The document discusses using machine learning and statistical modeling to predict and understand the impact of COVID-19 in North Dakota. Researchers used random forest models to predict county-level COVID-19 cases, hospitalizations, and deaths based on demographic, health, and geographic factors. They also used quasi-binomial models to investigate the effects of independent variables on the outcomes. The models helped identify counties most affected by COVID-19 and determine important predictors.
A look at two different Datasets (infection data & mobility data to make some predictions about Corona Virus. The main takeaways:
1. Without a vaccine Corona is here to stay for 18 months till herd immunity. We need to have cyclical lockdowns of 2 weeks lockdown 6 weeks opening.
2. The structure of a city dictates whether a lockdown works or not. Rural and Nature heavy cities like Utah can't follow the same strategy like NY or Manhattan.
THE ROLE OF STATE-WIDE STAY-AT-HOME POLICIES ON CONFIRMED COVID-19 CASES IN T...hiij
In January 2020, the first confirmed case of the novel severe acute respiratory syndrome coronavirus 2 emerged in the United States of America. By March 2020, the USA had eclared a national emergency and implemented stay-at-home policies subject to the individual initiative of health authorities of each state. However, ambiguity in the literature exists about the extent to which temporal variation of stay-at-home implementation contributes to an effective stay-at-home order. To examine the role of the implementation of stay-at-home policy at the county level on outbreak progression, we compiled the case count data and dates of policy commencement for 1720 counties from the US Counties: Socio-Health Data database. Measures of central tendency and rate of change identified correlation between the change of confirmed case counts compared to time, quantified by comparing four successive time points of 5 days to the initial date of each county’s stay-at-home implementation. We then used a deterministic county-level SIR epidemiological model to predict post stay-at-home case counts based on pre-stay-at-home parameters and compared the model to actual post-stay-at-home case counts to identify the degree of error Mean Squared Error (MSE). Our analyses demonstrated the high error between time since stay-at-home implementation and change in actual case counts compared to predicted case counts, which suggests an interaction between policy and COVID-19 transmission. Our findings shine light on the confounding variables of stay-at-home policy at the county level and the promising outlook of stay-at-home policy in the USA.
THE ROLE OF STATE-WIDE STAY-AT-HOME POLICIES ON CONFIRMED COVID-19 CASES IN T...hiij
In January 2020, the first confirmed case of the novel severe acute respiratory syndrome coronavirus 2
emerged in the United States of America. By March 2020, the USA had declared a national emergency and
implemented stay-at-home policies subject to the individual initiative of health authorities of each state.
However, ambiguity in the literature exists about the extent to which temporal variation of stay-at-home
implementation contributes to an effective stay-at-home order. To examine the role of the implementation
of stay-at-home policy at the county level on outbreak progression, we compiled the case count data and
dates of policy commencement for 1720 counties from the US Counties: Socio-Health Data database.
Measures of central tendency and rate of change identified correlation between the change of confirmed
case counts compared to time, quantified by comparing four successive time points of 5 days to the initial
date of each county’s stay-at-home implementation. We then used a deterministic county-level SIR
epidemiological model to predict post stay-at-home case counts based on pre-stay-at-home parameters
and compared the model to actual post-stay-at-home case counts to identify the degree of error Mean
Squared Error (MSE). Our analyses demonstrated the high error between time since stay-at-home
implementation and change in actual case counts compared to predicted case counts, which suggests an
interaction between policy and COVID-19 transmission. Our findings shine light on the confounding
variables of stay-at-home policy at the county level and the promising outlook of stay-at-home policy in the
USA.
Use of Digital Technologies in Public Health Responses to Tackle Covid-19: th...hiij
This paper aims to study the fight against COVID-19 in Bangladesh and digital intervention initiatives. To achieve the purpose of our research, we conducted a methodical review of online content. We have reviewed the first digital intervention that COVID-19 has been used to fight against worldwide. Then we reviewed the initiatives that have been taken in Bangladesh. Our paper has shown that while Bangladesh can take advantage of the digital intervention approach, it will require rigorous collaboration between government organizations and universities to get the most out of it. Public health can become increasingly digital in the future, and we are reviewing international alignment requirements. This exploration also focused on the strategies for controlling, evaluating, and using digital technology to strengthen epidemic management and future preparations for COVID-19.
USE OF DIGITAL TECHNOLOGIES IN PUBLIC HEALTH RESPONSES TO TACKLE COVID-19: TH...hiij
This paper aims to study the fight against COVID-19 in Bangladesh and digital intervention initiatives. To
achieve the purpose of our research, we conducted a methodical review of online content. We have
reviewed the first digital intervention that COVID-19 has been used to fight against worldwide. Then we
reviewed the initiatives that have been taken in Bangladesh. Our paper has shown that while Bangladesh
can take advantage of the digital intervention approach, it will require rigorous collaboration between
government organizations and universities to get the most out of it. Public health can become increasingly
digital in the future, and we are reviewing international alignment requirements. This exploration also
focused on the strategies for controlling, evaluating, and using digital technology to strengthen epidemic
management and future preparations for COVID-19.
Health Informatics - An International Journal (HIIJ)hiij
Health Informatics - An International Journal (HIIJ)
USE OF DIGITAL TECHNOLOGIES IN PUBLIC
HEALTH RESPONSES TO TACKLE COVID-19: THE
BANGLADESH PERSPECTIVE
https://aircconline.com/hiij/V11N1/11122hiij01.pdf
Vol.11, No.1, February 2022
DOI : 10.5121/hiij.2022.11101
A look at two different Datasets (infection data & mobility data to make some predictions about Corona Virus. The main takeaways:
1. Without a vaccine Corona is here to stay for 18 months till herd immunity. We need to have cyclical lockdowns of 2 weeks lockdown 6 weeks opening.
2. The structure of a city dictates whether a lockdown works or not. Rural and Nature heavy cities like Utah can't follow the same strategy like NY or Manhattan.
