In Europe, Lyme borreliosis is the most common vector-borne human disease, mainly caused by Borrelia afzelii and Borrelia garinii, two species of the Borrelia burgdorferi sensu lato (Bbsl) complex transmitted by Ixodes ricinus. Assessing the spatial risk of human exposure to these pathogens is essential for efficient public health surveillance, yet conventional monitoring often fails to produce geographically explicit, large-scale data capturing exposure heterogeneity and its drivers. Focusing on continental France, we used data from the CiTIQUE citizen science programme to analyse spatial variation of Bbsl infection in georeferenced human-biting ticks and to model its associations with environmental, ecological, and anthropogenic factors. From 2017 to 2019, 1,891 ticks were analysed, with 15% testing positive for Bbsl. The most prevalent genospecies were B. afzelii (7.2%) and B. garinii (4.2%), showing distinct spatial patterns. I. ricinus habitat suitability was the most consistent predictor of Bbsl infection probability. Genospecies-specific models highlighted host influences: B. afzelii occurrence increased with rodent species richness, whereas B. garinii was associated with Turdidae species and showed possible dilution effect by rodents. Our findings demonstrate the value of citizen science in complementing surveillance and provide large-scale, spatially explicit insights into Bbsl eco-epidemiology in France, offering an adaptable framework for vector-borne disease monitoring.

The growing public health burden of Aedes mosquito-borne diseases requires a comprehensive understanding of Aedes species biology, ecology, and vector competence. Eco-epidemiological modelling of Aedes vector species has grown significantly in recent years, driven by the increasing reports of outbreaks in endemic and non-endemic temperate areas, as well as the latitudinal and altitudinal range expansion of these vectors. A prominent example is the Asian tiger mosquito, Aedes albopictus, a competent arbovirus vector that has spread across most continents through the movement of humans and goods. Species distribution models and mechanistic models have been used to predict the spatio-temporal distribution and dynamics of this vector. However, despite the potential of these models to capture the vector distribution and dynamics, integrating them into practical monitoring, surveillance, and vector control activities remains challenging, often due to a lack of communication and model co-development between scientists and public health stakeholders. This paper reports the results of a workshop on vector modelling held in Bologna (Italy) in September 2024, which brought together European experts in disease modelling, public health stakeholders, and medical entomologists. The workshop identified key priorities for advancing the operational use of Aedes-focused quantitative models, including sustained investment in surveillance, improved representation of environmental and biological drivers, standardisation of model outputs, and the establishment of long-term, co-produced modelling frameworks embedded within public health workflows.

West Nile virus (WNV) is one of the most widespread arboviruses globally and is maintained primarily through a bird-mosquito-bird transmission cycle, while other vertebrates play more limited roles. Host contributions to transmission depend on both infection evidence in natural populations (reflecting exposure and susceptibility) and reservoir competence, determined by the magnitude and duration of viraemia sufficient to infect mosquitoes. Despite extensive surveillance and experimental research, no comprehensive, standardised resource has integrated evidence on host exposure and infection in natural populations together with experimental data on host competence across vertebrate taxa. Here, we present two harmonised datasets compiled through a systematic literature review: (i) a WNV host prevalence dataset, summarising infection and serological evidence in wild and captive vertebrates; and (ii) a WNV host competence dataset, derived from controlled experimental infections. The prevalence dataset aggregates records from 541 studies across 91 countries (1950-2023), comprising 535,568 tested individuals from 1,801 vertebrate species. The WNV host competence dataset compiles 113 experimental infection studies covering 103 species and 3,030 individuals, and provides standardised time-resolved viraemia and survival data with accompanying metadata, enabling reconstruction and/or modelling of species-specific viraemia trajectories and the derivation of quantitative competence metrics. Both datasets use standardised taxonomy and incorporate synonym crosswalks to facilitate linkage with trait databases, phylogenetic trees and species distribution products. Together, these resources provide a unified foundation for macroecological analyses, surveillance gap assessment, and modelling multi-host WNV transmission dynamics.

