The drivers of West Nile virus human illness in the Chicago, Illinois, USA area: Fine scale dynamic effects of weather, mosquito infection, social, and biological conditions

Host-feeding preferences and temperature shape the dynamics of West Nile virus: A mathematical model to predict the impacts of vector-host interactions and vector management on R0

West Nile virus (WNV) is prevalent across the United States, but its transmission patterns and spatio-temporal intensity vary significantly, particularly in the Eastern United States. For instance, Chicago has long been a hotspot for WNV cases due to its high cumulative incidence of infection, with the number of cases varying considerably from year to year. The abilities of host species to maintain and disseminate WNV, along with eco-epidemiological factors that influence vector-host contact rates underlie WNV transmission potential. There is growing evidence that several vectors exhibit strong feeding preferences towards different host communities. In our research study, we construct a process based weather driven ordinary differential equation (ODE) model to understand the impact of one vector species (Culex pipiens), its preferred avian and non-preferred human hosts on the basic reproduction number (R0). In developing this WNV transmission model, we account for the feeding index, which is defined as the relative preference of the vectors for taking blood meals from a competent avian host versus a non-competent mammalian host. We also include continuous introduction of infected agents into the model during the simulations as the introduction of WNV is not a single event phenomenon. We derive an analytic form of R0 to predict the conditions under which there will be an outbreak of WNV and the relationship between the feeding index and the efficacy of adulticide is highly nonlinear. In our mechanistic model, we also demonstrate that adulticide treatments produced significant reductions in the Culex pipiens population. Sensitivity analysis demonstrates that feeding index and rate of introduction of infected agents are two important factors beside the efficacy of adulticide. We validate our model by comparing simulations to surveillance data collected for the Culex pipiens complex in Cook County, Illinois, USA. Our results reveal that the interaction between the feeding index and mosquito abatement strategy is intricate, especially considering the fluctuating temperature conditions. This induces heterogeneous transmission patterns that need to be incorporated when modelling multi-host, multi-vector transmission models.

Forecasting West Nile Virus With Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data

Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks.

Mosquito Species Diversity and Abundance Patterns in Plots with Contrasting Land Use and Land Cover in Bloomington, Indiana

Land use and land cover (LULC) gradients are associated with differences in mosquito species composition and the entomological risk of mosquito-borne disease. Here, we present results from a season-long study of mosquito species richness and abundance with samples collected at 9 locations from 2 plots with contrasting LULC, an urban farm and a forest preserve, in Bloomington, IN, a city in the midwestern USA. With a total sampling effort of 234 trap-nights, we collected 703 mosquitoes from 9 genera and 21 species. On the farm, we collected 15 species (285 mosquitoes). In the preserve, we collected 19 species (418 mosquitoes). Thirteen species were common in both study plots, 2 were exclusive to the farm, and 6 were exclusive to the forest preserve. In both plots, we collected Aedes albopictus and Ae. japonicus. In the farm, the most common mosquito species were Culex restuans/Cx. pipiens and Coquillettidia perturbans. In the preserve, Ae. japonicus and Ae. triseriatus were the 2 most common mosquito species. Time series analysis suggests that weather factors differentially affected mosquito species richness and mosquito abundance in the plots. Temperature, relative humidity (RH), and precipitation were positively associated with richness and abundance at the farm, while increases in the SD of RH decreased both richness and abundance at the preserve. Our results highlight the importance that LULC has for mosquito species diversity and abundance and confirm the presence of Ae. albopictus and Ae. japonicus in southwestern Indiana.

Climate change and disorders of the nervous system

Anthropogenic climate change is affecting people's health, including those with neurological and psychiatric diseases. Currently, making inferences about the effect of climate change on neurological and psychiatric diseases is challenging because of an overall sparsity of data, differing study methods, paucity of detail regarding disease subtypes, little consideration of the effect of individual and population genetics, and widely differing geographical locations with the potential for regional influences. However, evidence suggests that the incidence, prevalence, and severity of many nervous system conditions (eg, stroke, neurological infections, and some mental health disorders) can be affected by climate change. The data show broad and complex adverse effects, especially of temperature extremes to which people are unaccustomed and wide diurnal temperature fluctuations. Protective measures might be possible through local forecasting. Few studies project the future effects of climate change on brain health, hindering policy developments. Robust studies on the threats from changing climate for people who have, or are at risk of developing, disorders of the nervous system are urgently needed.

