With sponsorship from the Wellcome Trust and in collaboration with New Light Technologies Inc. and UNICEF, the SDG Policy Initiative is developing an online infectious disease prediction platform using real-time climate conditions to map predicted spikes in disease rates and to inform decision makers of public health resources required for disease prevention and treatment. 

The project will with dengue as a proof-of-concept, and then incorporate other diseases.  Dengue fever is a climate-sensitive disease transmitted by the Aedes aegypti mosquito, infecting an estimated 100-400 million cases each year. There is a great need for publicly available early warning systems (EWS) and machine learning (ML) models that can forecast at-risk geographic regions for governing bodies to preemptively address transmission rates (through spraying for example) and distribute resources for patient care. This data will directly link the causal effects of weather on increased disease incidence rates allowing policymakers to make evidence-based, climate-focused policy that could stabilize the affected ecosystems. With dengue fever as a proof-of-concept, the goal is to expand the predictive model to predict incident rates of additional under-researched vector-borne diseases, like Zika Virus and Chagas Disease