This document presents estimates of the case fatality ratio (CFR) for COVID-19 infections in three contexts: Hubei province, China where the epidemic is currently centered; cases reported outside of mainland China among travellers, representing a broader spectrum of disease severity; and estimates of the overall CFR across all infections after adjusting for the level of undiagnosed mild or asymptomatic cases based on testing of evacuees from Wuhan. The CFR is estimated to be 18% for cases in Hubei, 1.2-5.6% for cases outside of China, and approximately 1% overall after adjusting for undiagnosed infections. All estimates have substantial uncertainty and should be viewed cautiously given
People Living with Human Immunodeficiency Virus in Hadhramout: Clinical Prese...asclepiuspdfs
The objective of the study was to determine the clinical features and outcome of patients living with human immunodeficiency virus (HIV) in Hadhramout and nearby governorates. Materials and Methods: This descriptive study was conducted in the antiretroviral therapy (ART) site at Ibn-Sina General Hospital, Mukalla, Hadhramout governorate. All 145 patients were enrolled in HIV treatment and care program from December 2008 to the end of December 2016 with confirmed HIV test. Data included all personal data, clinical staging, drugs taken, and outcomes. Patients were grouped according to the decades to five groups, ≤15 years, 16–30 years, 31–50 years, 51–70 years, and >70 years. Cases classify according to the antiretroviral drugs to ART group and Pre-ART group. The relevant data parameters were analyzed using SPSS statistical software version 21 and Excel 10. Results: A total of 145 cases, most adults (97.9%), males and females were104 (71.7%) and 41 (28.3%), respectively. Mean age was 36.46 years and 30–50 years the most affected age group (55.2%). Clinical Stages 3 and 4 were the common presentation in 73.8%, and most cases were from Mukalla city. Of the total cases, 74.5% were on ART 53.1 of them improved, pulmonary tuberculosis was found in 4 cases, and death cases were (18.5%), mostly due to late presentation and non-adherence, and mostly occurred in early 6 months of starting the ART. 37 patients were in a pre-treatment group (21.6%), where the mortality rate is 35.1%, mainly due to loss of follow-up. Conclusions: Most cases were adult males, young age and have had late presentation, where mortality is higher in the pre-treatment group due to loss of follow-up and in early 6 months of treatment.
People Living with Human Immunodeficiency Virus in Hadhramout: Clinical Prese...asclepiuspdfs
The objective of the study was to determine the clinical features and outcome of patients living with human immunodeficiency virus (HIV) in Hadhramout and nearby governorates. Materials and Methods: This descriptive study was conducted in the antiretroviral therapy (ART) site at Ibn-Sina General Hospital, Mukalla, Hadhramout governorate. All 145 patients were enrolled in HIV treatment and care program from December 2008 to the end of December 2016 with confirmed HIV test. Data included all personal data, clinical staging, drugs taken, and outcomes. Patients were grouped according to the decades to five groups, ≤15 years, 16–30 years, 31–50 years, 51–70 years, and >70 years. Cases classify according to the antiretroviral drugs to ART group and Pre-ART group. The relevant data parameters were analyzed using SPSS statistical software version 21 and Excel 10. Results: A total of 145 cases, most adults (97.9%), males and females were104 (71.7%) and 41 (28.3%), respectively. Mean age was 36.46 years and 30–50 years the most affected age group (55.2%). Clinical Stages 3 and 4 were the common presentation in 73.8%, and most cases were from Mukalla city. Of the total cases, 74.5% were on ART 53.1 of them improved, pulmonary tuberculosis was found in 4 cases, and death cases were (18.5%), mostly due to late presentation and non-adherence, and mostly occurred in early 6 months of starting the ART. 37 patients were in a pre-treatment group (21.6%), where the mortality rate is 35.1%, mainly due to loss of follow-up. Conclusions: Most cases were adult males, young age and have had late presentation, where mortality is higher in the pre-treatment group due to loss of follow-up and in early 6 months of treatment.
A Point Cross-sectional study of Swine Flu Cases admitted at a Tertiary Level Hospital, Jaipur (Rajasthan) India-Presently in India Swine Flu cases were reported maximum from Rajasthan in this year (2015). So this study was aimed to analyzed the swine flu cases on various grounds to know the reasons for this increase. 77 swine flu cases addimited on 10.3.15 in a tertiary level hospital were interrogated. Total 2603 swine flu cases and 101 deaths were confirmed upto 10.3.15 in this current year concluding CFR 3.88%. Mean age of identified 77 swine flu cases was 41.32 ± 16.19 years with age range 1.5 to 75 years and MF ratio 0.51. Significantly more females were affected with swine flu than males but no significant age wise difference was found in males and females. Out of total 77 cases, 32.47 % were in ICU. About one third (31%) were self motivated others were from government and private health institutes. They were correctly diagnosed symptomatically in 33.77% before referred and about half of cases were advised for investigation (44.16%) for swine flu and precautions (51.95%) regarding respiratory antiquates. And 63.64% were admitted within 24 hours shows good awareness. Co morbidity was found in 57.14% of admitted cases and maximum (84%) co morbidity was found in cases admitted in ICU.