Avian influenza (AI) is a highly contagious viral disease affecting poultry and wild water birds, posing significant global challenges due to its high mortality rates and economic impacts. Highly pathogenic avian influenza (HPAI) outbreaks, particularly those caused by H5N1 and its variants, have surged since 1959. The HPAI H5N1 clade 2.3.4.4b viruses have notably expanded their geographical reach, affecting numerous countries, diverse avian species, and now mammals. Using an ecological niche modelling approach, this study aims to elucidate the environmental factors associated with increased HPAI H5 cases since 2020, investigate potential shifts in ecological niches, and predict new areas suitable for viral circulation. We developed ecological niche models for HPAI cases in wild and domestic birds across two distinct periods: 2015–2020 and 2020–2022. Key environmental predictors include chicken and duck population density, human density, distance to water bodies, and land cover variables. Post-2020, we observe increased relative influence of predictors such as intensive chicken population density and cultivated vegetation. Risk maps reveal notable ecological suitability for HPAI H5 circulation in Europe, Asia, and the Americas, with significant expansions of at-risk areas post-2020. Wild bird H5 occurrences appear primarily correlated with urban areas and open water regions. Our analyses also highlight a potential shift in affected wild bird species diversity, with more avian species, particularly sea birds, impacted post-2020. Overall, these results further contribute to the understanding of HPAI epidemiology and identify regions where surveillance and control measures should be prioritised.

BackgroundWest Nile Virus (WNV) is a zoonotic arbovirus maintained in a transmission cycle between Culex mosquitoes and birds, occasionally spilling over into humans. The impact of avian biodiversity on WNV circulation remains debated, with studies reporting both negative and positive correlations (dilution and amplification effects respectively) across different settings. In Europe, this relationship remains largely unexplored, particularly in regions with high WNV transmission, such as Emilia-Romagna in Northern Italy.MethodsWe explored the association between avian biodiversity and WNV circulation in Culex mosquitoes in Emilia-Romagna using 11 years (2013-2023) of entomological surveillance data paired with two avian data sources. We calculated avian biodiversity indices (Shannon's, Simpson's, and Chao2) from observation records from the Farmland Bird Index project and applied linear regression models to assess their relationship with WNV detection frequency. Moreover, we used Bayesian spatiotemporal regression models and gridded weekly avian abundance estimates from the eBird project to analyse the associations between avian species richness indices and WNV transmission risk quantified by vector index (VI) at 68 geolocated mosquito traps across the region.ResultsWe observed consistent negative associations between WNV detection frequency in the Culex population and avian biodiversity indices, supporting the dilution effect hypothesis (DEH). We found that non-passerine species richness was negatively associated with VI while passerine species richness showed a positive association after adjusting for covariates and spatial random effects. These findings suggest that passerines may amplify WNV transmission, whereas the presence of non-passerine species is associated with reductions in WNV circulation.SignificanceThis study provides the first empirical evidence supporting the DEH for WNV in Europe. These findings have important implications for biodiversity conservation and integrated public health surveillance activities across Europe.

Background:Tick-borne encephalitis (TBE), caused by tick-borne encephalitis virus (TBEV), is a zoonotic disease that can lead to severe neurological symptoms. Given the increasing number of reported human TBE cases in Europe, we developed a spatio-temporal predictive model to infer the year-to-year probability of human TBE occurrence across Europe at the regional and municipal administrative levels. Methods:We derived the distribution of human TBE cases at the regional level during 2017-2022 by using data provided by the European Centre for Disease Prevention and Control (ECDC), and at the municipal level by using data provided by Austria, Finland, Italy, Lithuania, and Slovakia. We modeled the probability of presence of human TBE cases at the regional and municipal levels for the period 2017-2025 with a boosted regression trees model, including covariates that affect both the natural hazard of virus circulation and human exposure to tick bites. Findings:Areas with the highest probability of human TBE infections are located in central-eastern Europe, the Baltic states, and along the coastline of Nordic countries. Our results highlight a statistically significant rising trend in human TBE risk not only in north-western, but also in south-western European countries. Such areas are characterised by the presence of key tick host species, forested areas, intense human activity in forests, steep drops in late summer temperatures and high precipitation amounts during the driest months. The model showed good predictive performance, with a mean AUC of 0.84 (SD = 0.03), sensitivity of 0.83 (SD = 0.01), and specificity of 0.80 (SD = 0.01) at the regional level, and a mean AUC of 0.82 (SD = 0.03), sensitivity of 0.83 (SD = 0.01), and specificity of 0.69 (SD = 0.01) at the municipal level. Interpretation:With ongoing climate and land use changes, the number of human TBE cases is likely to increase and spread into new areas. This highlights the importance of predictive models that can identify potential risk areas to support disease prevention and control efforts by public health authorities. The approach adopted, by fitting a One Health framework and leveraging lagged covaries, enables timely one-year-ahead predictions and enhances our current understanding of TBE risk under a global change scenario.

