What is it about?
During the COVID-19 pandemic, exposure notification apps were developed to scale up manual contact tracing. They automated notifying contacts of infection using information on proximity of mobile devices. Here we present the next step beyond these apps, network data assimilation (DA). Network DA uses this data more effectively, by aggregating it with a model of disease transmission to produce individual user-level risk assessments. We inherit our methods from the way satellite data streams are combined with models of the Earths atmosphere to produce local weather forecasts. Supported by extensive simulations studies of New York City (NYC), we show that network DA predictions track outbreaks more accurately and reliably than exposure notification apps. We also show how individual risk information can be used to target intervention policy, achieving epidemic control by isolating only a small targeted fraction of the population at any given time.
Photo by Victor He on Unsplash
Why is it important?
Current means to prevent spread of COVID-19 (and other infectious diseases) involves sweeping population-wide mandates: quarantines, social distancing and lockdowns. Though they can be effective, such mandates come at a huge cost to economic and social environments. Our findings demonstrate that by leveraging currently available data and models, one can significantly reduce this disruption (isolating only 5-10% population at any time) while simultaneously reducing deaths (by over 50%) compared with blanket interventions. Moreover, network DA is general. It can use a multitude of data sources, and can be applied to other populations and transmissible diseases, giving it great future potential towards effective control of other transmissible disease outbreaks.
Read the Original
This page is a summary of: Epidemic management and control through risk-dependent individual contact interventions, PLoS Computational Biology, June 2022, PLOS, DOI: 10.1371/journal.pcbi.1010171.
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