Data-Model Fusion to Predict the Impacts of Climate Change on Mosquito-borne Diseases

Carrie Manore (Los Alamos National Laboratory)

Mosquito-borne diseases are among the many human-natural systems that will be impacted by climate change. All of the life stages and development rates of mosquitoes are impacted by temperature and other environmental factors, and often human infrastructure provides habitat  (irrigation, containers, water management, etc). This poses a very interesting mathematical modeling problem: how do we account for relevant factors, capture the nonlinearities, and understand the uncertainty in our models and in the data used to calibrate and validate the models? I will present several models, ranging from continental to fine scale and from statistical and machine learning to mechanistic, that we are using to predict mosquito-borne diseases and how they will be impacted by climate change. Over 30 people have worked together on this project, including students, postdocs, and staff. Our team is interdisciplinary and tasked with addressing critical national security problems around human health and climate change.