In Europe, Lyme borreliosis is the most common vector–borne human disease, caused mainly by Borrelia afzelii and Borrelia garinii , two species of the Borrelia burgdorferi sensu lato (Bbsl) complex transmitted by the tick Ixodes ricinus . Accurately assessing the spatial risk of human exposure to these pathogens is essential for efficient public health surveillance. However conventional mon-itoring often struggles to produce geographically explicit, large scale data that capture the heterogeneity of human exposure and its drivers. Focusing on continental France, we leveraged data from the French CiTIQUE citizen science pro-gramme to analyse spatial variation of Bbsl infection in georeferenced human–biting I. ricinus ticks and to model the relationship between Bbsl distribution and environmental, ecological, and an-thropogenic factors. From 2017–2019, 1,891 ticks were analysed, of which 15% tested positive for Bbsl. The most prevalent genospecies were B. afzelii (7.2%) and B. garinii (4.2%). Infection rates varied spatially, with distinct distribution patterns across pathogen groups. Tick habitat suitability was the most consistent predictor for overall Bbsl infection probability, genospecies specific models revealed the importance of their respective reservoir hosts: B. afzelii occurrence was positively associated with rodent species richness, whereas B. garinii was associated with Turdidae species and showed po-tential traces of a dilution effect due to rodents. Our findings demonstrate the value of citizen science for complementing formal surveillance and provide the first geographically explicit, large-scale insights into Bbsl eco–epidemiology in France. This scalable approach offers an adaptable framework for monitoring vector–borne disease risk and guiding public health strategies. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The post-doctoral grant of Thierno Madiou Bah was supported by the INRAE scientific divisions Animal Health and Ecology and Biodiversity. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Approval of the ethics committee was not required since participation in sending ticks was voluntary and the short questionnaire online was anonymous (no identification possible). The participants are given information about the study on the website of the project. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The dataset will be made publicly available through the Recherche Data Gouv repository (https://entrepot.recherche.data.gouv.fr) upon publication of this study.

Advanced outbreak analytics played a key role in governmental decision-making as the COVID-19 pandemic challenged health systems globally. This study assessed the evolution of European modelling practices, data usage, gaps, and interactions between modellers and decision-makers to inform future investments in epidemic-intelligence globally. We conducted a two-stage semi-quantitative survey among modellers in a large European epidemic-intelligence consortium. Responses were analysed descriptively across early, mid-, and late-pandemic phases. Policy citations in Overton were used to assess the policy impact of modelling. Our sample included 66 modelling contributions from 11 institutions in four European countries. COVID-19 modeling initially prioritised understanding epidemic dynamics, while evaluating non-pharmaceutical interventions and vaccination impacts became equally important in later phases. ‘Traditional’ surveillance data (e.g. case linelists) were widely used in near-real time, while real-time non-traditional data (notably social contact and behavioural surveys), and serological data were frequently reported as lacking. Data limitations included insufficient stratification and geographical coverage. Interactions with decision-makers were commonplace and informed modelling scope and, vice versa, supported recommendations. Conversely, fewer than half of the studies shared open-access code. We highlight the evolving use and needs of modelling during public health crises. The reported missing of non-traditional surveillance data, even two years into the pandemic, underscores the need to rethink sustainable data collection and sharing practices, including from for-profit providers. Future preparedness should focus on strengthening collaborative platforms, research consortia and modelling networks to foster data and code sharing and effective collaboration between academia, decision-makers, and data providers.

