Interactions between people and wildlife are increasing as development encroaches on natural habitats. Concurrently, young mammals suspected to be orphans are brought to wildlife rehabilitation centers. Four common orphaned mammals presented to rehabilitation facilities in North America are the Virginia opossum (Didelphis virginiana, DIVI), common raccoon (Procyon lotor, PRLO), eastern gray squirrel (Sciurus carolinensis, SCCA), and eastern cottontail rabbit (Sylvilagus floridanus, SYFL). This study examined the spatial relationship between the originating locations of orphans presented to the University of Illinois Wildlife Medical Clinic within Champaign County from 2015 to 2020 (99 Virginia opossums, 80 common raccoons, 441 eastern gray squirrels, and 602 eastern cottontails) and their environment. Environmental factors evaluated included land development, distance to water, and percentage of tree canopy. Spatial and multinomial logistic regression analysis showed that human-wildlife interactions frequently occurred near human developments and varied based on species-specific habitat preferences. Human-wildlife interactions tend to cluster around ecosystems that are selected by both parties. Our findings can inform outreach strategies aimed at reducing unnecessary wildlife admissions and improving resource allocation.

Managing chronic wasting disease (CWD) - a fatal, transmissible prion disease of cervids - has many challenges, such as determining the number of cervids to test to accurately estimate true prevalence with apparent prevalence. Options range from basic equations to complex techniques like agent-based modelling, but the former does not account for parameter uncertainty, and more detailed models may be less accessible for managers. Our study used Monte Carlo simulation to create a reference table showing the number of hunter-harvested deer to test to approximate herd true prevalence with a ± 2% margin of error and 95% confidence. A three-step process was simulated in which deer from the total population were harvested (testable subpopulation), and then a subset of those harvested deer were tested for CWD (tested group) with diagnostic testing error, with group assignment determined by random sampling. We evaluated true prevalences ranging from 0.5-20% and testable subpopulation sizes up to 5,000 deer from a total population of 20,000. An example from the reference table shows that detecting a 1% true prevalence in a testable subpopulation of 300 deer requires testing ≥280 deer, and that the only way to accurately detect a true prevalence of 0.5% is to test 600 out of 5,000 deer. Evaluating IDNR's CWD surveillance efforts found that 16.14% of counties had adequate sample sizes in a given year and could detect true prevalences as low as 1%, showing that these guidelines are achievable.

Chronic wasting disease (CWD) is a fatal, neurodegenerative disease of cervids, and its management heavily relies on diagnostic testing. Test results are commonly used to calculate 'apparent prevalence' (AP) - the percent of animals tested for CWD (CWD tests) with CWD-positive test results (CWD cases) - but this obscures how tests and cases individually contribute to this statistic. This is most relevant when CWD testing is limited because when few animals are tested, detection of even a single infected deer can result in a high AP that poorly reflects reality. We hypothesized that when CWD testing is limited, AP is negatively driven by testing - rather than cases - with more tests corresponding to lower APs. Graphed CWD surveillance data from townships in Illinois and Wisconsin, USA, indicate that CWD AP values ≥50% were only observed when <23 deer were tested. We used Bayesian multilevel zero-inflated Beta regression to model AP as a function of CWD tests, CWD cases and nonlinear transformations of these two terms separately for each state. The best-fit models of both identified a statistically significant negative relationship between AP and testing numbers that was modified by a positive nonlinear test covariate. This means adding tests when testing is low can have a big impact on decreasing the AP, but this relationship weakens as testing increases. We urge treating apparent prevalences ≥50% with caution and emphasize the importance of increasing the test results when initial surveillance has yielded <23 tests.

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.

Current modeling practices for environmental and sociological modulated infectious diseases remain inadequate to forecast the risk of outbreak(s) in human populations, partly due to a lack of integration of disciplinary knowledge, limited availability of disease surveillance datasets, and overreliance on compartmental epidemiological modeling methods. Harvesting data knowledge from virus transmission (aerosols) and detection (wastewater) of SARS-CoV-2, a heuristic score-based environmental predictive intelligence system was developed that calculates the risk of COVID-19 in the human population. Seasonal validation of the algorithm was uniquely associated with wastewater surveillance of the virus, providing a lead time of 7-14 days before a county-level outbreak. Using county-scale disease prevalence data from the United States, the algorithm could predict COVID-19 risk with an overall accuracy ranging between 81% and 98%. Similarly, using wastewater surveillance data from Illinois and Maryland, the SARS-CoV-2 detection rate was greater than 80% for 75% of the locations during the same time the risk was predicted to be high. Results suggest the importance of a holistic approach across disciplinary boundaries that can potentially allow anticipatory decision-making policies of saving lives and maximizing the use of available capacity and resources.

