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Leptospirosis is a neglected zoonosis which remains poorly known despite its epidemic potential, especially in tropical islands where outdoor lifestyle, vulnerability to invasive reservoir species and hot and rainy climate constitute higher risks for infections. Burden remains poorly documented while outbreaks can easily overflow health systems of these isolated and poorly populated areas. Identification of generic patterns driving leptospirosis dynamics across tropical islands would help understand its epidemiology for better preparedness of communities. In this study, we aim to model leptospirosis seasonality and outbreaks in tropical islands based on precipitation and temperature indicators. We adjusted machine learning models on leptospirosis surveillance data from seven tropical islands (Guadeloupe, Reunion Island, Fiji, Futuna, New Caledonia, and Tahiti) to investigate 1) the effect of climate on the disease's seasonal dynamic, i.e., the centered seasonal profile and 2) inter-annual anomalies, i.e., the incidence deviations from the seasonal profile. The model was then used to estimate seasonal dynamics of leptospirosis in Vanuatu and Puerto Rico where disease incidence data were not available. A robust model, validated across different islands with leave-island-out cross-validation and based on current and 2-month lagged precipitation and current and 1-month lagged temperature, can be constructed to estimate the seasonal dynamic of leptospirosis. In opposition, climate determinants and their importance in estimating inter-annual anomalies highly differed across islands. Climate appears as a strong determinant of leptospirosis seasonality in tropical islands regardless of the diversity of the considered environments and the different lifestyles across the islands. However, predictive and expandable abilities from climate indicators weaken when estimating inter-annual outbreaks and emphasize the importance of these local characteristics in the occurrence of outbreaks.

Leptospirosis is the most common bacterial zoonoses and has been identified as an important emerging global public health problem in Southeast Asia. Rodents are important reservoirs for human leptospirosis, but epidemiological data is lacking. We sampled rodents living in different habitats from seven localities distributed across Southeast Asia (Thailand, Lao PDR and Cambodia), between 2009 to 2010. Human isolates were also obtained from localities close to where rodents were sampled. The prevalence of Leptospira infection was assessed by real-time PCR using DNA extracted from rodent kidneys, targeting the lipL32 gene. Sequencing rrs and secY genes, and Multi Locus Variable-number Tandem Repeat (VNTR) analyses were performed on DNA extracted from rat kidneys for Leptospira isolates molecular typing. Four species were detected in rodents, L. borgpetersenii (56% of positive samples), L. interrogans (36%), L. kirschneri (3%) and L. weilli (2%), which were identical to human isolates. Mean prevalence in rodents was approximately 7%, and largely varied across localities and habitats, but not between rodent species. The two most abundant Leptospira species displayed different habitat requirements: L. interrogans was linked to humid habitats (rice fields and forests) while L. borgpetersenii was abundant in both humid and dry habitats (non-floodable lands). L. interrogans and L. borgpetersenii species are widely distributed amongst rodent populations, and strain typing confirmed rodents as reservoirs for human leptospirosis. Differences in habitat requirements for L. interrogans and L. borgpetersenii supported differential transmission modes. In Southeast Asia, human infection risk is not only restricted to activities taking place in wetlands and rice fields as is commonly accepted, but should also include tasks such as forestry work, as well as the hunting and preparation of rodents for consumption, which deserve more attention in future epidemiological studies.

Background Rodents are recognized as hosts for at least 60 zoonotic diseases and may represent a serious threat for human health. In the context of global environmental changes and increasing mobility of humans and animals, contacts between pathogens and potential animal hosts and vectors are modified, amplifying the risk of disease emergence. An accurate identification of each rodent at a specific level is needed in order to understand their implications in the transmission of diseases. Among the Muridae, the Rattini tribe encompasses 167 species inhabiting South East Asia, a hotspot of both biodiversity and emerging and re-emerging diseases. The region faces growing economical development that affects habitats, biodiversity and health. Rat species have been demonstrated as significant hosts of pathogens but are still difficult to recognize at a specific level using morphological criteria. DNA-barcoding methods appear as accurate tools for rat species identification but their use is hampered by the need of reliable identification of reference specimens. In this study, we explore and highlight the limits of the current taxonomy of the Rattini tribe. Results We used the DNA sequence information itself as the primary information source to establish group membership and estimate putative species boundaries. We sequenced two mitochondrial and one nuclear genes from 122 rat samples to perform phylogenetic reconstructions. The method of Pons and colleagues (2006) that determines, with no prior expectations, the locations of ancestral nodes defining putative species was then applied to our dataset. To give an appropriate name to each cluster recognized as a putative species, we reviewed information from the literature and obtained sequences from a museum holotype specimen following the ancient DNA criteria. Conclusions Using a recently developed methodology, this study succeeds in refining the taxonomy of one of the most difficult groups of mammals. Most of the species expected within the area were retrieved but new putative species limits were also indicated, in particular within Berylmys and Rattus genera, where future taxonomic studies should be directed. Our study lays the foundations to better investigate rodent-born diseases in South East Asia and illustrates the relevance of evolutionary studies for health and medical sciences.

