Optimizing respiratory virus surveillance networks using uncertainty propagation

Sen Pei (), Xian Teng, Paul Lewis and Jeffrey Shaman ()
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Sen Pei: Mailman School of Public Health, Columbia University
Xian Teng: University of Pittsburgh
Paul Lewis: Integrated Biosurveillance Section, Armed Forces Health Surveillance Branch
Jeffrey Shaman: Mailman School of Public Health, Columbia University

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Infectious disease prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for diseases with limited records. The proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens – human metapneumovirus and seasonal coronavirus – from 35 US states and can be used to guide systemic allocation of surveillance efforts.

Date: 2021
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DOI: 10.1038/s41467-020-20399-3

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