| Project Detail |
Revolutionising mosquito-borne disease control in Latin America
In the face of extreme climatic events, environmental degradation and socio-economic disparities, mosquito-borne diseases (MBDs) pose a growing threat. With the support of the Marie Sklodowska-Curie Actions programme, the REGIME project will introduce an innovative modelling tool that assesses the impact of various determinants on MBD emergence, spread and co-circulation in Latin America. By analysing data spanning the past two decades from Brazil’s 5 570 municipalities, REGIME employs multivariate cluster analysis to classify transmission regimes, such as endemic, epidemic or episodic. This data-driven approach dissects the role of diverse disease drivers using random effects and multi-source data, offering insights into spatial patterns and temporal changes. The findings will help shape effective disease control and prevention strategies.
Extreme climatic events, environmental degradation and socio-economic inequalities exacerbate the risk of mosquito-borne disease (MBD) epidemics and modulate the transmission regimes of these diseases. Most MBDs can coexist and share patterns, which are sensitive to biodiversity loss, land use and land cover change, as well as climate, and socioeconomic and demographic factors. Determining the spatial and temporal scales at which these complex factors interact and modulate disease transmission can help to target effective control and prevention strategies. This research goes beyond the state-of-the-art by developing an innovative modelling tool called REGIME to assess the relative impact of multiple determinants on MBD emergence, spread and co-circulation in Latin America. Models will be formulated using data for Brazil over the past 20 years at the municipality level (5,570 municipalities). Multivariate cluster analysis will be performed to propose a transmission regime classification (e.g. endemic, epidemic, episodic) using disease case time series for each municipality each year to identify temporal changes in the transmission regimes. A model based on the transmission regimes will be designed to disentangle the role of different disease drivers, using multi-source data and random effects, which account for additional layers of uncertainty and help identify unknown spatial patterns, inter-annual signatures and similarities or differences between the drivers for each disease. The knowledge gained from Brazil will be applicable in a broader context in Latin America, by validating the model framework in the context of disease risk scenarios shared by those countries. REGIME will facilitate the generation of actionable knowledge to inform local risk mapping and build robust early warning and response systems to build resilience in low-resource settings. |
