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Mathematical Model of the Role of Asymptomatic Infection in Outbreaks of Some Emerging Pathogens
Tropical Medicine and Infectious Disease Article Mathematical Model of the Role of Asymptomatic Infection in Outbreaks of Some Emerging Pathogens Nourridine Siewe 1,* , Bradford Greening, Jr. 2 and Nina H. Fefferman 3 1 School of Mathematical Sciences, Rochester Institute of Technology, College of Sciences, 1 Lomb Memorial Dr, Rochester, NY 14623, USA 2 Centers for Disease Control and Prevention, 1600 Clifton Rd NE, Atlanta, GA 30329, USA; [email protected] 3 Department of Ecology and Evolutionary Biology, College of Sciences, The University of Tennessee, 447 Hesler, 569 Dabney Hall, 1416 Circle Dr, Knoxville, TN 37996-1610, USA; [email protected] * Correspondence: [email protected] Received: 1 October 2020; Accepted: 1 December 2020; Published: 9 December 2020 Abstract: Preparation for outbreaks of emerging infectious diseases is often predicated on beliefs that we will be able to understand the epidemiological nature of an outbreak early into its inception. However, since many rare emerging diseases exhibit different epidemiological behaviors from outbreak to outbreak, early and accurate estimation of the epidemiological situation may not be straightforward in all cases. Previous studies have proposed considering the role of active asymptomatic infections co-emerging and co-circulating as part of the process of emergence of a novel pathogen. Thus far, consideration of the role of asymptomatic infections in emerging disease dynamics have usually avoided considering some important sets of influences. In this paper, we present and analyze a mathematical model to explore the hypothetical scenario that some (re)emerging diseases may actually be able to maintain stable, endemic circulation successfully in an entirely asymptomatic state. -
Endo, Backward Contact Tracing, WOR, 2020.Pdf
Wellcome Open Research 2020, 5:239 Last updated: 03 NOV 2020 RESEARCH ARTICLE Implication of backward contact tracing in the presence of overdispersed transmission in COVID-19 outbreaks [version 1; peer review: 2 approved] Akira Endo 1-3, Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Quentin J. Leclerc 1,3, Gwenan M. Knight 1,3, Graham F. Medley 3,4, Katherine E. Atkins 1,3,5, Sebastian Funk 1,3, Adam J. Kucharski1,3 1Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK 2The Alan Turing Institute, London, NW1 2DB, UK 3Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK 4Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK 5Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, EH16 4UX, UK v1 First published: 13 Oct 2020, 5:239 Open Peer Review https://doi.org/10.12688/wellcomeopenres.16344.1 Latest published: 13 Oct 2020, 5:239 https://doi.org/10.12688/wellcomeopenres.16344.1 Reviewer Status Abstract Invited Reviewers Introduction: Contact tracing has the potential to control outbreaks without the need for stringent physical distancing policies, e.g. civil 1 2 lockdowns. Unlike forward contact tracing, backward contact tracing identifies the source of newly detected cases. This approach is version 1 particularly valuable when there is high individual-level variation in 13 Oct 2020 report report the number of secondary transmissions (overdispersion). Methods: By using a simple branching process model, we explored 1. -
MMWR Early and Were Reported to Two Respective Local Health Jurisdictions, Release on the MMWR Website (
Morbidity and Mortality Weekly Report High SARS-CoV-2 Attack Rate Following Exposure at a Choir Practice — Skagit County, Washington, March 2020 Lea Hamner, MPH1; Polly Dubbel, MPH1; Ian Capron1; Andy Ross, MPH1; Amber Jordan, MPH1; Jaxon Lee, MPH1; Joanne Lynn1; Amelia Ball1; Simranjit Narwal, MSc1; Sam Russell1; Dale Patrick1; Howard Leibrand, MD1 On May 12, 2020, this report was posted as an MMWR Early and were reported to two respective local health jurisdictions, Release on the MMWR website (https://www.cdc.gov/mmwr). without indication of a common source of exposure. On On March 17, 2020, a member of a Skagit County, March 17, the choir director sent a second e-mail stating that Washington, choir informed Skagit County Public Health 24 members reported that they had developed influenza-like (SCPH) that several members of the 122-member choir had symptoms since March 11, and at least one had received test become ill. Three persons, two from Skagit County and one results positive for SARS-CoV-2. The email emphasized the from another area, had test results positive for SARS-CoV-2, importance of social distancing and awareness of symptoms the virus that causes coronavirus disease 2019 (COVID-19). suggestive of COVID-19. These two emails led many members Another 25 persons had compatible symptoms. SCPH to self-isolate or quarantine before a delegated member of the obtained the choir’s member list and began an investigation on choir notified SCPH on March 17. March 18. Among 61 persons who attended a March 10 choir All 122 members were interviewed by telephone either practice at which one person was known to be symptomatic, during initial investigation of the cluster (March 18–20; 53 cases were identified, including 33 confirmed and 20 115 members) or a follow-up interview (April 7–10; 117); most probable cases (secondary attack rates of 53.3% among con- persons participated in both interviews. -