Clinical Epidemiological Study of Secondary Syphilis - Current Scenarioiosrjce
IOSR Journal of Dental and Medical Sciences is one of the speciality Journal in Dental Science and Medical Science published by International Organization of Scientific Research (IOSR). The Journal publishes papers of the highest scientific merit and widest possible scope work in all areas related to medical and dental science. The Journal welcome review articles, leading medical and clinical research articles, technical notes, case reports and others.
The value of real-world evidence for clinicians and clinical researchers in t...Arete-Zoe, LLC
In the midst of a rapidly spreading global pandemic, real-world evidence can offer invaluable insight into the most promising treatments, risk factors, and not only predict but suggest how to improve outcomes. Despite overwhelming news coverage, significant knowledge gaps regarding COVID-19 persist. The current uncertainties regarding incidence and the case fatality rate can only be addressed by widespread testing. But the paucity of testing, and diversity of approaches implemented in different countries, particularly among the general asymptomatic public, perpetuates a lack of understanding about spread and infectivity. The essential indicators that would describe the pandemic more accurately can be obtained using real-world data (RWD). To that purpose, we designed a data collection tool to collect data from hospitals that treat COVID-19 patients. The captured data will enhance our understanding of the COVID-19 pandemic, identify risk factors relevant for triage, relate to other similar seasonal infections and gain insight into the safety and efficacy of experimental and off-label therapies. Knowledge derived from a focused data collection effort will enable clinicians to adjust rapidly clinical protocols and discontinue interventions that turn out to be ineffective or harmful. By deploying our elegantly designed survey to capture routine clinical indicators, we avoid placing an additional burden on practitioners. Systematically generating real-world evidence can decrease the time to insight compared to randomized clinical trials, improving the odds for patients in rapidly changing conditions.
A Point Cross-sectional study of Swine Flu Cases admitted at a Tertiary Level Hospital, Jaipur (Rajasthan) India-Presently in India Swine Flu cases were reported maximum from Rajasthan in this year (2015). So this study was aimed to analyzed the swine flu cases on various grounds to know the reasons for this increase. 77 swine flu cases addimited on 10.3.15 in a tertiary level hospital were interrogated. Total 2603 swine flu cases and 101 deaths were confirmed upto 10.3.15 in this current year concluding CFR 3.88%. Mean age of identified 77 swine flu cases was 41.32 ± 16.19 years with age range 1.5 to 75 years and MF ratio 0.51. Significantly more females were affected with swine flu than males but no significant age wise difference was found in males and females. Out of total 77 cases, 32.47 % were in ICU. About one third (31%) were self motivated others were from government and private health institutes. They were correctly diagnosed symptomatically in 33.77% before referred and about half of cases were advised for investigation (44.16%) for swine flu and precautions (51.95%) regarding respiratory antiquates. And 63.64% were admitted within 24 hours shows good awareness. Co morbidity was found in 57.14% of admitted cases and maximum (84%) co morbidity was found in cases admitted in ICU.
Clinical Epidemiological Study of Secondary Syphilis - Current Scenarioiosrjce
IOSR Journal of Dental and Medical Sciences is one of the speciality Journal in Dental Science and Medical Science published by International Organization of Scientific Research (IOSR). The Journal publishes papers of the highest scientific merit and widest possible scope work in all areas related to medical and dental science. The Journal welcome review articles, leading medical and clinical research articles, technical notes, case reports and others.
The value of real-world evidence for clinicians and clinical researchers in t...Arete-Zoe, LLC
In the midst of a rapidly spreading global pandemic, real-world evidence can offer invaluable insight into the most promising treatments, risk factors, and not only predict but suggest how to improve outcomes. Despite overwhelming news coverage, significant knowledge gaps regarding COVID-19 persist. The current uncertainties regarding incidence and the case fatality rate can only be addressed by widespread testing. But the paucity of testing, and diversity of approaches implemented in different countries, particularly among the general asymptomatic public, perpetuates a lack of understanding about spread and infectivity. The essential indicators that would describe the pandemic more accurately can be obtained using real-world data (RWD). To that purpose, we designed a data collection tool to collect data from hospitals that treat COVID-19 patients. The captured data will enhance our understanding of the COVID-19 pandemic, identify risk factors relevant for triage, relate to other similar seasonal infections and gain insight into the safety and efficacy of experimental and off-label therapies. Knowledge derived from a focused data collection effort will enable clinicians to adjust rapidly clinical protocols and discontinue interventions that turn out to be ineffective or harmful. By deploying our elegantly designed survey to capture routine clinical indicators, we avoid placing an additional burden on practitioners. Systematically generating real-world evidence can decrease the time to insight compared to randomized clinical trials, improving the odds for patients in rapidly changing conditions.
Since receiving unexplained pneumonia patients at the Jinyintan Hospital in Wu- han, China in December 2019, the new coronavirus (COVID-19) has rapidly spread in Wuhan, China and spread to the entire China and some neighboring countries.
Since receiving unexplained pneumonia patients at the Jinyintan Hospital in Wu- han, China in December 2019, the new coronavirus (COVID-19) has rapidly spread in Wuhan, China and spread to the entire China and some neighboring countries.
