This document presents a simulation study evaluating two architectural changes to reduce the spread of MRSA in a hospital dialysis unit. The simulation is based on real healthcare worker movement data collected from the unit. Splitting the central nursing station into two smaller stations had little effect on MRSA spread and slightly increased infections. Doubling the surface area of the nursing station substantially reduced MRSA infections, between 12-22%, by diluting pathogen loads. The results suggest the dynamics of environmentally-mediated infections like MRSA differ from other infections and reducing contacts may not help if it increases local pathogen concentrations.
Seven steps to reduce the risk of infectious disease in hospitalsBassam Gomaa
Healthcare organizations face growing challenges related to infectious disease control. While frequent hand washing and the use of personal protective equipment are the leading weapons against infectious disease spread and hospital-acquired infections, the built environment, including the HVAC systems, also plays an important role. Strides in the development of smart building operation management platforms that easily and cost-effectively integrate with a facility’s existing systems can give healthcare providers a powerful tool with which to enhance the effectiveness of their overall infection control programs.
Seven steps to reduce the risk of infectious disease in hospitalsBassam Gomaa
Healthcare organizations face growing challenges related to infectious disease control. While frequent hand washing and the use of personal protective equipment are the leading weapons against infectious disease spread and hospital-acquired infections, the built environment, including the HVAC systems, also plays an important role. Strides in the development of smart building operation management platforms that easily and cost-effectively integrate with a facility’s existing systems can give healthcare providers a powerful tool with which to enhance the effectiveness of their overall infection control programs.
SYSTEMS-LEVEL QUALITY IMPROVEMENTFrom Cues to Nudge A Knolisandrai1k
SYSTEMS-LEVEL QUALITY IMPROVEMENT
From Cues to Nudge: A Knowledge-Based Framework
for Surveillance of Healthcare-Associated Infections
Arash Shaban-Nejad1,2 & Hiroshi Mamiya2 & Alexandre Riazanov3 & Alan J. Forster4 &
Christopher J. O. Baker2,5 & Robyn Tamblyn2 & David L. Buckeridge2
Received: 3 June 2015 /Accepted: 30 September 2015 /Published online: 4 November 2015
# Springer Science+Business Media New York 2015
Abstract We propose an integrated semantic web framework
consisting of formal ontologies, web services, a reasoner and a
rule engine that together recommend appropriate level of
patient-care based on the defined semantic rules and guide-
lines. The classification of healthcare-associated infections
within the HAIKU (Hospital Acquired Infections – Knowl-
edge in Use) framework enables hospitals to consistently fol-
low the standards along with their routine clinical practice and
diagnosis coding to improve quality of care and patient safety.
The HAI ontology (HAIO) groups over thousands of codes
into a consistent hierarchy of concepts, along with relation-
ships and axioms to capture knowledge on hospital-associated
infections and complications with focus on the big four types,
surgical site infections (SSIs), catheter-associated urinary tract
infection (CAUTI); hospital-acquired pneumonia, and blood
stream infection. By employing statistical inferencing in our
study we use a set of heuristics to define the rule axioms to
improve the SSI case detection. We also demonstrate how the
occurrence of an SSI is identified using semantic e-triggers.
The e-triggers will be used to improve our risk assessment of
post-operative surgical site infections (SSIs) for patients un-
dergoing certain type of surgeries (e.g., coronary artery bypass
graft surgery (CABG)).
Keywords Ontologies . Knowledge modeling .
Healthcare-associated infections . Surveillance . Semantic
framework . Surgical site infections
Introduction
Healthcare-associated Infections (HAIs) affect millions of
patients around the world, killing hundreds of thousands
and imposing, directly or indirectly, a significant socio-
economic burden on healthcare systems [1]. According
to the Centers for Disease Control (CDC) [2], hospital-
acquired infections in the U.S., where the point preva-
lence of HAIs among hospitalized patients is 4 %, result
in an estimated 1.7 million infections, which lead to as
many as 99,000 deaths and cost up to $45 billion annually
[3, 4]. Similar or higher rates of HAI occur in other coun-
tries as well with an estimated 10.5 % of patients in Ca-
nadian hospitals having an HAI [5]. Clinical assessment
and laboratory testing are generally used to detect and
confirm an infection, identify its origin, and determine
appropriate infection control methods to stop the infection
from spreading within a healthcare institution. Failure to
monitor, and detect HAI in timely manner can delay di-
agnosis, leading to complications (e.g., sepsis), and
allowing an epid ...
SYSTEMS-LEVEL QUALITY IMPROVEMENTFrom Cues to Nudge A Kno.docxdeanmtaylor1545
SYSTEMS-LEVEL QUALITY IMPROVEMENT
From Cues to Nudge: A Knowledge-Based Framework
for Surveillance of Healthcare-Associated Infections
Arash Shaban-Nejad1,2 & Hiroshi Mamiya2 & Alexandre Riazanov3 & Alan J. Forster4 &
Christopher J. O. Baker2,5 & Robyn Tamblyn2 & David L. Buckeridge2
Received: 3 June 2015 /Accepted: 30 September 2015 /Published online: 4 November 2015
# Springer Science+Business Media New York 2015
Abstract We propose an integrated semantic web framework
consisting of formal ontologies, web services, a reasoner and a
rule engine that together recommend appropriate level of
patient-care based on the defined semantic rules and guide-
lines. The classification of healthcare-associated infections
within the HAIKU (Hospital Acquired Infections – Knowl-
edge in Use) framework enables hospitals to consistently fol-
low the standards along with their routine clinical practice and
diagnosis coding to improve quality of care and patient safety.
The HAI ontology (HAIO) groups over thousands of codes
into a consistent hierarchy of concepts, along with relation-
ships and axioms to capture knowledge on hospital-associated
infections and complications with focus on the big four types,
surgical site infections (SSIs), catheter-associated urinary tract
infection (CAUTI); hospital-acquired pneumonia, and blood
stream infection. By employing statistical inferencing in our
study we use a set of heuristics to define the rule axioms to
improve the SSI case detection. We also demonstrate how the
occurrence of an SSI is identified using semantic e-triggers.
The e-triggers will be used to improve our risk assessment of
post-operative surgical site infections (SSIs) for patients un-
dergoing certain type of surgeries (e.g., coronary artery bypass
graft surgery (CABG)).
Keywords Ontologies . Knowledge modeling .
Healthcare-associated infections . Surveillance . Semantic
framework . Surgical site infections
Introduction
Healthcare-associated Infections (HAIs) affect millions of
patients around the world, killing hundreds of thousands
and imposing, directly or indirectly, a significant socio-
economic burden on healthcare systems [1]. According
to the Centers for Disease Control (CDC) [2], hospital-
acquired infections in the U.S., where the point preva-
lence of HAIs among hospitalized patients is 4 %, result
in an estimated 1.7 million infections, which lead to as
many as 99,000 deaths and cost up to $45 billion annually
[3, 4]. Similar or higher rates of HAI occur in other coun-
tries as well with an estimated 10.5 % of patients in Ca-
nadian hospitals having an HAI [5]. Clinical assessment
and laboratory testing are generally used to detect and
confirm an infection, identify its origin, and determine
appropriate infection control methods to stop the infection
from spreading within a healthcare institution. Failure to
monitor, and detect HAI in timely manner can delay di-
agnosis, leading to complications (e.g., sepsis), and
allowing an epid.
CENTRAL LINE-ASSOCIATED BLOODSTREAM INFECTIONS2CENTRAL LINE-ASS.docxsleeperharwell
CENTRAL LINE-ASSOCIATED BLOODSTREAM INFECTIONS2
CENTRAL LINE-ASSOCIATED BLOODSTREAM INFECTIONS2
Central Line-Associated Bloodstream Infections Literature Review
Kerry S. Murphy
Grand Canyon University
Translational Research and Evidence-Based Practice
DNP-820-O501
Dr. Kari Lane
September 26, 2018
Running head: CENTRAL LINE-ASSOCIATED BLOODSTREAM INFECTIONS2
Central Line-Associated Bloodstream Infections Literature ReviewComment by Microsoft Office User: The heading of introduction is inferred in APA format
I. Introduction
Central Line-Associated Bloodstream Infection (CLABSIs) in a fatal infection that results from bacteria or viruses entering the bloodstream through the central line. A central line, also known as a central venous catheter, refers to a tube used by doctors to administer medication, fluids or to collect blood from the body of a patient (Deason & Gray, 2018). Central Line-Associated Bloodstream Infection is one of the leading causes of deaths each year in different countries across the globe. Central Line-Associated Bloodstream Infection has been an area of interest for many healthcare researchers representing a diverse body of knowledge about the infection while still expanding on what is already known. The paper is an analysis of articles related to CLABSIs with the major themes of concern to the authors including risk factors, interventions, CLABSIs and Hospital Acquired Infections (HAIs), benefits of the preventive measures and the common symptoms of CLABSIs. Comment by Microsoft Office User: Add a description of how the literature search was completed. Tell the reader how you did your literature search, which databases you searched, how many articles were found, and how you eliminated articles to come to the ones you included here.
II. Questions Posed in the Studies
a. Afonso, Blot, & Blot (2016) seeks to establish how hospital-acquired bloodstream infections can be prevented through the use of chlorhexidine gluconate-impregnated washcloth bathing in intensive care units. In the study by Chidambaram (2015), the question raised is, what associations dental procedure and CVCs have.
b. Kadium, M. (2015) inquired into how the education program for 1 month, based on the
evidence-based guidelines recommended by CDC, will improve registered dialysis nurses’ knowledge regarding CVC maintenance care?
c. CDC and NCBI (2011) raise the research question, how many people have been affected in the USA from 2001-2009?
d. Srinivasan, Wise, Bell, Cardo,Edwards, Fridkin, Jernigan, Kallen, McDonald, & Patel (2011) considers questioning the perception of central line-associated bloodstream infection.
e. Dougherty (2012) questions the potential solutions in reducing incidences of central-line associated bloodstream infections have to be created in line with the clinical setting and careful consideration of the patients and the organizational culture.
f. Lin, Apisarnthanarak, Jaggi, Harrington, Morikane, Thu, Ching, Villanueva.