Norovirus Background
DOH 420-180 Norovirus Background Clinical Syndrome of Norovirus Symptoms Norovirus can cause acute gastroenteritis in persons of all ages. Symptoms include acute onset non- bloody diarrhea, vomiting, nausea, and abdominal pain, sometimes accompanied by low-grade fever, body aches, and headache.2,3 Some individuals may only experience vomiting or diarrhea. Dehydration is a concerning secondary outcome.2 Symptoms typically resolve without treatment in 1-3 days in healthy individuals. Illness can last 4-6 days and may manifest more severely in young children elderly persons, and hospitalized patients.2,3 Diarrhea is more common in adults, while vomiting is more common among children.4 Up to 30% of norovirus infections are asymptomatic.2 Incubation The incubation period for norovirus is 12-48 hours.2 Transmission The only known reservoir for norovirus is humans. Transmission occurs by three routes: person-to- person, foodborne, or waterborne.2 Individuals can be infected by coming into contact with infected individuals (through the fecal-oral route or by ingestion of aerosolized vomitus or feces), contaminated foods or water, or contaminated surfaces or fomites.1,2 Viral shedding occurs for 4 weeks on average following infection, with peak viral shedding occurring 2-5 days after infection.2 Norovirus is extremely contagious, with an estimated infectious dose as low as 18 viral particles, indicating that even small amounts of feces can contain billions of infectious doses.2 The period of communicability includes the acute phase of illness up through 48 hours after conclusion of diarrhea. Treatment Treatment of norovirus gastroenteritis primarily includes oral rehydration through water, juice, or ice chips. -
Lesson 1 Introduction to Epidemiology
Lesson 1 Introduction to Epidemiology Epidemiology is considered the basic science of public health, and with good reason. Epidemiology is: a) a quantitative basic science built on a working knowledge of probability, statistics, and sound research methods; b) a method of causal reasoning based on developing and testing hypotheses pertaining to occurrence and prevention of morbidity and mortality; and c) a tool for public health action to promote and protect the public’s health based on science, causal reasoning, and a dose of practical common sense (2). As a public health discipline, epidemiology is instilled with the spirit that epidemiologic information should be used to promote and protect the public’s health. Hence, epidemiology involves both science and public health practice. The term applied epidemiology is sometimes used to describe the application or practice of epidemiology to address public health issues. Examples of applied epidemiology include the following: • the monitoring of reports of communicable diseases in the community • the study of whether a particular dietary component influences your risk of developing cancer • evaluation of the effectiveness and impact of a cholesterol awareness program • analysis of historical trends and current data to project future public health resource needs Objectives After studying this lesson and answering the questions in the exercises, a student will be able to do the following: • Define epidemiology • Summarize the historical evolution of epidemiology • Describe the elements of a case -
Managing Outbreaks of Sexually Transmitted Infections
Managing outbreaks of Sexually Transmitted Infections Operational guidance Managing outbreaks of Sexually Transmitted Infections: Operational guidance About Public Health England Public Health England exists to protect and improve the nation’s health and wellbeing, and reduce health inequalities. We do this through world-class science, knowledge and intelligence, advocacy, partnerships and the delivery of specialist public health services. We are an executive agency of the Department of Health, and are a distinct delivery organisation with operational autonomy to advise and support government, local authorities and the NHS in a professionally independent manner. Public Health England Wellington House 133-155 Waterloo Road London SE1 8UG Tel: 020 7654 8000 www.gov.uk/phe Twitter: @PHE_uk Facebook: www.facebook.com/PublicHealthEngland Prepared by: Dr Ian Simms, Dr Margot Nicholls, Dr Kirsty Foster, Lynsey Emmett, Dr Paul Crook and Dr Gwenda Hughes. For queries relating to this document, please contact Dr Ian Simms at [email protected] Resources for supporting STI outbreak management can be found at: www.gov.uk/government/publications/sexually-transmitted-infections-stis-managing- outbreaks © Crown copyright 2017 You may re-use this information (excluding logos) free of charge in any format or medium, under the terms of the Open Government Licence v3.0. To view this licence, visit OGL or email [email protected]. Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concerned. Any enquiries regarding this publication should be sent to [insert email address]. Published January 2017 PHE publications gateway number: 2016586 2 Managing outbreaks of Sexually Transmitted Infections: Operational guidance Contents About Public Health England 2 1. -