BACKGROUND:Environmental factors, such as fluctuations of climatic conditions and land cover, play a pivotal role in driving infectious disease epidemics, particularly those originating from wildlife reservoirs. Orthohantavirus puumalaense, hosted by bank voles in Europe, is the causative agent of a form of hemorrhagic fever and renal syndrome called nephropathia epidemica. Despite two decades of consistent presence in western Europe, nephropathia epidemica outbreaks still pose challenges due to localized periodic occurrences and a lack of understanding of its environmental drivers. OBJECTIVE:Our study aims to bridge this gap by investigating the specific ecological and climatic factors influencing nephropathia epidemica outbreaks in western Europe. METHODS:We compiled monthly, serologically confirmed nephropathia epidemica case data obtained from public health authorities in Belgium, France, Germany, and the Netherlands for the period 2004-2012. Cases were georeferenced to the finest available administrative unit. We selected 28 covariates, including climatic variables, land cover, tree species distributions, and human population, and implemented a Bayesian spatiotemporal model using integrated nested Laplace approximation (INLA) with zero-inflated Poisson distribution, including fixed effects and spatial, temporal, and nonstructured random effects. RESULTS:We identified key triggers for nephropathia epidemica outbreaks, particularly climate-mediated changes in all seasons up to 2 years before, favoring tree mast impacting bank vole abundance. Our findings revealed that while land-cover factors mostly determine hotspot locations, climatic fluctuation patterns rather tend to modulate outbreak intensity. DISCUSSION:Crucially, our model allows for the generation of yearly maps showcasing nephropathia epidemica incidence and risk factors, aiding in public health preparedness against climate change-induced disease emergence. This work represents a significant step toward developing targeted forecasting tools for Orthohantavirus puumalaense outbreaks, offering valuable insights for epidemic control strategies. https://doi.org/10.1289/EHP15457.

This report presents protocols developed for modelling vector habitat suitability using presence-absence data derived from sources such as VectorNet and GBIF. These datasets, while extensive, often suffer from spatial gaps and lack of absence data. To address this, the report outlines a comprehensive workflow involving the generation of pseudo-absences and the use of environmental unsuitability layers. Covariate datasets – including climatic, land cover, and topographic variables – are used to train machine learning models such as Random Forest and Boosted Regression Trees. Models are independently run and ensembled to improve predictive robustness. Emphasis is placed on automation using R's tidymodels framework, enabling reproducible and scalable modelling pipelines. The protocols include detailed steps for data acquisition, covariate extraction, model training, evaluation, and ensemble generation. Expert validation is incorporated to ensure ecological realism and methodological rigour. The goal is to produce spatially explicit habitat suitability maps at resolutions of 1 to 5 km, suitable for surveillance planning and risk assessment. This standardised approach allows for consistent modelling across diverse vector species and geographical contexts, forming a reference methodology for future VectorNet outputs and related public health applications.

The European Food Safety Authority (EFSA), through the VectorNet project, commissioned a survey to identify stakeholders involved in conducting risk assessments and to map existing platforms and dashboards that support or display outcomes of these assessments. The objective was to explore opportunities for joint risk assessments, with a focus on four priority vector-borne diseases: Bluetongue Virus (BTV), Epizootic Haemorrhagic Disease virus (EHDV), West Nile virus (WNV), and Crimean-Congo Haemorrhagic Fever virus (CCHF). Under the VectorNet3 contract (EFSA/2023/OP/0009), led by Avia-GIS, a structured EU-wide survey was developed using the EU Survey tool. The questionnaire underwent approval by EFSA before being launched on May 29, 2025, with invitations distributed on May 30 and reminders issued on June 26. This technical report outlines the approach and scope of the survey, serving as Deliverable 2.9.5.6 under the project's terms of reference.