The first three months of the COVID-19 epidemic:
Epidemiological evidence for two separate strains of SARSCoV-2 viruses spreading and implications for prevention
strategies
Characteristics of COVID-19 and Tuberculosis Co-Infection: A Cross-Sectional ...semualkaira
Coronavirus disease 2019 (COVID-19) and Tuberculosis (TB) are two major infectious diseases posing significant
public health threats. This study aimed to investigate the clinical
features of COVID-19 and TB co-infected patients.
The Gibraltar COVID-19 Cohort: Determining the True Incidence and Severity Ra...asclepiuspdfs
COVID-19 is a new infectious disease with an unclear incidence and an unknown rate of progression to severe disease. The Gibraltar COVID-19 Cohort utilises two distinct cohorts - a clinical cohort and a random population based cohort -, to provide an accurate assessment of case severity rate. Design: Retrospective analysis of a SARS-CoV2 RT-PCR point prevalence study and a RT-PCR confirmed positive clinical case cohort to calculate case severity rates. Settings and Participants: Over a three day period nasopharyngeal swabs were sampled from a randomly selected 1.2% of the population of Gibraltar and then analysed via RT-PCR to determine the background incidence of COVID-19 infection. The results were then analysed and compared to the clinical case cohort. The rate of progression to severe COVID-19 disease in those with COVID-19 infection was then calculated.
Test positivity – Evaluation of a new metric to assess epidemic dispersal med...Olutosin Ademola Otekunrin
Epidemic control may be hampered when the percentage of asymptomatic cases is high. Seeking remedies for this problem, test positivity was explored between the first 60 to 90 epidemic days in six countries that reported their first COVID-19 case between February and March 2020: Argentina, Bolivia, Chile, Cuba, Mexico, and Uruguay.
Test positivity (TP) is the percentage of test-positive individuals reported on a given day out of all individuals tested the same day. To generate both country-specific and multi-country information, this study was implemented in two stages. First, the epidemiologic data of the country infected last (Uruguay) were analyzed. If at
least one TP-related analysis yielded a statistically significant relationship, later assessments would investigate the six countries. The Uruguayan data indicated (i) a positive correlation between daily TP and daily new cases (r = 0.75); (ii) a negative correlation between TP and the number of tests conducted per million inhabitants (TPMI, r = 0.66); and (iii) three temporal stages, which differed from one another in both TP and TPMI medians (p < 0.01) and, together, revealed a negative relationship between TPMI and TP. No significant relationship
was found between TP and the number of active or recovered patients. The six countries showed a positive correlation between TP and the number of deaths/million inhabitants (DMI, r = 0.65, p < 0.01). With one exception –a country where isolation was not pursued , all countries showed a negative correlation between
TP and TPMI (r = 0.74). The temporal analysis of country-specific policies revealed four patterns, characterized by: (1) low TPMI and high DMI, (2) high TPMI and low DMI; (3) an intermediate pattern, and (4) high TPMI and
high DMI. Findings support the hypothesis that test positivity may guide epidemiologic policy-making, provided that policy-related factors are considered and high-resolution geographical data are utilized.
Similar to Imperial college-covid19-severity-10-02-2020 (18)
Maharashtra Adventure Gr 2021 Maharashtra government on Wednesday approved a policy for adventure tourism to streamline adventure activities across the state.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
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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
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
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
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!
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.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
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.
1. 10 February 2020 Imperial College London COVID-19 Response Team
DOI: https://doi.org/10.25561/77154 Page 1 of 12
Report 4: Severity of 2019-novel coronavirus (nCoV)
Ilaria Dorigatti+
, Lucy Okell+
, Anne Cori, Natsuko Imai , Marc Baguelin, Sangeeta Bhatia, Adhiratha Boonyasiri,
Zulma Cucunubá, Gina Cuomo-Dannenburg, Rich FitzJohn, Han Fu, Katy Gaythorpe , Arran Hamlet, Wes
Hinsley, Nan Hong , Min Kwun, Daniel Laydon, Gemma Nedjati-Gilani, Steven Riley, Sabine van Elsland, Erik
Volz, Haowei Wang, Raymond Wang, Caroline Walters , Xiaoyue Xi, Christl Donnelly, Azra Ghani, Neil
Ferguson*
. With support from other volunteers from the MRC Centre.1
WHO Collaborating Centre for Infectious Disease Modelling
MRC Centre for Global Infectious Disease Analysis
Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA)
Imperial College London
*
Correspondence: neil.ferguson@imperial.ac.uk
Summary
We present case fatality ratio (CFR) estimates for three strata of 2019-nCoV infections. For cases
detected in Hubei, we estimate the CFR to be 18% (95% credible interval: 11%-81%). For cases
detected in travellers outside mainland China, we obtain central estimates of the CFR in the range 1.2-
5.6% depending on the statistical methods, with substantial uncertainty around these central values.
Using estimates of underlying infection prevalence in Wuhan at the end of January derived from
testing of passengers on repatriation flights to Japan and Germany, we adjusted the estimates of CFR
from either the early epidemic in Hubei Province, or from cases reported outside mainland China, to
obtain estimates of the overall CFR in all infections (asymptomatic or symptomatic) of approximately
1% (95% confidence interval 0.5%-4%). It is important to note that the differences in these estimates
does not reflect underlying differences in disease severity between countries. CFRs seen in individual
countries will vary depending on the sensitivity of different surveillance systems to detect cases of
differing levels of severity and the clinical care offered to severely ill cases. All CFR estimates should
be viewed cautiously at the current time as the sensitivity of surveillance of both deaths and cases in
mainland China is unclear. Furthermore, all estimates rely on limited data on the typical time intervals
from symptom onset to death or recovery which influences the CFR estimates.
