The worldwide attention to influenza prevention has been resulted in increased availability of influenza surveillance data in the recent years. In 1997, WHO launched FluNet, the first web-based tool for global influenza virological surveillance. We are developing methods to examine factors that modify the timing and intensity of seasonal influenza in data-rich and data-limited settings. We are targeting countries with limited recourses or depleted economies due to natural disasters or humanitarian emergencies, where the quality and availability of reporting is still insufficient for developing reliable forecasts. Our research proposes a method to enable seasonal forecasts in data-sparse settings by triangulating temporal patterns observed in data-rich countries.
Calendar Effects in Forecasting Influenza
We examined the impact of school holidays, social events and religious observances for six age groups on four influenza outcomes (tests, positives, influenza A, and influenza B) as reported by the City of Milwaukee Health Department Laboratory, Milwaukee, Wisconsin from 2004 to 2009. Social outings can trigger influenza transmission, especially in children and elderly. In contrast, school closures are associated with reduced influenza incidence in school-aged children. Overall, calendar effects depend on the proximity and alignment of an individual holiday to age-specific and influenza outcome-specific peak timing.
Forecasting Influenza in Data Sparse Settings
After 2009 “swine flu” outbreak the national surveillance systems have improved worldwide. Yet, in countries with limited recourses or depleted economies due to natural disasters or humanitarian emergencies, the quality and availability of reporting is still insufficient for developing reliable forecasts. To understand influenza transmission dynamics in a continental scale, we extracted FluNet records in Latin America. spanning numerous timeframes with varying degrees of availability, accessibility, completeness, and comparability of records. This project is focusing on the complexity of forecasting in presence of missingness.