Impact of regional climate change on the mosquito vector Aedes albopictus in a tropical island environment: La Réunion

Spatial patterns and clustering of dengue incidence in Mexico: Analysis of Moran's index across 2,471 municipalities from 2022 to 2024

Dengue is an increasing public health challenge, with rising cases and expanding distribution. Its complex epidemiology is influenced by climate change, urbanization, and the circulation of multiple viral serotypes. This study aimed to characterize the spatial and temporal (2022-2024) patterns of dengue incidence across 2,471 municipalities in Mexico. Weekly case counts, obtained through the normative epidemiological surveillance system for vector-borne diseases, were used to calculate incidence rates per 100,000 population. Geographic Information Systems were employed to analyze spatial patterns, while Local Moran's I statistic and a k-nearest neighbors spatial weights matrix identified spatial clusters. A total of 622,689 dengue cases were analyzed, with incidence rates rising from 29.4 in 2022 to 279.0 per 100,000 in 2024. Dengue transmission expanded, affecting 38.0% of municipalities in 2022 and 68.6% by 2024 ([Formula: see text] < 0.001). Spatial clustering also increased, with positive clusters increasing from 28 municipalities in 2022-98 in both 2023 and 2024. Moran's I values indicated a peak in spatial autocorrelation in 2023 (I = 0.57). While DENV-2 was the predominant serotype in 2022, DENV-3 became dominant in 2023 and 2024. Over time, high-incidence areas shifted from southern and central regions to the southeast and Pacific coast. These findings suggest the growing burden of dengue in Mexico, driven by rising incidence, expanding geographic distribution, and evolving spatial patterns. A coordinated public health response is needed to mitigate the impact of dengue and prevent its spread to newly affected areas.

Understanding the Effect of a Changing Climate on the Re-Emergence of Mosquito-Borne Diseases in Vulnerable Small Island Nations: A Systematic Review

INTRODUCTION

Drastic changes in meteorological variables due to climate change will likely have an implication on the proliferation of vectors such as mosquitoes. Extreme weather events may therefore promote the emergence/re-emergence of mosquito-borne diseases (MBDs) and potentiate the risk of endemicity, particularly, in small island nations.

METHOD

A systematic review was chosen to methodically ascertain the knowledge gaps that exist in determining the influence of the changing climate on MBDs in small islands with vulnerable public health systems. This review was conducted using the PRISMA guidelines.

RESULTS

Following extraction of 600 articles from the databases, 16 studies were determined to meet the selection criteria. The majority of these research papers were from Sri Lanka (n = 9) while the remaining articles were distributed between islands in the Pacific and Atlantic Ocean. Several of these studies used regression modelling techniques to discuss the effect of multiple meteorological variables on the incidence of MBDs. A positive relationship was observed between temperature and the relative risk of MBDs in 72% of the papers. Rainfall enhanced dengue transmission in 84% of the studies included. All the articles discussing the effect of humidity illustrated a similar trend while wind speed was the only climatic variable demonstrating a negative relationship with MBDs.

DISCUSSION

Considering the intricate nature of the non-linear exposure-response link is crucial when estimating the lagged effect of the changing climate on MBDs transmission. Other challenges associated with bias and confounders in the selected studies as well as meteorological data accessibility, were highlighted. Therefore, it was not possible to conclusively establish that the changing climatic variables do influence the spread of MBDs which accentuated the need for conducting further studies to illustrate the effect of changing weather variables on the incidence of MBDs, with an emphasis on vulnerable small island nations.

A State-of-the-Science Review of Long-Term Predictions of Climate Change Impacts on Dengue Transmission Risk

BACKGROUND

Climate change is predicted to profoundly impact dengue transmission risk, yet a thorough review of evidence is necessary to refine understanding of climate scenarios, projection periods, spatial resolutions, and modeling approaches.

OBJECTIVES

We conducted a state-of-the-science review to comprehensively understand long-term dengue risk predictions under climate change, identify research gaps, and provide evidence-based guidelines for future studies.

METHODS

We searched three medical databases (PubMed, Embase, and Web of Science) up to 5 December 2024 to extract relevant modeling studies. An a priori search strategy, predefined eligibility criteria, and systematic data extraction procedures were implemented to identify and evaluate studies.