Prevention of Central Line-Associated Bloodstream Infections (CLAB.docxstilliegeorgiana
Prevention of Central Line-Associated Bloodstream Infections (CLABSIs)
Submitted by
Kerry Sean Murphy
Direct Practice Improvement Project Proposal
Doctor of Nursing Practice
Grand Canyon University
Phoenix, Arizona
Prevention of Central Line-Associated Bloodstream Infections (CLABSIs)1
Prevention of Central Line-Associated Bloodstream Infections (CLABSIs)
June 26, 2019
Appendix A
Ten Strategic Points
Comments or Feedback
Broad Topic Area
1. Broad Topic Area:
The topic is the Prevention of Central Line-Associated Bloodstream Infections (CLABSIs).
Literature Review
2. Literature Review:
a. Background of the problem/gap:
· There arises an urgency to intervene and develop effective measures to curtail the incidence of CLABSIs (Yaseen, et al., 2016).
· The use of proper hand hygiene and skin aseptic techniques over the insertion site is necessary for preventing microbial infections (Dick et al., 2015).
· The nurses need to have significant knowledge associated with evidence-based practices for the prevention of central line-associated bloodstream infections (CLABSIs). Their attitude towards the guidelines and the utilization of the hygienic measures for the Central Venous Catheter in adult Intensive Care (ICU) units with (CVC) patients needs to change for the better (Kadium, 2015).
· Research has shown the existence of variations in the practice of nursing regarding the prevention of CLABSI, thus stressing the need for monitoring and education/training (Elbilgahy et al., 2015).
· There is a need for implementation of educational interventions to deal with the practice and knowledge gaps about CLABSIs prevention as well as ensuring that nurses apply prevention interventions which are evidence-based (Esposito et al., 2017).
· The Quality Health Outcomes Model (QHOM) Investigating Prophylactic measures to eradicate the incidence of central line-associated bloodstream infections (CLABSIs), based on the opinions of the experts as well as the analysis of the research literature (Hentrich et al., 2014)
b. Carrying out a review of literature topics with a key theme for each one.
c. Prevention of Central Line-Associated Blood Stream Infection (CLABSI) (Masse, Edmond, & Diekema, 2018).
· The Quality Health Outcomes Model (QHOM) is used to study healthcare quality and outlines relationships existing between interventions, characteristics, and outcomes of patients, and the characteristics of the healthcare system. This model/framework has been used by various researchers to test the effectiveness of infection prevention measures (Gilmartin & Sousa, 2016).
· The Centers for Disease Control and Prevention’s (CDC’s) National Healthcare Safety Network (NHSN) developed uses Standardized Infection Ratios (SIRs). Clear Lake Regional Medical Center ("NHSN | CDC," 2018) uses SIRs, and this study will also utilize SIRs for measuring the effectiveness of preventive CLABSI maintenance bundles ("SIR Report | HAI | CDC," 2019).
Setting
· Adult ICU (El ...
Healthcare Associated Infections (HAIs): Research NewsletterErin K. Peavey
In the US “One in 25 patients have a hospital-acquired
infection...Each day, over 205 deaths occur from HAIs...”
— Centers for Disease Control, 2011
An overview of this month’s article follows with bullet-points of the advantages and disadvantages of various environmental interventions in the prevention of Healthcare-Associated Infections (HAIs), often referred to as hospital-acquired infections. Paragraph summaries of Cleaning Strategies, Materials, Room Design and Hand-Hygiene are listed below. These provide broad themes and findings from the article. Reading the full article is always of benefit for a fuller understanding and is recommended. Click here to access the full article on the HERD Journal website.
12Plan for Evaluating the Impact of the Inte.docxmoggdede
1
2
Plan for Evaluating the Impact of the Intervention
Anne Marie WouapetName
Walden University
NURS 8310 Section 03, Epidemiology and Population HealthClass
April 29, 2018Date
Plan for Evaluating the Impact of the Intervention
Hospital-acquired infections have been determined throughout this project to be a significant problem in the United States health care system. Epidemiologic data show that there is still a considerable number of patients who die as a result of infections that they have acquired while receiving care (Umscheid et al., 2011). The older population was found to be at a higher risk of acquiring these infections because of their deteriorating immune systems (Sievert et al., 2013). Therefore, a proposed intervention to eliminate the dangers of infection was created. The intervention proposes that nurses go through hand washing education for an extended period to enhance their compliance to hand hygiene after the education program. In studying the potential impacts of this intervention, it was determined that hand washing education is usually effective in changing perceptions and behaviors with regards to hand hygiene, but the compliance to what has been learned is often not maintained. Therefore, this intervention suggests that the education is based on the practice environment and that the nurses are monitored for an extended period. The following is an evaluation plan aiming at assessing the potential outcomes of the proposed intervention.
Evaluation Plan
This evaluation plan is designed to assess the expected outcomes from the implementation of the program (Friis & Sellers, 2014). This plan will investigate the extent to which the hand washing intervention plan will help to reduce the rate of hospital-acquired in infections in the healthcare facilities in which the intervention will be implemented. The plan includes an evaluation of the short-term, medium-term, and long-term changes expected to occur after the implementation of the intervention.
Stakeholders Involved in the Intervention
For the expected outcome to be achieved, the following stakeholders will be required to participate in the intervention program. Evaluating the participation of the stakeholders is essential in determining their contribution to the outcome of the program (Centers for Disease Control, 2011). The program will require the participation of the Director of Nursing, who will be responsible for guiding the nurses included in the intervention to ensure that they participate in the program as required. The intervention will also require the participation of the Directors of the respective health care facilities where the intervention will be implemented to ensure that they provide the resources needed for the program to be implemented and approve the use of the hospital data to evaluate the outcomes of the program. The hospitals included will also need to employ super ...
Adapting to adversity: insights from a stand-alone human immunodeficiency virus testing centre in india during the covid-19 pandemic
Authors:Sumathi Muralidhar*, Abhishek Lachyan
Int J Biol Med Res. 2024; 15(1): 7750-7755
https://www.biomedscidirect.com/2857/adapting-to-adversity-insights-from-a-stand-alone-human-immunodeficiency-virus-testing-centre-in-india-during-the-covid-19-pandemic
Adapting to adversity: insights from a stand-alone human immunodeficiency virus testing centre in india during the covid-19 pandemic
Authors:Sumathi Muralidhar*, Abhishek Lachyan
Int J Biol Med Res. 2024; 15(1): 7750-7755
https://www.biomedscidirect.com/articles.php
Abstract:
Background: The global healthcare landscape confronted unprecedented challenges during the COVID-19 pandemic in 2020. Materials and Methods: This study explores how the COVID-19 pandemic impacted healthcare services in India, with a focus on the Stand-alone HIV Testing Centre (SA-ICTC) at Safdarjung Hospital, New Delhi, during the period from April 1, 2020, to March 31, 2021. Amid the pandemic, specialized clinics for Sexually Transmitted Infections (STI) and Reproductive Tract Infections (RTI) saw a decline in outpatient attendance, while the SA-ICTC faced unique challenges. Results: To address these challenges, innovative solutions were implemented, including alternate-day duty rosters, leading to increased staff efficiency and reduced errors. The study noted a 47.9% reduction in the total number of HIV tests conducted, although the proportion of HIV-positive clients accessing services remained stable. Referrals from STI clinics and Targeted Intervention sites decreased, while referrals from the Tuberculosis (TB) center remained consistent. Client categories accessing ICTC services decreased, except for referrals from Facility Integrated Counseling and Testing Centres (F-ICTC). Conclusions: This research underscores the intricate interplay between COVID-19 and HIV, prompting positive changes in healthcare work ethics, documentation practices, and service delivery. It emphasizes the significance of strategic supply chain management, recommending a 1-2-month buffer of testing kits and consumables in HIV testing facilities to ensure uninterrupted service delivery during crises, thus safeguarding the healthcare needs of vulnerable populations.
About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...Subhajit Sahu
TrueTime is a service that enables the use of globally synchronized clocks, with bounded error. It returns a time interval that is guaranteed to contain the clock’s actual time for some time during the call’s execution. If two intervals do not overlap, then we know calls were definitely ordered in real time. In general, synchronized clocks can be used to avoid communication in a distributed system.
The underlying source of time is a combination of GPS receivers and atomic clocks. As there are “time masters” in every datacenter (redundantly), it is likely that both sides of a partition would continue to enjoy accurate time. Individual nodes however need network connectivity to the masters, and without it their clocks will drift. Thus, during a partition their intervals slowly grow wider over time, based on bounds on the rate of local clock drift. Operations depending on TrueTime, such as Paxos leader election or transaction commits, thus have to wait a little longer, but the operation still completes (assuming the 2PC and quorum communication are working).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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SYSTEMS-LEVEL QUALITY IMPROVEMENTFrom Cues to Nudge A Knolisandrai1k
SYSTEMS-LEVEL QUALITY IMPROVEMENT
From Cues to Nudge: A Knowledge-Based Framework
for Surveillance of Healthcare-Associated Infections
Arash Shaban-Nejad1,2 & Hiroshi Mamiya2 & Alexandre Riazanov3 & Alan J. Forster4 &
Christopher J. O. Baker2,5 & Robyn Tamblyn2 & David L. Buckeridge2
Received: 3 June 2015 /Accepted: 30 September 2015 /Published online: 4 November 2015
# Springer Science+Business Media New York 2015
Abstract We propose an integrated semantic web framework
consisting of formal ontologies, web services, a reasoner and a
rule engine that together recommend appropriate level of
patient-care based on the defined semantic rules and guide-
lines. The classification of healthcare-associated infections
within the HAIKU (Hospital Acquired Infections – Knowl-
edge in Use) framework enables hospitals to consistently fol-
low the standards along with their routine clinical practice and
diagnosis coding to improve quality of care and patient safety.