The Failure of R0
Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2011, Article ID 527610, 17 pages doi:10.1155/2011/527610 Review Article The Failure of R0 Jing Li,1 Daniel Blakeley,2 and Robert J. Smith?3 1 Department of Mathematics, Pennsylvania State University, University Park, State College, PA 16802, USA 2 School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH, UK 3 Department of Mathematics and Faculty of Medicine, The University of Ottawa, 585 King Edward Avenue, Ottawa ON, Canada K1N 6N5 Correspondence should be addressed to Robert J. Smith?, [email protected] Received 30 January 2011; Revised 18 May 2011; Accepted 18 May 2011 Academic Editor: Haitao Chu Copyright © 2011 Jing Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The basic reproductive ratio, R0, is one of the fundamental concepts in mathematical biology. It is a threshold parameter, intended to quantify the spread of disease by estimating the average number of secondary infections in a wholly susceptible population, giving an indication of the invasion strength of an epidemic: if R0 < 1, the disease dies out, whereas if R0 > 1, the disease persists. R0 has been widely used as a measure of disease strength to estimate the effectiveness of control measures and to form the backbone of disease-management policy. However, in almost every aspect that matters, R0 is flawed. Diseases can persist with R0 < 1, while diseases with R0 > 1 can die out. -
Epidemiology of and Risk Factors for COVID-19 Infection Among Health Care Workers: a Multi-Centre Comparative Study
International Journal of Environmental Research and Public Health Article Epidemiology of and Risk Factors for COVID-19 Infection among Health Care Workers: A Multi-Centre Comparative Study Jia-Te Wei 1, Zhi-Dong Liu 1, Zheng-Wei Fan 2, Lin Zhao 1,* and Wu-Chun Cao 1,2,* 1 Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China; [email protected] (J.-T.W.); [email protected] (Z.-D.L.) 2 State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China; [email protected] * Correspondence: [email protected] (L.Z.); [email protected] (W.-C.C.) Received: 31 August 2020; Accepted: 28 September 2020; Published: 29 September 2020 Abstract: Healthcare workers (HCWs) worldwide are putting themselves at high risks of coronavirus disease 2019 (COVID-19) by treating a large number of patients while lacking protective equipment. We aim to provide a scientific basis for preventing and controlling the COVID-19 infection among HCWs. We used data on COVID-19 cases in the city of Wuhan to compare epidemiological characteristics between HCWs and non-HCWs and explored the risk factors for infection and deterioration among HCWs based on hospital settings. The attack rate (AR) of HCWs in the hospital can reach up to 11.9% in Wuhan. The time interval from symptom onset to diagnosis in HCWs and non-HCWs dropped rapidly over time. From mid-January, the median time interval of HCW cases was significantly shorter than in non-HCW cases. -
2015 Infectious Disease Outbreak Summary Report
nfectious IDisease Outbreak Summary Report 2015 Arizona Department of Health Services Office of Infectious Disease Services 150 N 18th Ave., Suite 140 Phoenix, AZ 85007 602-364-3676 This page has intentionally been left blank. Contents 1.0 Executive Summary 1 2.0 Overview 2 2.1 Outbreak detection and response 2 2.2 Outbreak data sources 3 2.3 Outbreak performance goals 3 2.4 Outbreak reporting requirements. 4 2.5 2015 Outbreak | A High-level View 5 2.5.1 Number of confirmed outbreaks 5 2.5.2 Rule-out outbreaks, out-of-Arizona outbreaks, and clusters 5 2.5.3 Outbreaks by county 6 2.5.4 Outbreaks by month 7 2.5.5 Outbreak size 8 2.5.6 Method of identification 8 3.0 Outbreaks by disease category highlights 9 3.1 Outbreaks of gastrointestinal diseases 10 3.1.1 PFGE-matched clusters 11 3.1.2 Foodborne GI outbreaks 14 3.1.3 Person-to-person GI outbreaks 16 3.1.4 Zoonotic outbreaks 18 3.2 Outbreaks of vaccine-preventable diseases 18 3.2.1 Pertussis outbreaks 19 3.3 Outbreaks of respiratory illnesses. 19 3.3.1 Influenza outbreaks 20 4.0 Outbreaks by setting 21 4.1 Outbreaks in child care or school settings 22 4.2 Outbreaks in healthcare settlings 24 4.3 Outbreaks that occurred outside of Arizona 25 5.0 Notable outbreaks 26 5.1 Salmonella Paratyphi B var. L(+) tartrate(+) linked to imported raw tuna 26 5.2 Salmonella Poona linked to imported cucumbers 28 5.3 St. -