RESULTS

Of 5,035 studies retrieved, 57 met inclusion criteria. Prediction for dengue risk ranged from 1950 to 2115, and 52.63% (n = 30 ) of all studies used Representative Concentration Pathways (RCPs). Specifically, RCP 8.5 (34.94%; n = 29 ), Shared Socioeconomic Pathways (SSPs) 2 (32.35%; n = 11 ), and the Special Report on Emissions Scenarios (SRES) A1 (58.33%; n = 7 ) were utilized the most among all the RCPs, SSPs, and SRES climate change scenarios. Most studies (57.89%; n = 33 ) used only climatic variables for the prediction, and 21.05% (n = 12 ) of studies employed fine spatial resolution (≈ 1 km ) for the climate data. We identified that correlative approach was used mostly across the studies for modeling the future risk (61.40%; n = 35 ). Among mechanistic models, 35% (n = 7 ) lacked outcome validation, and 75% (n = 15 ) did not report model evaluation metrics.

DISCUSSION

We identified the urgent need to strengthen dengue databases, use finer spatial resolutions to integrate big data, and incorporate potential socioenvironmental factors such as human movement, vegetation, microclimate, and vector control efficacy in modeling. Utilizing appropriate spatiotemporal models and validation techniques will be crucial for developing functional climate-driven early warning systems for dengue fever. https://doi.org/10.1289/EHP14463.

Assessing AedesTech mosquito home system on yellow fever mosquito Aedes aegypti (Linnaeus) in Northern Malaysia

This research evaluated the AedesTech Mosquito Home System (AMHS), an ovitrap employing autodissemination with pyriproxyfen, to monitor and manage mosquito populations. It involved 3 studies of the AMHS: a baseline study, an effectiveness study, and an autodissemination study on Aedes (Diptera: Culicidae) mosquitoes. Forty AHMS units filled with water were deployed for the baseline study. During the effectiveness study, 40 untreated AMHS units with water were placed alongside 40 AMHS units treated with Mosquito Home Aqua (MHAQ) solution, the retail solution used for AMHS. The autodissemination study featured 40 AMHS units treated with MHAQ alongside 40 control AMHS units without MHAQ, together with 25 Aedes aegypti (Linnaeus) larvae. Notably, treated traps in the effectiveness study exhibited a significant reduction in the Ovitrap Index (OI) compared to the baseline traps in the baseline study. The effectiveness study validated AMHS's efficacy, with treated traps displaying a significantly lower OI than untreated counterparts (P < 0.05). The study also showed a decrease in the percentage of egg hatching and percentage of adult emergence in treated traps compared to untreated traps. Autodissemination was evident, marked by a significant percentage of adult emergence decrease of Ae. aegypti larvae, without affecting sex ratios. It strongly suggests that AMHS can effectively reduce Aedes populations through direct contact and autodissemination without affecting sex ratios.

Chemical Profiling, Sensory Qualities, and Bioactivities of Essential Oils Obtained from Aloysia citrodora and Bursera graveolens Ecuadorian Plants Against the Mosquito Aedes albopictus (Skuse) (Diptera: Culicidae)

The mosquito Aedes albopictus is a vector of several harmful viruses, necessitating the development of safer and more sustainable repellents than DEET. This study evaluated the potential of plant-based EOs extracted via steam distillation from Aloysia citrodora and Bursera graveolens plants harvested in Ecuador. A. citrodora EO exhibited a high sensory pleasantness with a citral-like aroma, while B. graveolens EO had lower sensory appeal due to off-flavors. At 200 µL L-1, B. graveolens EO demonstrated strong ovideterrent activity, reducing egg laying by 64%, whereas A. citrodora EO displayed the highest larvicidal activity with an LC50 value of 88.54 µL L-1. Both EOs, applied at 0.20 µL cm-2 on human skin, provided comparable repellency for over 45 min. The RD50 values were 0.104 µL cm-2 for A. citrodora EO and 0.136 µL cm-2 for B. graveolens EO. When compared to DEET at 0.04 µL cm-2, no significant differences in repellency were observed within the first 15 min post-application. These findings suggest that both EOs are effective alternatives to synthetic repellents, and A. citrodora EO emerges as a promising candidate due to its superior sensory attributes and larvicidal activity.

[The effects of climate change on the emergence of dengue]

In recent decades, dengue has become a global issue due to its rapid spread and significant public health impact. Climate change is recognized as a key factor in the geographical spread of dengue and its vectors. Climate change affects dengue transmission through changes in temperature and precipitation, which affect both vectors and arboviruses. Climate change can also disrupt human migration patterns facilitating the spread of the virus and the invasion of vectors into new regions. Understanding the impact of climate change on dengue and its vectors is essential for developing strategies to prevent and control the disease. Appropriate mosquito control strategies, enhanced epidemiological surveillance and tailored public health systems are needed to mitigate the increasing burden of dengue in the context of climate change.

