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

Autor: Oliver Elison Timm, Nicholas DeFelice, Kelly Helm Smith, Matthew J. Ward, A. Marm Kilpatrick, Marta S. Shocket, Christopher M. Barker, Charlotte G. Rhodes, Eliza Little, Ilia Rochlin, Johnny A. Uelmen, Imelda K. Moise, Andrew J. Tyre, Justin K. Davis, Luis Fernando Chaves, Michael C. Wimberly, Morgan E. Gorris, Patrick Irwin, Alexander C. Keyel, Chris L. Fredregill, Gabriel L. Hamer, Rebecca L. Smith, Karen M. Holcomb
Přispěvatelé: Viennet, Elvina
Jazyk: angličtina
Rok vydání: 2021
Předmět:
RNA viruses
Epidemiology
RC955-962
Social Sciences
Review
Disease Vectors
Mosquitoes
Medical and Health Sciences
Geographical locations
California
Value of information
Medical Conditions
Cognition
Models
Arctic medicine. Tropical medicine
Agency (sociology)
Medicine and Health Sciences
Psychology
West Nile Virus
Public and Occupational Health
Temporal scales
Pathology and laboratory medicine
Scope (project management)
Eukaryota
Medical microbiology
Biological Sciences
Insects
Outreach
Mosquito control
Infectious Diseases
Geography
Viruses
Seasons
Pathogens
Public aspects of medicine
RA1-1270
West Nile virus
medicine.medical_specialty
Arthropoda
Infectious Disease Control
Decision Making
Models
Biological
Microbiology
Rare Diseases
Tropical Medicine
medicine
Animals
Humans
Environmental planning
Biology and life sciences
Flaviviruses
Prevention
Public health
Organisms
Viral pathogens
Cognitive Psychology
Public Health
Environmental and Occupational Health
Statistical model
Biological
Invertebrates
United States
Microbial pathogens
Insect Vectors
Vector-Borne Diseases
Species Interactions
Good Health and Well Being
Emerging Infectious Diseases
Medical Risk Factors
North America
Earth Sciences
Cognitive Science
People and places
Zoology
Entomology
Public Health Administration
West Nile Fever
Neuroscience
Zdroj: PLoS Neglected Tropical Diseases, Vol 15, Iss 9, p e0009653 (2021)
PLoS neglected tropical diseases, vol 15, iss 9
PLoS Neglected Tropical Diseases
ISSN: 1935-2735
1935-2727
Popis: 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.
Databáze: OpenAIRE
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