A climate-dependent spatial epidemiological model for the transmission risk of West Nile virus at local scale

Patterns of West Nile virus vector co-occurrence and spatial overlap with human cases across Europe

Its geographic range expansion and rising incidence make West Nile Virus (WNV) a major public health challenge in Europe. Although numerous studies have investigated geographic variation in WNV incidence in humans or equines, most have focused on climate and land-use factors; however, the implications of vector co-occurrence and niche overlap remain largely unexplored. Identifying areas where highly competent vectors overlap with favourable environmental conditions is crucial for determining areas at risk for future WNV outbreaks. We analysed the distribution and habitat suitability of four Culex mosquito vectors across Europe using an ensemble of six modelling techniques and relevant environmental variables. We generated probability maps, converted them into binary distribution maps through threshold-based methods, and weighted them by WNV vector competence to identify hotspots of vector co-occurrence and human cases. Our findings indicate that WNV vectors are unevenly distributed across Europe, with southern regions emerging as hotspots, particularly due to the presence of highly competent vectors such as Culex univittatus s.l., Culex modestus, and Culex pipiens. The overlap of Cx. modestus, Cx. torrentium, and Cx. pipiens in central, western, and eastern Europe indicates that competent WNV vectors are present in nearly all European regions. Among the environmental factors analysed, mean winter temperatures were the most influential, suggesting that mild winters could increase the distribution of WNV competent vectors. Our results also revealed a strong spatial overlap between hotspots of human WNV cases and vector co-occurrence, highlighting regions of elevated transmission risk. The high-risk hotspots identified in this large-scale study can guide local surveillance efforts and optimize resource allocation, ultimately enhancing the effectiveness of WNV surveillance.

Early warning system of the seasonal west nile virus infection risk in humans in northern greece, 2020-2024

This study introduces a novel forecasting tool for West Nile Virus (WNV) risk at the municipal level, based on a previously developed climate-dependent epidemiological model coupled with a data-driven model and field data in an ensemble framework. The modelling system was evaluated at the municipal level in the regions of Central Macedonia (RCM) and Thessaly (RTH) in Greece for the period 2020-2024 and classifies the municipalities according to the risk level of occurrence of a WNV human case (WNVhc) within each forecast season. The modelling system produces seasonal forecasts updated monthly throughout the entire mosquito breeding season (April-September). From the first forecast of the year, WNVhc were correctly predicted at 71% of the infected municipalities in RCM and 60% of the infected municipalities in RTH, percentages that increased by at least 20% after July when surveillance data were incorporated. The observed number of infected humans was within the predicted range, highlighting the part of the region with an expected outbreak. The approach shows that the coupling of a climate-dependent epidemiological model at local level with a data-driven model, incorporating entomological data to predict the risk of WNVhc, can be a useful tool in planning control strategies.

Spatiotemporally Explicit Epidemic Model for West Nile Virus Outbreak in Germany: An Inversely Calibrated Approach

Since the first autochthonous transmission of West Nile Virus was detected in Germany (WNV) in 2018, it has become endemic in several parts of the country and is continuing to spread due to the attainment of a suitable environment for vector occurrence and pathogen transmission. Increasing temperature associated with a changing climate has been identified as a potential driver of mosquito-borne disease in temperate regions. This scenario justifies the need for the development of a spatially and temporarily explicit model that describes the dynamics of WNV transmission in Germany. In this study, we developed a process-based mechanistic epidemic model driven by environmental and epidemiological data. Functional traits of mosquitoes and birds of interest were used to parameterize our compartmental model appropriately. Air temperature, precipitation, and relative humidity were the key climatic forcings used to replicate the fundamental niche responsible for supporting mosquito population and infection transmission risks in the study area. An inverse calibration method was used to optimize our parameter selection. Our model was able to generate spatially and temporally explicit basic reproductive number (R0) maps showing dynamics of the WNV occurrences across Germany, which was strongly associated with the deviation from daily means of climatic forcings, signaling the impact of a changing climate in vector-borne disease dynamics. Epidemiological data for human infections sourced from Robert Koch Institute and animal cases collected from the Animal Diseases Information System (TSIS) of the Friedrich-Loeffler-Institute were used to validate model-simulated transmission rates. From our results, it was evident that West Nile Virus is likely to spread towards the western parts of Germany with the rapid attainment of environmental suitability for vector mosquitoes and amplifying host birds, especially short-distance migratory birds. Locations with high risk of WNV outbreak (Baden-Württemberg, Bavaria, Berlin, Brandenburg, Hamburg, North Rhine-Westphalia, Rhineland-Palatinate, Saarland, Saxony-Anhalt and Saxony) were shown on R0 maps. This study presents a path for developing an early warning system for vector-borne diseases driven by climate change.

Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization

Mathematical models within the Ross-Macdonald framework increasingly play a role in our understanding of vector-borne disease dynamics and as tools for assessing scenarios to respond to emerging threats. These threats are typically characterized by a high degree of heterogeneity, introducing a range of possible complexities in models and challenges to maintain the link with empirical evidence. We systematically identified and analysed a total of 77 published papers presenting compartmental West Nile virus (WNV) models that use parameter values derived from empirical studies. Using a set of 15 criteria, we measured the dissimilarity compared with the Ross-Macdonald framework. We also retrieved the purpose and type of models and traced the empirical sources of their parameters. Our review highlights the increasing refinements in WNV models. Models for prediction included the highest number of refinements. We found uneven distributions of refinements and of evidence for parameter values. We identified several challenges in parametrizing such increasingly complex models. For parameters common to most models, we also synthesize the empirical evidence for their values and ranges. The study highlights the potential to improve the quality of WNV models and their applicability for policy by establishing closer collaboration between mathematical modelling and empirical work.

Empirical dynamic modelling and enhanced causal analysis of short-length Culex abundance timeseries with vector correlation metrics

Employing Empirical Dynamic Modelling we investigate whether model free methods could be applied in the study of Culex mosquitoes in Northern Greece. Applying Simplex Projection and S-Map algorithms on yearly timeseries of maximum abundances from 2011 to 2020 we successfully predict the decreasing trend in the maximum number of mosquitoes which was observed in the rural area of Thessaloniki during 2021. Leveraging the use of vector correlation metrics we were able to deduce the main environmental factors driving mosquito abundance such as temperature, rain and wind during 2012 and study the causal interaction between neighbouring populations in the industrial area of Thessaloniki between 2019 and 2020. In all three cases a chaotic and non-linear behaviour of the underlying system was observed. Given the health risk associated with the presence of mosquitoes as vectors of viral diseases these results hint to the usefulness of EDM methods in entomological studies as guides for the construction of more accurate and realistic mechanistic models which are indispensable to public health authorities for the design of targeted control strategies and health prevention measures.

An epidemiological model for mosquito host selection and temperature-dependent transmission of West Nile virus

We extend a previously developed epidemiological model for West Nile virus (WNV) infection in humans in Greece, employing laboratory-confirmed WNV cases and mosquito-specific characteristics of transmission, such as host selection and temperature-dependent transmission of the virus. Host selection was defined by bird host selection and human host selection, the latter accounting only for the fraction of humans that develop symptoms after the virus is acquired. To model the role of temperature on virus transmission, we considered five temperature intervals (≤ 19.25 °C; > 19.25 and < 21.75 °C; ≥ 21.75 and < 24.25 °C; ≥ 24.25 and < 26.75 °C; and > 26.75 °C). The capacity of the new model to fit human cases and the week of first case occurrence was compared with the original model and showed improved performance. The model was also used to infer further quantities of interest, such as the force of infection for different temperatures as well as mosquito and bird abundances. Our results indicate that the inclusion of mosquito-specific characteristics in epidemiological models of mosquito-borne diseases leads to improved modelling capacity.

Impact of climate change on West Nile virus distribution in South America

BACKGROUND

West Nile virus (WNV) is a vector-borne pathogen of global relevance and is currently the most widely distributed flavivirus causing encephalitis worldwide. Climate conditions have direct and indirect impacts on vector abundance and virus dynamics within the mosquito. The significance of environmental variables as drivers in WNV epidemiology is increasing under the current climate change scenario. In this study we used a machine learning algorithm to model WNV distributions in South America.

METHODS

Our model evaluated eight environmental variables for their contribution to the occurrence of WNV since its introduction in South America in 2004.

RESULTS

Our results showed that environmental variables can directly alter the occurrence of WNV, with lower precipitation and higher temperatures associated with increased virus incidence. High-risk areas may be modified in the coming years, becoming more evident with high greenhouse gas emission levels. Countries such as Bolivia, Paraguay and several Brazilian areas, mainly in the northeast and midwest regions and the Pantanal biome, will be greatly affected, drastically changing the current WNV distribution.

CONCLUSIONS

Understanding the linkages between climatological and ecological change as determinants of disease emergence and redistribution will help optimize preventive strategies. Increased virus surveillance, integrated modelling and the use of geographically based data systems will provide more anticipatory measures by the scientific community.

Case Report: Neurologic Presentation of West Nile Virus: Difficult Diagnosis

West Nile virus infections have surged across the globe. South Texas, located on the path of bird migration, with Culex quinquefasciatus and other Culex species, and biotic primers that predispose the area to epidemics (floods, amplifying hosts, and lack of mosquito control and prevention) remains a highly endemic area for arbovirus spread. West Nile virus infection ranges from mild febrile illness to severe central nervous system involvement. The purpose of this report is to highlight complex presentations of WNV and how confounding presenting symptoms delay diagnosis. The secondary goal is to describe how pandemics, such as SARS-CoV-2, can overwhelm the system and result in medical decision bias errors.