The HAI ontology (HAIO) groups over thousands of codes
into a consistent hierarchy of concepts, along with relation-
ships and axioms to capture knowledge on hospital-associated
infections and complications with focus on the big four types,
surgical site infections (SSIs), catheter-associated urinary tract
infection (CAUTI); hospital-acquired pneumonia, and blood
stream infection. By employing statistical inferencing in our
study we use a set of heuristics to define the rule axioms to
improve the SSI case detection. We also demonstrate how the
occurrence of an SSI is identified using semantic e-triggers.
The e-triggers will be used to improve our risk assessment of
post-operative surgical site infections (SSIs) for patients un-
dergoing certain type of surgeries (e.g., coronary artery bypass
graft surgery (CABG)).
Keywords Ontologies . Knowledge modeling .
Healthcare-associated infections . Surveillance . Semantic
framework . Surgical site infections
Introduction
Healthcare-associated Infections (HAIs) affect millions of
patients around the world, killing hundreds of thousands
and imposing, directly or indirectly, a significant socio-
economic burden on healthcare systems [1]. According
to the Centers for Disease Control (CDC) [2], hospital-
acquired infections in the U.S., where the point preva-
lence of HAIs among hospitalized patients is 4 %, result
in an estimated 1.7 million infections, which lead to as
many as 99,000 deaths and cost up to $45 billion annually
[3, 4]. Similar or higher rates of HAI occur in other coun-
tries as well with an estimated 10.5 % of patients in Ca-
nadian hospitals having an HAI [5]. Clinical assessment
and laboratory testing are generally used to detect and
confirm an infection, identify its origin, and determine
appropriate infection control methods to stop the infection
from spreading within a healthcare institution. Failure to
monitor, and detect HAI in timely manner can delay di-
agnosis, leading to complications (e.g., sepsis), and
allowing an epid ...
SYSTEMS-LEVEL QUALITY IMPROVEMENTFrom Cues to Nudge A Kno.docxdeanmtaylor1545
SYSTEMS-LEVEL QUALITY IMPROVEMENT
From Cues to Nudge: A Knowledge-Based Framework
for Surveillance of Healthcare-Associated Infections
Arash Shaban-Nejad1,2 & Hiroshi Mamiya2 & Alexandre Riazanov3 & Alan J. Forster4 &
Christopher J. O. Baker2,5 & Robyn Tamblyn2 & David L. Buckeridge2
Received: 3 June 2015 /Accepted: 30 September 2015 /Published online: 4 November 2015
# Springer Science+Business Media New York 2015
Abstract We propose an integrated semantic web framework
consisting of formal ontologies, web services, a reasoner and a
rule engine that together recommend appropriate level of
patient-care based on the defined semantic rules and guide-
lines. The classification of healthcare-associated infections
within the HAIKU (Hospital Acquired Infections – Knowl-
edge in Use) framework enables hospitals to consistently fol-
low the standards along with their routine clinical practice and
diagnosis coding to improve quality of care and patient safety.
The HAI ontology (HAIO) groups over thousands of codes
into a consistent hierarchy of concepts, along with relation-
ships and axioms to capture knowledge on hospital-associated
infections and complications with focus on the big four types,
surgical site infections (SSIs), catheter-associated urinary tract
infection (CAUTI); hospital-acquired pneumonia, and blood
stream infection. By employing statistical inferencing in our
study we use a set of heuristics to define the rule axioms to
improve the SSI case detection. We also demonstrate how the
occurrence of an SSI is identified using semantic e-triggers.
The e-triggers will be used to improve our risk assessment of
post-operative surgical site infections (SSIs) for patients un-
dergoing certain type of surgeries (e.g., coronary artery bypass
graft surgery (CABG)).
Keywords Ontologies . Knowledge modeling .
Healthcare-associated infections . Surveillance . Semantic
framework . Surgical site infections
Introduction
Healthcare-associated Infections (HAIs) affect millions of
patients around the world, killing hundreds of thousands
and imposing, directly or indirectly, a significant socio-
economic burden on healthcare systems [1]. According
to the Centers for Disease Control (CDC) [2], hospital-
acquired infections in the U.S., where the point preva-
lence of HAIs among hospitalized patients is 4 %, result
in an estimated 1.7 million infections, which lead to as
many as 99,000 deaths and cost up to $45 billion annually
[3, 4]. Similar or higher rates of HAI occur in other coun-
tries as well with an estimated 10.5 % of patients in Ca-
nadian hospitals having an HAI [5]. Clinical assessment
and laboratory testing are generally used to detect and
confirm an infection, identify its origin, and determine
appropriate infection control methods to stop the infection
from spreading within a healthcare institution. Failure to
monitor, and detect HAI in timely manner can delay di-
agnosis, leading to complications (e.g., sepsis), and
allowing an epid.
CENTRAL LINE-ASSOCIATED BLOODSTREAM INFECTIONS2CENTRAL LINE-ASS.docxsleeperharwell
CENTRAL LINE-ASSOCIATED BLOODSTREAM INFECTIONS2
CENTRAL LINE-ASSOCIATED BLOODSTREAM INFECTIONS2
Central Line-Associated Bloodstream Infections Literature Review
Kerry S. Murphy
Grand Canyon University
Translational Research and Evidence-Based Practice
DNP-820-O501
Dr. Kari Lane
September 26, 2018
Running head: CENTRAL LINE-ASSOCIATED BLOODSTREAM INFECTIONS2
Central Line-Associated Bloodstream Infections Literature ReviewComment by Microsoft Office User: The heading of introduction is inferred in APA format
I. Introduction
Central Line-Associated Bloodstream Infection (CLABSIs) in a fatal infection that results from bacteria or viruses entering the bloodstream through the central line. A central line, also known as a central venous catheter, refers to a tube used by doctors to administer medication, fluids or to collect blood from the body of a patient (Deason & Gray, 2018). Central Line-Associated Bloodstream Infection is one of the leading causes of deaths each year in different countries across the globe. Central Line-Associated Bloodstream Infection has been an area of interest for many healthcare researchers representing a diverse body of knowledge about the infection while still expanding on what is already known. The paper is an analysis of articles related to CLABSIs with the major themes of concern to the authors including risk factors, interventions, CLABSIs and Hospital Acquired Infections (HAIs), benefits of the preventive measures and the common symptoms of CLABSIs. Comment by Microsoft Office User: Add a description of how the literature search was completed. Tell the reader how you did your literature search, which databases you searched, how many articles were found, and how you eliminated articles to come to the ones you included here.
II. Questions Posed in the Studies
a. Afonso, Blot, & Blot (2016) seeks to establish how hospital-acquired bloodstream infections can be prevented through the use of chlorhexidine gluconate-impregnated washcloth bathing in intensive care units. In the study by Chidambaram (2015), the question raised is, what associations dental procedure and CVCs have.
b. Kadium, M. (2015) inquired into how the education program for 1 month, based on the
evidence-based guidelines recommended by CDC, will improve registered dialysis nurses’ knowledge regarding CVC maintenance care?
c. CDC and NCBI (2011) raise the research question, how many people have been affected in the USA from 2001-2009?
d. Srinivasan, Wise, Bell, Cardo,Edwards, Fridkin, Jernigan, Kallen, McDonald, & Patel (2011) considers questioning the perception of central line-associated bloodstream infection.
e. Dougherty (2012) questions the potential solutions in reducing incidences of central-line associated bloodstream infections have to be created in line with the clinical setting and careful consideration of the patients and the organizational culture.
f. Lin, Apisarnthanarak, Jaggi, Harrington, Morikane, Thu, Ching, Villanueva.
Prevention of Central Line-Associated Bloodstream Infections (CLAB.docxstilliegeorgiana
Prevention of Central Line-Associated Bloodstream Infections (CLABSIs)
Submitted by
Kerry Sean Murphy
Direct Practice Improvement Project Proposal
Doctor of Nursing Practice
Grand Canyon University
Phoenix, Arizona
Prevention of Central Line-Associated Bloodstream Infections (CLABSIs)1
Prevention of Central Line-Associated Bloodstream Infections (CLABSIs)
June 26, 2019
Appendix A
Ten Strategic Points
Comments or Feedback
Broad Topic Area
1. Broad Topic Area:
The topic is the Prevention of Central Line-Associated Bloodstream Infections (CLABSIs).
Literature Review
2. Literature Review:
a. Background of the problem/gap:
· There arises an urgency to intervene and develop effective measures to curtail the incidence of CLABSIs (Yaseen, et al., 2016).
· The use of proper hand hygiene and skin aseptic techniques over the insertion site is necessary for preventing microbial infections (Dick et al., 2015).
· The nurses need to have significant knowledge associated with evidence-based practices for the prevention of central line-associated bloodstream infections (CLABSIs). Their attitude towards the guidelines and the utilization of the hygienic measures for the Central Venous Catheter in adult Intensive Care (ICU) units with (CVC) patients needs to change for the better (Kadium, 2015).
· Research has shown the existence of variations in the practice of nursing regarding the prevention of CLABSI, thus stressing the need for monitoring and education/training (Elbilgahy et al., 2015).
· There is a need for implementation of educational interventions to deal with the practice and knowledge gaps about CLABSIs prevention as well as ensuring that nurses apply prevention interventions which are evidence-based (Esposito et al., 2017).
· The Quality Health Outcomes Model (QHOM) Investigating Prophylactic measures to eradicate the incidence of central line-associated bloodstream infections (CLABSIs), based on the opinions of the experts as well as the analysis of the research literature (Hentrich et al., 2014)
b. Carrying out a review of literature topics with a key theme for each one.
c. Prevention of Central Line-Associated Blood Stream Infection (CLABSI) (Masse, Edmond, & Diekema, 2018).