Two-step light gradient boosted model to identify human west nile virus infection risk factor in Chicago

West Nile virus (WNV), a flavivirus transmitted by mosquito bites, causes primarily mild symptoms but can also be fatal. Therefore, predicting and controlling the spread of West Nile virus is essential for public health in endemic areas. We hypothesized that socioeconomic factors may influence human risk from WNV. We analyzed a list of weather, land use, mosquito surveillance, and socioeconomic variables for predicting WNV cases in 1-km hexagonal grids across the Chicago metropolitan area. We used a two-stage lightGBM approach to perform the analysis and found that hexagons with incomes above and below the median are influenced by the same top characteristics. We found that weather factors and mosquito infection rates were the strongest common factors. Land use and socioeconomic variables had relatively small contributions in predicting WNV cases. The Light GBM handles unbalanced data sets well and provides meaningful predictions of the risk of epidemic disease outbreaks.

Multi-Model Prediction of West Nile Virus Neuroinvasive Disease With Machine Learning for Identification of Important Regional Climatic Drivers

West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental United States (CONUS). Spatial heterogeneity in historical incidence, environmental factors, and complex ecology make prediction of spatiotemporal variation in WNV transmission challenging. Machine learning provides promising tools for identification of important variables in such situations. To predict annual WNV neuroinvasive disease (WNND) cases in CONUS (2015-2021), we fitted 10 probabilistic models with variation in complexity from naïve to machine learning algorithm and an ensemble. We made predictions in each of nine climate regions on a hexagonal grid and evaluated each model's predictive accuracy. Using the machine learning models (random forest and neural network), we identified the relative importance and variation in ranking of predictors (historical WNND cases, climate anomalies, human demographics, and land use) across regions. We found that historical WNND cases and population density were among the most important factors while anomalies in temperature and precipitation often had relatively low importance. While the relative performance of each model varied across climatic regions, the magnitude of difference between models was small. All models except the naïve model had non-significant differences in performance relative to the baseline model (negative binomial model fit per hexagon). No model, including the ensemble or more complex machine learning models, outperformed models based on historical case counts on the hexagon or region level; these models are good forecasting benchmarks. Further work is needed to assess if predictive capacity can be improved beyond that of these historical baselines.

Spatiotemporal organization of cryptic North American Culex species along an urbanization gradient

Landscape heterogeneity creates diverse habitat and resources for mosquito vectors of disease. A consequence may be varied distribution and abundance of vector species over space and time dependent on niche requirements.We tested the hypothesis that landscape heterogeneity driven by urbanization influences the distribution and relative abundance of Culex pipiens, Cx. restuans, and Cx. quinquefasciatus, three vectors of West Nile virus (WNv) in the eastern North American landscape. We collected 9,803 cryptic Culex from urban, suburban, and rural sites in metropolitan Washington, District of Columbia, during the months of June-October, 2019-2021. In 2021, we also collected mosquitoes in April and May to measure early-season abundance and distribution. Molecular techniques were used to identify a subset of collected Culex to species (n = 2,461). Ecological correlates of the spatiotemporal distribution of these cryptic Culex were examined using constrained and unconstrained ordination.Seasonality was not associated with Culex community composition in June-October over three years but introducing April and May data revealed seasonal shifts in community composition in the final year of our study. Culex pipiens were dominant across site types, while Cx. quinquefasciatus were associated with urban environments, and Cx. restuans were associated with rural and suburban sites. All three species rarely coexisted.Our work demonstrates that human-mediated land-use changes influence the distribution and relative abundance of Culex vectors of WNv, even on fine geospatial scales. Site classification, percent impervious surface, distance to city center, and longitude predicted Culex community composition. We documented active Culex months before vector surveillance typically commences in this region, with Culex restuans being most abundant during April and May. Active suppression of Cx. restuans in April and May could reduce early enzootic transmission, delay the seasonal spread of WNv, and thereby reduce overall WNv burden. By June, the highest risk of epizootic spillover of WNv to human hosts may be in suburban areas with high human population density and mixed Culex assemblages that can transmit WNv between birds and humans. Focusing management efforts there may further reduce human disease burden.