Identification of climate-sensitive disease incidences in vietnam: A longitudinal retrospective analysis of infectious disease rates between 2014 and 2022

Objective

There is a growing correlation between the rise in infectious diseases and climate change; however, little is known about the interactions and mixed effects of climate factors on infectious diseases.

Method

We conducted a retrospective longitudinal study spanning 108 consecutive months from 2014 to 2022 in Can Tho, Vietnam to identify common infectious diseases (excluding tuberculosis, HIV, and COVID-19) and their associations with climate change and determine which common diseases presented concurrently with the COVID-19 period using multivariate linear regression, receiver operating characteristic (ROC) curve analysis, Bayesian kernel machine regression (BKMR) and orthogonal partial least squares discriminant analysis.

Result

The five infectious diseases with the highest average incidence rates per 100,000 people were diarrhea; hand, foot, and mouth disease (HFMD); dengue fever; viral hepatitis; and influenza. Positive associations with humidity were observed for dengue fever and HFMD. Temperature was positively associated with malaria. Negative associations were found between humidity and both chickenpox and tetanus. Diarrhea (AUC = 0.79; 95 % CL = 0.70-0.87) and dengue fever (AUC = 0.74; 95 % CL = 0.62-0.83) emerged as the most influential diseases both before and during the COVID-19 period. In our BKMR analysis, we found a significant association between the combined influence of temperature and humidity and the occurrence of dengue fever and HFMD, especially when all climate factors were at or above their 60th percentile relative to their values at the 50th percentile. Temperature emerged as the primary driver associated with the occurrence of infectious diseases.

Conclusion

These findings underscore the importance of implementing robust surveillance, prevention, and control measures by public health authorities in Can Tho. Initiatives like vaccination campaigns, vector control programs, public education on hygiene practices, and strengthening healthcare infrastructure are crucial for mitigating the spread of infectious diseases and safeguarding public health in the region.

Pandemic-Proofing: Intercepting Zoonotic Spillover Events

Zoonotic spillover events pose a significant and growing threat to global health. By focusing on preventing these cross-species transmissions, we can significantly mitigate pandemic risks. This review aims to analyze the mechanisms of zoonotic spillover events, identify key risk factors, and propose evidence-based prevention strategies to reduce future pandemic threats. Through a comprehensive literature review and analysis of major databases including PubMed, Web of Science, and Scopus from 1960-2024, we examined documented spillover events, their outcomes, and intervention strategies. This article emphasizes that targeting the root cause-the spillover event itself-is key to averting future pandemics. By analyzing historical and contemporary outbreaks, we extract crucial insights into the dynamics of zoonotic transmission. Factors underlying these events include increased human-animal contact due to habitat encroachment, agricultural intensification, and wildlife trade. Climate change, global travel, and inadequate healthcare infrastructure exacerbate risks. The diversity of potential viral reservoirs and rapid viral evolution present major challenges for prediction and prevention. Solutions include enhancing surveillance of wildlife populations, improving biosecurity measures, investing in diagnostic capabilities, and promoting sustainable wildlife management. A "One Health" approach integrating human, animal, and environmental health is crucial. Predictive modelling, international cooperation, and public education are key strategies. Developing pre-exposure prophylactics and post-exposure treatments is essential for mitigating outbreaks. While obstacles remain, advances in genomics and ecological modelling offer hope. A proactive, comprehensive approach addressing the root causes of spillover events is vital for safeguarding global health against future pandemics.

Spatial analysis of dengue fever incidence and serotype distribution in Vientiane Capital, Laos: A multi-year study