Future epidemics and/or pandemics may likely arise from zoonotic viruses with bat- and rodent-borne pathogens being among the prime candidates. To improve preparedness and prevention strategies, we predicted the global distribution of bat- and rodent-borne viral infectious disease outbreaks using geospatial modeling. We developed species distribution models based on published outbreak occurrence data, applying machine learning and Bayesian statistical approaches to assess disease risk. Our models demonstrated high predictive accuracy (TSS = 0.87 for bat-borne, 0.90 for rodent-borne diseases), identifying precipitation and bushmeat activities as key drivers for bat-borne diseases, while deforestation, human population density, and minimum temperature influenced rodent-borne diseases. The predicted risk areas for bat-borne diseases were concentrated in Africa, whereas rodent-borne diseases were widespread across the Americas and Europe. Our findings provide geospatial tools for policymakers to prioritize surveillance and resource allocation, enhance early detection and rapid response efforts. By improving reporting and data quality, predictive models can be further refined and strengthen public health preparedness against potential future emerging infectious disease threats.

BACKGROUNDAdvanced outbreak analytics were instrumental in informing governmental decision-making during the COVID-19 pandemic. However, systematic evaluations of how modelling practices, data use and science-policy interactions evolved during this and previous emergencies remain scarce.AIMThis study assessed the evolution of modelling practices, data usage, gaps, and engagement between modellers and decision-makers to inform future global epidemic intelligence.METHODSWe conducted a two-stage semiquantitative survey among modellers in a large European epidemic intelligence consortium. Responses were analysed descriptively across early, mid- and late-pandemic phases. We used policy citations in Overton to assess policy impact.RESULTSOur sample included 66 modelling contributions from 11 institutions in four European countries. COVID-19 modelling initially prioritised understanding epidemic dynamics; evaluating non-pharmaceutical interventions and vaccination impacts later became equally important. Traditional surveillance data (e.g. case line lists) were widely available in near-real time. Conversely, real-time non-traditional data (notably social contact and behavioural surveys) and serological data were frequently reported as lacking. Gaps included poor stratification and incomplete geographical coverage. Frequent bidirectional engagement with decision-makers shaped modelling scope and recommendations. However, fewer than half of the studies shared open-access code.CONCLUSIONSWe highlight the evolving use and needs of modelling during public health crises. Persistent gaps in the availability of non-traditional data underscore the need to rethink sustainable data collection and sharing practices, including from for-profit providers. Future preparedness should focus on strengthening collaborative platforms, research consortia and modelling networks to foster data and code sharing and effective collaboration between academia, decision-makers and data providers.

Abstract In the frame of the entomological VectorNet network and its capacity building activities, we collected original mosquito distribution data in southern Poland and bordering areas of the Czech Republic, Germany and Slovakia, in June and September–November 2023. Because of the suspected occurrence of Aedes japonicus or Ae. koreicus in Poland, provided by a photo posted early 2022 on iNaturalist, we targeted the exotic Aedes species in our sampling strategy, but also collected data on other mosquito species. Besides some adult catches, we mainly collected mosquito immature stages from artificial and natural water containers but occasionally from other aquatic habitats. In addition, we collated citizen data and modelled the distribution of Ae. japonicus in Europe incorporating the newly collected data. During this snapshot field study, a total of 162 samples, including 139 yielding mosquitoes, were taken from 111 locations across 47 administrative units, resulting on the detection of 22 mosquito taxa. Our study provides the first substantiated records of Ae. japonicus and Anopheles petragnani in Poland (the second confirmed by molecular identification). While Ae. japonicus is clearly established over a large part of the country, no other exotic mosquito species was detected. The presence of Ae. japonicus was also confirmed at one location by four citizen records submitted to MosquitoAlert in 2023. Regarding native mosquitoes, we identified their presence in 127 species/NUTS3 combinations (113 for Poland, including a single record for An. petragnani). An updated modelling of the distribution of Ae. japonicus suggests higher environment suitability in Central and Eastern Europe than has been previously estimated. Aedes japonicus is probably widespread in the Czech Republic and Slovakia, and might soon colonise the bordering region of Ukraine. Its establishment extends the putative mosquito vector list for West Nile and Rift Valley fever viruses in Central Europe.

