The Section of Computational epidemiology and modeling of infectious diseases at DIEPS, FIC, NIH leads a capacity-building program toward modeling, outbreak analytics, and surveillance for infectious diseases in low-resource settings. Endemic and pandemic pathogens continue to cause a significant global burden, particularly in low- and middle-income countries (LMICs) that might not have as robust surveillance, reporting, and analytics infrastructure. Therefore, understanding the transmission of pathogens circulating locally, and nowcasting/forecasting epidemiologic patterns in the weeks ahead, can help local public health communities assess the effectiveness of past interventions and optimize control strategies.
Our Capacity-Building in Computational epidemiology and modeling of infectious diseases aims to train epidemiologists, virologists and public health experts in analytic approaches to analyze surveillance data. There have been more than 20 regional in-person training workshops since the inception of the program in 2005, and virtual trainings during COVID19. Past trainings have focused on time series approaches to disease surveillance, disease burden studies, mathematical transmission models, and short-term forecasts using digital surveillance.
Contact