· The Quality Health Outcomes Model (QHOM) is used to study healthcare quality and outlines relationships existing between interventions, characteristics, and outcomes of patients, and the characteristics of the healthcare system. This model/framework has been used by various researchers to test the effectiveness of infection prevention measures (Gilmartin & Sousa, 2016).
· The Centers for Disease Control and Prevention’s (CDC’s) National Healthcare Safety Network (NHSN) developed uses Standardized Infection Ratios (SIRs). Clear Lake Regional Medical Center ("NHSN | CDC," 2018) uses SIRs, and this study will also utilize SIRs for measuring the effectiveness of preventive CLABSI maintenance bundles ("SIR Report | HAI | CDC," 2019).
Setting
· Adult ICU (El ...
Healthcare Associated Infections (HAIs): Research NewsletterErin K. Peavey
In the US “One in 25 patients have a hospital-acquired
infection...Each day, over 205 deaths occur from HAIs...”
— Centers for Disease Control, 2011
An overview of this month’s article follows with bullet-points of the advantages and disadvantages of various environmental interventions in the prevention of Healthcare-Associated Infections (HAIs), often referred to as hospital-acquired infections. Paragraph summaries of Cleaning Strategies, Materials, Room Design and Hand-Hygiene are listed below. These provide broad themes and findings from the article. Reading the full article is always of benefit for a fuller understanding and is recommended. Click here to access the full article on the HERD Journal website.
12Plan for Evaluating the Impact of the Inte.docxmoggdede
1
2
Plan for Evaluating the Impact of the Intervention
Anne Marie WouapetName
Walden University
NURS 8310 Section 03, Epidemiology and Population HealthClass
April 29, 2018Date
Plan for Evaluating the Impact of the Intervention
Hospital-acquired infections have been determined throughout this project to be a significant problem in the United States health care system. Epidemiologic data show that there is still a considerable number of patients who die as a result of infections that they have acquired while receiving care (Umscheid et al., 2011). The older population was found to be at a higher risk of acquiring these infections because of their deteriorating immune systems (Sievert et al., 2013). Therefore, a proposed intervention to eliminate the dangers of infection was created. The intervention proposes that nurses go through hand washing education for an extended period to enhance their compliance to hand hygiene after the education program. In studying the potential impacts of this intervention, it was determined that hand washing education is usually effective in changing perceptions and behaviors with regards to hand hygiene, but the compliance to what has been learned is often not maintained. Therefore, this intervention suggests that the education is based on the practice environment and that the nurses are monitored for an extended period. The following is an evaluation plan aiming at assessing the potential outcomes of the proposed intervention.
Evaluation Plan
This evaluation plan is designed to assess the expected outcomes from the implementation of the program (Friis & Sellers, 2014). This plan will investigate the extent to which the hand washing intervention plan will help to reduce the rate of hospital-acquired in infections in the healthcare facilities in which the intervention will be implemented. The plan includes an evaluation of the short-term, medium-term, and long-term changes expected to occur after the implementation of the intervention.
Stakeholders Involved in the Intervention
For the expected outcome to be achieved, the following stakeholders will be required to participate in the intervention program. Evaluating the participation of the stakeholders is essential in determining their contribution to the outcome of the program (Centers for Disease Control, 2011). The program will require the participation of the Director of Nursing, who will be responsible for guiding the nurses included in the intervention to ensure that they participate in the program as required. The intervention will also require the participation of the Directors of the respective health care facilities where the intervention will be implemented to ensure that they provide the resources needed for the program to be implemented and approve the use of the hospital data to evaluate the outcomes of the program. The hospitals included will also need to employ super ...
Adapting to adversity: insights from a stand-alone human immunodeficiency virus testing centre in india during the covid-19 pandemic
Authors:Sumathi Muralidhar*, Abhishek Lachyan
Int J Biol Med Res. 2024; 15(1): 7750-7755
https://www.biomedscidirect.com/2857/adapting-to-adversity-insights-from-a-stand-alone-human-immunodeficiency-virus-testing-centre-in-india-during-the-covid-19-pandemic
Adapting to adversity: insights from a stand-alone human immunodeficiency virus testing centre in india during the covid-19 pandemic
Authors:Sumathi Muralidhar*, Abhishek Lachyan
Int J Biol Med Res. 2024; 15(1): 7750-7755
https://www.biomedscidirect.com/articles.php
Abstract:
Background: The global healthcare landscape confronted unprecedented challenges during the COVID-19 pandemic in 2020. Materials and Methods: This study explores how the COVID-19 pandemic impacted healthcare services in India, with a focus on the Stand-alone HIV Testing Centre (SA-ICTC) at Safdarjung Hospital, New Delhi, during the period from April 1, 2020, to March 31, 2021. Amid the pandemic, specialized clinics for Sexually Transmitted Infections (STI) and Reproductive Tract Infections (RTI) saw a decline in outpatient attendance, while the SA-ICTC faced unique challenges. Results: To address these challenges, innovative solutions were implemented, including alternate-day duty rosters, leading to increased staff efficiency and reduced errors. The study noted a 47.9% reduction in the total number of HIV tests conducted, although the proportion of HIV-positive clients accessing services remained stable. Referrals from STI clinics and Targeted Intervention sites decreased, while referrals from the Tuberculosis (TB) center remained consistent. Client categories accessing ICTC services decreased, except for referrals from Facility Integrated Counseling and Testing Centres (F-ICTC). Conclusions: This research underscores the intricate interplay between COVID-19 and HIV, prompting positive changes in healthcare work ethics, documentation practices, and service delivery. It emphasizes the significance of strategic supply chain management, recommending a 1-2-month buffer of testing kits and consumables in HIV testing facilities to ensure uninterrupted service delivery during crises, thus safeguarding the healthcare needs of vulnerable populations.
About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...Subhajit Sahu
TrueTime is a service that enables the use of globally synchronized clocks, with bounded error. It returns a time interval that is guaranteed to contain the clock’s actual time for some time during the call’s execution. If two intervals do not overlap, then we know calls were definitely ordered in real time. In general, synchronized clocks can be used to avoid communication in a distributed system.
The underlying source of time is a combination of GPS receivers and atomic clocks. As there are “time masters” in every datacenter (redundantly), it is likely that both sides of a partition would continue to enjoy accurate time. Individual nodes however need network connectivity to the masters, and without it their clocks will drift. Thus, during a partition their intervals slowly grow wider over time, based on bounds on the rate of local clock drift. Operations depending on TrueTime, such as Paxos leader election or transaction commits, thus have to wait a little longer, but the operation still completes (assuming the 2PC and quorum communication are working).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting Bitset for graph : SHORT REPORT / NOTESSubhajit Sahu
Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is commonly used for efficient graph computations. Unfortunately, using CSR for dynamic graphs is impractical since addition/deletion of a single edge can require on average (N+M)/2 memory accesses, in order to update source-offsets and destination-indices. A common approach is therefore to store edge-lists/destination-indices as an array of arrays, where each edge-list is an array belonging to a vertex. While this is good enough for small graphs, it quickly becomes a bottleneck for large graphs. What causes this bottleneck depends on whether the edge-lists are sorted or unsorted. If they are sorted, checking for an edge requires about log(E) memory accesses, but adding an edge on average requires E/2 accesses, where E is the number of edges of a given vertex. Note that both addition and deletion of edges in a dynamic graph require checking for an existing edge, before adding or deleting it. If edge lists are unsorted, checking for an edge requires around E/2 memory accesses, but adding an edge requires only 1 memory access.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Experiments with Primitive operations : SHORT REPORT / NOTESSubhajit Sahu
This includes:
- Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
- Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
- Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
- Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...Subhajit Sahu
Below are the important points I note from the 2020 paper by Martin Grohe:
- 1-WL distinguishes almost all graphs, in a probabilistic sense
- Classical WL is two dimensional Weisfeiler-Leman
- DeepWL is an unlimited version of WL graph that runs in polynomial time.
- Knowledge graphs are essentially graphs with vertex/edge attributes
ABSTRACT:
Vector representations of graphs and relational structures, whether handcrafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view.
Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.
DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTESSubhajit Sahu
https://gist.github.com/wolfram77/54c4a14d9ea547183c6c7b3518bf9cd1
There exist a number of dynamic graph generators. Barbasi-Albert model iteratively attach new vertices to pre-exsiting vertices in the graph using preferential attachment (edges to high degree vertices are more likely - rich get richer - Pareto principle). However, graph size increases monotonically, and density of graph keeps increasing (sparsity decreasing).
Gorke's model uses a defined clustering to uniformly add vertices and edges. Purohit's model uses motifs (eg. triangles) to mimick properties of existing dynamic graphs, such as growth rate, structure, and degree distribution. Kronecker graph generators are used to increase size of a given graph, with power-law distribution.
To generate dynamic graphs, we must choose a metric to compare two graphs. Common metrics include diameter, clustering coefficient (modularity?), triangle counting (triangle density?), and degree distribution.
In this paper, the authors propose Dygraph, a dynamic graph generator that uses degree distribution as the only metric. The authors observe that many real-world graphs differ from the power-law distribution at the tail end. To address this issue, they propose binning, where the vertices beyond a certain degree (minDeg = min(deg) s.t. |V(deg)| < H, where H~10 is the number of vertices with a given degree below which are binned) are grouped into bins of degree-width binWidth, max-degree localMax, and number of degrees in bin with at least one vertex binSize (to keep track of sparsity). This helps the authors to generate graphs with a more realistic degree distribution.
The process of generating a dynamic graph is as follows. First the difference between the desired and the current degree distribution is calculated. The authors then create an edge-addition set where each vertex is present as many times as the number of additional incident edges it must recieve. Edges are then created by connecting two vertices randomly from this set, and removing both from the set once connected. Currently, authors reject self-loops and duplicate edges. Removal of edges is done in a similar fashion.
