How experts use disease modeling to help inform policymakers

Harry Stevens and John Muyskens for The Washington Post put you in the spot of an epidemiologist receiving inquiries from policymakers about what might happen:

Imagine you are an epidemiologist, and one day the governor sends you an email about an emerging new disease that has just arrived in your state. To avoid the complexities of a real disease like covid-19, the illness caused by the novel coronavirus, we have created a fake disease called Simulitis. In the article below, we’ll give you the chance to model some scenarios — and see what epidemiologists are up against as they race to understand a new contagion.

Fuzzy numbers, meet real-world decisions.

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Johns Hopkins providing course on using epidemiology to understand the Covid-19 numbers

The numbers are fuzzy. You take them at face value, and you end up with fuzzy interpretations. Starting at the end of this month, Johns Hopkins is providing a two-week epidemiology course on understanding these numbers better:

This free Teach-Out is for anyone who has been curious about how we identify and measure outbreaks like the COVID-19 epidemic and wants to understand the epidemiology of these infections.

The COVID-19 epidemic has made many people want to understand the science behind pressing questions like: “How many people have been infected?” “How do we measure who is infected?” “How infectious is the virus?” “What can we do?” Epidemiology has the tools to tell us how to collect and analyze the right data to answer these questions.

Yes.

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