ABSTRACT Nucleic acid-based assays, such as polymerase chain reaction (PCR), that amplify and detect organism-specific genome sequences are a standard method for infectious disease surveillance. However, challenges arise for virus surveillance because of their genetic diversity. Here, we calculated the variability of nucleotides within the genomes of 10 human viral species in silico and found that endemic viruses exhibit a high percentage of variable nucleotides (e.g., 51.4% for norovirus genogroup II). This genetic diversity led to the variable probability of detection of PCR assays (the proportion of viral sequences that contain the assay’s target sequences divided by the total number of viral sequences). We then experimentally confirmed that the probability of the target sequence detection is indicative of the number of mismatches between PCR assays and norovirus genomes. Next, we developed a degenerate PCR assay that detects 97% of known norovirus genogroup II genome sequences and recognized norovirus in eight clinical samples. By contrast, previously developed assays with 31% and 16% probability of detection had 1.1 and 2.5 mismatches on average, respectively, which negatively impacted RNA quantification. In addition, the two PCR assays with a lower probability of detection also resulted in false negatives for wastewater-based epidemiology. Our findings suggest that the probability of detection serves as a simple metric for evaluating nucleic acid-based assays for genetically diverse virus surveillance. IMPORTANCE Nucleic acid-based assays, such as polymerase chain reaction (PCR), that amplify and detect organism-specific genome sequences are employed widely as a standard method for infectious disease surveillance. However, challenges arise for virus surveillance because of the rapid evolution and genetic variation of viruses. The study analyzed clinical and wastewater samples using multiple PCR assays and found significant performance variation among the PCR assays for genetically diverse norovirus surveillance. This finding suggests that some PCR assays may miss detecting certain virus strains, leading to a compromise in detection sensitivity. To address this issue, we propose a metric called the probability of detection, which can be simply calculated in silico using a code developed in this study, to evaluate nucleic acid-based assays for genetically diverse virus surveillance. This new approach can help improve the sensitivity and accuracy of virus detection, which is crucial for effective infectious disease surveillance and control.

The ongoing COVID-19 pandemic is among the worst in recent history, resulting in excess of 520,000,000 cases and 6,200,000 deaths worldwide. The United States (U.S.) has recently surpassed 1,000,000 deaths. Individuals who are elderly and/or immunocompromised are the most susceptible to serious sequelae. Rising sentiment often implicates younger, less-vulnerable populations as primary introducers of COVID-19 to communities, particularly around colleges and universities. Adjusting for more than 32 key socio-demographic, economic, and epidemiologic variables, we (1) implemented regressions to determine the overall community-level, age-adjusted COVID-19 case and mortality rate within each American county, and (2) performed a subgroup analysis among a sample of U.S. colleges and universities to identify any significant preliminary mitigation measures implemented during the fall 2020 semester. From January 1, 2020 through March 31, 2021, a total of 22,385,335 cases and 374,130 deaths were reported to the CDC. Overall, counties with increasing numbers of university enrollment showed significantly lower case rates and marginal decreases in mortality rates. County-level population demographics, and not university level mitigation measures, were the most significant predictor of adjusted COVID-19 case rates. Contrary to common sentiment, our findings demonstrate that counties with high university enrollments may be more adherent to public safety measures and vaccinations, likely contributing to safer communities.

Epizootic hemorrhagic disease (EHD) leads to high mortality in white-tailed deer (Odocoileus virginianus) and is caused by a double-stranded RNA (dsRNA) virus. Toll-like receptor 3 (TLR3) plays a role in host immune detection and response to dsRNA viruses. We, therefore, examined the role of genetic variation within the TLR3 gene in EHD among 84 Illinois wild white-tailed deer (26 EHD-positive deer and 58 EHD-negative controls). The entire coding region of the TLR3 gene was sequenced: 2715 base pairs encoding 904 amino acids. We identified 85 haplotypes with 77 single nucleotide polymorphisms (SNPs), of which 45 were synonymous mutations and 32 were non-synonymous. Two non-synonymous SNPs differed significantly in frequency between EHD-positive and EHD-negative deer. In the EHD-positive deer, phenylalanine was relatively less likely to be encoded at codon positions 59 and 116, whereas leucine and serine (respectively) were detected less frequently in EHD-negative deer. Both amino acid substitutions were predicted to impact protein structure or function. Understanding associations between TLR3 polymorphisms and EHD provides insights into the role of host genetics in outbreaks of EHD in deer, which may allow wildlife agencies to better understand the severity of outbreaks.