THE ROLE OF STATE-WIDE STAY-AT-HOME POLICIES ON CONFIRMED COVID-19 CASES IN T...hiij
In January 2020, the first confirmed case of the novel severe acute respiratory syndrome coronavirus 2 emerged in the United States of America. By March 2020, the USA had eclared a national emergency and implemented stay-at-home policies subject to the individual initiative of health authorities of each state. However, ambiguity in the literature exists about the extent to which temporal variation of stay-at-home implementation contributes to an effective stay-at-home order. To examine the role of the implementation of stay-at-home policy at the county level on outbreak progression, we compiled the case count data and dates of policy commencement for 1720 counties from the US Counties: Socio-Health Data database. Measures of central tendency and rate of change identified correlation between the change of confirmed case counts compared to time, quantified by comparing four successive time points of 5 days to the initial date of each county’s stay-at-home implementation. We then used a deterministic county-level SIR epidemiological model to predict post stay-at-home case counts based on pre-stay-at-home parameters and compared the model to actual post-stay-at-home case counts to identify the degree of error Mean Squared Error (MSE). Our analyses demonstrated the high error between time since stay-at-home implementation and change in actual case counts compared to predicted case counts, which suggests an interaction between policy and COVID-19 transmission. Our findings shine light on the confounding variables of stay-at-home policy at the county level and the promising outlook of stay-at-home policy in the USA.
THE ROLE OF STATE-WIDE STAY-AT-HOME POLICIES ON CONFIRMED COVID-19 CASES IN T...hiij
In January 2020, the first confirmed case of the novel severe acute respiratory syndrome coronavirus 2
emerged in the United States of America. By March 2020, the USA had declared a national emergency and
implemented stay-at-home policies subject to the individual initiative of health authorities of each state.
However, ambiguity in the literature exists about the extent to which temporal variation of stay-at-home
implementation contributes to an effective stay-at-home order. To examine the role of the implementation
of stay-at-home policy at the county level on outbreak progression, we compiled the case count data and
dates of policy commencement for 1720 counties from the US Counties: Socio-Health Data database.
Measures of central tendency and rate of change identified correlation between the change of confirmed
case counts compared to time, quantified by comparing four successive time points of 5 days to the initial
date of each county’s stay-at-home implementation. We then used a deterministic county-level SIR
epidemiological model to predict post stay-at-home case counts based on pre-stay-at-home parameters
and compared the model to actual post-stay-at-home case counts to identify the degree of error Mean
Squared Error (MSE). Our analyses demonstrated the high error between time since stay-at-home
implementation and change in actual case counts compared to predicted case counts, which suggests an
interaction between policy and COVID-19 transmission. Our findings shine light on the confounding
variables of stay-at-home policy at the county level and the promising outlook of stay-at-home policy in the
USA.
Use of Digital Technologies in Public Health Responses to Tackle Covid-19: th...hiij
This paper aims to study the fight against COVID-19 in Bangladesh and digital intervention initiatives. To achieve the purpose of our research, we conducted a methodical review of online content. We have reviewed the first digital intervention that COVID-19 has been used to fight against worldwide. Then we reviewed the initiatives that have been taken in Bangladesh. Our paper has shown that while Bangladesh can take advantage of the digital intervention approach, it will require rigorous collaboration between government organizations and universities to get the most out of it. Public health can become increasingly digital in the future, and we are reviewing international alignment requirements. This exploration also focused on the strategies for controlling, evaluating, and using digital technology to strengthen epidemic management and future preparations for COVID-19.
USE OF DIGITAL TECHNOLOGIES IN PUBLIC HEALTH RESPONSES TO TACKLE COVID-19: TH...hiij
This paper aims to study the fight against COVID-19 in Bangladesh and digital intervention initiatives. To
achieve the purpose of our research, we conducted a methodical review of online content. We have
reviewed the first digital intervention that COVID-19 has been used to fight against worldwide. Then we
reviewed the initiatives that have been taken in Bangladesh. Our paper has shown that while Bangladesh
can take advantage of the digital intervention approach, it will require rigorous collaboration between
government organizations and universities to get the most out of it. Public health can become increasingly
digital in the future, and we are reviewing international alignment requirements. This exploration also
focused on the strategies for controlling, evaluating, and using digital technology to strengthen epidemic
management and future preparations for COVID-19.
Health Informatics - An International Journal (HIIJ)hiij
Health Informatics - An International Journal (HIIJ)
USE OF DIGITAL TECHNOLOGIES IN PUBLIC
HEALTH RESPONSES TO TACKLE COVID-19: THE
BANGLADESH PERSPECTIVE
https://aircconline.com/hiij/V11N1/11122hiij01.pdf
Vol.11, No.1, February 2022
DOI : 10.5121/hiij.2022.11101
ANALYZING THE EFFECTS OF DIFFERENT POLICIES AND STRICTNESS LEVELS ON MONTHLY ...IJDKP
The corona virus is one of the most unprecedented events of recent decades. Countries struggled to identify
appropriate COVID-19 policies to prevent virus spread effectively. Although much research has been
done, little focused on policy effectiveness and their enforcement levels. As corona virus cases and death
numbers fluctuated among countries, questions of which policies are most effective in preventing corona
virus spread and how strict they should be implemented have yet to be answered. Countries are prone to
making policy and implementation errors that could cost lives. This research identified the most effective
policies and their most effective enforcement levels through data analysis of 12 common coronavirus
policies. A monthly case increase rate prediction model was developed to enable decision makers to
evaluate the effectiveness of COVID-19 policies and their enforcement levels so that they can implement
policies efficiently to save lives, time, and money.
Morbid and Mortal Inequities among Indigenous Peoples during the COVID-19 Pan...AmyAlberton1
The COVID-19 pandemic has illuminated gross racialized health inequities and injustices (Mackey et al., 2021). Evidence of the widespread and harmful impacts of the COVID-19 pandemic across diverse populations in Canada and the United States of America (USA) is voluminous (Clark et al., 2021; Mateen et al., 2020; Wendt et al., 2021). While the pandemic has revealed the much greater relative health risks experienced by racialized/ethnic people, the primary and synthetic evidence thus far has focused primarily on Latinx and Black people (Mackey et al., 2021). To date, there has been a relative lack of primary study and a complete absence of synthetic study of the relative morbid and mortal COVID-19-related risks experienced by Indigenous peoples (Douglas et al., 2021; Waldner et al., 2021).
