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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.

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.

Extreme weather events can lead to infectious disease outbreaks, especially those spread by hematophagous flies, and The Gambia is particularly vulnerable to climate change. To the best of our knowledge, no one has ever documented the relationship between climate variability and change and the distribution of the hematophagous flies belonging to the families Glossinidae, Tabanidae, and Stomoxyinae. This paper aims to study the association of temperature and humidity on the distribution of the above species and their families in The Gambia in the recent past and to provide predictions of species abundance and occurrence in the future. A line transect survey was carried out in all the administrative regions of The Gambia to study the prevalence of the flies. Generalized additive models were used to analyze the relationships between the distribution of the insects and their families and the variability in climate conditions in the recent past and in three different future periods. Regarding the recent past, our results show that temperature has significantly impacted the presence of Glossinidae and Tabanidae species, with maximum temperature being the most important factor. Relative humidity was also statistically significantly associated with Tabanidae species. None of the climate variables was found to be associated with the Tabanus par and Tabanus sufis. Minimum temperature and relative humidity were statistically significantly associated with Glossina morsitan submorsitan, while maximum temperature was statistically significantly associated with Atylotus agrestis and Stomoxys calcitrans. Only relative humidity was statistically significantly associated with the Glossina palpalis gambiense. As for the future projections, the results show that rising temperatures impacted the distribution of Tabanus species, Glossina species, and Stomoxys calcitrans in The Gambia. The distribution of Trypanosoma vectors in The Gambia is mostly influenced by maximum temperature. The research’s conclusions gave climate and public health policymakers crucial information to take into account.

The case fatality ratio (CFR) is a vital metric for assessing the disease severity of novel pathogens. The widely used direct method of CFR estimation—the ratio of total confirmed deaths to total confirmed cases—is inherently simplistic, as it fails to account for the essential time lag between case confirmation to death, and reporting delays. These limitations often lead to biased CFR estimates, particularly in the early stages of outbreaks. This study introduces a novel approach—the distributed-delay method that, like the direct method, utilizes publicly available aggregate time-series data on cases and deaths. It estimates CFR by flexibly incorporating a case-to-death time distribution without requiring a priori assumptions on distribution parameters. Using a fitting approach to forecast case fatalities based on known or assumed case-to-death time distributions, the method consistently recovers true CFR much earlier than the direct method under various simulation settings. These settings reflect variability in disease severity, uncertainties in case-to-death time parameters, and limited knowledge of case-to-death time distributions. It outperforms other methods such as Baud’s, which assumes a non-zero constant case-to-death time, and the Generalized Baud’s method, which allows for a direct comparison with our new approach. While evaluations based on empirical data are challenging, our conclusions are supported by CFR estimates obtained using empirical COVID-19 data from 34 countries. As an added value, this analysis also demonstrates a significant negative association between eventual CFR and the expected case-to-death time within the context of COVID-19 data. Our study highlights the complexities of inferring real-time CFR from aggregate time-series case and death data, highlighting that refining this method can lead to accurate real-time CFR estimations for actual outbreaks.

Context: Dengue is a major public health threat in the Lao People’s Democratic Republic (Laos) and Thailand. Dengue transmission is ecologically complex. Concurrently identifying both climate and landscape-based risk factors for dengue virus transmission is necessary to improve dengue prevention and control efforts in Laos and Thailand. Objectives: The objective of this study was to determine how changes in climate (temperature and rainfall), and land use (e.g. built-up areas, agricultural crops, fruit orchards, rubber plantations) and land cover (e.g. evergreen and deciduous forests, permanent and temporary wetlands) affect dengue risk in four provinces in southern Laos and north-eastern Thailand during 2002 to 2019.

Methods: A conditional autoregressive Bayesian spatiotemporal modeling framework was used to analyze the risk of dengue by spatiotemporal variations in land use and land cover (LULC) and climatic parameters.

Results: The average annual temperatures in the study area increased by 0.44–0.94 °C during the study period. The model indicated that an increase of 1 °C in weekly average temperatures (up to a 29 °C threshold level) increased the average dengue risk by up to 24% in the two Lao provinces and 18.9% in the two Thai provinces. The model suggested that a rainfall increase of 1 mm up to 60 mm increased dengue risk by 1.8–3.2%. A 0.6–1.6% increase in built-up land use increased dengue risk by 1.8–6.9%. Built-up areas and rubber plantations were positively associated with dengue in the Ubon Ratchathani province of Thailand, while wetlands were negatively associated with dengue cases in the Savannakhet province of Laos.