Ultra-low volume (ULV) adulticide treatment impacts age structure of Culex species (Diptera: Culicidae) in a West Nile virus hotspot

West Nile virus (WNV) invaded the continental United States over 20 years ago and continues to cause yearly seasonal outbreaks of human and veterinary disease. In the suburbs of Chicago, Illinois, ultra-low volume (ULV) truck-mounted adulticide spraying frequently is performed to reduce populations of Culex restuans Theobald and Cx. pipiens L. mosquitoes (Diptera: Culicidae) in an effort to lower the risk of WNV transmission. The effectiveness of this control method has not been rigorously evaluated, and evidence for Culex population reduction after ULV adulticide spraying has been inconclusive. Therefore, we evaluated the results of 5 sequential weekly truck-mounted adulticide applications of Zenivex® E20 (etofenprox) in 2 paired sites located in Cook County, IL, during the summer of 2018. Mosquito population abundance, age structure, and WNV infection prevalence were monitored and compared between paired treatment and nearby control sites. Adulticide treatment did not result in consistent short-term or long-term reductions in target WNV vector Culex abundance. However, there was a significant increase in the proportion of nulliparous females in the treated sites compared to control sites and a decrease in Cx. pipiens WNV infection rates at one of the treated sites. This evidence that ULV adulticide spraying altered the age structure and WNV infection prevalence in a vector population has important implications for WNV transmission risk management. Our findings also underscore the importance of measuring these important indicators in addition to abundance metrics when evaluating the efficacy of control methods.

A Habitat Model for Disease Vector Aedes aegypti in the Tampa Bay Area, FloridA

Within the contiguous USA, Florida is unique in having tropical and subtropical climates, a great abundance and diversity of mosquito vectors, and high rates of human travel. These factors contribute to the state being the national ground zero for exotic mosquito-borne diseases, as evidenced by local transmission of viruses spread by Aedes aegypti, including outbreaks of dengue in 2022 and Zika in 2016. Because of limited treatment options, integrated vector management is a key part of mitigating these arboviruses. Practical knowledge of when and where mosquito populations of interest exist is critical for surveillance and control efforts, and habitat predictions at various geographic scales typically rely on ecological niche modeling. However, most of these models, usually created in partnership with academic institutions, demand resources that otherwise may be too time-demanding or difficult for mosquito control programs to replicate and use effectively. Such resources may include intensive computational requirements, high spatiotemporal resolutions of data not regularly available, and/or expert knowledge of statistical analysis. Therefore, our study aims to partner with mosquito control agencies in generating operationally useful mosquito abundance models. Given the increasing threat of mosquito-borne disease transmission in Florida, our analytic approach targets recent Ae. aegypti abundance in the Tampa Bay area. We investigate explanatory variables that: 1) are publicly available, 2) require little to no preprocessing for use, and 3) are known factors associated with Ae. aegypti ecology. Out of our 4 final models, none required more than 5 out of the 36 predictors assessed (13.9%). Similar to previous literature, the strongest predictors were consistently 3- and 4-wk temperature and precipitation lags, followed closely by 1 of 2 environmental predictors: land use/land cover or normalized difference vegetation index. Surprisingly, 3 of our 4 final models included one or more socioeconomic or demographic predictors. In general, larger sample sizes of trap collections and/or citizen science observations should result in greater confidence in model predictions and validation. However, given disparities in trap collections across jurisdictions, individual county models rather than a multicounty conglomerate model would likely yield stronger model fits. Ultimately, we hope that the results of our assessment will enable more accurate and precise mosquito surveillance and control of Ae. aegypti in Florida and beyond.