This rapid review, the first synthetic study of Indigneny-COVID-19 inequities in North America, hypothesized certain Indigenous protections based upon Indigenous cultural strengths and certain risks based upon Indigenous peoples’ long histories of structural violence in North America. First, the pooled relative risk of COVID-19 among Indigenous peoples compared with otherwise similar non-Indigenous people was statistically and practically significant, indicating that Indigenous peoples were two-thirds more likely to be infected or die with COVID-19 as the primary or contributing cause of death (RR = 1.65). Second, Indigenous peoples’ risk of death (RR = 2.45) was significantly greater than their risk of infection (RR = 1.40), Indigenous peoples being about one and a half times as like to become ill with COVID-19 and two and a half times as likely to die as a result. Pre-existing, chronic health conditions secondary to lifetime structural violence exposures were likely responsible for the much worse mortal outcomes among Indigenous peoples. Third, despite long histories of oppression, providing Indigenous peoples with every reason to mistrust governments, their vaccination uptake rate was on par with that of non-Indigenous people, who were primarily non-Hispanic White people (RR = 1.02).
This rapid review provided evidence that inequalities exist among Indigenous and non-Indigenous people on COVID-19 related outcomes. Consistent with their lifetime exposures to discrimination and structural violence (Alberton, 2020), Indigenous peoples seemed clearly to be at relatively grave risk of having the most serious and deadly COVID-19 infections. However, consistent with cultural strengths theory, COVID-19 infection occurrences and vaccination uptake seemed much more equitably distributed with certain Indigenous people in some places even demonstrating significant protective advantages over non-Hispanic White people.
Planning for Community Resiliency in Recovery from COVID-19 in NBDataNB
The aim of this knowledge transfer session is to describe our research on a series of population-based indicators using data available at the NB-IRDT. These indicators can identify New Brunswick communities and citizens that may be more vulnerable to negative consequences of COVID-19 or provide evidence to support planning for targeted intervention and resource allocation. This session will describe the six high-level indicators in each of the 33 health council communities and will provide a more in-depth look at specific vulnerabilities. For example, seniors who live alone, individuals with COVID-19 relevant physical health conditions or those with diagnosed mental health disorders. Population-based risk indicators such as these can inform regional efforts to limit spread and exacerbation of infection in those most at-risk, and in helping to identify the at-risk groups likely impacted by measures to combat the spread of COVID-19.
Health equity through data and mapping on PolicyMapPolicyMap
Where do vulnerable populations live? Where are there inequalities in health outcomes? Data and mapping can be an extraordinary resource when trying to understand questions on health disparities. Join PolicyMap for a webinar examining the role of mapping in taking a data-driven approach towards achieving health equity.
Complications in big data-based communication in the wake of COVID-19: A comp...Selcen Ozturkcan
Johnson, E., R., Martínez, M. C., and Ozturkcan, S., "Complications in big data-based communication in the wake of COVID-19: A comparison of North American and Nordic practices of multinational healthcare corporations," Engineering and Technology Management Summit 2022, 20-21 Oct 2022, Istanbul, Turkey.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Role of Data Accessibility During PandemicDatabricks
This talk focuses on the importance of data access and how crucial it is, to have the granular level of data availability in the open-source space as it helps researchers and data teams to fuel their work.
We present to you the research conducted by the DS4C (Data Science for Covid-19) team who made a huge and detailed level of South Korea Covid-19 data available to a wider community. The DS4C dataset was one of the most impactful datasets on Kaggle with over fifty thousand cumulative downloads and 300 unique contributors. What makes the DS4C dataset so potent is the sheer amount of data collected for each patient. The Korean government has been collecting and releasing patient information with unprecedented levels of detail. The data released includes infected people’s travel routes, the public transport they took, and the medical institutions that are treating them. This extremely fine-grained detail is what makes the DS4C dataset valuable as it makes it easier for researchers and data scientists to identify trends and more evidence to support hypotheses to track down the cause and gain additional insights. We will cover the data challenges, impact that it had on the community by making this data available on a public forum and conclude it with an insightful visual representation.
Mapping Community-Level Prevalence of Modifiable Risk Factors for Dementia in...DataNB
A large proportion of dementia risk is attributable to modifiable factors such as physical inactivity, hypertension, and social isolation. Prevention strategies will be essential to mitigate the expected increased number of people living with dementia. Data on the distribution of risk factors can help support these efforts.
The objective of this study was to derive community-level prevalence estimates for dementia specific modifiable risk factors.
Statistics Canada Canadian Community Health Survey (CCHS; 2001-2020) data were used to develop prediction models for several mid-life (age 45-64; heavy drinking, obesity, hypertension) and late-life (age 65+; smoking, physical inactivity, social isolation, diabetes) risk factors. Prevalence was estimated from the prediction model using age and sex stratified Census (2001-2016) population counts for communities across New Brunswick. Spatial-temporal models were used to increase the robustness of predicted prevalence estimates.
The risk factors with the highest prevalence were physical inactivity (67%), obesity (34%), and hypertension (31%). These three risk factors, in addition to risk factors for social isolation and smoking, were also found to have highest variability across communities. The prevalence of obesity, hypertension and diabetes increased over time, whereas smoking and social isolation remained consistent. While physical inactivity had the highest prevalence, this was found to decrease over time.
National population-based survey and Census data can be used to inform of the burden of dementia risk factors at the community-level. Community-level risk factor data may be helpful in directing resources to communities with the highest burden and to monitor changes in risk for these communities.
Geospatial Analysis: Innovation in GIS for Better Decision MakingMEASURE Evaluation
Discussion led by John Spencer and Mark Janko. This webinar shared new techniques in geospatial analysis and how they have the potential to transform data-informed decision making.
Review of Recent COVID-19 Science ~ Denis G. Rancourt, PhDPandataAnalytics
Measures do not prevent deaths, transmission is not by contact, masks provide no benefit, vaccines are inherently dangerous: Review update of recent science relevant to COVID-19 policy.
Economic analysis of malaria burden in kenyaNanyingi Mark
This framework uses a cost of illness approach to evaluate the burden of malaria. The evaluation is based on private direct costs (PDC) and private indirect cost (PIC) of malaria attack per episode.
Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Explorato...Konstantinos Demertzis
Within the complex framework of anti-COVID-19 health management, where the criteria of diagnostic testing, the availability of public-health resources and services, and the applied anti-COVID-19 policies vary between countries, the reliability and accuracy in the modeling of temporal spread can prove to be effective in the worldwide fight against the disease. This paper applies an exploratory time-series analysis to the evolution of the disease in Greece, which currently suggests a success story of COVID-19 management. The proposed method builds on a recent conceptualization of detecting connective communities in a time-series and develops a novel spline regression model where the knot vector is determined by the community detection in the complex network. Overall, the study contributes to the COVID-19 research by proposing a free of disconnected past-data and reliable framework of forecasting, which can facilitate decision-making and management
of the available health resources.