1. Introduction: Challenges in assessing the spectrum of severity
There are two main challenges in assessing the severity of clinical outcomes during an epidemic of a
newly emerging infection:
1. Surveillance is typically biased towards detecting clinically severe cases, particularly at the
start of an epidemic when diagnostic capacity is limited (Figure 1). Estimates of the proportion
of fatal cases (the case fatality ratio, CFR) may thus be biased upwards until the extent of
clinically milder disease is determined [1].
2. There can be a period of two to three weeks between a case developing symptoms,
subsequently being detected and reported and observing the final clinical outcome. During a
growing epidemic the final clinical outcome of the majority of the reported cases is typically
unknown. Dividing the cumulative reported deaths by reported cases will underestimate the
CFR among these cases early in an epidemic [1-3].
1
See full list at end of document. +
These two authors contributed equally.
2. 10 February 2020 Imperial College London COVID-19 Response Team
DOI: https://doi.org/10.25561/77154 Page 2 of 12
Figure 1 illustrates the first challenge. Published data from China suggest that the majority of detected
and reported cases have moderate or severe illness, with atypical pneumonia and/or acute respiratory
distress being used to define suspected cases eligible for testing. In these individuals, clinical outcomes
are likely to be more severe, and hence any estimates of the CFR are likely to be high.
Outside mainland China, countries alert to the risk of infection being imported via international travel
have instituted surveillance for 2019-nCoV infection with a broader set of clinical criteria for defining
a suspected case, typically including a combination of symptoms (e.g. cough + fever) combined with
recent travel history to the affected region (Wuhan and/or Hubei Province). Such surveillance is
therefore likely to pick up clinically milder cases as well as the more severe cases also being detected
in mainland China. However, by restricting testing to those with a travel history or link, it is also likely
to miss other symptomatic cases (and possibly hospitalised cases with atypical pneumonia) that have
occurred through local transmission or through travel to other affected areas of China.
Figure 1: Spectrum of cases for 2019-nCoV, illustrating imputed sensitivity of surveillance in
mainland China and in travellers arriving in other countries or territories from mainland China.
Finally, the bottom of the pyramid represents the likely largest population of those infected with
either mild, non-specific symptoms or who are asymptomatic. Quantifying the extent of infection
overall in the population requires random population surveys of infection prevalence. The only such
data at present for 2019-nCoV are the PCR infection prevalence surveys conducted in exposed
expatriates who have recently been repatriated to Japan, Germany and the USA from Wuhan city (see
below).
To obtain estimates of the severity of 2019-nCoV across the full severity range we examined aggregate
data from Hubei Province, China (representing the top two levels – deaths and hospitalised cases – in
Figure 1) and individual-level data from reports of cases outside mainland China (the top three levels
and perhaps part of the fourth level in Figure 1). We also analysed data on infections in repatriated
expatriates returning from Hubei Provence (representing all levels in Figure 1).
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2. Current estimates of the case fatality ratio
The CFR is defined as the proportion of cases of a disease who will ultimately die from the disease. For
a given case definition, once all deaths and cases have been ascertained (for example at the end of an
epidemic), this is simply calculated as deaths/cases. However, at the start of the epidemic this ratio
underestimates the true CFR due to the time-lag between onset of symptoms and death [1-3]. We
adopted several approaches to account for this time-lag and to adjust for the unknown final clinical
outcome of the majority of cases reported both inside and outside China (cases reported in mainland
China and those reported outside mainland China) (see Methods section below). We present the range
of resulting CFR estimates in Table 1 for two parts of the case severity pyramid. Note that all estimates
have high uncertainty and therefore point estimates represent a snapshot at the current time and
may change as additional information becomes available. Furthermore, all data sources have inherent
potential biases due to the limits in testing capacity as outlined earlier.
Table 1: Estimates of CFR for two severity ranges: cases reported in mainland China, and those
reported outside. All estimates quoted to two significant figures.
1Mode quoted for Bayesian estimates, given uncertainty in the tail of the onset-to-death distribution. 2Estimates made
without imputing onset dates in traveller cases for whom onset dates are unknown are slightly higher than when onset dates
are imputed. 3Maximum likelihood estimate. 4This estimate relies on information from just 2 deaths reported outside
mainland China thus far and therefore has wide uncertainty. Both of these deaths occurred a relatively short time after onset
compared with the typical pattern in China.
Severity range Method and data used Time to outcome
distributions used
CFR
China: Epidemic
currently in
Hubei
Parametric model fitted to publicly
reported number of cases and deaths
in Hubei as of 5th
February, assuming
exponential growth at rate 0.14/day.
Onset-to-death estimated
from 26 deaths in China;
assume 5-day period from
onset to report and 1-day
period from death to report.
18%1
(95% credible
interval: 11-81%)
Outside mainland
China: cases in
travellers from
mainland China
to other
countries or
territories
(showing a
broader
spectrum of
symptoms than
cases in Hubei,
including milder
disease)
Parametric model fitted to reported
traveller cases up to 8th
February using
both death and recovery outcomes
and inferring latest possible dates of
onset in traveller cases2
.