In northern Illinois, chronic wasting disease (CWD) was first identified in free-ranging white-tailed deer (Odocoileus virginianus; hereafter referred to as "deer") in 2002. To reduce CWD transmission rates in Illinois, wildlife biologists have conducted locally focussed culling of deer since 2003 in areas where CWD has been detected. We used retrospective spatial, temporal and space-time scan statistical models to identify areas and periods where culling removed higher than expected numbers of CWD-positive deer. We included 490 Public Land Survey "sections" (∼2.59 km2 ) from 15 northern Illinois counties in which at least one deer tested positive for CWD between 2003 and 2020. A negative binomial regression model compared the proportion of CWD positive cases removed from sections with at least one CWD case detected in the previous years, "local area 1 (L1)," to the proportion of CWD cases in adjacent sections-L2, L3, and L4-designated by their increasing distance from L1. Of the 14,661 deer removed and tested via culling, 325 (2.22 %) were CWD-positive. A single temporal CWD cluster occurred in 2020. Three spatial clusters were identified, with a primary cluster located at the border of Boone and Winnebago counties. Four space-time clusters were identified with a primary cluster in the northern portion of the study area from 2003 to 2005 that overlapped with the spatial cluster. The proportion of CWD cases removed from L1 (3.92, 95% CI, 2.56-6.01) and L2 (2.32, 95% CI, 1.50-3.59) were significantly higher compared to L3. Focussing culling efforts on accessible properties closest to L1 areas results in more CWD-infected deer being removed, which highlights the value of collaborations among landowners, hunters, and wildlife management agencies to control CWD. Continuous evaluation and updating of the culling and surveillance programs are essential to mitigate the health burden of CWD on deer populations in Illinois.

Chronic Wasting Disease (CWD), the prion disease of the Cervidae family, has been managed in Illinois deer since it was first detected in the Fall of 2002. Management uses a state-sponsored localized focus culling (LFC) program, implemented as close as possible to previously identified CWD-infected locations (TRSs (township/range/section)). We used hunter-harvest and LFC deer from 4621 and 435 unique TRSs, respectively, over 16 years of surveillance and management (2003-2017). We divided the study area into groups of TRSs with similar landcover types (SPLT) to assess CWD hunter-harvest prevalence at LFC and non-LFC sites by landcover composition. We also evaluate the importance of the month when LFC was implemented and determine whether the density of LFC sites or the total number of deer removed by LFC predicts hunter-harvest CWD prevalence. The percentage of CWD positive samples from hunters was lower than for LFC samples (for the study area and SPLTs). The probability of CWD increased by 5.24% for all the SPLT groups combined in the study area, by 4.6% from areas without an LFC nearby, and by 1.21% for areas with a prior LFC nearby. For all the TRS in the study area, low CWD odds (<1) in hunter-harvest deer were found in three SPLTs, in two SPLTs within TRSs with non-LFC, and five in five SPLTs within TRSs with LFC. The results suggest the importance of accounting for landcover composition to implement and sustain management in habitats with a higher risk of CWD. Our findings support that hunter-harvest alone cannot control CWD and the critical need for continued LFC intervention. For the whole study area-regardless of landcover composition-LFC in January was more important in decreasing hunter-harvest CWD prevalence than when LFC was conducted in March. However, the LFC conducted in January, February, and March were equally important when evaluating the month per habitat. Furthermore, the density of LFC sites in proximity to known infected areas is a better predictor of CWD than the number of deer removed by LFC, suggesting that increasing the density of LFC sites has a greater impact on CWD. The proximity of LFC to infected areas helps control CWD. Ultimately, landowners' and hunters' collaborations with the CWD surveillance and management programs are critical to protecting the Illinois wild deer herd; this study demonstrates their ongoing and valuable contributions to protecting this natural and public resource.

