Research highlight

Epidemiology: A website to assess COVID-19 event risk in the US in real time

Nature Human Behaviour

November 9, 2020

An interactive website that estimates the risk that at least one individual with SARS-CoV-2 is present in gatherings of different sizes in the United States is presented in a paper published in Nature Human Behaviour. This website provides data-driven information to help individuals and policy-makers make decisions that could help control the spread of SARS-CoV-2, particularly in hard-hit regions.

Despite large-scale efforts to suppress disease spread through lockdown orders and other non-pharmaceutical interventions (such as wearing masks and physical distancing), there was a resurgence of SARS-CoV-2 cases in the United States in late summer 2020, particularly in the South and West, followed by a resurgence of cases in the Midwest in early autumn 2020. The strong and often undocumented spread of SARS-CoV-2 is exacerbated by large transmission incidents, referred to as ‘super-spreading’ events. Super-spreading of SARS-CoV-2 has been documented in multiple indoor events or large gatherings in which a single infector is associated with the infection of dozens (or more) individuals.

Joshua Weitz, Clio Andris and colleagues developed the COVID-19 Real-Time Event Risk Assessment interactive website to quantify and visualize the expected risk associated with gatherings of different sizes. This website connects circulating case reports with risk assessments by adapting a mathematical risk model to real-time estimates at the county level. The authors designed the site to translate epidemiology into real-world contexts to provide policy makers, public health departments and individuals with up-to-date information relevant to decision-making that could help reduce transmission of SARS-CoV-2. The website has already been extended to provide real-time risk estimates for several European countries, and has the potential to be applied globally.

The authors note that the assessment tool could improve local estimates with the addition of data on several factors, including cases reported by zip code and improvements in estimates of under-reporting rates. The model also assumes that individuals are equally likely to attend an event, even though symptomatic individuals are less likely to attend gatherings.

doi: 10.1038/s41562-020-01000-9

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