Optimizing respiratory virus surveillance networks using uncertainty propagation.

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

SUBMITTER: Pei S 

PROVIDER: S-EPMC7801666 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature