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Aedes albopictus, a key vector for Dengue, Chikungunya, Zika, and Yellow Fever, is expanding its range beyond its tropical and subtropical origins, driven by suitable climate, population mobility, trade, and urbanization. Since its introduction to Europe, Ae. albopictus has rapidly spread and triggered recurrent outbreaks. Past model attempts have handled vector suitability and vector introduction as independent drivers. Here we develop a highly predictive spatio-temporal vector diffusion model based on climate suitability and human population predictors, integrated in one simultaneous framework. The model explains how short- and long-range spread of Ae. albopictus interacts with vector suitability, predicting areas of presence or absence with high accuracy (99% and 79%). These results show that the expansion of Ae. albopictus in Europe is predictable and provide a basis for anticipating future outbreaks in situations of dependent interacting co-drivers.

This chapter explores the intricate relationship between climate change and infectious diseases, examining the various mechanisms through which climate change influences the distribution, prevalence, and transmission. Over half of all infectious diseases are susceptible to climate change, affecting 3 billion people worldwide. The nexus of climate hazards, exposure, and vulnerability creates cascading risk pathways, with events such as hurricanes and floods disrupting infrastructure and fostering disease outbreaks. Vectorborne, foodborne, waterborne, and respiratory diseases are exceptionally sensitive to climate changes with altered temperature and precipitation patterns affecting pathogen transmission, leading to the expansion of disease ranges, seasonality, and shifting transmission dynamics. Disruptions to ecosystems also lead to increased contact between wildlife and human populations, heightening the risk of zoonotic disease spillover events. Addressing these challenges demands the strengthening of healthcare systems, surveillance, and infrastructure resilience. Global warming will continue to alter the landscape of infectious diseases, with expansion of transmission seasons and geographic ranges of disease, increased risk of infections, and introduction of emerging infections to naïve populations. By understanding climate change and these complex connections, a multifaceted approach to adaptation efforts, mitigation, and effective public health strategies can be devised to confront an evolving disease burden in a changing world.

Aedes and Culex mosquitoes, known for spreading arboviruses like dengue and West Nile, thrive in cities, posing health risks to urban populations. Climate change can create suitable climatic conditions for these vectors to spread further in Europe. Cities contain numerous landscape and infrastructure elements, such as storm drains, that allow stagnant water build-up facilitating mosquito breeding. Modifying urban infrastructure to prevent water accumulation can reduce mosquito populations, but evidence is limited. The Public Health Agency of Barcelona, Spain, introduced a structural modification of storm drains to prevent water accumulation. Together with the Agency, we designed a randomised controlled trial (RCT) to experimentally assess the effectiveness of these modifications on adult Aedes albopictus and Culex pipiens populations. It is a parallel-arm RCT with equal randomization of 44 drains to receive mosquito-proofing modifications (intervention) or not (control). Primary outcomes are adult mosquito counts and secondary outcomes are larvae and mosquito presence, assessed weekly at each drain until no mosquitoes are detected. Data analyses include generalised linear mixed models to estimate the time-averaged and highest intervention effects, subgroup and sensitivity analyses. The trial results will guide a city-wide expansion of the storm drain modifications and provide valuable evidence to enhance existing vector control measures.

Background: The rapid spread of the Asian tiger mosquito (Aedes albopictus) poses a notable public health threat in Europe due to its ability to transmit tropical diseases such as dengue and chikungunya. We aimed to quantify the underlying drivers facilitating and accelerating Europe's transition from sporadic arbovirus outbreaks to Aedes-borne disease endemicity, focusing on dengue and chikungunya outbreaks.

Methods: We conducted a time-to-event analysis to investigate the period between establishment of Ae albopictus and autochthonous dengue and chikungunya outbreaks across Nomenclature of Territorial Units for Statistics (NUTS) 3 regions in the EU. We incorporated data from the European Centre for Disease Prevention and Control, WHO, technical and surveillance reports, and other entomological data sources on regional Ae albopictus establishment and subsequent dengue and chikungunya outbreaks from 1990 (when Ae albopictus was first introduced to an EU country) to 2024. The main outcome was survival time (ie, the time from Ae albopictus establishment to an outbreak of dengue or chikungunya), accounting for land-use types, demographic and socioeconomic factors, imported cases, and climatic variables via univariable and multivariable regression. To address recurrent outbreaks, we applied the Andersen–Gill extension of the Cox proportional hazards model to analyse all events. We further stratified regions into warm and cool groups on the basis of mean summer temperatures above or below 20°C and conducted a stratified analysis with Kaplan–Meier curves and the log-rank test to evaluate differences between these groups. We also estimated projected outbreak hazards from the 2030s to the 2060s at a decadal scale under three distinct shared socioeconomic pathways (SSPs; SSP1–2·6, SSP3–7·0, and SSP5–8·5) to assess the future impact of climate change on outbreak hazard estimates.

Findings: Between 1990 and 2024, the interval from the first NUTS 3 regional establishment of Ae albopictus to the first outbreak of dengue or chikungunya decreased from 25 years to less than 5 years. Similarly, the interval from the first outbreak to the second outbreak decreased from 12 years in 1990 to less than 1 year in 2024. Our regression analyses indicate that increasingly favourable climatic conditions play a significant role in this trend. A 1°C rise in mean summer temperature was associated with a hazard ratio of 1·55 (95% CI 1·30–1·85; p<0·0001) after controlling for health-care expenditure and imported cases and land-use type. First outbreak events might have occurred more frequently and earlier in warmer regions than cooler ones (log-rank p=0·088), reflecting a lower probability of remaining outbreak-free over time. This trend is expected to intensify under extreme climate change scenarios, with projections under the SSP5–8·5 scenario suggesting an almost five-fold increase in dengue or chikungunya outbreaks by the 2060s, relative to the 1990–2024 baseline period.

