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

Unveiling spatial patterns of West Nile virus emergence in northern Greece, 2010-2023

The Region of Central Macedonia (RCM) in Northern Greece recorded the highest number of human West Nile virus (WNV) infections in Greece, despite considerable local mosquito control actions. We examined spatial patterns and associations of mosquito levels, infected mosquito levels, and WNV human cases (WNVhc) across the municipalities of this region over the period 2010-2023 and linked it with climatic characteristics. We combined novel entomological and available epidemiological and climate data for the RCM, aggregated at the municipality level and used Local and Global Moran's I index to assess spatial associations of mosquito levels, infected mosquito levels, and WNVhc. We identified areas with strong interdependencies between adjacent municipalities in the Western part of the region. Furthermore, we employed a Generalized Linear Mixed Model to first, identify the factors driving the observed levels of mosquitoes, infected mosquitoes and WNVhc and second, estimate the influence of climatic features on the observed levels. This modeling approach indicates a strong dependence of the mosquito levels on the temperatures in winter and spring and the total precipitation in early spring, while virus circulation relies on the temperatures of late spring and summer. Our findings highlight the significant influence of climatic factors on mosquito populations (∼60 % explained variance) and the incidence of WNV human cases (∼40 % explained variance), while the unexplained ∼40 % of the variance suggests that targeted interventions and enhanced surveillance in identified hot-spots can enhance public health response.

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.

Population-specific thermal responses contribute to regional variability in arbovirus transmission with changing climates

Temperature is increasing globally, and vector-borne diseases are particularly responsive to such increases. While it is known that temperature influences mosquito life history traits, transmission models have not historically considered population-specific effects of temperature. We assessed the interaction between Culex pipiens population and temperature in New York State (NYS) and utilized novel empirical data to inform predictive models of West Nile virus (WNV) transmission. Genetically and regionally distinct populations from NYS were reared at various temperatures, and life history traits were monitored and used to inform trait-based models. Variation in Cx. pipiens life history traits and population-dependent thermal responses account for a predicted 2.9°C difference in peak transmission that is reflected in regional differences in WNV prevalence. We additionally identified genetic signatures that may contribute to distinct thermal responses. Together, these data demonstrate how population variation contributes to significant geographic variability in arbovirus transmission with changing climates.

Exploring the role of temperature and other environmental factors in West Nile virus incidence and prediction in California counties from 2017-2022 using a zero-inflated model

West Nile virus (WNV) is the most common mosquito-borne disease in the United States, resulting in hundreds of reported cases yearly in California alone. The transmission cycle occurs mostly in birds and mosquitoes, making meteorological conditions, such as temperature, especially important to transmission characteristics. Given that future increases in temperature are all but inevitable due to worldwide climate change, determining associations between temperature and WNV incidence in humans, as well as making predictions on future cases, are important to public health agencies in California. Using surveillance data from the California Department of Public Health (CDPH), meteorological data from the National Oceanic and Atmospheric Administration (NOAA), and vector and host data from VectorSurv, we created GEE autoregressive and zero-inflated regression models to determine the role of temperature and other environmental factors in WNV incidence and predictions. An increase in temperature was found to be associated with an increase in incidence in 11 high-burden Californian counties between 2017-2022 (IRR = 1.06), holding location, time of year, and rainfall constant. A hypothetical increase of two degrees Fahrenheit-predicted for California by 2040-would have resulted in upwards of 20 excess cases per year during our study period. Using 2017-2021 as a training set, meteorological and host/vector data were able to closely predict 2022 incidence, though the models did overestimate the peak number of cases. The zero-inflated model closely predicted the low number of cases in winter months but performed worse than the GEE model during high-transmission periods. These findings suggests that climate change will, and may be already, altering transmission dynamics and incidence of WNV in California, and provides tools to help predict incidence into the future.

The Arbovirus Mapping and Prediction (ArboMAP) system for West Nile virus forecasting

Objectives

West Nile virus (WNV) is the most common mosquito-borne disease in the United States. Predicting the location and timing of outbreaks would allow targeting of disease prevention and mosquito control activities. Our objective was to develop software (ArboMAP) for routine WNV forecasting using public health surveillance data and meteorological observations.

Materials and Methods

ArboMAP was implemented using an R markdown script for data processing, modeling, and report generation. A Google Earth Engine application was developed to summarize and download weather data. Generalized additive models were used to make county-level predictions of WNV cases.

Results

ArboMAP minimized the number of manual steps required to make weekly forecasts, generated information that was useful for decision-makers, and has been tested and implemented in multiple public health institutions.

Discussion and Conclusion

Routine prediction of mosquito-borne disease risk is feasible and can be implemented by public health departments using ArboMAP.

A Spatially Resolved and Environmentally Informed Forecast Model of West Nile Virus in Coachella Valley, California

West Nile virus (WNV) is the most significant arbovirus in the United States in terms of both morbidity and mortality. West Nile exists in a complex transmission cycle between avian hosts and the arthropod vector, Culex spp. mosquitoes. Human spillover events occur when humans are bitten by an infected mosquito and predicting these rates of infection and therefore the risk to humans may be associated with fluctuations in environmental conditions. In this study, we evaluate the hydrological and meteorological drivers associated with mosquito biology and viral development to determine if these associations can be used to forecast seasonal mosquito infection rates with WNV in the Coachella Valley of California. We developed and tested a spatially resolved ensemble forecast model of the WNV mosquito infection rate in the Coachella Valley using 17 years of mosquito surveillance data and North American Land Data Assimilation System-2 environmental data. Our multi-model inference system indicated that the combination of a cooler and dryer winter, followed by a wetter and warmer spring, and a cooler than normal summer was most predictive of the prevalence of West Nile positive mosquitoes in the Coachella Valley. The ability to make accurate early season predictions of West Nile risk has the potential to allow local abatement districts and public health entities to implement early season interventions such as targeted adulticiding and public health messaging before human transmission occurs. Such early and targeted interventions could better mitigate the risk of WNV to humans.

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.

The current landscape of software tools for the climate-sensitive infectious disease modelling community

Climate-sensitive infectious disease modelling is crucial for public health planning and is underpinned by a complex network of software tools. We identified only 37 tools that incorporated both climate inputs and epidemiological information to produce an output of disease risk in one package, were transparently described and validated, were named (for future searching and versioning), and were accessible (ie, the code was published during the past 10 years or was available on a repository, web platform, or other user interface). We noted disproportionate representation of developers based at North American and European institutions. Most tools (n=30 [81%]) focused on vector-borne diseases, and more than half (n=16 [53%]) of these tools focused on malaria. Few tools (n=4 [11%]) focused on food-borne, respiratory, or water-borne diseases. The under-representation of tools for estimating outbreaks of directly transmitted diseases represents a major knowledge gap. Just over half (n=20 [54%]) of the tools assessed were described as operationalised, with many freely available online.