The Fight Against COVID-19: A National Patient RegistryHealth Catalyst
Comprehensive COVID-19 understanding is a critical asset for adapting to pandemic needs, directing resources, developing vaccines, and planning for surges in a timely, informed manner. Because common barriers have impeded the progress of comprehensive data repositories, researchers have relied on surveillance data from population-level viral testing, which has proven insufficient. To significantly advance COVID-19 understanding, the medical community needs a digital patient registry that captures national-level data on how the virus impacts individuals differently according to comorbidities, lifestyle factors, and more. These essential insights lie in real-world evidence, which a registry can only deliver when it applies value sets to leverage clinical and claims data from health systems across the United States.
ANALYZING THE EFFECTS OF DIFFERENT POLICIES AND STRICTNESS LEVELS ON MONTHLY ...IJDKP
The corona virus is one of the most unprecedented events of recent decades. Countries struggled to identify
appropriate COVID-19 policies to prevent virus spread effectively. Although much research has been
done, little focused on policy effectiveness and their enforcement levels. As corona virus cases and death
numbers fluctuated among countries, questions of which policies are most effective in preventing corona
virus spread and how strict they should be implemented have yet to be answered. Countries are prone to
making policy and implementation errors that could cost lives. This research identified the most effective
policies and their most effective enforcement levels through data analysis of 12 common coronavirus
policies. A monthly case increase rate prediction model was developed to enable decision makers to
evaluate the effectiveness of COVID-19 policies and their enforcement levels so that they can implement
policies efficiently to save lives, time, and money.
Morbid and Mortal Inequities among Indigenous Peoples during the COVID-19 Pan...AmyAlberton1
The COVID-19 pandemic has illuminated gross racialized health inequities and injustices (Mackey et al., 2021). Evidence of the widespread and harmful impacts of the COVID-19 pandemic across diverse populations in Canada and the United States of America (USA) is voluminous (Clark et al., 2021; Mateen et al., 2020; Wendt et al., 2021). While the pandemic has revealed the much greater relative health risks experienced by racialized/ethnic people, the primary and synthetic evidence thus far has focused primarily on Latinx and Black people (Mackey et al., 2021). To date, there has been a relative lack of primary study and a complete absence of synthetic study of the relative morbid and mortal COVID-19-related risks experienced by Indigenous peoples (Douglas et al., 2021; Waldner et al., 2021).
This rapid review, the first synthetic study of Indigneny-COVID-19 inequities in North America, hypothesized certain Indigenous protections based upon Indigenous cultural strengths and certain risks based upon Indigenous peoples’ long histories of structural violence in North America. First, the pooled relative risk of COVID-19 among Indigenous peoples compared with otherwise similar non-Indigenous people was statistically and practically significant, indicating that Indigenous peoples were two-thirds more likely to be infected or die with COVID-19 as the primary or contributing cause of death (RR = 1.65). Second, Indigenous peoples’ risk of death (RR = 2.45) was significantly greater than their risk of infection (RR = 1.40), Indigenous peoples being about one and a half times as like to become ill with COVID-19 and two and a half times as likely to die as a result. Pre-existing, chronic health conditions secondary to lifetime structural violence exposures were likely responsible for the much worse mortal outcomes among Indigenous peoples. Third, despite long histories of oppression, providing Indigenous peoples with every reason to mistrust governments, their vaccination uptake rate was on par with that of non-Indigenous people, who were primarily non-Hispanic White people (RR = 1.02).
This rapid review provided evidence that inequalities exist among Indigenous and non-Indigenous people on COVID-19 related outcomes. Consistent with their lifetime exposures to discrimination and structural violence (Alberton, 2020), Indigenous peoples seemed clearly to be at relatively grave risk of having the most serious and deadly COVID-19 infections. However, consistent with cultural strengths theory, COVID-19 infection occurrences and vaccination uptake seemed much more equitably distributed with certain Indigenous people in some places even demonstrating significant protective advantages over non-Hispanic White people.
Planning for Community Resiliency in Recovery from COVID-19 in NBDataNB
The aim of this knowledge transfer session is to describe our research on a series of population-based indicators using data available at the NB-IRDT. These indicators can identify New Brunswick communities and citizens that may be more vulnerable to negative consequences of COVID-19 or provide evidence to support planning for targeted intervention and resource allocation. This session will describe the six high-level indicators in each of the 33 health council communities and will provide a more in-depth look at specific vulnerabilities. For example, seniors who live alone, individuals with COVID-19 relevant physical health conditions or those with diagnosed mental health disorders. Population-based risk indicators such as these can inform regional efforts to limit spread and exacerbation of infection in those most at-risk, and in helping to identify the at-risk groups likely impacted by measures to combat the spread of COVID-19.
Health equity through data and mapping on PolicyMapPolicyMap
Where do vulnerable populations live? Where are there inequalities in health outcomes? Data and mapping can be an extraordinary resource when trying to understand questions on health disparities. Join PolicyMap for a webinar examining the role of mapping in taking a data-driven approach towards achieving health equity.
Complications in big data-based communication in the wake of COVID-19: A comp...Selcen Ozturkcan
Johnson, E., R., Martínez, M. C., and Ozturkcan, S., "Complications in big data-based communication in the wake of COVID-19: A comparison of North American and Nordic practices of multinational healthcare corporations," Engineering and Technology Management Summit 2022, 20-21 Oct 2022, Istanbul, Turkey.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
Role of Data Accessibility During PandemicDatabricks
This talk focuses on the importance of data access and how crucial it is, to have the granular level of data availability in the open-source space as it helps researchers and data teams to fuel their work.
We present to you the research conducted by the DS4C (Data Science for Covid-19) team who made a huge and detailed level of South Korea Covid-19 data available to a wider community. The DS4C dataset was one of the most impactful datasets on Kaggle with over fifty thousand cumulative downloads and 300 unique contributors. What makes the DS4C dataset so potent is the sheer amount of data collected for each patient. The Korean government has been collecting and releasing patient information with unprecedented levels of detail. The data released includes infected people’s travel routes, the public transport they took, and the medical institutions that are treating them. This extremely fine-grained detail is what makes the DS4C dataset valuable as it makes it easier for researchers and data scientists to identify trends and more evidence to support hypotheses to track down the cause and gain additional insights. We will cover the data challenges, impact that it had on the community by making this data available on a public forum and conclude it with an insightful visual representation.
