A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making.

Eliza Little, Rebecca L Smith, Charlotte G Rhodes, Christopher M Barker, Matthew J Ward, Michael C Wimberly, Karen M Holcomb, Oliver Elison Timm, Chris L Fredregill, Kelly Helm Smith, Andrew J Tyre, Patrick Irwin, Alexander C Keyel, Justin K Davis, Nicholas B DeFelice, A Marm Kilpatrick, Marta Shocket, Imelda K Moise, Gabriel L Hamer, Luis F Chaves, Johnny A Uelmen, Ilia Rochlin, Morgan E Gorris

Journal: PLoS neglected tropical diseases 2021;15(9):e0009653

PMID: 34499656

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Abstract

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m-km, days-weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.

Address: Division of Infectious Diseases, Wadsworth Center, New York State Department of Health, Albany, New York, United States of America.; Department of Atmospheric and Environmental Sciences, University at Albany, Albany, New York, United States of America.; Information Systems and Modeling & Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.; Center for Vector Biology, Rutgers University, New Brunswick, New Jersey, United States of America.; Department of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.; Instituto Costarricense de Investigación y Enseñanza en Nutrición y Salud (INCIENSA), Tres Rios, Cartago, Costa Rica.; Department of Entomology, Texas A&M University, College Station, Texas, United States of America.; Department of Geography & Regional Studies, University of Miami, Coral Gables, Florida, United States of America.; Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, United States of America.; Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, California, United States of America.; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.; Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.; Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.; Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, Oklahoma, United States of America.; Connecticut Agricultural Experimental Station, New Haven, Connecticut, United States of America.; Northwest Mosquito Abatement District, Wheeling, Illinois, United States of America.; Department of Entomology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.; School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America.; National Drought Mitigation Center, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America.; Mosquito and Vector Control Division, Harris County Public Health, Houston, Texas, United States of America.; Department of Pathology, Microbiology, and Immunology, University of California Davis, California, United States of America.; Environmental Analytics Group, Universities Space Research Association, NASA Ames Research Center, Moffett Field, California, United States of America.; Department of Tropical Medicine, Tulane University School of Public Health & Tropical Medicine, New Orleans, Louisiana, United States of America.

DOI: 10.1371/journal.pntd.0009653

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MeSH Terms: Decision Making, Humans, Models, Biological, Public Health Administration, West Nile Fever