Conclusions: Changes in dengue risk were clearly related to increases in rainfall and temperature as well as changes in LULC in both Laos and Thailand. These insights may inform community-based dengue control activities by targeting geographically localized areas (microgeographic scale) to deploy dengue control activities more effectively in these highly endemic regions.

Accurate malaria predictions are essential for implementing timely interventions, particularly in Mozambique, where climate factors strongly influence transmission. This study aims to develop and evaluate a spatial–temporal prediction model for malaria incidence in Mozambique for potential use in a malaria early warning system (MEWS). We used monthly data on malaria cases from 2001 to 2018 in Mozambique, the model incorporated lagged climate variables selected through Deviance Information Criterion (DIC), including mean temperature and precipitation (1–2 months), relative humidity (5–6 months), and Normalized Different Vegetation Index (NDVI) (3–4 months). Predictive distributions from monthly cross-validations were employed to calculate threshold exceedance probabilities, with district-specific thresholds set at the 75th percentile of historical monthly malaria incidence. The model’s ability to predict high and low malaria seasons was evaluated using receiver operating characteristic (ROC) analysis. Results indicated that malaria incidence in Mozambique peaks from November to April, offering a predictive lead time of up to 4 months. The model demonstrated high predictive power with an area under the curve (AUC) of 0.897 (0.893–0.901), sensitivity of 0.835 (0.827–0.843), and specificity of 0.793 (0.787–0.798), underscoring its suitability for integration into a MEWS. Thus, incorporating climate information within a multisectoral approach is essential for enhancing malaria prevention interventions effectiveness.

Expressions in social media can provide a rapid insight into people's reactions to events, such as periods of climatic stress. This study explored the link between climatic stressors and negative sentiment on the X platform in Germany to inform climate-related health policies and interventions. Natural language processing was used to standardize the text, and a comprehensive approach for sentiment analysis was utilized. We then conducted spatiotemporal modeling fitted using integrated nested laplace approximation (INLA). Our findings indicate that higher and lower level of temperature and precipitation is correlated with an increase and decrease in the relative risk of negative sentiments, respectively. The findings of this study illustrate that human sentiment of distress in social media varies with space and time about exposure to climate stressors. This emotional indicator of human exposure and responses to climate stress indicates potential physical and mental health impacts among the affected populations.

Temperature influences the transmission of mosquito-borne pathogens with significant implications for disease risk under climate change. Mathematical models of mosquito-borne infections rely on functions that capture mosquito-pathogen interactions in response to temperature to accurately estimate transmission dynamics. For deriving these functions, experimental studies provide valuable data on the temperature sensitivity of mosquito life-history traits and pathogen transmission. However, the scarcity of experimental data and inconsistencies in methodologies for analysing temperature responses across mosquito species, pathogens, and experiments present major challenges. Here, we introduce a new approach to address these challenges. We apply this framework to study the thermal biology of West Nile virus (WNV). We reviewed existing experimental studies, obtaining temperature responses for eight mosquito-pathogen traits across 15 mosquito species. Using these data, we employed Bayesian hierarchical models to estimate temperature response functions for each trait and their variation between species and experiments. We incorporated the resulting functions into mathematical models to estimate the temperature sensitivity of WNV transmission, focusing on six mosquito species of the genus Culex. Our study finds a general optimal transmission temperature around 24°C among Culex species with only small species-specific deviations. We demonstrate that differing mechanistic assumptions underlying published mosquito population models result in temperature optima estimates that differ by up to 3°C. Additionally, we find substantial variability between trait temperature responses across experiments on the same species, possibly indicating significant intra-species variation in trait performance. We identify mosquito biting rate, lifespan, and egg viability as priorities for future experiments, as they strongly influence estimates of temperature limits, optima, and overall uncertainty in transmission suitability. Experimental studies on vector competence traits are also essential, because limited data on these currently require model simplifications. These data would enhance the accuracy of our estimates, critical for anticipating future shifts in WNV risk under climate change