Erin Mordecai
NIGMS R35 ESI MIRA
Summary
Leveraging environmental drivers to predict vector-borne disease transmission
Vector-borne diseases are an increasingly urgent public health crisis worldwide. Traditional biomedical
approaches such as vaccines and drugs alone will not sustainably control vector-borne diseases or prevent
future emergence. More proactive, ecological approaches that discover and disrupt the environmental
drivers of vector transmission are critical for understanding and sustainably controlling disease epidemics.
Predicting infectious disease dynamics from ecological drivers like climate and land use is appealing because
these drivers are readily observable and often predictable, and their impacts on disease transmission are
supported by mechanistic hypotheses. However, vector-borne diseases, like other ecological systems, are
nonlinear, complex, and dynamic, making prediction challenging in a stochastic and changing world. My
research uses brings in techniques from quantitative ecology, statistics, mathematics, econometrics, and
geography as well as newly available data sources to understand and predict vector-borne disease dynamics
in response to global change. Our preliminary work has shown that climate and land use are powerful
predictors of geographical and seasonal patterns of disease transmission. I now propose to extend this work to
understand disease dynamics using cutting edge quantitative techniques and time series data. Specifically, we
will investigate how climate, habitat, behavior, and immunity interact to determine disease dynamics over
space and time for malaria, Zika, dengue, and other vector-borne pathogens, building a portfolio of evidence
and predictive tools from multiple complementary quantitative approaches. These include fitting increasingly
sophisticated dynamic models to time series data, applying empirical dynamic modeling to infer, rather than
assume, mechanistic relationships with ecological drivers, and applying econometric panel analysis to remotely
sensed and geographic data to evaluate evidence for bidirectional causation between disease and human land
use activities.
Recent decades have witnessed both unprecedented expansions in both vector-borne disease and
technological and computational capacity. In response, vector-borne disease modeling research is rapidly
accelerating, with the goal of improving prospective prediction and thereby opening opportunities for proactive
control. By developing and testing new theory, this project will finally allow us to leverage environmental
drivers of vector-borne disease to understand the mechanisms underlying complex disease dynamics,
and to predict future disease risk in changing environments.