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MIA Talks

The impact of climate, social setting, and susceptibility on dengue dynamics: a case study using compartmental models, empirical dynamic modeling, and meta-analysis; Part II

April 13, 2022
Dept. of Biology, Stanford University; Center for Computational, Evolutionary and Human Genomics
Dept. of Biology, Stanford University; University of British Columbia

Temperature and precipitation are fundamental drivers of dengue fever, a mosquito-borne viral disease estimated to cause nearly 100 million symptomatic cases every year that has no widely effective therapy or vaccine. Yet, the dynamics of dengue often prove difficult to predict despite clear, mechanistic impacts of temperature and precipitation on the vector mosquito and virus because the disease is embedded in a complex socio-immunological context that is difficult to ascertain directly. Understanding the impact of climate change on dengue dynamics and predicting the future of dengue in the varying global settings in which dengue poses a threat are imperative research goals. Here, we explore how the social and immunological context modulates the effects of climate on dengue dynamics, and how when these drivers are considered together, dengue becomes much more predictable.


In the first hour, Erin Mordecai gives an overview of the biological impacts of temperature and precipitation on dengue transmission and provides a foundational understanding of how nonlinear effects of temperature affect the basic reproduction number, R0, for dengue. She then presents the challenges of using this information to predict dengue dynamics in the field. Next, Jamie Caldwell explains how dynamic compartmental models based on ordinary differential equations (i.e., susceptible, exposed, infectious, recovered, or SEIR models) can be parameterized to predict dengue dynamics in two distinct geographic settings based on weather.


In Part II, Nicole Nova presents how we can use empirical dynamic modeling to empirically reconstruct the dynamical attractor in which dengue, temperature, rainfall, and host susceptibility are embedded, and how we can use this attractor to infer causal relationships (and thereby test theory) and to make the most accurate predictions of dengue dynamics to date. Finally, Devin Kirk presents a meta-analysis that shows evidence that the theoretically predicted nonlinear effects of temperature on dengue transmission occur across a wide range of real-world transmission settings and examines the social, geographic, and study design factors that affect how temperature affects dengue in a range of global settings.