Wastewater-based epidemiology (WBE), an emerging approach for community-wide COVID-19 surveillance, was primarily characterized at large sewersheds such as wastewater treatment plants serving a large population. Although informed public health measures can be better implemented for a small population, WBE for neighborhood-scale sewersheds is less studied and not fully understood. This study applied WBE to seven neighborhood-scale sewersheds (average population of 1471) from January to November 2021. Community testing data showed an average of 0.004 % incidence rate in these sewersheds (97 % of monitoring periods reported two or fewer daily infections). In 92 % of sewage samples, SARS-CoV-2 N gene fragments were below the limit of quantification. We statistically determined 10-2.6 as the threshold of the SARS-CoV-2 N gene concentration normalized to pepper mild mottle virus (N/PMMOV) to alert high COVID-19 incidence rate in the studied sewershed. This threshold of N/PMMOV identified neighborhood-scale outbreaks (COVID-19 incidence rate higher than 0.2 %) with 82 % sensitivity and 51 % specificity. Importantly, neighborhood-scale WBE can discern local outbreaks that would not otherwise be identified by city-scale WBE. Our findings suggest that neighborhood-scale WBE is an effective community-wide disease surveillance tool when COVID-19 incidence is maintained at a low level.

Chronic wasting disease (CWD) is a fatal, highly infectious prion disease that affects captive and wild cervids. Chronic wasting disease is the only known transmissible spongiform encephalopathy affecting free-ranging wildlife. In CWD-positive deer, some haplotypes of the prion protein gene PRNP are detected at lower frequencies as compared to CWD-negative deer, as are some variants of the prion protein PrP. Here, we examined wild, hunter-harvested CWD-negative white-tailed deer (Odocoileus virginianus) to determine whether there were geographical or temporal differences in the PRNP haplotypes, PRNP diplotypes, PrP proteoforms, and in the proportion of deer with at least one protective haplotype. We sampled 96-100 hunter-harvested deer per county at two time points in the Illinois counties of Jo Daviess, LaSalle, and Winnebago, chosen based on their geographic locations and known occurrence of CWD. The entire coding region of PRNP was sequenced, with haplotypes, diplotypes, and PrP proteoforms inferred. Across time, in Winnebago there was a significant increase in PrP proteoform F (p = 0.034), which is associated with a lower vulnerability to CWD. In every county, there was an increase over time in the frequency of deer carrying at least one protective haplotype to CWD, with a significant increase (p = 0.02) in the Jo Daviess County CWD infected region. We also found that primer combination was important as there was an 18.7% difference in the number of the deer identified as homozygous depending on primer usage. Current Illinois state management practices continue to remove CWD infected deer from locally infected areas helping to keep CWD prevalence low. Nonetheless, continued research on spatial and temporal changes in PRNP haplotypes, PrP proteoforms, and levels of deer vulnerability among Illinois deer will be important for the management of CWD within the state of Illinois and beyond.

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 × 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.

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.

West Nile virus (WNV) has consistently been reported to be associated with human cases of illness in the region near Chicago, Illinois. However, the number of reported cases of human illness varies across years, with intermittent outbreaks. Several dynamic factors, including temperature, rainfall, and infection status of vector mosquito populations, are responsible for much of these observed variations. However, local landscape structure and human demographic characteristics also play a key role. The geographic and temporal scales used to analyze such complex data affect the observed associations. Here, we used spatial and statistical modeling approaches to investigate the factors that drive the outcome of WNV human illness on fine temporal and spatial scales. Our approach included multilevel modeling of long-term weekly data from 2005 to 2016, with weekly measures of mosquito infection, human illness and weather combined with more stable landscape and demographic factors on the geographical scale of 1000m hexagons. We found that hot weather conditions, warm winters, and higher MIR in earlier weeks increased the probability of an area of having a WNV human case. Higher population and the proportion of urban light intensity in an area also increased the probability of observing a WNV human case. A higher proportion of open water sources, percentage of grass land, deciduous forests, and housing built post 1990 decreased the probability of having a WNV case. Additionally, we found that cumulative positive mosquito pools up to 31 weeks can strongly predict the total annual human WNV cases in the Chicago region. This study helped us to improve our understanding of the fine-scale drivers of spatiotemporal variability of human WNV cases.

