Version 1
: Received: 2 September 2020 / Approved: 4 September 2020 / Online: 4 September 2020 (12:21:32 CEST)
How to cite:
Gayle, A.A. Perspectives onApplying Artificial Intelligence to Model Outbreak Risk: Contextualizing the Impact of Climate Change on Vector-borne Disease. Preprints2020, 2020090103. https://doi.org/10.20944/preprints202009.0103.v1
Gayle, A.A. Perspectives on Applying Artificial Intelligence to Model Outbreak Risk: Contextualizing the Impact of Climate Change on Vector-borne Disease. Preprints 2020, 2020090103. https://doi.org/10.20944/preprints202009.0103.v1
Gayle, A.A. Perspectives onApplying Artificial Intelligence to Model Outbreak Risk: Contextualizing the Impact of Climate Change on Vector-borne Disease. Preprints2020, 2020090103. https://doi.org/10.20944/preprints202009.0103.v1
As recent history has shown, changing climate not only threatens to increase the spread of known disease, but also the emergence of new and dangerous phenotypes. This occurred most recently with West Nile virus: a virus previously known for mild febrile illness rapidly emerged to become a major cause of mortality and long-term disability throughout the world. As we move forward, into increasingly uncertain times, public health research must begin to incorporate a broader understanding of the determinants of disease emergence– what, how, why, and when. The increasingmainstream availability of high-quality open data and high-poweredanalytical methods presents promising new opportunities. Up to now, quantitative models of disease outbreak risk have been largely based on just a few key drivers, namely climate and large-scale climatic effects. Such limited assessments, however, often overlook key interacting processes and downstream determinantsmore likely to drive local manifestation of disease. Such pivotal determinants may include local host abundance, human behavioral variability, and population susceptibility dynamics.The results of such analyses can therefore be misleading in cases where necessary downstream requirements are not fulfilled. It is therefore important to develop models that include climateand higher-level climatic effects alongside the downstream non-climatic factors that ultimately determine individual disease manifestation. Today, few models attempt to comprehensively address such dynamics: up until very recently, the technology simply hasn’t been available. Herein, we present an updated overview of current perspectives on the varying drivers and levels of interactions that drive disease spread. We review the predominant analytical paradigms, discuss their strengths and weaknesses, and highlight promising new analytical solutions. Our focus is on the prediction of arboviruses, particularly West Nile virus, as these diseases represent the pinnacle of epidemiological complexity – solution to which would serve as an effective “gatekeeper”. We present the current state-of-the-art with respect to known drivers of arbovirus outbreak risk and severity, differentially highlighting the impact of climate and non-climatic drivers. The reality of multiple classes of drivers interacting at different geospatial and temporal scales requires advanced new methodologies. We therefore close out by presenting and discussing some promising new applications of AI. Given the reality of accelerating disease risks due to climate change, public health and other related fields must begin the process of updating their research programs to incorporate these much needed, new capabilities.
Medicine and Pharmacology, Epidemiology and Infectious Diseases
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