Apply epidemiological principles to describe the spread of COVID-19, and evaluate the potential impact of public health interventions via modeling and historical and contemporary examples.
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Authors: Vineet Desai; Jessie Duggan; Jakub Glowala; Manav Gupta; Kiryung Kim; Gina Liu; Katherine McDaniel, MSc; Katherine Nabel; Himaja Nagireddy; Deborah Plana; Emily Rencsok; Connor Verheyen; Lily Zhong
Editor:
Reviewers: Wolfram Goessling, MD, PhD; Andrea Wershof Schwartz, MD, MPH; William Hanage, PhD; Rebecca Kahn, MS; James Hay, PhD
Update Disclaimer: Thank you for visiting Module 2! Due to the wide availability of high-quality resources at the medical student level, we are no longer updating this module regularly. Information on the last major update on 1/3/21 can be found below. We hope that the material, including our learning objectives, cases, and thought questions can still be useful as an educational resource. If you find that material is incorrect or that a link is broken, please do let us know by emailing .
Highlight of Updates (1/03/21):
Clarified learning objectives in and updated
: Added definition of presymptomatic vs. asymptomatic
Added section on differential
Included information on in nursing homes
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By the end of this module, medical students should be able to:
Define R0, Re, incubation period, serial interval, epidemic curve, community transmission, social distancing, and flattening the curve as they pertain to COVID-19
Access a reliable source of the latest epidemiologic information about COVID-19
Describe how changing epidemiological parameters changes disease dynamics
Contrast three cases that illustrate how nonpharmaceutical interventions save lives in a pandemic
In this module, we move from the biology and pathophysiology of SARS-CoV-2 to its implications at a population level. We start with an introduction to epidemiological terms. To understand where the epidemic is now, we link a curated set of continuously updating resources. Current estimates for the U.S. indicate a caseload of what is currently recognized, with asymptomatic, presymptomatic, and mildly symptomatic people as a major contributor to transmission. Next, we give an overview of the factors used to predict where the epidemic is going in the U.S., focusing on the concept of . Mathematically and empirically, small modifications to the parameters of this growth can “," which lengthens the time over which severely ill people present, providing the healthcare system more time to prepare to treat patients and scientists time to test and optimize new treatment strategies to reduce mortality. At this phase, the U.S. is primarily attempting to flatten the curve by “.” Modeling from the UK indicates social distancing may be required for months.
We end with three case studies to contextualize these epidemiology principles. The influenza pandemic of prompted different responses from three U.S. cities, with three dramatically different outcomes for morbidity and mortality. The 2009 H1N1 pandemic, a frequent foil to COVID-19, required less disruption to control due to a lower R0 and case fatality rate, as well as faster testing, prior population immunity and pre-existing antiviral treatments. In confronting COVID-19, South Korea presents a contemporary example of a country that rapidly scaled up testing, contact tracing, and social distancing without nationwide lockdown, and has brought new cases to a minimum.
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