Understanding the geographic distribution and clustering of chronic wasting disease (CWD) among free-ranging white-tailed deer (Odocoileus virginianus) populations can inform disease management practices. We used a retrospective analysis of surveillance data to evaluate CWD's spatial and temporal dynamics within 16 CWD-infected northern Illinois counties. Of 42,541 deer samples collected and tested for CWD from recreational hunter harvest between 2008 and 2019, we recorded 359 (0.84%) CWD-positive samples. We observed variability in CWD cases over time and space. By county, the median CWD-positive proportion was 0.84%, varying from a minimum of 0.14% in McHenry County to a maximum of 6.28% in Boone County. Across years, there were differences among CWD-positive proportions with a median of 0.90%, ranging from a minimum of 0.27% in 2012 to a maximum of 1.60% in 2019. We used a retrospective discrete Poisson scan statistic model to evaluate the space-time clustering of CWD-positive deer. We identified a statistically significant (p < .001) primary cluster C1 (area = 23.59 km2 ; RR = 10.48), occurring from 2010 to 2015 in the north-central part of the study area, and a secondary cluster C2, occurring from 2014 to 2019 (area = 9.27 km2 ; RR = 3.88) in the north-west of the study area. Detected CWD-positive space-time clusters suggest that the risk of CWD is not random. Space-time clusters of CWD can be used to evaluate the effectiveness of the Illinois CWD management programme. The area surrounding the older C1 cluster has undergone longer and more intense CWD management compared with C2. Currently, the older C1 cluster is no longer as high risk compared with the newer cluster C2, suggesting that management efforts in C2 should be increased. However, all CWD clusters should be targeted with surveillance, prevention and management programmes, including reducing deer densities to limit further spread of CWD.

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 level of mosquito infection (MIR) vary across time and space and are related to 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, are among the areas hardest hit by 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 in the past 5 years in Cook County alone. Methods: A previous WNV model for the greater Chicago area identified the fifty-five most high and low risk study areas in the Northwest Mosquito Abatement District (NWMAD), an enclave ¼ the size of the previous study area. In these locations, human WNV risk was stratified by strength of predictive success, as indicated by differences in studentized residuals. Within these areas, an additional two-years of field collections and data processing was added to a 10-year WNV dataset and assessed by an ultra-fine-scale multivariate logistic regression model.Results: Multivariate statistical approaches revealed that this ultra-fine-scale model resulted in fewer explanatory variables while improving upon the fit of the existing model. Beyond mosquito infection rates and climatic factors, efforts to acquire additional covariates only slightly improve 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, this study suggests a large variation in the significance to model performance, and provides guidance in covariate selection for optimal WNV human illness modeling.

Since the 1970s, mosquito-borne pathogens have spread to previously disease-free areas, as well as causing increased illness in endemic areas. In particular, dengue and chikungunya viruses, transmitted primarily by Aedes aegypti and secondarily by Aedes albopictus mosquitoes, represent a threat for up to a third of the world population, and are a growing public health concern. In this study, we assess the spatial and temporal factors related to the occurrences of historic dengue and chikungunya outbreaks in 76 nations focused geographically on the Indian Ocean, with outbreak data from 1959 to 2009. First, we describe the historical spatial and temporal patterns of outbreaks of dengue and chikungunya in the focal nations. Second, we use a boosted regression tree approach to assess the statistical relationships of nations' concurrent outbreak occurrences and annual occurrences with their spatial proximity to prior infections and climatic and socio-economic characteristics. We demonstrate that higher population density and shorter distances among nations with outbreaks are the dominant factors that characterize both dengue and chikungunya outbreaks. In conclusion, our analysis provides crucial insights, which can be applied to improve nations' surveillance and preparedness for future vector-borne disease epidemics.

Surveillance for West Nile virus (WNV) and other mosquito-borne pathogens involves costly and time-consuming collection and testing of mosquito samples. One difficulty faced by public health personnel is how to interpret mosquito data relative to human risk, thus leading to a failure to fully exploit the information from mosquito testing. The objective of our study was to use the information gained from historic West Nile virus mosquito testing to determine human risk relative to mosquito infection and to assess the usefulness of our mosquito infection forecasting models to give advance warning. We compared weekly mosquito infection rates from 2004 to 2013 to WNV case numbers in Illinois. We then developed a weather-based forecasting model to estimate the WNV mosquito infection rate one to 3 weeks ahead of mosquito testing both statewide and for nine regions of Illinois. We further evaluated human illness risk relative to both the measured and the model-estimated infection rates to provide guidelines for public health messages. We determined that across 10 years, over half of human WNV cases occurred following the 29 (of 210) weeks with the highest mosquito infection rates. The values forecasted by the models can identify those time periods, but model results and data availability varied by region with much stronger results obtained from regions with more mosquito data. The differences among the regions may be related to the amount of surveillance or may be due to diverse landscape characteristics across Illinois. We set the stage for better use of all surveillance options available for WNV and described an approach to modelling that can be expanded to other mosquito-borne illnesses.