Onset-to-death estimated
from 26 deaths in China;
onset-to-recovery estimated
from 36 cases detected
outside mainland China4
.
5.1%3
(95% credible
interval: 1.1%-38%)
Parametric model fitted to reported
traveller cases up to 8th
February using
only death outcome and inferring
latest possible unreported dates of
onset in traveller cases2
.
Onset-to-death estimated
from 26 deaths in China.
5.6%1
(95% credible
interval: 2.0%-85%)
Kaplan-Meier-like non-parametric
model (CASEFAT Stata module [4])
fitted to reported traveller cases up to
8th
February using both death and
recovery outcomes2
.
Hazards of death and
recovery estimated as part
of method.
1.2%3,4
(95% confidence
interval: 0.9%-26%)
All infections
Scaling CFR estimate for Hubei for the
level of infection under-ascertainment
estimated from infection prevalence
detected in repatriation flights,
assuming infected individuals test
positive for 14 days
As first row 0.9%
(95% confidence
interval: 0.5%-4.0%)
As previous row, but assuming
infected individuals test positive for 7
days
As first row 0.8%
(95% confidence
interval: 0.4%-3.0%)
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Use of data on those who have recovered among exported cases gives very similar point estimates to
just relying on death data, but a rather narrower uncertainty range. This highlights the value of case
follow-up data on both fatal and non-fatal cases.
Given that the estimates of CFR across all infections rely on a single point estimate of infection
prevalence, they should be treated cautiously. In particular, the sensitivity of the diagnostics used to
test repatriated passengers is not known, and it is unclear when infected people might test positive,
or how representative those passengers were of the general population of Wuhan (their infection risk
might have been higher or lower than the general population). Additional representative studies to
assess the extent of mildly symptomatic or asymptomatic infection are therefore urgently needed.
Figure 2 shows projected expected numbers of deaths detected in cases detected up to 4th
February
outside mainland China over the next few weeks for different values of the CFR. If no further deaths
are reported amongst this group (and indeed if many of those now in hospital recover and are
discharged) in the next 5 to 10 days, then we expect the upper bound on estimates of the CFR in this
population to reduce. We note that the coming one to two weeks should allow CFR estimates to be
refined.
Figure 2: Projected numbers of deaths in cases detected outside China up to 8th
February for
different values of the CFR in that population.
0
2
4
6
8
10
12
21/01/2020 26/01/2020 31/01/2020 05/02/2020 10/02/2020 15/02/2020 20/02/2020
Expectednumberofdeaths(cumulative)
Date of death
Case Fatality Ratio
1% 3% 5%
7% 9% 11%
Observed
5. 10 February 2020 Imperial College London COVID-19 Response Team
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3. Methods
A) Intervals between onset of symptoms and outcome
During a growing epidemic, the reported cases are generally identified some time before knowing the
clinical outcome of each case. For example, during the 2003 SARS epidemic, the average time between
onset of symptoms and either death or discharge from hospital was approximately three weeks. To
interpret the relationship between reported cases and deaths, we therefore need to account for this
interval. Two factors need to be considered; a) that we have not observed the full distribution of
outcomes of the reported cases (i.e. censoring) and b) that our sample of cases is from a growing
epidemic and hence more reported cases have been infected recently compared to one to two weeks
ago. The latter effect is frequently ignored in analyses but leads to a downwards biased central
estimate of the CFR.
If fOD(.) denotes the probability density function (PDF) of time from symptom onset to death, then the
PDF that we observe a death at time dt with assumed onset days ago is
0
( ) ( )
( | )
( ') ( ') '
OD d
OD d
OD d
f o t
g t
f o t d
−
=
−
,
where ( )o t denotes the observed number of onsets that occurred at time t. For an exponentially
growing epidemic, we assume that 0( ) rt
o t o e= where 0o is the initial number of onsets (at t=0) and
r is the epidemic growth rate. Substituting this, we get
'
0
( )
( | ) .
( ') '
r
OD
OD d
r
OD
f e
g t
f e d
=
We can therefore fit the distribution (.)ODg to the observed data and correct for the epidemic growth
rate to estimate parameters for (.)ODf , the true distribution for a given estimate of r.
If we additionally assume that onsets were poorly observed prior to time Tmin then we can include
censoring:
min
'
0
( )
( | ) .
( ') '
d
r
OD
OD d t T
r
OD
f e
g t
f e d
−
=
For the special case that we model ( )ODf as a gamma distribution parameterised in terms of its
mean m and the ratio of the standard deviation to the mean, s, namely ( | , )ODf m s , it can be shown
that
min
2
2
0
( | / (1 ), )
( | , ', ') ,
( '| / (1 ), ) '
d
OD
OD d t T
OD
f m rms s
g t m s
f m rms s d
−
+
=
+
6. 10 February 2020 Imperial College London COVID-19 Response Team
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where the transformed mean and standard deviation-to-mean ratios are 2
' , ' .