Mapping Community-Level Prevalence of Modifiable Risk Factors for Dementia in...DataNB
A large proportion of dementia risk is attributable to modifiable factors such as physical inactivity, hypertension, and social isolation. Prevention strategies will be essential to mitigate the expected increased number of people living with dementia. Data on the distribution of risk factors can help support these efforts.
The objective of this study was to derive community-level prevalence estimates for dementia specific modifiable risk factors.
Statistics Canada Canadian Community Health Survey (CCHS; 2001-2020) data were used to develop prediction models for several mid-life (age 45-64; heavy drinking, obesity, hypertension) and late-life (age 65+; smoking, physical inactivity, social isolation, diabetes) risk factors. Prevalence was estimated from the prediction model using age and sex stratified Census (2001-2016) population counts for communities across New Brunswick. Spatial-temporal models were used to increase the robustness of predicted prevalence estimates.
The risk factors with the highest prevalence were physical inactivity (67%), obesity (34%), and hypertension (31%). These three risk factors, in addition to risk factors for social isolation and smoking, were also found to have highest variability across communities. The prevalence of obesity, hypertension and diabetes increased over time, whereas smoking and social isolation remained consistent. While physical inactivity had the highest prevalence, this was found to decrease over time.
National population-based survey and Census data can be used to inform of the burden of dementia risk factors at the community-level. Community-level risk factor data may be helpful in directing resources to communities with the highest burden and to monitor changes in risk for these communities.
Geospatial Analysis: Innovation in GIS for Better Decision MakingMEASURE Evaluation
Discussion led by John Spencer and Mark Janko. This webinar shared new techniques in geospatial analysis and how they have the potential to transform data-informed decision making.
Review of Recent COVID-19 Science ~ Denis G. Rancourt, PhDPandataAnalytics
Measures do not prevent deaths, transmission is not by contact, masks provide no benefit, vaccines are inherently dangerous: Review update of recent science relevant to COVID-19 policy.
Economic analysis of malaria burden in kenyaNanyingi Mark
This framework uses a cost of illness approach to evaluate the burden of malaria. The evaluation is based on private direct costs (PDC) and private indirect cost (PIC) of malaria attack per episode.
Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Explorato...Konstantinos Demertzis
Within the complex framework of anti-COVID-19 health management, where the criteria of diagnostic testing, the availability of public-health resources and services, and the applied anti-COVID-19 policies vary between countries, the reliability and accuracy in the modeling of temporal spread can prove to be effective in the worldwide fight against the disease. This paper applies an exploratory time-series analysis to the evolution of the disease in Greece, which currently suggests a success story of COVID-19 management. The proposed method builds on a recent conceptualization of detecting connective communities in a time-series and develops a novel spline regression model where the knot vector is determined by the community detection in the complex network. Overall, the study contributes to the COVID-19 research by proposing a free of disconnected past-data and reliable framework of forecasting, which can facilitate decision-making and management
of the available health resources.
The Fight Against COVID-19: A National Patient RegistryHealth Catalyst
Comprehensive COVID-19 understanding is a critical asset for adapting to pandemic needs, directing resources, developing vaccines, and planning for surges in a timely, informed manner. Because common barriers have impeded the progress of comprehensive data repositories, researchers have relied on surveillance data from population-level viral testing, which has proven insufficient. To significantly advance COVID-19 understanding, the medical community needs a digital patient registry that captures national-level data on how the virus impacts individuals differently according to comorbidities, lifestyle factors, and more. These essential insights lie in real-world evidence, which a registry can only deliver when it applies value sets to leverage clinical and claims data from health systems across the United States.
Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator days and deaths by US state in the next 4 months
Similar to NDGeospatialSummit2022 - Using Machine Learning and Quasi Binomial Model to Predict and Understand the Impact of COVID 19 in North Dakota (20)
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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NDGeospatialSummit2022 - Using Machine Learning and Quasi Binomial Model to Predict and Understand the Impact of COVID 19 in North Dakota
1. Using Machine Learning and Quasi-
Binomial Model to Predict and Understand
the Impact of COVID-19 in North Dakota
Valquiria F. Quirino;
Avram Slone;
Jerry Dogbey-Gakpetor;
Karen L. Olson;
Nancy M. Hodur
North Dakota Geospatial Summit - Sept. 14-15, 2022
2. Funding and Genesis
• The NDSU Center for Social Research was the recipient of a CDC grant to study
COVID-19 disparities in underserved populations in North Dakota.
• We’ve partnered with the ND Dept. of Health (NDDoH), and are using data
provided by them to research COVID-19 disparities from various perspectives.
• One such perspective is a county-level model of COVID-19 infections and
outcomes based on the sociodemographic, health, and geographic features of
each county’s population.
North Dakota Geospatial Summit - Sept. 14-15, 2022
3. Project Goals
1. Use random forest to predict the proportion of the county level population
with confirmed and probable COVID-19 cases (Y1), confirmed and probable
COVID-19 cases resulting in hospitalization (Y2), and confirmed and probable
COVID-19 cases resulting in death (Y3).
2. Use quasi-binomial models to investigate the effect of various independent
variables on county level population with confirmed and probable COVID-19
cases (Y1), confirmed and probable COVID-19 cases resulting in hospitalization
(Y2), and confirmed and probable COVID-19 cases resulting in death (Y3).
North Dakota Geospatial Summit - Sept. 14-15, 2022
4. Modeling COVID-19
• From the early days of the pandemic, COVID-19 models were used to inform
public policy decisions worldwide.
• The first model to garner substantial public attention was published on March
16th, 2020 and predicted that with no coordinated intervention COVID-19 would
kill 2.2 million people in the USA and infect over 80% of the country’s
population.1,2
• Modeling has also been used frequently to determine predictors of serious illness
and mortality from COVID-19.3
North Dakota Geospatial Summit - Sept. 14-15, 2022
5. Data-Driven and Epidemiological Forecasts
• Predictive models of the COVID-19 pandemic can be broadly categorized as
either data-driven or epidemiological.4
• Data-driven models typically take the form of predictive curves, using past data
to predict future outcomes.