West Nile virus (WNV) is an emerging mosquito-borne pathogen in Europe where it represents a new public health threat. While climate change has been cited as a potential driver of its spatial expansion on the continent, a formal evaluation of this causal relationship is lacking. Here, we investigate the extent to which WNV spatial expansion in Europe can be attributed to climate change while accounting for other direct human influences such as land-use and human population changes. To this end, we trained ecological niche models to predict the risk of local WNV circulation leading to human cases to then unravel the isolated effect of climate change by comparing factual simulations to a counterfactual based on the same environmental changes but a counterfactual climate where long-term trends have been removed. Our findings demonstrate a notable increase in the area ecologically suitable for WNV circulation during the period 1901–2019, whereas this area remains largely unchanged in a no-climate-change counterfactual. We show that the drastic increase in the human population at risk of exposure is partly due to historical changes in population density, but that climate change has also been a critical driver behind the heightened risk of WNV circulation in Europe.

Introduction: Caused by the tick-borne encephalitis virus (TBEV), tick-borne encephalitis (TBE) is a zoonotic disease that can cause severe neurological symptoms. Despite the availability of a vaccine, it remains a public health concern in Europe, with an increasing number of reported human cases and new hotspots of virus circulation, also in previously non-endemic areas. To geolocate and predict new areas at risk of human TBE infections, we developed a spatio-temporal predictive model to infer the year-to-year probability of human TBE occurrence across Europe at the regional and municipal administrative levels. Methods: We derived the distribution of human TBE cases at the regional (NUTS-3) level during the period 2017-2022 using data provided by the European surveillance system (TESSy, ECDC), while the distribution of human TBE cases at the municipal level during the same years was obtained using data from five European countries (Austria, Finland, Italy, Lithuania, and Slovakia). We modelled the probability of TBE occurrence at regional and municipal levels for the period 2017-2024 using a boosted regression trees approach, including both hazard and exposure variables affecting TBE risk: climate, land cover, presence of tick hosts to account for the natural hazard of virus circulation, forest road density and human population density as proxies for the probability of human exposure to tick bites. Results: Our modelling framework provides a multi-scale approach to predict yearly variations in the risk of occurrence of human TBE cases in Europe. Our results highlight a significant rising trend in the probability of human infection with TBE not only in north-western, but also in south-western European countries and show that areas at high risk of TBE are characterized by the presence of key tick host species, intense human recreational activity in forests, steep drops in late summer temperatures and high annual precipitation. Discussion: Our study provides a modelling framework for the early annual assessment and identification of European regions and municipalities at risk of human TBE infection, based on covariates reflecting both the hazard and exposure dimensions. Being based on lagged covariates, our approach can also be used to predict risk areas one year in advance, thus supporting surveillance, prevention, and control of human TBE infections by public health authorities. ### Competing Interest Statement The authors have declared no competing interest.

Background: The natural transmission cycle of tick-borne encephalitis (TBE) virus is enhanced by complex interactions between ticks and key hosts strongly connected to habitat characteristics. The diversity of wildlife host species and their relative abundance is known to affect transmission of tick-borne diseases. Therefore, in the current context of global biodiversity loss, we explored the relationship between habitat richness and the pattern of human TBE cases in Europe to assess biodiversity's role in disease risk mitigation. Methods: We assessed human TBE case distribution across 879 European regions using official epidemiological data reported to The European Surveillance System (TESSy) between 2017 and 2021 from 15 countries. We explored the relationship between TBE presence and the habitat richness index (HRI1) by means of binomial regression. We validated our findings at local scale using data collected between 2017 and 2021 in 227 municipalities located in Trento and Belluno provinces, two known TBE foci in northern Italy. Findings: Our results showed a significant parabolic effect of HRI on the probability of presence of human TBE cases in the European regions included in our dataset, and a significant, negative effect of HRI on the local presence of TBE in northern Italy. At both spatial scales, TBE risk decreases in areas with higher values of HRI. Interpretation: To our knowledge, no efforts have yet been made to explore the relationship between biodiversity and TBE risk, probably due to the scarcity of high-resolution, large-scale data about the abundance or density of critical host species. Hence, in this study we considered habitat richness as proxy for vertebrate host diversity. The results suggest that in highly diverse habitats TBE risk decreases. Hence, biodiversity loss could enhance TBE risk for both humans and wildlife. This association is relevant to support the hypothesis that the maintenance of highly diverse ecosystems mitigates disease risk.