(1 )
m
m s s
rms
= =
+
Therefore, the Bayesian posterior distribution for m and s (up to a constant factor equal to the total
probability) is proportional to the likelihood (over all intervals):
,( , |{ , }) ( | , , ) ( , ),d OD i d i
i
P m s t g t m s P m s
where the product is over a dataset of observed intervals and times of death { , }dt and ( , )P m s is
the prior distribution for m and s. This is constant for a uniform prior distribution or can be derived,
for instance, by fitting this model to the complete dataset of observed onset-to-death intervals from
previous epidemics (e.g. in this case the 2003 SARS epidemic in Hong Kong). Note that for a fully
observed epidemic, it is not necessary to account for epidemic growth provided there was no change
in clinical management (and thus the interval distribution) over time.
We can infer other interval distributions such as the onset-to-recovery distribution, (.)ORf (but also
the serial interval distribution and incubation period distribution) in a similar manner, given relevant
data on the timing of events. It should be noted that inferring all such interval distributions needs to
take account of epidemic growth.
For the analyses presented here, we fitted (.)ODf to data from 26 deaths from 2019-nCoV reported
in mainland China early in the epidemic and we fitted (.)ORf to 29 cases detected outside mainland
China. Uninformative uniform prior distributions were used for both.
The estimates of key parameters are shown in Table 2.
Table 2: Estimates of parameters for onset-to-death and onset-to-recovery distributions
Distribution Data Source mean (mode, 95%
credible interval)
SD/mean (mode, 95%
credible interval)
Onset-to-recovery 29 2019-CoV cases
detected outside
mainland China
22.2 days (18-83) 0.45 (0.35-0.62)
Onset-to-death 26 2019-nCoV deaths
from mainland China
22.3 days (18-82) 0.42 (0.33-0.6)
B) Estimates of the Case Fatality Ratio from individual case data
Parametric models
We can infer the CFR from individual data on dates of symptom onset, death and recovery. Continuing
our notation from above, let (.)ODf denote the distribution of times from symptom onset to death,
(.)ORf denote the distribution of times from onset to recovery, and c denote the CFR.
The probability that a patient dies on day dt given onset at time ot , conditional on survival to that
time is given by:
( )
1
| , , ( ) .
d o
d o
t t
d d o OD OD OD
t t
p t t c m s c f d
− +
−
− =
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Similarly, the probability that a patient recovers on day rt , given onset at time ot ,is given by:
( )
1
| , , (1 ) ( ) .
d o
d o
t t
r r o OR OR OR
t t
p t t c m s c f d
− +
−
− = −
Here ,OD ODm s are the mean and standard deviation-to-mean ratio for the onset-to-death
distribution, and ,OR ORm s are those for the onset-to-recovery distribution.
Finally, the probability that a patient remains in hospital at the last date for which data are available,
T, is
( )| , , , , (1 ) ( ) ( ) .
o o
h o OD OD OR OR OR OD
T t T t
p T t c m s m s c f d c f d
− −
− = − +
The overall likelihood of all observed deaths, recoveries and cases remaining in hospital is
( ) ( ) ( ), , , , ,
{dead by } {recovered by } {hospitalised at }
( , , | , , , , , )
| , , , | , , , | , , , , ,
OD OD OR OR
d d i o i OD OD r r i o i OR OR h i o i OD OD OR OR
i T i T i T
P T c m s m s
p t t c m s p t t c m s p T t c m s m s
=
d r ot t t
It is also possible to infer c from data just on deaths and ‘non-deaths’, grouping the currently
hospitalised and recoveries together:
( ) ( ), , ,
{dead by } {not dead at }
( , , | , , , ) | , , , | , , , , ,OD OD d d i o i OD OD h o i OD OD OR OR
i T i T
P T c m s p t t c m s p T t c m s m s
= d r ot t t
In a Bayesian context, the posterior distribution is given by
old old
( | , , , )
( , , | , , , , , ) ( , |{ , } ) ( , |{ , } ) ( ) dOD OD OR OR OR OR OD OD OD OD OR OR
P c T
P T c m s m s P m s P m s P c m ds dm ds
d r o
d r o d d
t t t
t t t t t
where old( , |{ , } )OD ODP m s dt is the prior distribution for the onset-to-death distribution obtained by
fitting to previous epidemics (e.g. in this case the 2003 SARS epidemic in Hong Kong) old{ , } dt , and
old( , |{ , } )OR OR rP m s t is the comparable prior distribution for time from onset to recovery.
We assumed gamma-distributed onset-to-death and onset-to-recovery distributions (see above).
We fitted this model to the observed onset, recovery and death times in 290 international travellers
from mainland China reported up to 8th
February. For approximately 50% of these travellers, the date
of onset was not reported. To allow us to fit the model to all cases, for those travellers we imputed an
estimate of the onset date as the first known contact with healthcare services – taken as the earliest
of the date of hospitalisation, date of report or date of confirmation. We note this is the latest possible
onset date and may therefore increase our estimates of CFR.
We also fitted a variant of this model where recoveries were ignored, given that they may be
systematically under-ascertained and hence introduce a bias in the estimate.
Posterior distributions were calculated numerically on a hypercube grid of the parameters to be
inferred ( , , , ,OD OD OR ORc m s m s ). Marginal distributions were computed for c.