• Epidemiological models divide the population of study into recognized
epidemiological groups and model the movement of individuals between groups
based on individual and environmental features.
North Dakota Geospatial Summit - Sept. 14-15, 2022
6. Data-Driven Forecasts
• These models are built using assumptions about the future. For example, the
implementation of a mask mandate, or a social distancing policy.
• As such, they are most useful as short-term predictive tools, since policies are
changed and updated frequently, and practical policy implementation usually
does not match theoretical policy goals.5
• Highlight the need for harm-reductive policy, but fail to account for the shifting
dynamics of spread.6
North Dakota Geospatial Summit - Sept. 14-15, 2022
7. Epidemiological Forecasts
• These models are built by separating the population into recognizable
epidemiological groups such as Susceptible, Infected, and Recovered (SIR
Model).7
• Dividing the population in this way allows for modeling that can be catered to
social, health, and geographical patterns that impact each group differently.
• Because they are models within models, they are particularly susceptible to
changes in on-the-ground pandemic dynamics, but are also more easily
adaptable to them.
North Dakota Geospatial Summit - Sept. 14-15, 2022
8. Random Forest Modeling and COVID-19
• A machine learning algorithm commonly used predictively and in variable
selection.
• Random forest has been used frequently in modeling COVID-19 patient outcomes
as well as ranking the importance of various sociodemographic, geographic,
socioeconomic, and health variables.8,9,10,11
• Among the most popular and reliable machine learning algorithms for predicting
health outcomes.12,13,14
North Dakota Geospatial Summit - Sept. 14-15, 2022
9. Quasi-Binomial Model and COVID-19
• Quasi-binomial logistic regression allows for fitting a model while taking
dispersion into account.
• COVID-19 mortality data has been used to show that quasi-binomial distribution
is an effective model in the analysis of overly-dispersed data.15
• Has been frequently used in predictive analyses of COVID-19 spread and
mortality, as well as in studies of pandemic behaviors.16,17,18
North Dakota Geospatial Summit - Sept. 14-15, 2022
10. Distribution of COVID-19 Cases (Y1)
• Slope County was the
least affected by COVID-
19 between 2020 and
2022.
• The eight counties shown
in light green – Rolette,
Eddy, Cass, Stutsman,
Burleigh, Morton,
Hettinger and Stark -
were the most affected
by COVID-19.
North Dakota Geospatial Summit - Sept. 14-15, 2022
11. Distribution of COVID-19 Cases Resulting in Hospitalization
(Y2)
• The percent of the
population
hospitalized due to
COVID-19 was less
than 4 percent for all
ND counties.
• The five counties
shown in light green
– Sheridan, Grant,
Sioux, Emmons and
McIntosh - had the
highest percent of
COVID-19
hospitalizations.
North Dakota Geospatial Summit - Sept. 14-15, 2022
12. Distribution of COVID-19 Cases that Resulted in Deaths (Y3)
• The percentage of
the population that
died due to COVID-19
was less than 1
percent for all ND
counties.
• The two counties
shown in light green
(Pierce and Dickey)
had the highest
percentage of COVID-
19 deaths.
North Dakota Geospatial Summit - Sept. 14-15, 2022
Note: COVID-19 deaths in Slope County are unknown.
13. Data Acquisition
The data used for this work come from several different sources:
1. North Dakota Dept. of Health (NDDoH)
• A dataset with information about 234,998 individuals who were diagnosed with
COVID-19 between March 11th, 2020 and February 13th, 2022 was provided by
NDDoH. The dataset had information about demographics, vaccination status,
self-reported underlying diseases, and county of residency of individuals who
had or probably had COVID-19.
• Additionally, two-class county categorization (Rural or Urban) and the total
number of COVID-19 tests per county from March 11, 2020 to February 13,
2022 - which is the sum of PCR test and rapid antigen tests - was obtained from
NDDoH’s publicly available data.
North Dakota Geospatial Summit - Sept. 14-15, 2022
14. Data Acquisition
2. United States Census Bureau
• The population estimates from the 2020 Decennial Census were bridged
with race categories and the vintage 2020 population estimates. These
datasets were used to obtain county level estimates of total population
and population by gender, race, and age range.
• The 2020 Decennial Census state redistricting summary file was used to
obtain the total group quarters population. This population includes
persons living in both institutionalized and non-institutionalized group
quarters.
• Current county land areas were obtained from the US Census Bureau.
• The Census was also the source for three economic variables (per capita
income, median household income and median family income).
North Dakota Geospatial Summit - Sept. 14-15, 2022
15. Data Acquisition
3. 2020 North Dakota Behavioral Risk Factor Surveillance System (BRFSS) data
• Per-county weighted estimates of four comorbid conditions (Asthma, chronic
lung disease, diabetes and angina or coronary heart disease).
4. Center for Disease Control and Prevention (CDC)
• The number of individuals who received COVID-19 vaccines per county
between March 11, 2020 and February 13, 2022 was obtained from the CDC’s
publicly available data.
5. Other
• The source for the percentage of the adult population (age 18 and older) that
reports a body mass index (BMI) greater than or equal to 30 kg/m2 (age-
adjusted) in 2019 was the county health rankings website.19
16. Materials and Methods
• Case data from NDDoH were cleaned. 223,349 observations were used. This
included each individual’s first known COVID-19 infection.
• Using various data sources, general population variables and variables for the
COVID-19 infected population were calculated (see next slide).
• Descriptive statistics were obtained for the dependents and independent variables.
• Random forest models were produced to predict the proportion of the county level
population with confirmed and probable COVID-19 cases (Y1), hospitalization (Y2),
and death (Y3).
• The models were produced using twenty-six variables.
• Prediction accuracies as well as the most important variables for each model are
reported.
• Finally, quasi-binomial models were produced. They were used to investigate the
impact of the independent variables on the dependent variables.
North Dakota Geospatial Summit - Sept. 14-15, 2022
17. Variables Used
General Population
1. Population demographics (gender,
age, race)
2. Comorbidities
3. County and place of residence
4. Population density
5. COVID-19 tests performed
6. COVID-19 vaccination status
7. Household and per-capita income
variables
Population with COVID-19
1. Population demographics (gender,
age, race) of those with COVID-19
2. Comorbidities of those with COVID-
19
3. COVID-19 vaccination status of
those with COVID-19
4. Place of residence of those with
COVID-19
North Dakota Geospatial Summit - Sept. 14-15, 2022
18. How does random forest work?
• Random forest is a supervised machine learning algorithm.
