Fig 1.
Map of specific locations where WNV models included in this comparison have been applied.
Some models (Spatial Risk Random Forest, not shown) have been applied across the entire US. Green corresponds to analyses with state extents, blue to county extents, and pink to subcounty extents. State outlines are from Natural Earth (https://www.naturalearthdata.com/downloads/50m-cultural-vectors/). City of Chicago boundary is publicly available from the City of Chicago (https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-City/ewy2-6yfk), and county boundaries and the outline for Coachella Valley were derived from US Census tract boundaries (https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html) dissolved to provide a single outline using the Dissolve algorithm in QGIS (https://qgis.org/en/site/). WNV, West Nile virus.
Fig 2.
Examples of key model outputs.
(A) A summary of key outputs for 1 year. (B) Cumulative human cases (annual human cases), (C) Culex mosquito abundance per trap night, (D) vector index (Culex abundance times infection rate by week), and (E) MIR per 1,000 mosquitoes. Peak MLE/IR is the mosquito infection rate in the peak week, Peak week for MLE/IR is the week in which the peak is reached, while Seasonal MLE/MIR is the infection rate over the season when the mosquitoes are active (using either MLEs or MIRs). Culex, Culex abundance; IR, mosquito infection rate, either as MIR or MLE; HC, human cases; MIR, minimum infection rate; MLE, maximum likelihood estimate of infection rate; VI, vector index.
Fig 3.
Generalized overview of major factors, tools, and decisions utilized by mosquito control agencies.
This figure is based on 4 representative mosquito abatement districts: 2 in Chicago (IL), Slidell (LA), and Houston (TX). Management practices may differ from program to program, but similar challenges and decisions are made from across varying spatial (local to district-wide) and temporal (days to multiple months) scales.
Fig 4.
The 13 models reviewed in this paper arranged by spatial and temporal resolution.
Rectangles with decreasing shades of gray indicate less coverage identifying potential knowledge gaps. These gaps may guide future model development or require additional data collection, as many models are at the county-annual scale due to data availability.
Fig 5.
A summary of the spatial and temporal resolution for the 41 models reviewed in [17] that are not included in Fig 4.
Numbers indicate the number of models at that spatial and temporal scale.
Table 1.
Model overview: A comparison of model class, spatial, temporal resolution, software implementation, and code availability.
Table 2.
Model inputs.
Table 3.
Prediction targets included human case counts, mosquito infection rates as either MIRs or as MLEs. Probabilistic models are those that generate predictions as probability distributions rather than single mean values. The additional prediction targets column indicates whether the model generates additional outputs not otherwise included in the table.
Table 4.
Only published model applications were included. Each line corresponds to a separate model test; therefore, some models appear more than once. References are listed for further details.
Table 5.
List of common decisions made regarding a public health and vector control response to WNV.
Letters correspond to models in Tables 1–4 and indicate models with an appropriate spatial or temporal resolution to inform the decision. Note that this pertains to the scale on which predictions are made and provides no information on the accuracy of the model predictions. As such, models with appropriate scale, but insufficient accuracy, would not be useful in an operational context.
Fig 6.
Examples of the 3 spatial scales described in Table 6 for Long Island, NY.
(a) Coarse-grain: county, (b) medium-grain: county subdivision, and (c) fine-grain: 30 × 30 m resolution for vegetation types [40], with the NY county outlines in gray for context. County outlines and county subdivisions from the 2017 US Census https://www.census.gov/geo/maps-data/data/tiger-line.html).
Table 6.
Classification of temporal and spatial resolutions relevant to vector control and public health decision-making.