Comparison of West Nile Virus Disease in Humans and Horses: Exploiting Similarities for Enhancing Syndromic Surveillance

West Nile virus (WNV) neuroinvasive disease threatens the health and well-being of horses and humans worldwide. Disease in horses and humans is remarkably similar. The occurrence of WNV disease in these mammalian hosts has geographic overlap with shared macroscale and microscale drivers of risk. Importantly, intrahost virus dynamics, the evolution of the antibody response, and clinicopathology are similar. The goal of this review is to provide a comparison of WNV infection in humans and horses and to identify similarities that can be exploited to enhance surveillance methods for the early detection of WNV neuroinvasive disease.

Human biting mosquitoes and implications for West Nile virus transmission

BACKGROUND

West Nile virus (WNV), primarily vectored by mosquitoes of the genus Culex, is the most important mosquito-borne pathogen in North America, having infected thousands of humans and countless wildlife since its arrival in the USA in 1999. In locations with dedicated mosquito control programs, surveillance methods often rely on frequent testing of mosquitoes collected in a network of gravid traps (GTs) and CO2-baited light traps (LTs). Traps specifically targeting oviposition-seeking (e.g. GTs) and host-seeking (e.g. LTs) mosquitoes are vulnerable to trap bias, and captured specimens are often damaged, making morphological identification difficult.

METHODS

This study leverages an alternative mosquito collection method, the human landing catch (HLC), as a means to compare sampling of potential WNV vectors to traditional trapping methods. Human collectors exposed one limb for 15 min at crepuscular periods (5:00-8:30 am and 6:00-9:30 pm daily, the time when Culex species are most actively host-seeking) at each of 55 study sites in suburban Chicago, Illinois, for two summers (2018 and 2019).

RESULTS

A total of 223 human-seeking mosquitoes were caught by HLC, of which 46 (20.6%) were mosquitoes of genus Culex. Of these 46 collected Culex specimens, 34 (73.9%) were Cx. salinarius, a potential WNV vector species not thought to be highly abundant in upper Midwest USA. Per trapping effort, GTs and LTs collected > 7.5-fold the number of individual Culex specimens than HLC efforts.

CONCLUSIONS

The less commonly used HLC method provides important insight into the complement of human-biting mosquitoes in a region with consistent WNV epidemics. This study underscores the value of the HLC collection method as a complementary tool for surveillance to aid in WNV vector species characterization. However, given the added risk to the collector, novel mitigation methods or alternative approaches must be explored to incorporate HLC collections safely and strategically into control programs.

Using machine learning to understand microgeographic determinants of the Zika vector, Aedes aegypti

There are limited data on why the 2016 Zika outbreak in Miami-Dade County, Florida was confined to certain neighborhoods. In this research, Aedes aegypti, the primary vector of Zika virus, are studied to examine neighborhood-level differences in their population dynamics and underlying processes. Weekly mosquito data were acquired from the Miami-Dade County Mosquito Control Division from 2016 to 2020 from 172 traps deployed around Miami-Dade County. Using random forest, a machine learning method, predictive models of spatiotemporal dynamics of Ae. aegypti in response to meteorological conditions and neighborhood-specific socio-demographic and physical characteristics, such as land-use and land-cover type and income level, were created. The study area was divided into two groups: areas affected by local transmission of Zika during the 2016 outbreak and unaffected areas. Ae. aegypti populations in areas affected by Zika were more strongly influenced by 14- and 21-day lagged weather conditions. In the unaffected areas, mosquito populations were more strongly influenced by land-use and day-of-collection weather conditions. There are neighborhood-scale differences in Ae. aegypti population dynamics. These differences in turn influence vector-borne disease diffusion in a region. These results have implications for vector control experts to lead neighborhood-specific vector control strategies and for epidemiologists to guide vector-borne disease risk preparations, especially for containing the spread of vector-borne disease in response to ongoing climate change.