Authors observe that adding edges with power-law properties dominates the execution time, and consider parallelizing DyGraph as part of future work.
My notes on shared memory parallelism.
Shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs. Using memory for communication inside a single program, e.g. among its multiple threads, is also referred to as shared memory [REF].
A Dynamic Algorithm for Local Community Detection in Graphs : NOTESSubhajit Sahu
**Community detection methods** can be *global* or *local*. **Global community detection methods** divide the entire graph into groups. Existing global algorithms include:
- Random walk methods
- Spectral partitioning
- Label propagation
- Greedy agglomerative and divisive algorithms
- Clique percolation
https://gist.github.com/wolfram77/b4316609265b5b9f88027bbc491f80b6
There is a growing body of work in *detecting overlapping communities*. **Seed set expansion** is a **local community detection method** where a relevant *seed vertices* of interest are picked and *expanded to form communities* surrounding them. The quality of each community is measured using a *fitness function*.
**Modularity** is a *fitness function* which compares the number of intra-community edges to the expected number in a random-null model. **Conductance** is another popular fitness score that measures the community cut or inter-community edges. Many *overlapping community detection* methods **use a modified ratio** of intra-community edges to all edges with atleast one endpoint in the community.
Andersen et al. use a **Spectral PageRank-Nibble method** which minimizes conductance and is formed by adding vertices in order of decreasing PageRank values. Andersen and Lang develop a **random walk approach** in which some vertices in the seed set may not be placed in the final community. Clauset gives a **greedy method** that *starts from a single vertex* and then iteratively adds neighboring vertices *maximizing the local modularity score*. Riedy et al. **expand multiple vertices** via maximizing modularity.
Several algorithms for **detecting global, overlapping communities** use a *greedy*, *agglomerative approach* and run *multiple separate seed set expansions*. Lancichinetti et al. run **greedy seed set expansions**, each with a *single seed vertex*. Overlapping communities are produced by a sequentially running expansions from a node not yet in a community. Lee et al. use **maximal cliques as seed sets**. Havemann et al. **greedily expand cliques**.
The authors of this paper discuss a dynamic approach for **community detection using seed set expansion**. Simply marking the neighbours of changed vertices is a **naive approach**, and has *severe shortcomings*. This is because *communities can split apart*. The simple updating method *may fail even when it outputs a valid community* in the graph.
Scalable Static and Dynamic Community Detection Using Grappolo : NOTESSubhajit Sahu
A **community** (in a network) is a subset of nodes which are _strongly connected among themselves_, but _weakly connected to others_. Neither the number of output communities nor their size distribution is known a priori. Community detection methods can be divisive or agglomerative. **Divisive methods** use _betweeness centrality_ to **identify and remove bridges** between communities. **Agglomerative methods** greedily **merge two communities** that provide maximum gain in _modularity_. Newman and Girvan have introduced the **modularity metric**. The problem of community detection is then reduced to the problem of modularity maximization which is **NP-complete**. **Louvain method** is a variant of the _agglomerative strategy_, in that is a _multi-level heuristic_.
https://gist.github.com/wolfram77/917a1a4a429e89a0f2a1911cea56314d
In this paper, the authors discuss **four heuristics** for Community detection using the _Louvain algorithm_ implemented upon recently developed **Grappolo**, which is a parallel variant of the Louvain algorithm. They are:
- Vertex following and Minimum label
- Data caching
- Graph coloring
- Threshold scaling
With the **Vertex following** heuristic, the _input is preprocessed_ and all single-degree vertices are merged with their corresponding neighbours. This helps reduce the number of vertices considered in each iteration, and also help initial seeds of communities to be formed. With the **Minimum label heuristic**, when a vertex is making the decision to move to a community and multiple communities provided the same modularity gain, the community with the smallest id is chosen. This helps _minimize or prevent community swaps_. With the **Data caching** heuristic, community information is stored in a vector instead of a map, and is reused in each iteration, but with some additional cost. With the **Vertex ordering via Graph coloring** heuristic, _distance-k coloring_ of graphs is performed in order to group vertices into colors. Then, each set of vertices (by color) is processed _concurrently_, and synchronization is performed after that. This enables us to mimic the behaviour of the serial algorithm. Finally, with the **Threshold scaling** heuristic, _successively smaller values of modularity threshold_ are used as the algorithm progresses. This allows the algorithm to converge faster, and it has been observed a good modularity score as well.
From the results, it appears that _graph coloring_ and _threshold scaling_ heuristics do not always provide a speedup and this depends upon the nature of the graph. It would be interesting to compare the heuristics against baseline approaches. Future work can include _distributed memory implementations_, and _community detection on streaming graphs_.
Application Areas of Community Detection: A Review : NOTESSubhajit Sahu
This is a short review of Community detection methods (on graphs), and their applications. A **community** is a subset of a network whose members are *highly connected*, but *loosely connected* to others outside their community. Different community detection methods *can return differing communities* these algorithms are **heuristic-based**. **Dynamic community detection** involves tracking the *evolution of community structure* over time.
https://gist.github.com/wolfram77/09e64d6ba3ef080db5558feb2d32fdc0
Communities can be of the following **types**:
- Disjoint
- Overlapping
- Hierarchical
- Local.
The following **static** community detection **methods** exist:
- Spectral-based
- Statistical inference
- Optimization
- Dynamics-based
The following **dynamic** community detection **methods** exist:
- Independent community detection and matching
- Dependent community detection (evolutionary)
- Simultaneous community detection on all snapshots
- Dynamic community detection on temporal networks
**Applications** of community detection include:
- Criminal identification
- Fraud detection
- Criminal activities detection
- Bot detection
- Dynamics of epidemic spreading (dynamic)
- Cancer/tumor detection
- Tissue/organ detection
- Evolution of influence (dynamic)
- Astroturfing
- Customer segmentation
- Recommendation systems
- Social network analysis (both)
- Network summarization
- Privary, group segmentation
- Link prediction (both)
- Community evolution prediction (dynamic, hot field)
<br>
<br>
## References
- [Application Areas of Community Detection: A Review : PAPER](https://ieeexplore.ieee.org/document/8625349)
This paper discusses a GPU implementation of the Louvain community detection algorithm. Louvain algorithm obtains hierachical communities as a dendrogram through modularity optimization. Given an undirected weighted graph, all vertices are first considered to be their own communities. In the first phase, each vertex greedily decides to move to the community of one of its neighbours which gives greatest increase in modularity. If moving to no neighbour's community leads to an increase in modularity, the vertex chooses to stay with its own community. This is done sequentially for all the vertices. If the total change in modularity is more than a certain threshold, this phase is repeated. Once this local moving phase is complete, all vertices have formed their first hierarchy of communities. The next phase is called the aggregation phase, where all the vertices belonging to a community are collapsed into a single super-vertex, such that edges between communities are represented as edges between respective super-vertices (edge weights are combined), and edges within each community are represented as self-loops in respective super-vertices (again, edge weights are combined). Together, the local moving and the aggregation phases constitute a stage. This super-vertex graph is then used as input fof the next stage. This process continues until the increase in modularity is below a certain threshold. As a result from each stage, we have a hierarchy of community memberships for each vertex as a dendrogram.
Approaches to perform the Louvain algorithm can be divided into coarse-grained and fine-grained. Coarse-grained approaches process a set of vertices in parallel, while fine-grained approaches process all vertices in parallel. A coarse-grained hybrid-GPU algorithm using multi GPUs has be implemented by Cheong et al. which grabbed my attention. In addition, their algorithm does not use hashing for the local moving phase, but instead sorts each neighbour list based on the community id of each vertex.
https://gist.github.com/wolfram77/7e72c9b8c18c18ab908ae76262099329
Survey for extra-child-process package : NOTESSubhajit Sahu
Useful additions to inbuilt child_process module.
📦 Node.js, 📜 Files, 📰 Docs.
Please see attached PDF for literature survey.
https://gist.github.com/wolfram77/d936da570d7bf73f95d1513d4368573e
Dynamic Batch Parallel Algorithms for Updating PageRank : POSTERSubhajit Sahu
For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May.
https://gist.github.com/wolfram77/692d263f463fd49be6eb5aa65dd4d0f9
Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...Subhajit Sahu
For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May.
https://gist.github.com/wolfram77/1c1f730d20b51e0d2c6d477fd3713024
Fast Incremental Community Detection on Dynamic Graphs : NOTESSubhajit Sahu
In this paper, the authors describe two approaches for dynamic community detection using the CNM algorithm. CNM is a hierarchical, agglomerative algorithm that greedily maximizes modularity. They define two approaches: BasicDyn and FastDyn. BasicDyn backtracks merges of communities until each marked (changed) vertex is its own singleton community. FastDyn undoes a merge only if the quality of merge, as measured by the induced change in modularity, has significantly decreased compared to when the merge initially took place. FastDyn also allows more than two vertices to contract together if in the previous time step these vertices eventually ended up contracted in the same community. In the static case, merging several vertices together in one contraction phase could lead to deteriorating results. FastDyn is able to do this, however, because it uses information from the merges of the previous time step. Intuitively, merges that previously occurred are more likely to be acceptable later.
https://gist.github.com/wolfram77/1856b108334cc822cdddfdfa7334792a
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
2. include good hand hygiene and sanitation practices [4]. Studies
have shown that improved hand hygiene and surface/room
cleaning effectively decrease MRSA infection rates [5]–[7].
The specific question we focus on is whether there are simple,
inexpensive architectural or process changes one can make in
the dialysis unit to reduce the spread of MRSA. In answering
this question, we seek a better understanding of the role
of the environment in the diffusion of MRSA. A one-time
intervention such as an architectural change would not only
complement standard hand-hygiene and cleaning policies, but
could result in a long-term impact.