Laos is a hyperendemic country of all 4 dengue serotypes. Various factors contribute to the spread of the disease including viral itself, vectors, and environment. This study aims to analyze dengue data and its incidence in nine districts of Vientiane Capital, Laos spanning from 2019 to 2021 by data collected from Mittaphab Hospital. The Maximum Entropy algorithm (MaxEnt) was applied to assess spatial distribution and identify high-probability locations for dengue occurrence by analyzing crucial environmental and climatic conditions. Dengue cases were more prominent in female (54.88 %) and highest case number was found in worker group (29.02 %) followed by student (28.47 %) and officer (16.92 %). In this study, the age group 21-30 years old had the highest infection rate (42.23 %), followed by 10-20 years old (24.21 %). Most of dengue cases was primary infection (91.61 %). Dengue serotype 2 predominated in 2019 and 2020 and substitute by serotype 1 in 2021. Across the nine districts of Vientiane Capital, the highest incidence of dengue was found in Xaythany district population in 2019, shifting to Chanthabouly district in 2020 and 2021. The MaxEnt revealed potentially most suitable areas for dengue were widely distributed central south part of Vientiane, Laos. Additionally, the best predictive variable for dengue occurrence was normalized difference vegetation index. Understanding of case characteristics and spatial distribution features of dengue will be helpful in effective surveillance and disease control in the future.

Application of circular statistics in temporal distribution of adult mosquitoes in Qingdao, Shandong Province, China, 2021-2023

BACKGROUND

Analyses of the temporal distribution of mosquitoes are presented in statistical charts, but it is difficult to prove in statistics whether differences in peak periods exist among different years or habitats. This study aimed to investigate the application of circular statistics in determining the peak period and a comparison of differences.

METHODS

Surveillance of adult mosquitoes was conducted twice a month by light traps in five different habitats from March to November for 3 years (2021-2023) in Qingdao, Shandong Province, China. The Kruskal-Wallis test was performed to determine the differences in mosquito density among different years and habitats. Circular statistics and line charts were employed to determine the peak period and a comparison of differences.

RESULTS

Among a total of 14,834 adult mosquitoes comprising five mosquito species from four genera, Culex pipiens pallens was dominant and accounted for 89.6% of the specimens identified. Aedes albopictus, Armigeres subalbatus, and Anopheles sinensis made up 5.7%, 4.2%, and 0.5%, respectively. Culex tritaeniorhynchus accounted for less than 0.1%. The mean mosquito density (females/trap night) for the trapping period was 10.3 in 2021, 5.6 in 2022, and 3.6 in 2023. Among five habitats, the highest mosquito density was 8.9 in livestock sheds, followed by 6.8 in parks, 5.9 in rural dwellings, 5.5 in urban dwellings, and 5.4 in hospitals. No statistically significant differences were found among different years (H = 1.96, d.f. 2, P = 0.376) and habitats (H = 0.45, d.f. 4, P = 0.978). Overall, the peak period of mosquito activity fell in the months from June to September. The peak period among 3 years differed significantly (F(2,7022) = 119.17, P < 0.01), but there were no statistically significant differences in peak period among different habitats (F(4,7020) = -159.09, P > 0.05).

CONCLUSION

Circular statistics could be effectively combined with statistical charts to elucidate the peak period of mosquitoes and determine the differences in statistics among different years and habitats. These findings will provide valuable information for mosquito control and public health management.

Potential climate change effects on the distribution of urban and sylvatic dengue and yellow fever vectors

Climate change may increase the risk of dengue and yellow fever transmission by urban and sylvatic mosquito vectors. Previous research primarily focused on Aedes aegypti and Aedes albopictus. However, dengue and yellow fever have a complex transmission cycle involving sylvatic vectors. Our aim was to analyze how the distribution of areas favorable to both urban and sylvatic vectors could be modified as a consequence of climate change. We projected, to future scenarios, baseline distribution models already published for these vectors based on the favorability function, and mapped the areas where mosquitoes' favorability could increase, decrease or remain stable in the near (2041-2060) and distant (2061-2080) future. Favorable areas for the presence of dengue and yellow fever vectors show little differences in the future compared to the baseline models, with changes being perceptible only at regional scales. The model projections predict dengue vectors expanding in West and Central Africa and in South-East Asia, reaching Borneo. Yellow fever vectors could spread in West and Central Africa and in the Amazon. In some locations of Europe, the models suggest a reestablishment of Ae. aegypti, while Ae. albopictus will continue to find new favorable areas. The results underline the need to focus more on vectors Ae. vittatus, Ae. luteocephalus and Ae. africanus in West and Central sub-Saharan Africa, especially Cameroon, Central Africa Republic, and northern Democratic Republic of Congo; and underscore the importance of enhancing entomological monitoring in areas where populations of often overlooked vectors may thrive as a result of climate changes.