Semi-field and surveillance data define the natural diapause timeline for Culex pipiens across the United States

Reproductive diapause serves as biological mechanism for many insects, including the mosquito Culex pipiens, to overwinter in temperate climates. While Cx. pipiens diapause has been well-studied in the laboratory, the timing and environmental signals that promote diapause under natural conditions are less understood. In this study, we examine laboratory, semi-field, and mosquito surveillance data to define the approximate timeline and seasonal conditions that contribute to Cx. pipiens diapause across the United States. While confirming integral roles of temperature and photoperiod in diapause induction, we also demonstrate the influence of latitude, elevation, and mosquito population genetics in shaping Cx. pipiens diapause incidence across the country. Coinciding with the cessation of WNV activity, these data can have important implications for mosquito control, where targeted efforts prior to diapause induction can decrease mosquito populations and WNV overwintering to reduce mosquito-borne disease incidence the following season.

Relative Influence of Land Use, Mosquito Abundance, and Bird Communities in Defining West Nile Virus Infection Rates in Culex Mosquito Populations

Since its introduction to North America in 1999, the West Nile virus (WNV) has resulted in over 50,000 human cases and 2400 deaths. WNV transmission is maintained via mosquito vectors and avian reservoir hosts, yet mosquito and avian infections are not uniform across ecological landscapes. As a result, it remains unclear whether the ecological communities of the vectors or reservoir hosts are more predictive of zoonotic risk at the microhabitat level. We examined this question in central Iowa, representative of the midwestern United States, across a land use gradient consisting of suburban interfaces with natural and agricultural habitats. At eight sites, we captured mosquito abundance data using New Jersey light traps and monitored bird communities using visual and auditory point count surveys. We found that the mosquito minimum infection rate (MIR) was better predicted by metrics of the mosquito community than metrics of the bird community, where sites with higher proportions of Culex pipiens group mosquitoes during late summer (after late July) showed higher MIRs. Bird community metrics did not significantly influence mosquito MIRs across sites. Together, these data suggest that the microhabitat suitability of Culex vector species is of greater importance than avian community composition in driving WNV infection dynamics at the urban and agricultural interface.

Integrated Forecasts Based on Public Health Surveillance and Meteorological Data Predict West Nile Virus in a High-Risk Region of North America

BACKGROUND

West Nile virus (WNV), a global arbovirus, is the most prevalent mosquito-transmitted infection in the United States. Forecasts of WNV risk during the upcoming transmission season could provide the basis for targeted mosquito control and disease prevention efforts. We developed the Arbovirus Mapping and Prediction (ArboMAP) WNV forecasting system and used it in South Dakota from 2016 to 2019. This study reports a post hoc forecast validation and model comparison.

OBJECTIVES

Our objective was to validate historical predictions of WNV cases with independent data that were not used for model calibration. We tested the hypothesis that predictive models based on mosquito surveillance data combined with meteorological variables were more accurate than models based on mosquito or meteorological data alone.

METHODS

The ArboMAP system incorporated models that predicted the weekly probability of observing one or more human WNV cases in each county. We compared alternative models with different predictors including a) a baseline model based only on historical WNV cases, b) mosquito models based on seasonal patterns of infection rates, c) environmental models based on lagged meteorological variables, including temperature and vapor pressure deficit, d) combined models with mosquito infection rates and lagged meteorological variables, and e) ensembles of two or more combined models. During the WNV season, models were calibrated using data from previous years and weekly predictions were made using data from the current year. Forecasts were compared with observed cases to calculate the area under the receiver operating characteristic curve (AUC) and other metrics of spatial and temporal prediction error.

RESULTS

Mosquito and environmental models outperformed the baseline model that included county-level averages and seasonal trends of WNV cases. Combined models were more accurate than models based only on meteorological or mosquito infection variables. The most accurate model was a simple ensemble mean of the two best combined models. Forecast accuracy increased rapidly from early June through early July and was stable thereafter, with a maximum AUC of 0.85. The model predictions captured the seasonal pattern of WNV as well as year-to-year variation in case numbers and the geographic pattern of cases.

DISCUSSION

The predictions reached maximum accuracy early enough in the WNV season to allow public health responses before the peak of human cases in August. This early warning is necessary because other indicators of WNV risk, including early reports of human cases and mosquito abundance, are poor predictors of case numbers later in the season. https://doi.org/10.1289/EHP10287.

