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Review
. 2019 May 6;6(2):40.
doi: 10.3390/vetsci6020040.

Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment

Andrew W Bartlow  1 , Carrie Manore  2 , Chonggang Xu  3 , Kimberly A Kaufeld  4 , Sara Del Valle  5 , Amanda Ziemann  6 , Geoffrey Fairchild  7 , Jeanne M Fair  8
Affiliations
Review

Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment

Andrew W Bartlow et al. Vet Sci. .

Abstract

Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty.

Keywords: climate change; epidemiology; infectious disease; mosquito; range expansion; vector-borne; zoonotic.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Data requirements to bring together climate and epidemiological models and ground truth real-time data. Arrows indicate data that can be used to inform additional data categories. Bold represents Data Categories and italics are representative subsets of Best Available Data.
Figure 2
Figure 2
Disease and climate systems for mosquito borne diseases. Each system must be coupled together with validation from ground truth real-time data. Data from Figure 1 feeds into each of these systems and data fusion issues are addressed throughout the process.
See this image and copyright information in PMC

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