The results presented here are fruit of an agent-based
simulation study based on ten days of fine-grained healthcare
worker (HCW) movement and interaction data collected by
our research group in the fall of 2013. The data were obtained
by instrumenting HCWs working in a nine-chair hospital
dialysis facility at the UIHC with small tracking sensor devices
(see Fig. 1). The subsequent simulations replay the HCW
interactions with patients, the environment, and each other,
while layering a detailed model of MRSA pathogen transfer,
die-off, and shedding over the agent interaction model. The
simulation maintains MRSA pathogen loads on all surfaces
including chairs, the nurses’ station, HCW hands, and pa-
tient skin and uses a dose-response function to model the
probability of a patient acquiring MRSA as a function of
their pathogen load. The simulation also models the effect
of HCW hand hygiene behaviors and environmental cleaning
strategies. To this baseline simulation, we add two distinct
architectural modifications: (i) splitting the central nurses’
station (labeled 10 in Fig. 1) into two smaller distinct nurses’
stations and (ii) doubling the surface area of the nursing
station, in effect diluting the level of surface contamination.
The first of these changes is motivated by the idea that if
the nurses’ station is split then HCWs will assort themselves
into two groups and interactions across the two groups of
HCWs at the nurses’ station will be minimized. This idea of
reducing contacts among HCWs is motivated by examples of
“staff cohorting” used in infection control to reduce infection
spread [8]. To obtain HCW partitions that could lead to the
greatest reduction in HCW contact duration, we solve a graph
partitioning problem on a HCW contact network obtained from
our HCW movement data.
Somewhat counter-intuitively, our results suggest that the
first architectural modification and the resulting reduction in
HCW-HCW contacts has little to no effect on the spread of
MRSA. In fact, as shown in Table V, this change actually leads
to a small percentage increase in the mean MRSA infection
counts (for these particular parameter settings). In contrast,
the second modification (increasing the surface area of the
nurses’ station) leads to a substantial reduction – between
12% and 22% for simulations with different parameters –
in the number of patients infected by MRSA. These results
suggest that the dynamics of an environmentally mediated
infection such as MRSA may be quite different from that of an
infection which is not substantially affected by environmental
contamination (e.g., respiratory infections or influenza). What
seems to matter in our model critically is the concentration of
pathogen among few individuals or surfaces. Reducing HCW-
HCW contacts seems to have no effect on this and, in fact,
may have the unintended effect of increasing local pathogen
concentration. On the other hand, increasing the surface area of
the nurses’ station dilutes its pathogen load and has significant
downstream effects on pathogen load on HCW hands and, in
turn, pathogen load on the patient skin. This effect seems to
have an analogue in the “dilution effect” studied in ecology
[9].
II. HCW MOVEMENT AND INTERACTION
In previous work, we have developed an accurate yet
inexpensive and easily deployed individual movement-tracking
and contact-tracing technology that directly captures the full
spatiotemporal contact network of HCWs as they go about
their duties [10]–[12].
A. Dialysis Unit Instrumentation
Our technology incorporates two types of elements: in-
dividual HCWs wear rechargeable badges, while additional
line-powered beacons (shown as green triangles in Fig. 1)
are placed in static locations to serve as spatial references.
Both badges and beacons consist of commercially available
wireless sensors, or motes. Whenever a beacon detects a
badge’s message, it records the unique identifier of the sender,
the received signal strength index (RSSI) associated with the
message, and the time the message was received. In a similar
fashion, badges record timestamps and identifiers of beacons
and other badges.
Four out of the 10 deployment days were short (i.e., approx
6.5 hrs, 1 shift) and the remaining six days were long (i.e.,
approx 14.75 hrs, 2 shifts). Badges were randomly distributed
to HCWs within job categories at the start of each shift 1
.
B. Extracting HCW Locations from Sensor Data
Because RSSI increases with proximity, the aggregation of
time-stamped badge messages recorded by the beacons can
be used to reconstruct badge positions over time, grounded in
space by the known locations of the beacons. Note, however,
that using badge-to-beacon RSSIs alone may not suffice, given
that the human body effectively absorbs RF energy and that
the physical orientation of the badge with respect to the
beacon may also reduce RSSI. Thus, in addition to badge-
to-beacon RSSI levels, we also incorporate the previous latent
positions of each badge as well as validated (i.e., confirmed by
both badges) badge-to-badge sightings in our reconstruction.
Our reconstruction algorithm’s utility function thus combines
the following three criteria: (i) minimize distances between
badges and beacons with high badge-to-beacon RSSIs, (ii)
minimize badge distance from its previous location, and (iii)
minimize badge distance to other badges with high mutual
1Note that, because we are not collecting identifiable patient data or the
mapping of badges to HCW identities, our institutional review board (IRB)
has determined that this research does not meet the regulatory definition of
human subjects research.
2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
962
Spatial simulation
3. Fig. 1. Schematic of the UIHC dialysis unit showing 9 patient chairs
and the nurses’ station in the central area. There are 22 stationary sensors
(“beacons”; shown as green triangles) placed in the unit to provide HCW
localization. Mobile sensors (“badges”) are randomly distributed within job
types to HCWs at the beginning of each shift.
0
25
C7 C6
0
25
C8 C5
0
25
C9 C4
0
25
C3
07:41
07:51
08:01
08:11
08:21
08:31
0
25
C1
07:41
07:51
08:01
08:11
08:21
08:31
C2
Distance of HCW3 from Chairs 2013-12-02 07:31:14~08:31:14
Distance(ft)
HorizontalLines:1ft,3ft,5ft
Fig. 2. Distance with respect to time for a particular HCW from each
of the nine chairs in the dialysis unit. The nine individual chair distance
plots are arranged to correspond, roughly speaking, to the position of the
chairs in the dialysis unit depicted in Fig 1. The three horizontal lines in
each distance plot correspond to 1ft, 3ft, and 5ft thresholds measured from
the corresponding dialysis chair, and represent three different definitions of
what it means for a HCW to be near a chair.
RSSI. Latent positions of these badges are taken to reflect the
latent positions of their respective HCWs for the remainder of
this paper.
C. Imputing Patient Dialysis Sessions from Sensor Data
Because we do not have access to patient records, we do not
know when a dialysis patient is being treated, nor do we know
when a dialysis chair is occupied. As modeling the spread
of infection requires knowledge of HCW-patient contacts, we
must first identify dialysis sessions; that is, the mapping of
patients to dialysis chairs and time intervals. To do so, we
exploit domain-specific knowledge about dialysis to impute
the start/end times of each dialysis session while making some
additional assumptions about the recurrence of each patient’s
treatment sessions.
At the start of a dialysis session, a HCW will necessarily
spend some time connecting the patient to the dialysis machine
[13]. Each session lasts for three to four hours [14], and, at the
end of the session, a HCW again must attend to the patient in
order to disconnect the machine. We can use these extended
interactions between HCW and patient at the beginning and
end of each dialysis sessions along with temporal constraints
on session duration and knowledge of the number of patients
treated per week to impute the likeliest pattern of dialysis
sessions. Fig. 2 is an example of one particular HCW’s
distance from each of the nine dialysis chairs over the course
of the morning of Day 10; we can clearly see a period
of extended interaction between this particular HCW and a
presumed patient in chair 3 from 8:00 to 8:25 in the morning.
We train a machine learning system to recognize the patterns
of extended interaction that typify the beginning or end of
a dialysis session in our HCW location data. We cast the
problem as a binary classification problem: we manually select
32 distance profiles, ranging from 7.5 minutes to 28.5 minutes
in length as positive examples and then generate a training set
by sliding a 7.5 minute (56 8-second timesteps) window over
the examples to generate 2196 equal length positive training
examples. Negative training examples consist of distance se-
quences of the same length from the next closest chair and
from randomly selected sequences that do not fit the desired
pattern; these negative instances are further augmented with
random noise to improve the robustness of the model, resulting
in a total of 4392 negative training examples. We held back
20% of the 6588 training instances, and then trained a multi-
layer perceptron on the remaining 80% of the instances using
mini-batch gradient descent. Finally, applying the classifier
to all of our HCW/chair data, we generated a set of daily
patient sessions on long days by selecting entry and exit
times randomly from several candidates of predicted patterns.
The average delivered treatment time of 227 (std. dev. 21
min) roughly matched the average conventional hemodialysis
treatment duration [14]. Between 13 to 20 patient sessions
were detected by our system for the 6 long deployment days.
III. SIMULATING THE SPREAD OF MRSA IN THE
DIALYSIS UNIT
We next perform a series of agent-based simulations that
explore the impact of our architectural design changes on
the transmission of MRSA over a fixed period of time. A
simulation consists of multiple replicates, where each replicate
replays a single day of HCW-patient, HCW-HCW and HCW-
environment interactions derived from the HCW location data
just described. Each interaction represents an opportunity for
pathogen exchange with another HCW, patient or environ-
mental surface according to a stochastic model of pathogen
transmission. Under certain conditions, a patient becomes
infected, and begins to shed pathogen which can subsequently
be spread to other patients in the same fashion. By varying
the structure of these interactions in accordance with our
proposed architectural changes, we can compare the impact
of these changes on the outcome of interest (here, the mean
number of infected patients over a collection of replicates). We
implemented the simulator in Python and allowed simulation
2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
963
4. replicates to run on multiple cores. A 30-day simulation runs
in 2.5 seconds for each core on Intel(R) Xeon(R) CPU E5-
2683 v4 @ 2.10GHz.
A. Patient Scheduling and Disease Model
For each simulation, we pick a long day and repeatedly
replay the interactions of all HCWs and all patients present in
the dialysis unit on that day. The results presented in this paper
are obtained by replaying Day 10 (a long day) during which 11
HCWs were present and there were 20 dialysis sessions. This
implies a cohort of 40 patients divided into two equal groups.