BackgroundTick-borne encephalitis (TBE) is a disease which can lead to severe neurological symptoms, caused by the TBE virus (TBEV). The natural transmission cycle occurs in foci and involves ticks as vectors and several key hosts that act as reservoirs and amplifiers of the infection spread. Recently, the incidence of TBE in Europe has been rising in both endemic and new regions.AimIn this study we want to provide comprehensive understanding of the main ecological and environmental factors that affect TBE spread across Europe.MethodsWe searched available literature on covariates linked with the circulation of TBEV in Europe. We then assessed the best predictors for TBE incidence in 11 European countries by means of statistical regression, using data on human infections provided by the European Surveillance System (TESSy), averaged between 2017 and 2021.ResultsWe retrieved data from 62 full-text articles and identified 31 different covariates associated with TBE occurrence. Finally, we selected eight variables from the best model, including factors linked to vegetation cover, climate, and the presence of tick hosts.DiscussionThe existing literature is heterogeneous, both in study design and covariate types. Here, we summarised and statistically validated the covariates affecting the variability of TBEV across Europe. The analysis of the factors enhancing disease emergence is a fundamental step towards the identification of potential hotspots of viral circulation. Hence, our results can support modelling efforts to estimate the risk of TBEV infections and help decision-makers implement surveillance and prevention campaigns.

The Asian tiger mosquito, Aedes albopictus, is an invasive vector species. It is capable of transmitting more than 20 arboviruses, and is responsible for chikungunya, dengue, and zika transmission. Urbanisation, globalisation, and climate change are expected to expand its habitable range and increase the global vector-borne disease burden in the coming decades. To plan effective control strategies, early-warning and decision support systems are urgently needed.We developed a climate- and environment-driven population dynamics model of Aedes albopictus with extensive geospatial applicability. The foundation of the model is the age- and stage-structured population dynamics model of Erguler et al. (2016)1. We replaced its rainfall- and human population density-dependent breeding site component with a large-scale mechanistic ecological model. The extension effectively created an ecological-dynamic model hybrid capable of representing niche dependence and response to changing environmental and meteorological conditions over time and under various land characteristics. To the best of our knowledge, this is the first spatiotemporal mechanistic model developed with a capacity to learn from both vector presence and longitudinal abundance data.We calibrated the model with an extensive field surveillance dataset by combining the data collected through the AIMSurv project, the first pan-European harmonized surveillance of Aedes invasive mosquito species of relevance for human vector-borne diseases, and the global surveillance records available from VectorBase MapVEu. By deriving the model structure and environmental dependencies from the literature and allowing a complete re-configuration of the entire parameter set, we asserted the biological relevance and geospatial applicability, which extends over Europe and North America.We corroborate that temperate northern territories are becoming increasingly suitable for Aedes albopictus establishment, while neighbouring southern territories become less suitable, as climate continues to change. We identify potential hotspots over Europe and North America by employing the combination of vector abundance and activity as a proxy to pathogen transmission risk. By investigating routes of introduction to new territories, we demonstrate the significant role of dynamic environmental suitability in the highly efficient spread of this invasive mosquito.The model is scheduled for integration into the "Climate-driven vector-borne disease risk assessment platform", to predict habitat suitability and dynamic abundance of important disease vectors and the risk of diseases transmitted by them at any location and time up to the end of the century. With the continental model of Aedes albopictus, the platform will reliably inform public health professionals and policy makers and contribute to the global strategies of integrated vector management.1 Erguler K, Smith-Unna SE, Waldock J, Proestos Y, Christophides GK, Lelieveld J, Parham PE. Large-scale modelling of the environmentally-driven population dynamics of temperate Aedes albopictus (Skuse). PloS one. 2016 Feb 12;11(2):e0149282.