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 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 11, 2021
- Source ID
- 10.1038/s41467-020-20399-3
Entities
People
- Jeffrey Shaman
- Paul Lewis
- Sen Pei
- Xian Teng
Organizations
- National Institute of General Medical Sciences
- United States Department of Defense