Description
Interacting and successive extreme climatic events, such as droughts and floods, can trigger outbreaks of multiple infectious diseases. These compound hazards can devastate communities if risk reduction plans are not implemented to protect vulnerable populations. Recent methodologicaladvances in climate-sensitive disease modelling have allowed the quantification of the combinedimpact of hydrometeorological extremes on disease risk. However, this research has not been developed into user-friendly and sustainable tools to serve anticipatory action planning of a diverse set of users. Our goal is to develop an infectious disease modelling tool called IDExtremes within an existing open-source framework for climate science, to predict the probability of outbreaks using observed and forecast hydrometeorological indicators. The flexible design will allow users in different settings to input observed (long-lag) and forecast (short-lag) hydrometeorological indicators, such adrought and flood indicators, and output the probability of an outbreak of a given climate-sensitive disease (e.g., dengue, malaria or cholera) several months in advance.
IDExtremes will be integrated in existing communities of practice, as a new health service for the Barbados Meteorological Service,a new climate service for the Brazilian Ministry of Health and an early action trigger tool forhumanitarian agencies operating in Asia and Africa.