• It builds a number of decision trees on bootstrapped samples of the dataset.
• It uses averaging to improve prediction accuracy and control overfitting.
• When building these trees, a random sample of m predictors is chosen as split
candidates from the full set of p predictors (usually m is approximately sqrt(p)).
• Random forest makes it easy to evaluate variable importance.
• The variable importance feature of random forest modelling serves as a source
for variable selection for simpler models.
North Dakota Geospatial Summit - Sept. 14-15, 2022
22. Descriptive Statistics for the
Dependent Variables and Categorical Variable
North Dakota Geospatial Summit - Sept. 14-15, 2022
Variables Counties N N Missing Min Mean* Median Max Std Dev
Y1 - Percent of Population with COVID-19 All 53 0 8.2 25.4 25.7 37.8 5.3
Y2 – Percent of Population who was Hospitalized with COVID-19 All 53 0 0.1 1.1 1.1 2.4 0.5
Y3 – Percent of Population who Died with COVID-19 All 52 1 0.1 0.4 0.3 0.8 0.2
Y1 – Percent of Population with COVID-19 Rural 45 0 8.2 24.5 25.3 37.8 5.1
Y2 – Percent of Population who was Hospitalized with COVID-19 Rural 45 0 0.1 1.2 1.1 2.4 0.5
Y3 – Percent of Population who Died with COVID-19 Rural 44 1 0.1 0.4 0.3 0.8 0.2
Y1 – Percent of Population with COVID-19 Urban 8 0 25.5 30.5 30.5 33.8 2.8
Y2 – Percent of Population who was Hospitalized with COVID-19 Urban 8 0 0.7 1.0 1.0 1.6 0.3
Y3 – Percent of Population who Died with COVID-19 Urban 8 0 0.2 0.3 0.3 0.5 0.1
There are differences in the COVID-19 statistics for the dependent variables and for rural and
urban counties.
Note: Statistics were calculated for each column independently without regard of missing values in
other columns.
*See graph in the next slide
23. *Y1 vs Y2 vs Y3 and Rural vs Urban
North Dakota Geospatial Summit - Sept. 14-15, 2022
25.4
1.1 0.4
24.5
1.2 0.4
30.5
1 0.3
Percent of the Population with
Covid-19 (Y1)
Percent of Population
Hospitalized with Covid-19
(Y2)
Percent of Population that
Died with Covid-19 (Y3)
Averages of Dependent Variables
Mean of All Counties Mean of Rural Counties Mean of Urban Counties
24. Descriptive
Statistics for
the
Numerical
Independent
Variables
North Dakota Geospatial Summit - Sept. 14-15, 2022
Note 1: Statistics were calculated for each column independently without regard for missing values in other columns.
Note 2: Variable names shown in black are measures of a general population characteristic. Variable names shown in blue are measures of the population with COVID-19.
Variable # Variable Names N
N
Missing Min Mean Median Max Std Dev
X1 Percent Difference in the Female and Male Population 53 0 -8.0 -2.7 -2.0 2.0 2.6
X2 Percent Difference in the Female and Male Population with COVID-19 53 0 -0.5 4.1 4.1 10.6 2.2
X3 Percent of the Population younger than 17-years-old 53 0 17.7 23.3 22.3 36.0 3.9
X4 Percent of the Population between 18- and 34-years-old 53 0 12.7 18.8 17.1 35.1 4.8
X5 Percent of the Population between 35- and 64-years-old 53 0 29.9 35.9 36.0 39.4 2.0
X6 Percent of the Population 65-years-old and older 53 0 9.0 22.1 23.2 34.0 6.1
X7 Percent of the Population with COVID-19 younger than 17-years-old 53 0 6.2 18.4 19.0 40.4 6.0
X8 Percent of the Population with COVID-19 between 18- and 34-years-old 53 0 8.6 32.6 32.7 43.8 7.1
X9 Percent of the Population with COVID-19 between 35- and 64-years-old 53 0 9.4 28.7 28.8 38.0 5.7
X10 Percent of the Population with COVID-19 65-years-old and older 53 0 6.1 21.3 21.5 30.9 5.3
X11 Percent of the Population who is Black 53 0 0.1 1.3 0.8 6.4 1.3
X12 Percent of the Population with COVID-19 who is Black 53 0 0.0 21.1 17.4 166.7 25.3
X13 Percent of the Population who has Two or More Races 53 0 0.4 1.9 1.9 3.9 0.8
X14 Percent of the Population with COVID-19 who has Two or More Races 53 0 2.3 25.3 21.8 100.0 17.3
X15 Percent of the Population who is Asian 53 0 0.0 0.9 0.6 4.3 0.9
X16 Percent of the Population with COVID-19 who is Asian 52 1 0.0 16.6 13.7 57.1 15.1
X17 Percent of the Population who is Native Hawaiian or Pacific Islander 53 0 0.0 0.1 0.0 0.2 0.1
X18 Percent of the Population with COVID-19 who is Native Hawaiian or Pacific Islander 36 17 0.0 29.7 8.1 300.0 56.9
X19 Percent of the Population who is American Indian 53 0 0.6 7.1 2.0 82.4 16.9
X20 Percent of the Population with COVID-19 who is American Indian 53 0 0 17.6 15.4 56.5 12.0
X21 Percent of the Population who is White 53 0 12.9 88.8 94.1 98.2 17.1
X22 Percent of the Population with COVID-19 who is White 53 0 6.7 18.7 18.7 25.8 4.5
X23 Population Density (in people per square miles) 53 0 0.6 9.4 3.8 104.2 17.3
X24 COVID-19 Tests performed per Capita 53 0 0.9 4.4 4.2 8.5 1.7
X25 Percent of Individuals with COVID-19 within those who Reside in Congregate Settings 44 9 8.9 48.7 52.3 98.3 18.8
X26 Percent of Total Population who live in Institutionalized and Not-Institutionalized Group Quarters 53 0 0.0 2.7 2.3 9.1 2.1
X27 Percent of Individuals within each County with Asthma 53 0 0.0 9.7 8.9 37.7 7.1
X28 Percent of Individuals within each County with Diabetes 53 0 0.0 12.7 11.6 31.4 8.0
X29 Percent of Individuals within each County with Lung Disease 53 0 0.0 4.9 5.0 14.7 3.5
X30 Percent of Individuals within each County with Angina or Coronary Heart Disease 53 0 0.0 5.7 4.3 25.1 4.7
