Optimizing respiratory virus surveillance networks using uncertainty propagation

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. Lack of a widespread surveillance network hampers accurate infectious disease forecasting. Here the authors provide a framework to optimize the selection of surveillance site locations and show that accurate forecasting of respiratory diseases for locations without surveillance is feasible.

Automated summary

By optimizing the selection of surveillance site locations, accurate forecasting of respiratory diseases is feasible for locations without surveillance. Monitoring regional population centers serves as a reasonable proxy to guide surveillance efforts.