Integrated surveillance and modelling systems for climate-sensitive diseases: two case studies

Abstract

BackgroundInfectious disease surveillance has traditionally focused on tracking human cases along with arthropod vectors and zoonotic hosts. For climate-sensitive diseases, there is potential to strengthen surveillance and predict future outbreaks by monitoring environmental risk factors using broad-scale sensor networks. We aim to highlight the opportunities and challenges of this integration by presenting two case studies of operational surveillance and forecasting systems for mosquito-borne diseases. MethodsThe Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) system integrates malaria case surveillance with remotely sensed environmental data to predict malaria outbreaks in the Amhara region of Ethiopia and has been producing weekly forecasts since 2015. The South Dakota Mosquito Information System (SDMIS) combines entomological surveillance with gridded meteorological data to generate weekly risk maps for West Nile virus in the north-central USA. Both systems use a variety of earth science datasets, including meteorological fields from the North American Land Data Assimilation System (NLDAS); rainfall data from the Global Precipitation Measurement (GPM) mission; and land surface temperature and surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS). We assessed these projects by conducting structured interviews and soliciting written reports from stakeholders. FindingsDespite differences in disease ecology and geographic setting, feedback from the stakeholders revealed common themes that can inform future efforts at disease early warning based on climatic models. These include the value of assimilating multiple data streams to constrain model predictions with recent observations of infection, the crucial role of automated workflows to facilitate timely data processing and integration, and the challenge of linking forecasts to specific public health responses. InterpretationInformation systems that integrate climate data with disease surveillance are critical enabling technologies that support data access, model-based predictions, and continuous evaluation and improvement of forecasts. These systems must also include networks of individuals and institutions that create a broader enabling environment to support disease forecasting. FundingNational Institute of Allergy and Infectious Diseases and NASA Applied Sciences Public Health and Air Quality Program.