Riding the Wave: Reactive Vector-Borne Disease Policy Renders the United States Vulnerable to Outbreaks and Insecticide Resistance

Funding for vector-borne disease surveillance, management, and research is cyclical and reactive in the United States. The subsequent effects have yielded gross inequities nationally that unintentionally support recurrent outbreaks. This policy forum is comprised of four primary subsections that collectively identify specific areas for improvement and offer innovative solutions to address national inadequacies in vector borne disease policy and infrastructure.

The Impact of Adulticide on Culex Abundance and Infection Rate in North Shore of Cook County, Illinois

Mosquito surveillance is critical to reduce the risk of West Nile virus (WNV) transmission to humans. In response to surveillance indicators such as elevated mosquito abundance or increased WNV levels, many mosquito control programs will perform truck-mounted ultra-low volume (ULV) adulticide application to reduce the number of mosquitoes and associated virus transmission. Despite the common use of truck-based ULV adulticiding as a public health measure to reduce WNV prevalence, limited evidence exists to support a role in reducing viral transmission to humans. We use a generalized additive and fused ridge regression model to quantify the location-specific impact of truck-mounted ULV adulticide spray efforts from 2010 to 2018 in the North Shore Mosquito Abatement District (NSMAD) in metropolitan Chicago, IL, on commonly assessed risk factors from NSMAD surveillance gravid traps: Culex abundance, infection rate, and vector index. Our model also takes into account environmental variables commonly associated with WNV, including temperature, precipitation, wind speed, location, and week of year. Since it is unlikely ULV adulticide spraying will have the same impact at each trap location, we use a spatially varying spray effect with a fused ridge penalty to determine how the effect varies by trap location. We found that ULV adulticide spraying has an immediate temporary reduction in abundance followed by an increase after 5 days. It is estimated that mosquito abundance increased more in sprayed areas than if left unsprayed in all but 3 trap locations. The impact on infection rate and vector index were inconclusive due to the large error associated with estimating trap-specific infection rates.

Development of an Index to Measure West Nile Virus Transmission Risk in New Jersey Counties

We developed an index for use by New Jersey counties to measure West Nile virus (WNV) transmission risk to the human population. We used a latent profile analysis to develop the index, identifying categories of environmental conditions associated with WNV transmission risk to humans. The final model included 4 indicators of transmission risk: mosquito abundance and minimum field infection rate, temperature, and human case count. We used data from 2004 to 2018 from all 21 New Jersey counties aggregated into 11 2-wk units per county per year (N = 3,465). Three WNV risk classes were identified. The Low Risk class had low levels of all variables. The Moderate Risk class had high abundance, average temperature levels, and low levels of the other variables. The High Risk class had substantially above average human case likelihood, average temperature, and high mosquito infection rates. These results suggest the presence of 3 distinct WNV risk profiles, which can be used to guide the development of public health actions intended to mitigate WNV transmission risk to the human population.

Laboratory and Field Evaluations of a Commercially Available Real-Time Loop-Mediated Isothermal Amplification Assay for the Detection of West Nile Virus in Mosquito Pools

Although the specific cDNA amplification mechanisms of reverse-transcriptase polymerase chain reaction (RT-PCR) and RT loop-mediated isothermal amplification (RT-LAMP) are very different, both molecular assays serve as options to detect arboviral RNA in mosquito pools. Like RT-PCR, RT-LAMP uses a reverse transcription step to synthesize complementary DNA (cDNA) from an RNA template and then uses target-specific primers to amplify cDNA to detectable levels in a single-tube reaction. Using laboratory-generated West Nile virus (WNV) samples and field-collected mosquito pools, we evaluated the sensitivity and specificity of a commercially available WNV real-time RT-LAMP assay (Pro-AmpRT™ WNV; Pro-Lab Diagnostics, Inc., Round Rock, Texas) and compared the results to a validated real-time RT-PCR assay. Laboratory generated virus stock samples containing ≥ 2.3 log10 plaque-forming units (PFU)/ml and intrathoracically inoculated mosquitoes containing ≥ 2.4 log10 PFU/ml produced positive results in the Pro-AmpRT WNV assay. Of field-collected pools that were WNV positive by real-time RT-PCR, 74.5% (70 of 94) were also positive by the Pro-AmpRT WNV assay, resulting in an overall Cohen's kappa agreement of 79.4% between the 2 tests. The Pro-AmpRT WNV assay shows promise as a suitable virus screening tool for vector surveillance programs provided agencies are aware of its characteristics and limitations.