Pesticide Efficiency of Environment-Friendly Transition Metal-Doped Magnetite Nanoparticles

This study explored the potential of Fe3O4, SnFe2O4, and CoFe2O4 nanoparticles as larvicidal and adulticidal agents against Aedes aegypti (A. aegypti) larvae and adults, which are vectors for various diseases. This research involved the synthesis of these nanoparticles using the coprecipitate method. The results indicate that CoFe2O4 nanoparticles are the most effective in both larvicidal and adulticidal activities, with complete mortality achieved after 96 h of exposure. SnFe2O4 nanoparticles also showed some larvicidal and adulticidal efficacy, although to a lesser extent than the CoFe2O4 nanoparticles. Fe3O4 nanoparticles exhibited minimal larvicidal and adulticidal effects at low concentrations but showed increased efficacy at higher concentrations. The study also revealed the superparamagnetic nature of these nanoparticles, making them potentially suitable for applications in aquatic environments, where A. aegypti larvae often thrive. Additionally, the nanoparticles induced observable damage to the gut structure of the mosquitoes and larvae, which could contribute to their mortality. Overall, this research suggests that CoFe2O4 nanoparticles, in particular, hold promise as environment-friendly and effective agents for controlling A. aegypti mosquitoes, which are responsible for the transmission of diseases such as dengue fever, Zika virus, and Chikungunya. Further studies and field trials are needed to validate their practical use in mosquito control programs.

Comparing the Performance of Three Models Incorporating Weather Data to Forecast Dengue Epidemics in Reunion Island, 2018-2019

We developed mathematical models to analyze a large dengue virus (DENV) epidemic in Reunion Island in 2018-2019. Our models captured major drivers of uncertainty including the complex relationship between climate and DENV transmission, temperature trends, and underreporting. Early assessment correctly concluded that persistence of DENV transmission during the austral winter 2018 was likely and that the second epidemic wave would be larger than the first one. From November 2018, the detection probability was estimated at 10%-20% and, for this range of values, our projections were found to be remarkably accurate. Overall, we estimated that 8% and 18% of the population were infected during the first and second wave, respectively. Out of the 3 models considered, the best-fitting one was calibrated to laboratory entomological data, and accounted for temperature but not precipitation. This study showcases the contribution of modeling to strengthen risk assessments and planning of national and local authorities.

Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review

BACKGROUND

Creating a spatial model of dengue fever risk is challenging duet to many interrelated factors that could affect dengue. Therefore, it is crucial to understand how these critical factors interact and to create reliable predictive models that can be used to mitigate and control the spread of dengue.

METHODS

This scoping review aims to provide a comprehensive overview of the important predictors, and spatial modelling tools capable of producing Dengue Haemorrhagic Fever (DHF) risk maps. We conducted a methodical exploration utilizing diverse sources, i.e., PubMed, Scopus, Science Direct, and Google Scholar. The following data were extracted from articles published between January 2011 to August 2022: country, region, administrative level, type of scale, spatial model, dengue data use, and categories of predictors. Applying the eligibility criteria, 45 out of 1,349 articles were selected.

RESULTS

A variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and machine learning technique. We found that there was no pattern of predictor use associated with particular approaches. Instead, a wide range of predictors was used to create the DHF risk maps. These predictors may include climatology factors (e.g., temperature, rainfall, humidity), epidemiological factors (population, demographics, socio-economic, previous DHF cases), environmental factors (land-use, elevation), and relevant factors.

CONCLUSIONS

DHF risk spatial models are useful tools for detecting high-risk locations and driving proactive public health initiatives. Relying on geographical and environmental elements, these models ignored the impact of human behaviour and social dynamics. To improve the prediction accuracy, there is a need for a more comprehensive approach to understand DHF transmission dynamics.

Effects of the Weather on the Seasonal Population Trend of Aedes albopictus (Diptera: Culicidae) in Northern Italy

BACKGROUND

Aedes albopictus, the Asian tiger mosquito, has become a prevalent pest in Italy, causing severe nuisance and posing a threat of transmission of arboviruses introduced by infected travelers. In this study, we investigated the influence of weather parameters on the seasonal population density of Aedes albopictus.

METHODS

A Bayesian approach was employed to identify the best meteorological predictors of species trend, using the eggs collected monthly from 2010 to 2022 by the Emilia-Romagna regional monitoring network.

RESULTS

The findings show that the winter-spring period (January to May) plays a crucial role in the size of the first generation and seasonal development of the species.

CONCLUSIONS

A temperate winter and a dry and cold March, followed by a rainy and hot spring and a rainy July, seem to favor the seasonal development of Ae. albopictus.