In the Long Shadow of Our Best Intentions: Model-Based Assessment of the Consequences of School Reopening During the COVID-19 Pandemic
medRxiv preprint doi: https://doi.org/10.1101/2020.09.18.20197400; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license . In the long shadow of our best intentions: model-based assessment of the consequences of school reopening during the COVID-19 pandemic Authors: Kaitlyn E. Johnson1,2,3, Madison Stoddard3, Ryan P. Nolan4, Douglas E. White5, Natasha S. Hochberg6,7, Arijit Chakravarty3 Affiliations: 1) Department of Integrative Biology, The University of Texas at Austin 2) Department of BioMedical Engineering, The University of Texas at Austin 3) Fractal Therapeutics, Cambridge, MA 4) HalozyMe Therapeutics, San Diego, CA 5) Independent Researcher 6) Department of EpideMiology, Boston University School of Public Health 7) Department of Medicine, Boston University School of Medicine NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.09.18.20197400; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license . Abstract As the United States grapples with the ongoing COVID-19 pandeMic, a particularly thorny set of questions surrounds the reopening of K-12 schools and universities. -
Arxiv:1705.01135V2 [Q-Bio.QM] 17 Jul 2020 Author Summary
Estimating and interpreting secondary attack risk: Binomial considered harmful Yushuf Sharker1, Eben Kenah2, * 1 Division of Biometrics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA 2 Biostatistics Division, College of Public Health, The Ohio State University, Columbus, Ohio, USA * [email protected] Abstract The household secondary attack risk (SAR), often called the secondary attack rate or secondary infection risk, is the probability of infectious contact from an infectious household member A to a given household member B, where we define infectious contact to be a contact sufficient to infect B if he or she is susceptible. Estimation of the SAR is an important part of understanding and controlling the transmission of infectious diseases. In practice, it is most often estimated using binomial models such as logistic regression, which implicitly attribute all secondary infections in a household to the primary case. In the simplest case, the number of secondary infections in a household with m susceptibles and a single primary case is modeled as a binomial(m; p) random variable where p is the SAR. Although it has long been understood that transmission within households is not binomial, it is thought that multiple generations of transmission can be safely neglected when p is small. We use probability generating functions and simulations to show that this is a mistake. The proportion of susceptible household members infected can be substantially larger than the SAR even when p is small. As a result, binomial estimates of the SAR are biased upward and their confidence intervals have poor coverage probabilities even if adjusted for clustering. -
Global Report for Research on Infectious Diseases of Poverty 2012
global reporT for research on infectious diseases of poverty 2012 wiTh financial supporT of European Union global report for research on infectious diseases of poverty with financial support of European Union WHo library Cataloguing-in-publication Data: Global report for research on infectious diseases of poverty. 1.communicable disease control. 2.research 3.poverty. 4.health behavior. 5.developing countries. i.World health organiza- tion. ii.unicef/undp/World Bank/Who special programme for research and training in tropical diseases. isBn 978 92 4 156 448 9 (nLM classification: aW 110) Copyright © World Health organization on behalf of the Special programme for research and training in tropical Diseases 2012 all rights reserved. the use of content from this health information product for all non-commercial education, training and information purposes is encouraged, including translation, quotation and reproduction, in any medium, but the content must not be changed and full acknowledgement of the source must be clearly stated. a copy of any resulting product with such content should be sent to tdr, World health organization, avenue appia, 1211 Geneva 27, switzerland. tdr is a World health organi- zation (Who) executed unicef/undp/World Bank/World health organization special programme for research and training in tropical diseases. this information product is not for sale. the use of any information or content whatsoever from it for publicity or advertising, or for any commercial or income-generating purpose, is strictly prohibited. no elements of this information product, in part or in whole, may be used to promote any specific individual, entity or product, in any manner whatsoever.