We assume that each group of 20 patients dialyzes three
times per week, on either a Monday-Wednesday-Friday or a
Tuesday-Thursday-Saturday dialysis schedule (the unit does
not operate on Sunday), for a total of 520 dialysis sessions
over 30 days, or 13 dialysis sessions per patient in just over
four weeks. Patients are randomly assigned to chairs on a
particular shift (morning, afternoon, and evening) each day
in the simulation.
Patients adhere to a simple Susceptible-Infected-Susceptible
(SIS) model [15]. On the first simulated day, a single patient in
the morning session is randomly identified as infected; all other
patients are initially assumed to be susceptible. An infected
patient continuously sheds pathogen at the shedding rate α
[5]; we assume this pathogen immediately settles on nearby
surfaces, including the patient’s skin, the hands of any HCW
who is presently in contact with the patient, and the patient’s
chair. After 10 days, a patient returns to the susceptible state.
HCWs do not adhere to the SIS model; they never become
infected, but can instead be colonized, serving to transfer
pathogen within the simulation.
B. Pathogen Transfer Model
Pathogen transfer occurs via 5 different types of contacts:
(i) HCW-patient, (ii) HCW-HCW, (iii) HCW-chair, (iv) patient-
chair, and (v) HCW-nurses’ station. We measure contacts in
8-second quanta (based on the granularity of our sensor data),
and in most cases a contact is said to occur between two
entities if they are separated by a distance of at most 1 ft. The
single exception to this rule concerns HCW-HCW contacts,
where an additional parameter τhcw denotes the fraction of
times (sampled uniformly at random) that the presence of two
HCWs within 1 ft of each other represents an effective contact.
The τhcw parameter reflects the fact that while HCWs are
likely to pass close to one another in the course of their duties,
most of these interactions do not involve any actual touching
or exchanging of physical objects that may transfer pathogen
between them.
Each contact between two entities A and B results in some
quantity of MRSA pathogen being transferred bidirectionally
between the entities [5]. Specifically, the volume of pathogen
transferred from A to B is the volume of pathogen in the
contact area of A times the transfer efficiency between surfaces
of A and B (and vice-versa). We operationalize this principle
by making two assumptions: (i) contact between HCWs and
other entities occurs only via HCW hands and (ii) pathogen is
Fig. 3. Diagram of the simulation model. Circles represent agents in the
simulation (green: HCW, red: infected patient, blue: susceptible patient) and
compartments surrounded by black dashed lines are environments (left: nurses
station, right: chairs). Arrows represent pathogen transfer (red dashed arrow:
shedding of an infected patient, yellow arrow: contacts between agents, brown
arrow: contacts between agents and the environment).
always spread uniformly over all the surfaces we model (i.e.,
HCW hands, patients’ skin, chair surfaces, and the surface of
the nurses’ station). With these assumptions, we can simulate
MRSA transfer during contacts by tracking the volume of
MRSA pathogen on different surfaces. We use MRSAhcw i,
1 ≤ i ≤ 11, MRSApt j, 1 ≤ j ≤ 40, MRSAch k,
1 ≤ k ≤ 9, and MRSAns to denote the volume of pathogen
on the hands of HCW i, the skin of patient j, the surface of
chair k, and the surface of the nurses’ station, respectively.
An example should help make this process clear. Consider
the case where HCW i comes in contact with patient j (Fig. 3
shows all the possible transfer types used in our simulations).
Letting ρsk-sk denote skin-to-skin transfer efficiency of the
MRSA pathogen, Ah denote the surface area of a hand, and
Apt denote the surface area of a patient, we see that ρsk-sk ·
MRSAhcw i is the amount of MRSA transfered from HCW
i’s hand to patient j’s skin, while ρsk-sk ·MRSApt j ·Ah/Apt
is the amount of MRSA transfer from patient j’s hand to HCW
i’s hand. In the latter expression, Ah/Apt denotes the fraction
of the patient’s skin surface that is in contact with the HCW’s
hands and therefore, thanks to our uniform pathogen mixing
assumption, MRSApt j · Ah/Apt is the volume of pathogen
on patient j that has the potential to be transferred to HCW
i’s hands. It should be noted that Ah and Apt are considered
identical for all hands and all patients respectively. See Table
I for formulae for the volume of pathogen transfer for all 5
types of contacts.
C. Pathogen Reduction Model
MRSA is introduced into the simulation by shedding of
pathogen from the initially infected patient, and subsequently
increased by the addition of other infected patients. In a
similar fashion, MRSA pathogen can also be removed from
the simulation via three distinct mechanisms: (i) HCWs per-
forming hand hygiene, (ii) environmental cleaning, and (iii)
natural decay. Every HCW starts the day with clean hands
and accumulates pathogen over the course of the day. We use
λ to denote the fraction of MRSAhcw i removed from HCW
i’s hands by performing hand hygiene [16]. In the simulation,
2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
964
5. we view the arrival of a HCW at a patient’s chair or the
nurses’ station as a hand hygiene opportunity. We assume that
all HCWs share the same hand hygiene compliance rate γ
[12]. In a similar fashion, we use to denote the fraction of
pathogen removed from surfaces when cleaned [17] (note that
chairs are cleaned after each dialysis session, while the nurses’
station is cleaned only at the end of the day). Finally, MRSA
pathogen naturally decays at a rate of µsk from skin and a
rate of µnp from non-porous surfaces (such as chairs and the
nurses’ stations) [5]. And while patients do not dialyze on
Sunday, our simulations do apply this natural decay process
every seventh day.
D. Disease State Transition Model
Colonization, or the presence of MRSA pathogen on a
patient’s skin, may result in the patient becoming infected. We
model this process using dose-response functions that define
the probability of infection f(MRSApt j) for patient j in
terms of MRSApt j, the volume of MRSA pathogen present
on patient j’s skin [5], [18]. In our simulations, we used two
different dose-response models: (i) a linear model and (ii) an
exponential model. The linear model has the form f(x) = πx
where π is the infectivity of a pathogen. This model assumes
a linear relationship between the volume of pathogen and
the probability of an individual transitioning from susceptible
to infected. In contrast, the exponential model has the form
f(x) = 1−e−πx
, which assumes that each pathogen can infect
an individual independent of other pathogens [18]. For the
simulations, we used π ∈ { 1
5M , 1
7.5M } because these values
produce an infection rate consistent with the observed MRSA
infection statistic in a dialysis unit [3]. An infected patient can
shed pathogen for days or weeks [7], but usually sheds for less
than ten days if treated [19]. We assume that infected patients
transition from infected to susceptible after 10 days.
This completes the description of our baseline model. Table
III shows all the parameters used in the baseline simulation
along with their values and in most cases, sources for these
values.
IV. MODELING ARCHITECTURAL CHANGES
We propose two simple, low-cost architectural changes to
the dialysis unit and evaluate their impact on MRSA diffusion.
TABLE I
VOLUME OF PATHOGEN TRANSFER
MRSA Transfer Between Entities
Source Target MRSA Transfer (Source to Target)
HCW i patient j ρsk-sk · MRSAhcw i
patient j HCW i ρsk-sk · MRSApt j · Ah/Apt
HCW i HCW l ρsk-sk · MRSAhcw i
HCW l HCW i ρsk-sk · MRSAhcw l
HCW i chair k ρsk-np · MRSAhcw i
chair k HCW i ρsk-np · MRSAch k · Ah/Ach
patient j chair k ρsk-np · MRSApt j · Ah/Apt
chair k patient j ρsk-np · MRSAch k · Ah/Ach
HCW i nurses’ station ρsk-np · MRSAhcw i
nurses’ station HCW i ρsk-np · MRSAns · Ah/Ans
These simple architectural changes suffice to help us better
understand the role of the environment in MRSA diffusion.
A. Splitting the Nurses’ Station into Two Stations
The first simple architectural change we consider is to split
the nurses’ station NS into two stations NS1 and NS2, each
with surface area half of the surface area of NS. This change
is motivated by the idea that HCWs will assort themselves into
two-equal-size groups and interaction across the two groups of
HCWs at the nurses’ station will be minimized. As a result,
MRSA pathogen transfer across the groups is substantially
reduced. We hypothesize that this architectural change will
reduce mean infection counts. This idea is motivated by
examples of “staff cohorting” used in infection control to
reduce infection spread [8].
To implement this change in the simulation, we partition the
HCWs equitably into two groups H1 and H2 and assign Hi to
NSi, i = 1, 2. Each contact between a HCW h ∈ Hi and NS
in the original simulation is replaced by a contact between h
and NSi. Also, each contact between h ∈ H1 and h ∈ H2
that occurs at NS is removed, modeling the fact that HCWs h
and h now visit different nurses’ stations. We evaluate three
methods for partitioning the HCWs equitably into H1 and H2.
The first two methods are motivated by the aim of maximizing
the duration of contact between HCWs in H1 and HCWs in H2
so that when we delete all contacts between H1 and H2, this
will result in the greatest reduction of contact duration between
the HCWs. We formalize this idea via the MAXBISECTION
problem which takes as input an edge-weighted graph G =
(V, E) and whose output is required to be a partition (V1, V2)
of the vertex set such that |V1| = |V |/2 , |V2| = |V |/2
and the weight the edges in {{u, v} | u ∈ V1, v ∈ V2} is
maximized. Let Gns (respectively, Gall) be the HCW contact
graph whose vertices are HCWs and each of whose edges
{h, h } are weighted by the total contact duration between h
and h that occurs at the nurses’ station (respectively, anywhere
in the unit).
1) Solve MAXBISECTION on Gns and used the returned
partition.
2) Solve MAXBISECTION on Gall and used the returned
partition.
3) Partition the HCWs into two (roughly) equal-sized
groups and picked uniformly at random from the 11
5
possible grouping of HCWs into five and six members.
The first two grouping strategies require a priori infor-
mation on HCW mobility, whereas the random strategy is
independent of HCW mobility and is easier to implement in
practice.