Interpretation: The findings in this study underscore the pressing need for robust control measures, enhanced surveillance, and early warning systems in the EU to mitigate the impending risk of Aedes-borne disease endemicity in the region.

Background: In recent years, Europe has experienced several outbreaks of West Nile Virus (WNV), a mosquito-borne pathogen. This study aims to quantify the impact of weekly mean temperature and cumulative precipitation on human cases of West Nile Neuroinvasive Disease (WNND), to assess the feasibility of climate-informed early warning systems for severe forms of WNV infection.

Methods: Using a space-time-stratified case-crossover design, the short-term effects of meteorological factors on WNND cases reported in Europe from 2014 to 2022 were examined. Distributed lag nonlinear models were implemented in conditional logistic regressions to assess the delayed and nonlinear effects of temperature and precipitation on WNND risk as well as to estimate the Attributable Fraction (AF) of cases to extreme values of the two meteorological factors.

Findings: Between 2014 and 2022, Europe reported 3437 WNND cases. Both meteorological factors recorded in the 8 weeks before symptom onset showed positive and delayed effects on WNND risk. The strongest effect was found for weekly mean temperatures at 2 weeks lag (Odds Ratio (OR): 1.15; 95% Confidence Interval (CI) 1.12–1.19) and for weekly cumulative precipitation at 3 weeks lag (OR: 1.12; 95% CI 1.09–1.16). Of all WNND cases analyzed, 36.4% (95% CI, 31.3%–40.3%) could be attributed to weekly mean temperatures exceeding the 25 °C, while 13.1% (95% CI, 9.5%–16.4%) to weekly cumulative precipitations exceeding 40 mm.

Interpretation: These findings emphasize the significance of short-term variations in temperature and precipitation in driving WNND incidence in Europe. Meteorological factors can be used to operationalize early warning systems to reduce the disease burden from WNV infections, which are continually increasing across the continent.

Global risk maps are an important tool for assessing the global threat of mosquito and tick-transmitted arboviral diseases. Public health officials increasingly rely on risk maps to understand the drivers of transmission, forecast spread, identify gaps in surveillance, estimate disease burden, and target and evaluate the impact of interventions. Here, we describe how current approaches to mapping arboviral diseases have become unnecessarily siloed, ignoring the strengths and weaknesses of different data types and methods. This places limits on data and model output comparability, uncertainty estimation and generalisation that limit the answers they can provide to some of the most pressing questions in arbovirus control. We argue for a new generation of risk mapping models that jointly infer risk from multiple data types. We outline how this can be achieved conceptually and show how this new framework creates opportunities to better integrate epidemiological understanding and uncertainty quantification. We advocate for more co-development of risk maps among modellers and end-users to better enable risk maps to inform public health decisions. Prospective validation of risk maps for specific applications can inform further targeted data collection and subsequent model refinement in an iterative manner. If the expanding use of arbovirus risk maps for control is to continue, methods must develop and adapt to changing questions, interventions and data availability.

Background: Leishmaniases are neglected diseases transmitted by sand flies. They disproportionately affect vulnerable groups globally. Understanding the relationship between climate and disease transmission allows the development of relevant decision-support tools for public health policy and surveillance. The aim of this modelling study was to develop an indicator that tracks climatic suitability for Leishmania infantum transmission in Europe at the subnational level.

Methods: Historical records of sand fly vectors, human leishmaniasis, bioclimatic indicators, and environmental variables were integrated in a machine learning framework (XGBoost) to predict suitability in two past periods (2001–2010 and 2011–2020). We further assessed if predictions were associated with human and animal disease data from selected countries (France, Greece, Italy, Portugal, and Spain).

Findings: An increase in the number of climatically suitable regions for leishmaniasis was detected, especially in southern and eastern countries, coupled with a northward expansion towards central Europe. The final model had excellent predictive ability (AUC = 0.970 [0.947–0.993]), and the suitability predictions were positively associated with human leishmaniasis incidence and canine seroprevalence for Leishmania.

Interpretation: This study demonstrates how key epidemiological data can be combined with open-source climatic and environmental information to develop an indicator that effectively tracks spatiotemporal changes in climatic suitability and disease risk. The positive association between the model predictions and human disease incidence demonstrates that this indicator could help target leishmaniasis surveillance to transmission hotspots.

Funding: European Union Horizon Europe Research and Innovation Programme (European Climate-Health Cluster), United Kingdom Research and Innovation.

Unsustainable globalisation of economic activities, lifestyles and social structures has contributed to environmental degradation, posing major threats to human health at the local and global levels. All these problems including climate change, pollution, and biodiversity loss represent challenges that are unlikely to be met with existing approaches, capabilities and tools. This article acknowledges the need for well-prepared practitioners from many walks of life to contribute to environmental public health (EPH) functions thus strengthening society’s capacity and capability to respond effectively and in a timely manner to such complex situations and multiple challenges. It envisions a new EPH practice addressing questions on: Why do this? What needs to be addressed? Who will do it? How can it be implemented? This article focuses on the main challenging EPH issues worldwide and how they could be addressed using a conceptual framework for training. A companion article shows how they have been tackled in practice, providing ideas and experiences.