X31 Percent of the Total Population with COVID-19 and Asthma 53 0 0.1 0.7 0.7 1.4 0.2
X32 Percent of the Total Population with COVID-19 and Diabetes 53 0 0.5 1.6 1.5 3.5 0.5
X33 Percent of the Total Population with COVID-19 and Chronic Lung Disease 53 0 0.3 1.5 1.4 2.6 0.4
X34 Percent of the Total Population with COVID-19 and Cardiovascular Disease (CVD) 53 0 0.5 1.5 1.4 2.5 0.4
X35 Percent of the Adult Population who is Obese 53 0 32.0 37.3 37.0 49.0 3.1
X36 Percent of Total Population with COVID-19 who is Unvaccinated 53 0 6.8 20.7 21.3 28.6 4.0
X37 Percent of Total Population who Received 1-Dose of the COVID-19 Vaccine 53 0 14.3 52.1 54.1 85.2 14.0
X38 Percent of Total Population with COVID-19 who Received 1-Dose of the COVID-19 Vaccine 53 0 0.1 0.4 0.4 1.2 0.2
X39 Percent of Total Population who Received 2-Doses of the COVID-19 Vaccine 53 0 11.0 44.8 46.0 72.3 12.5
X40 Percent of Total Population with COVID-19 who Received 2-Doses of the COVID-19 Vaccine 53 0 0.9 3.4 3.4 8.7 1.3
X41 Percent of Total Population who Received 3-Doses of the COVID-19 Vaccine 53 0 5.0 20.4 20.9 42.1 7.1
X42 Percent of Total Population with COVID-19 who Received 3-Doses of the COVID-19 Vaccine 53 0 0.2 0.8 0.7 3.2 0.5
X43 Median Household Income in the Past 12 Months in 2020 (Inflation Adjusted Dollars) 53 0 41,893.0 62,256.8 61,477.0 82,750.0 9,711.6
X44 Per Capita Income in the Past 12 Months in 2020 (Inflation Adjusted Dollars) 53 0 17,460.0 35,216.8 35,278.0 45,782.0 5,030.3
X45 Median Family Income in the Past 12 Months in 2020 (Inflation Adjusted Dollars) 53 0 46,071.0 81,682.0 81,439.0 108,816.0 11,899.1
25. Random Forest
REMINDER: 26 variables were used.
Data partitioning: Training (70%) and Testing (30%).
Number of trees & number of variables considered in
deciding how to partition the models: 500 and 5.
North Dakota Geospatial Summit - Sept. 14-15, 2022
Model Development
Dependent Variable Mean of squared
residuals (n=37)
% Var
Explained
Y1 – Percent of the Population
with COVID-19
12.098 60.70
Y2 – Percent of the Population
Hospitalized with COVID-19
0.180 20.01
Y3 – Percent of the Population
who Died with COVID-19
0.026 3.62
26. Random Forest Results
North Dakota Geospatial Summit - Sept. 14-15, 2022
Model Evaluation
Dependent variable Testing Dataset –
Pseudo R-square (n=16)
Full Model -
Pseudo R-square (n=53)
Y1 – Percent of the Population
with COVID-19
53.1% 84.7%
Y2 - Percent of the Population
Hospitalized with COVID-19
54.3% 78.9%
Y3 - Percent of the Population
who Died with COVID-19
14.6% 61.4%
31. Quasi-Binomial Model Results for the Analysis of Percent of
the Population who was Hospitalized with COVID-19 (Y2)
North Dakota Geospatial Summit - Sept. 14-15, 2022
32. Quasi-Binomial Model Results for the Analysis of Percent of
the Population who Died with COVID-19 (Y3)
North Dakota Geospatial Summit - Sept. 14-15, 2022
Pr(>|z|)
33. Take Away Points
• From the random forest models:
• The accuracy of the models produced using the full dataset varied from about
61% for COVID-19 cases resulting in deaths (Y3) to 85% for COVID-19 cases
(Y1).
• There was a large discrepancy between the results of the models obtained
using the training and the testing datasets for hospitalization due to COVID-
19 (Y2) (20.01% vs 78.94%) and COVID-19 cases resulting in deaths (Y3)
(3.62% vs 61.38%).
• The variable importance results showed that:
• COVID-19 vaccines (1 dose), Population density and Covid-19 tests performed per capita
were important variables to predict COVID-19 cases (Y1).
• Income is an important predictor for COVID-19 hospitalizations (Y2) and COVID-19
deaths (Y3).
North Dakota Geospatial Summit - Sept. 14-15, 2022
34. Take Away Points
• From the quasi-binomial model:
• 84.53% of the variation in the percentage of the population with COVID-19 (Y1)
was explained by the independent variables.
• Based on the variable of importance for Y1 and holding all variables constant;
• Y1 increases by 51% for a percent increase in proportion COVID-19 test per Capita
• Y1 increases by 50% for a percent increase in the proportion of population density in people
per square miles
• Y1 decreases by 50% for a percent increase in the proportion of total Dose 1 vaccine
administered.
• 52.85% of the variation explained in the population who was hospitalized with
COVID-19 (Y2).
• 46.2% of the variation explained in the percent of the population who died of
COVID-19 (Y3).
North Dakota Geospatial Summit - Sept. 14-15, 2022
35. Next Steps
• We will reevaluate initial variables used in the random forest models and add
additional variables (e.g., distance to COVID-19 test sites and health professional
shortage areas).
• Run partial dependencies for each dependent variable in random forest to learn
the direction of influence of the most important predictive variables.
North Dakota Geospatial Summit - Sept. 14-15, 2022
37. How to find us?
Email:
• Valquiria Quirino (valquiria.qurino@ndsu.edu)
• Avram Slone (avram.slone@ndsu.edu)
• Jerry Dogbey-Gakpetor(jerry.dogbeygakpetor@ndsu.edu)
Address:
Center for Social Research at NDSU
1616 12th Ave. N
Prairie Hall, Room 210
Fargo, ND 58102
North Dakota Geospatial Summit - Sept. 14-15, 2022
39. Sources
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