B. Doubling the Surface Area of the Nurses Station
In our simulation model, we assume that MRSA spreads
uniformly on surfaces whenever MRSA transfer between en-
tities occur. Based on this assumption, increasing the surface
area of the nurses’ station has the effect of diluting MRSA
concentration at the nurses’ station. In our second, simple
architectural change, we double the surface area of the nurses’
2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
965
6. station. Note that we do not split the nurses’ station when we
make this architectural change. We hypothesize that doubling
the surface of the nurses’ station will reduce mean MRSA
infection counts.
The specific policies we implement based on the architec-
tural changes are summarized in Table II.
V. RESULTS
There are several sources of stochasticity in our simulation
model, namely (i) chair placement of initially infected patient,
(ii) chair placement of remaining patients, (iii) dampening
of HCW-HCW contacts based on parameter τhcw, (iv) HCW
hand hygiene compliance based on parameter γ, (v) infectivity
of patients according to the probability given by the dose-
response function, and (vi) variation (by a few minutes) of
patient entry and exit times at chairs. To account for this
stochasticity, we report statistics over 1000 replicates of each
simulation. Fig. 4 shows the distribution of infection counts,
i.e., the number of additional infected patients beyond the
initially infected patient, over the 1000 simulation replicates
performed using the baseline simulation parameters in Ta-
ble III. The mean and median infection counts for the baseline
simulation are 3.287 and 2 respectively with a std. dev. of
4.129.
Fig. 5 contains four plots comparing mean infection counts
from simulations using the five different policies mentioned
above. The simulations in Fig. 5a use the baseline simulation
parameters, whereas the remaining figures are obtained by
TABLE II
NURSES STATION ARCHITECTURE CHANGE POLICIES
Policy Architecture Change HCW Grouping
0 None (baseline) No Grouping
1 Split into two stations Random Grouping
2 Split into two stations Max Bisection on Gns
3 Split into two stations Max Bisection on Gall
4 Double the surface area No Grouping
TABLE III
BASELINE SIMULATION PARAMETERS
Parameter Symbol Value Ref
Shedding rate (cfu/cm2/8s) α 0.001333 [5]
Die-off rate on skin (/8s) µsk 0.000471 [5]
Die-off rate on environments (/8s) µnp 0.000027 [5]
Transfer efficiency: skin-skin ρsk-sk 0.35 [5]
Transfer efficiency: skin-env ρsk-np 0.4 [5]
Area of patient’s exposed skin (cm2) Apt 2000 [5]
Area of HCW’s exposed skin (cm2) Ahcw 150 -
Area of hand contact surface (cm2) Ah 150 [5]
Area of chair surface (cm2) Ach 3600 -
Area of nurses’ station (cm2) Ans 41000 -
Decontamination efficacy 0.5 [17]
Hand hygiene compliance γ 0.279 [12]
Hand hygiene efficacy λ 0.83 [16]
Rate of HCW-HCW contact τhcw 0.05 -
Infection duration d 10 [7]
Dose-response function f(x) exponential [18]
MRSA Infectivity π 1
7.5M
-
Fig. 4. Distribution of infection counts in 1000 repetitions of the baseline
simulation using the model parameters in Table III. The mean and median
infection counts on the baseline simulation are 3.287 and 2, respectively with
a std. dev. of 4.129. The mean infection count is depicted as a vertical line.
varying one parameter each relative to the baseline. In Fig. 5b,
we replace the exponential dose-response function with a
linear dose-response function. In Fig. 5c we use τhcw = 0.5
instead of τhcw = 0.05, i.e., we assume that 50% of HCW-
HCW contacts are “touch” contacts as opposed to just 5%.
In Fig. 5d we use a higher infectivity of π = 1
5M instead of
π = 1
7.5M . Infection counts are cumulative over 30 days, and
the count per day is averaged over 1000 simulation replicates
for each policy, which are depicted as line graphs. For the
random HCW grouping policy, we repeat this procedure ten
times – each repetition likely yielding a different partition of
the HCWs. We show the distribution of the mean infection
counts over ten random HCW groups with an associated
boxplot showing the distribution over the ten repetitions.
Our main discovery is that, somewhat counter-intuitively,
splitting the nurses’ station into two stations does not much
affect mean infection counts. However, doubling the surface
area of the nurses’ station substantially reduces infection
counts for all four simulations in Fig. 5 (a t-test on the 1000
replicates of policy 0 and policy 4 for the 4 simulations in
Fig. 5a yielded p-values 0.030, 0.041, 0.001, and 2.14e−7
respectively).
Table IV shows the probability of an outbreak for various
policies and parameter settings, where an outbreak is defined
as a simulation having an attack rate larger than 0.05 (more
than two additionally infected patients) [20]. Doubling the
surface area of the nurses’ station reduced the probability of
an outbreak compared to other policies.
TABLE IV
PROBABILITY OF AN OUTBREAK WHILE USING DIFFERENT POLICIESa
Parameters Policy0 Policy1 Policy2 Policy3 Policy4
Baseline (Fig. 5a) 0.410 0.416 0.377 0.432 0.367
f(x) = linear (Fig. 5b) 0.405 0.422 0.411 0.422 0.355
τhcw = 0.5 (Fig. 5c) 0.413 0.424 0.422 0.430 0.362
π = 1
5M
(Fig. 5d) 0.657 0.678 0.679 0.663 0.608
aOutbreak if an attack rate for a simulation is larger than 0.05 [20]
2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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7. (a) Baseline Parameters (b) Baseline Parameters with f(x) = linear
(c) Baseline Parameters with τhcw = 0.5 (d) Baseline Parameters with π = 1
5M
Fig. 5. Cumulative infection counts for four different parameter settings. Fig. 5a uses the baseline simulation parameters; the remaining plots are
obtained by changing one parameter value each from the baseline parameters. Five different graphs are present in each plot (blue line: baseline policy, purple
boxplot: random grouping policy, orange line: max bisection on Gns, green line: max bisection on Gall, and red stared line: nurses station area doubling).
In supplementary material [21] we provide the results of
a number of additional simulations, showing that our results
are robust. Specifically, using other long days (besides Day
10) as the basis for simulations does not change our results.
Neither does using other values of the parameter τhcw (rate of
HCW-HCW contact).
A. Discussion
The fact that a reduction in HCW-HCW contacts that results
from the first architectural change does not consistently reduce
MRSA spread is an important take away from our work.
TABLE V
PERCENTAGE CHANGES IN MEAN INFECTION COUNTS OF DIFFERENT
POLICIESa
Parameters Policy1 Policy2 Policy3 Policy4
Baseline (Fig. 5a) 1% -3% 9% -12%
f(x) = linear (Fig. 5b) 11% -1% 3% -12%
τhcw = 0.5 (Fig. 5c) 7% 6% 2% -19%
π = 1
5M
(Fig. 5d) 4% 2% 1% -22%
aPercentage changes are relative to that of Policy0.
This result suggests that the dynamics of an environmentally
mediated infection such as MRSA may be quite different from
that of other infections, such as respiratory infections or in-
fluenza in which environmental contamination may not play a
substantial role. The result also cautions against the unintended
consequences of reducing HCW interactions. In seeking an
explanation for this finding, we note that the exponential dose-
response function f(x) = 1 − e−π·x
is concave and therefore
subadditive, i.e., f(x1) + f(x2) ≥ f(x1 + x2). Thus the
expected number of infections is higher when the pathogen
load “mixes” among patients, leading to loads x1 and x2 at
two patients rather than x1 +x2 at a single patient. We expect
to see more “mixing” in the baseline simulation relative to
the simulation with the first architectural change and due to
the above-mentioned property of the dose-response function,
we expect to see more infection in the baseline simulation.
However, this is not what we observe and this implies that
there are other factors at play that counter the role of “mixing”
of pathogen loads. Our hypothesis is that the extra HCW-
HCW contacts in the baseline simulation are playing a role in
spreading more MRSA among the HCWs and on the nurses’
2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
967
8. station, thereby reducing the overall MRSA load on patients.
We aim to test this hypothesis in future work.
The fact that increasing the surface area of the nurses’
station plays a significant role in reducing MRSA spread is
another important take away from our work. Our hypothesis
is that increasing the nurses’ station area dilutes pathogen load
at the nurses’ station, which has significant downstream effects
on pathogen load on HCW hands and, in turn, pathogen load
on the patient skin. Another way to view this phenomenon is
that a larger nurses’ station is a larger reservoir for pathogen,
thereby diluting the volume of pathogen that reaches patients.
This effect seems to have an analogue in the “dilution effect”
studied in ecology [9]. We aim to test this hypothesis in
future work, as well, by measuring pathogen loads on various
surfaces over the course of our simulations.
B. Limitations
Studies have shown that HCWs play a significant role in
the spread of HAIs and that hands of HCWs act as vectors
for pathogen transmission [22]. However, it is unclear if the
role of HCWs in this process is just as a vector or whether
HCWs could serve as a source of pathogen shedding. Albrich
et al. [23] show that among 33,000 HCWs, 4.6% carry MRSA
and these colonized HCWs may be viewed as the source of
MRSA transmission. Our simulation model view HCWs as
vectors of MRSA transmission only. MRSA dynamics could
change if our simulations allow HCWs to start shedding
MRSA in the dialysis unit.
Additional careful sensitivity analysis over the parameter
space is needed before our results can be considered robust.
For example, our model assumes that patients start shedding
MRSA at a fixed level immediately after they get infected.
However, the amount and duration of MRSA shedding, as
well as the point in time at which an infected patient starts
shedding, may differ from patient to patient.
ACKNOWLEDGMENT
This project is funded by CDC MInD-Healthcare via CDC
cooperative agreement U01CK000531. The authors acknowl-
edge the work of Ted Herman in instrumentation and data-
gathering efforts in the UIHC dialysis unit. The authors also
acknowledge feedback received on a presentation of these
results from the CDC MiND-Healthcare group. The authors
thank feedback from other University of Iowa CompEpi group
members.
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