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

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.

Dynamics of data availability in disease modeling: An example evaluating the trade-offs of ultra-fine-scale factors applied to human West Nile virus disease models in the Chicago area, USA

Background

Since 1999, West Nile virus (WNV) has moved rapidly across the United States, resulting in tens of thousands of human cases. Both the number of human cases and the minimum infection rate (MIR) in vector mosquitoes vary across time and space and are driven by numerous abiotic and biotic forces, ranging from differences in microclimates to socio-demographic factors. Because the interactions among these multiple factors affect the locally variable risk of WNV illness, it has been especially difficult to model human disease risk across varying spatial and temporal scales. Cook and DuPage Counties, comprising the city of Chicago and surrounding suburbs, experience some of the highest numbers of human neuroinvasive cases of WNV in the United States. Despite active mosquito control efforts, there is consistent annual WNV presence, resulting in more than 285 confirmed WNV human cases and 20 deaths from the years 2014–2018 in Cook County alone.

Methods

A previous Chicago-area WNV model identified the fifty-five most high and low risk locations in the Northwest Mosquito Abatement District (NWMAD), an enclave ¼ the size of the combined Cook and DuPage county area. In these locations, human WNV risk was stratified by model performance, as indicated by differences in studentized residuals. Within these areas, an additional two-years of field collections and data processing was added to a 12-year WNV dataset that includes human cases, MIR, vector abundance, and land-use, historical climate, and socio-economic and demographic variables, and was assessed by an ultra-fine-scale (1 km spatial x 1 week temporal resolution) multivariate logistic regression model.

Results

Multivariate statistical methods applied to the ultra-fine-scale model identified fewer explanatory variables while improving upon the fit of the previous model. Beyond MIR and climatic factors, efforts to acquire additional covariates only slightly improved model predictive performance.

Conclusions

These results suggest human WNV illness in the Chicago area may be associated with fewer, but increasingly critical, key variables at finer scales. Given limited resources, these findings suggest large variations in model performance occur, depending on covariate availability, and provide guidance in variable selection for optimal WNV human illness modeling.

Effects of Scale on Modeling West Nile Virus Disease Risk

Modeling vector-borne diseases is best conducted when heterogeneity among interacting biotic and abiotic processes is captured. However, the successful integration of these complex processes is difficult, hindered by a lack of understanding of how these relationships influence disease transmission across varying scales. West Nile virus (WNV) is the most important mosquito-borne disease in the United States. Vectored by Culex mosquitoes and maintained in the environment by avian hosts, the virus can spill over into humans and horses, sometimes causing severe neuroinvasive illness. Several modeling studies have evaluated drivers of WNV disease risk, but nearly all have done so at broad scales and have reported mixed results of the effects of common explanatory variables. As a result, fine-scale relationships with common explanatory variables, particularly climatic, socioeconomic, and human demographic, remain uncertain across varying spatial extents. Using an interdisciplinary approach and an ongoing 12-year study of the Chicago region, this study evaluated the factors explaining WNV disease risk at high spatiotemporal resolution, comparing the human WNV model and covariate performance across three increasing spatial extents: ultrafine, local, and county scales. Our results demonstrate that as spatial extent increased, model performance increased. In addition, only six of the 23 assessed covariates were included in best-fit models of at least two scales. These results suggest that the mechanisms driving WNV ecology are scale-dependent and covariate importance increases as extent decreases. These tools may be particularly helpful for public health, mosquito, and disease control personnel in predicting and preventing disease within local and fine-scale jurisdictions, before spillover occurs.