8. 10 February 2020 Imperial College London COVID-19 Response Team
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Kaplan-Meier-like non-parametric model
We used a non-parametric Kaplan-Meier-like method originally developed and applied to the 2003
SARS epidemic in Hong Kong [2]. The analysis was implemented using the CASEFAT Stata Module [4].
C) Estimates of the Case Fatality Ratio from aggregated case data
With posterior estimates of (.)ODf derived from case data collected in the early epidemic in Wuhan,
it is possible to estimate the CFR from daily reports of confirmed cases and deaths in China, under the
assumption that the daily new incidence figures reported represent recent deaths and cases.
Let the incidence of deaths and onsets (newly symptomatic cases at time t be ( )D t and ( )C t ,
respectively. Given knowledge of the onset-to-death distribution, (.)ODf , the expected number of
deaths at time t is given by
0
( ) ( ) ( )ODD t c C t f d
= −
Assuming cases are growing exponentially as 0( ) exp( )C t C rt= , we have
0
( ) ( ) ( ) ( )r
ODD t cC t f e d czC t
−
= =
where
0
( ) r
ODz f e d
−
=
Assuming a gamma distribution form for (.)ODf , and parameterising as above in terms of the mean
and the standard deviation-to-mean ratio, m and s, respectively, one can show that z is:
( )
2
1/2
1
( , , )
1
s
z r m s
rms
=
+
Thus we assumed the probability of observing ( )D t deaths given ( )C t at time t is a binomial draw
from ( )C t with probability cz. The term z is a downscaling of the actual CFR, c, to reflect epidemic
growth. Heuristically, if the mean onset-to-death interval is 20 days, and the doubling time of the
epidemic is, say, 5 days, then deaths now correspond to onsets occurring when incidence of cases was
24
=16 fold smaller than today, meaning the crudely estimated CFR (cumulative deaths/ cumulative
cases) needs to be scaled up by the same factor.
Ignoring constant terms in the binomial probability not involving ,c m or s , the posterior distribution
for c is:
( )
( | ( ), ( ), , ) ( ( , , ) ( )) exp( ( , , ) ( )) ( , ) ( )D t
P c C t D t m s cz r m s C t cz r m s C t P m s P c −
where ( )P c is the prior distribution on c (assumed uniform) and ( , )P m s is the prior distribution
on the onset-to-death distribution, (.)ODf , which we took to be the posterior distribution obtained
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by fitting to observed onset-to-death distribution for 26 cases in the early epidemic in Wuhan, itself
fitted with a prior distribution based on SARS data (see above).
The official case reports do not give dates of symptom onset or death, so we assumed that deaths
were reported 4 days more promptly than onsets, given the delays in healthcare seeking and testing
involved in confirming new cases, versus the follow-up and recording of deaths of the cases already
in the database. Assuming this difference in reporting delays is longer than 4 days results in lower
estimates of the CFR, while assuming the difference is shorter than 4 days gives higher estimates.
Thus, we compared 45 new deaths reported on 1st
February with 3156 new cases reported on 5th
February.
In addition, while both cases and deaths were growing approximately exponentially in the 10 days
prior to 5th
February, the numbers of cases have been growing faster than deaths. We assumed this
reflects improved surveillance of milder cases over time, and thus used an estimate of the growth
rate in deaths of 0.14 / dayr = , corresponding to a 5-day doubling time. Assuming a higher value of
r gives a higher estimate of the CFR.
Resulting estimates of the CFR showed little variation if calculated for each of the 7 days prior to 5th
February.
D) Translating prevalence to incidence and estimating a CFR for all infections
Translating the severity estimates in Table 1 into estimates of CFR for all cases of infection with 2019-
nCoV requires knowledge of the proportion of all infections being detected in either China or overseas.
To do so we use a single point estimate of prevalence of infection from the testing of all passengers
returning on four repatriation flights to Japan and Germany in the period 29th
January – 1st
February.
Infection was detected in passengers from each flight. In total 10 infections were confirmed in
approximately 750 passengers (passenger numbers are known for 3 flights and were estimated to be
~200 for the fourth). This gives an estimate of detectable infection prevalence of 1.3% (exact 95%
binomial confidence interval: 0.7%-2.4%).
Let us assume infected individuals test positive by PCR to 2019-nCoV infection from l days before onset
of clinical symptoms to n-l days after. Then the infection prevalence at time t, y(t) is related to the
incidence of new cases, C(t) by:
( ) ( ) /
n l
l
y t C t d N
−
−
= −
Here N is the population of the area sampled (here assumed to be Wuhan). Assuming incidence is
growing as 0( ) exp( )C t C rt= , with r=0.14/day (5-day doubling time), this gives
( ) ( ) 1 exp( ) /y t C t l rn N= + − −
Here we assume l=1 day and examine n=7 and 14 days.
Thus we estimated a daily incidence estimate of 220 (95% confidence interval: 120-400) case onsets
per day per 100,000 of population in Wuhan on 31st
January assuming infections are detectable for 14
days, and 300 (95% confidence interval: 160-550) case onsets per day per 100,000 assuming infections
are detectable for 7 days. Taking the 11 million population of Wuhan city, this implied a total of 24,000
10. 10 February 2020 Imperial College London COVID-19 Response Team
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(95% confidence interval: 13,000-44,000) case onsets in the city on that date assuming infections are
detectable for 14 days, and 33,000 (95% confidence interval: 18,000-60,000) assuming infections are
detectable for 7 days. It should be noted that a number of the detected infections on the repatriation
flights were asymptomatic (at least at the time of testing), therefore these total estimates of incidence
might include a proportion of very mildly symptomatic or asymptomatic cases.
