In:
PLOS Neglected Tropical Diseases, Public Library of Science (PLoS), Vol. 15, No. 9 ( 2021-9-9), p. e0009653-
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.
Type of Medium:
Online Resource
ISSN:
1935-2735
DOI:
10.1371/journal.pntd.0009653
DOI:
10.1371/journal.pntd.0009653.g001
DOI:
10.1371/journal.pntd.0009653.g002
DOI:
10.1371/journal.pntd.0009653.g003
DOI:
10.1371/journal.pntd.0009653.g004
DOI:
10.1371/journal.pntd.0009653.g005
DOI:
10.1371/journal.pntd.0009653.g006
DOI:
10.1371/journal.pntd.0009653.t001
DOI:
10.1371/journal.pntd.0009653.t002
DOI:
10.1371/journal.pntd.0009653.t003
DOI:
10.1371/journal.pntd.0009653.t004
DOI:
10.1371/journal.pntd.0009653.t005
DOI:
10.1371/journal.pntd.0009653.t006
DOI:
10.1371/journal.pntd.0009653.s001
DOI:
10.1371/journal.pntd.0009653.s002
DOI:
10.1371/journal.pntd.0009653.s003
DOI:
10.1371/journal.pntd.0009653.s004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2429704-5
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