Assuming an average 4 days2
between the onset of symptoms and case report in Wuhan City, the
above estimates can be compared with the 1242 reported confirmed cases on 3rd
February in Wuhan
City [5]. This implies 19-fold (95% confidence interval: 11-35) under-ascertainment of infections in
Wuhan assuming infections are detectable for 14 days (including from 1 day prior to symptoms), and
26-fold (95% confidence interval: 15-48) assuming infections are detectable for 7 days (including 1 day
prior to symptoms).
Under the assumption that all 2019-nCoV deaths are being reported in Wuhan city, we can then divide
our estimates of CFR in China by these under-ascertainment factors. Taking our 18% CFR among cases
in Hubei (first row of Table 1), this implies a CFR among all infections of 0.9% (95% confidence interval:
0.5%-4.3%) assuming infections are detectable via PCR for 14 days, and 0.8% (95% confidence interval:
0.4%-3.1%) assuming infections are detectable for 7 days.
Similar estimates are obtained if one uses estimates of CFR in exported cases as the comparator: we
estimate that surveillance outside mainland China is approximately 4 to 5-fold more sensitive at
detecting cases than that in China.
E) Forward Projections of Expected Deaths in Travellers
Using our previous notation, let ( )travellerso t denote the onsets in travellers from mainland China at time
maxt T where maxT is the most recent time (8th
February in this analysis). Using our central estimate
of the onset-to-death interval obtained from the 26 deaths in mainland China, ( , , )ODf m s t , we obtain
an estimate of the expected number of deaths occurring at time t, ( )D t from:
0
( ) ( ) ( )travellers ODD t c o t f d
= −
where c is the CFR.
4. Data Sources
A) Data on early deaths from mainland China
Data on the characteristics of 39 cases who died from 2019-nCoV infection in Hubei Province were
collated from several websites. Of these, the date of onset of symptoms was not available for 5 cases.
We restricted our analysis to those who died up to 21st
January leaving 26 deaths for analysis. These
data are available from website as hubei_early_deaths_2020_07_02.csv
2
This value is plausible from publicly available case reports. Longer durations between onset of symptoms and
report will lead to higher estimates of the degree of under-ascertainment.
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B) Data on cases in international travellers
We collated data on 290 cases in international travellers from websites and media reports up to 8th
February. These data are available from website as international_cases_2020_08_02.csv
C) Data on infection in repatriated international Wuhan residents
Data on infection prevalence in repatriated expatriates returning to their home countries were
obtained from media reports. These data are summarised in Table 3. Further data from a flight
returning to Malaysia reported two positive cases on 5th
February – giving a prevalence at this time
point of 2% which remains consistent with our estimate.
Table 3: Data on confirmed infections in passengers on repatriation flights from Wuhan.
*Not used in our analysis but noted here for completeness.
5. Acknowledgements
We are grateful to the following hackathon participants from the MRC Centre for Global Infectious
Disease Analysis for their support in extracting data: Kylie Ainsile, Lorenzo Cattarino, Giovanni Charles,
Georgina Charnley, Paula Christen, Victoria Cox, Zulma Cucunubá, Joshua D'Aeth, Tamsin Dewé, Amy
Dighe, Lorna Dunning, Oliver Eales, Keith Fraser, Katy Gaythorpe, Lily Geidelberg, Will Green,
David Jørgensen, Mara Kont, Alice Ledda, Alessandra Lochen, Tara Mangal, Ruth McCabe, Kate
Mitchell, Andria Mousa, Rebecca Nash, Daniela Olivera, Saskia Ricks, Nora Schmit, Ellie Sherrard-
Smith, Janetta Skarp, Isaac Stopard, Hayley Thompson, Juliette Unwin, Juan Vesga, Caroline Walters.
Country of
Destination
Number of
Passengers
Number
Confirmed
Number
Confirmed
who were
Symptomatic
Number
Confirmed
who were
Asymptomatic
1 Japan 206 4 2 2
2 Japan 210 2 0 2
3 Japan Not reported –
assume 200
2 1 1
4 Germany 124 2 - -
5* Malaysia 207 2 0 2
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6. References
1. Garske, T., et al., Assessing the severity of the novel influenza A/H1N1 pandemic. BMJ, 2009.
339: p. b2840.
2. Ghani, A.C., et al., Methods for estimating the case fatality ratio for a novel, emerging
infectious disease. Am J Epidemiol, 2005. 162(5): p. 479-86.
3. Lipsitch, M., et al., Potential Biases in Estimating Absolute and Relative Case-Fatality Risks
during Outbreaks. PLoS Negl Trop Dis, 2015. 9(7): p. e0003846.
4. Griffin, J. and A. Ghani, CASEFAT: Stata module for estimating the case fatality ratio of a new
infectious disease. Statistical Software Components, 2005. S454601.
5. People's Republic of China. National Health Commission of the People's Republic of China.
2020 Accessed 03/02/2020]; Available from: www.nhc.gov.cn/.