Dengue fever, one of the most widespread vector-borne diseases globally, is mainly transmitted by Aedes aegypti and Ae. albopictus mosquitoes. In Cambodia, dengue has been a recurrent public health challenge, with major outbreaks occurring in 1995, 2007, 2012, and 2019. The latter epidemic severely impacted the capital, Phnom Penh, yet the spatial and temporal dynamics of the two key vector species had not been studied in this urban context. This study aimed to investigate how the distribution of Ae. aegypti and Ae. albopictus is organized in the urban and peri-urban landscapes of Phnom Penh. Ovitraps were deployed every two months over a year in 40 pagodas randomly selected across Phnom Penh, chosen to ensure future replicability of the study. The larvae collected were reared to adulthood for accurate species identification. High-resolution satellite imagery (SPOT7) and daily rainfall data were used to analyze the surrounding environments through remote sensing techniques. The results revealed distinct spatio-temporal patterns for each species: Ae. albopictus was associated with peri-urban areas rich in vegetation and water, while Ae. aegypti predominated in highly urbanized and construction-dense environments. Spatial analysis using buffer zones (250 m, 500 m, 1000 m) around trapping sites confirmed that the use of pagodas as proxies for urban sampling is effective. These findings highlight the importance of monitoring these vector species, particularly as Phnom Penh continues to undergo rapid environmental transformation. The identification of simple, remotely sensed environmental indicators offer a valuable tool for predicting future outbreaks and guiding targeted vector control strategies. This study also provides a replicable methodological framework to assess the impact of urbanization and climate change on dengue vector distribution in Phnom Penh and similar urban settings.

Mosquitoes are responsible for the transmission of major pathogens worldwide. Modelling their population dynamics and mapping their distribution can contribute effectively to disease surveillance and control systems. Two main approaches are classically used to understand and predict mosquito abundance in space and time, namely empirical (or statistical) and process-based models. In this work, we used both approaches to model the population dynamics in Reunion Island of the 'Tiger mosquito', Aedes albopictus, a vector of dengue and chikungunya viruses, using rainfall and temperature data. We aimed to i) evaluate and compare the two types of models, and ii) develop an operational tool that could be used by public health authorities and vector control services. Our results showed that Ae. albopictus dynamics in Reunion Island are driven by both rainfall and temperature with a non-linear relationship. The predictions of the two approaches were consistent with the observed abundances of Ae. albopictus aquatic stages. An operational tool with a user-friendly interface was developed, allowing the creation of maps of Ae. albopictus densities over the whole territory using meteorological data collected from a network of weather stations. It is now routinely used by the services in charge of vector control in Reunion Island.

Outdoor and early biting by mosquitoes challenge the efficacy of bed nets and indoor residual spraying against malaria in the Greater Mekong Subregion. The objective of this study was to assess the efficacy of outdoor residual spraying (ORS) for malaria vector-control in this region. A cluster randomized controlled trial was conducted between July 2018 and April 2019 in twelve villages in Karen (Kayin) state, Myanmar. Villages were randomly assigned to receive either a single round of ORS with a capsule suspension of lambda-cyhalothrin for two days in October or no intervention (six villages per group). The primary endpoint was the biting rate of malaria mosquitoes assessed with human-landing catch and cow-baited trap collection methods, and was analyzed with a Bayesian multi-level model. In the intervention villages, the proportion of households located within the sprayed area ranged between 42 and 100% and the application rate ranged between 63 and 559 g of active ingredient per hectare. At baseline, the median of Anopheles biting rate estimates in the twelve villages was 2 bites per person per night (inter-quartile range [IQR] 0–5, range 0–48) indoors, 6 bites per person per night (IQR 2–16, range 0–342) outdoors and 206 bites per cow per night (IQR 83–380, range 19–1149) in the cow-baited trap. In intention-to-treat analysis, it was estimated that ORS reduced biting rate by 72% (95% confidence interval [CI] 63–79) from Month 0 to Month 3 and by 79% (95% CI 62–88) from Month 4 to Month 6, considering control villages as the reference. In conclusion, ORS rapidly reduces the biting rates of malaria mosquitoes in a Southeast Asian setting where the vectors bite mostly outdoors and at a time when people are not protected by mosquito bed nets.

Understanding vegetation-climate interactions is essential amid escalating global climate change. This study investigates spatial-temporal and seasonal variations in Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) across six regions of Thailand (2007–2022). Results reveal distinct regional and seasonal characteristics, with significant negative correlations between LST and NDVI ( R  = 0.61 dry; 0.39 rainy; 0.72 winter). The strongest negative correlation occurred during the rainy season in 2017, highlighting complex interannual variations. Seasonal LST fluctuations (winter-summer: 1.24, winter-rainy: -1.54, summer-rainy: -2.78, p  < 0.001) and NDVI variations (winter-summer: 0.09, winter-rainy: 0.07, summer-rainy: -0.03, p  < 0.001) were statistically significant. These findings emphasize monitoring LST and NDVI as vital for understanding ecological impacts of climate change and urbanization. The study specifically explores whether increased vegetation consistently is associated with lower temperatures, underscoring the importance of strategies to mitigate heat and enhance climate resilience, particularly in rapidly urbanizing regions.

BACKGROUND: Aedes aegypti and Ae. albopictus mosquitoes are major vectors for several human diseases of global importance, such as dengue and yellow fever. Their life cycles and hosted arboviruses are climate sensitive and thus expected to be impacted by climate change. Most studies investigating climate change impacts on Aedes at global or continental scales focused on their future global distribution changes, whereas a single study focused on its effects on Ae. aegypti densities regionally. OBJECTIVES: A process-based approach was used to model densities of Ae. aegypti and Ae. albopictus and their potential evolution with climate change using a panel of nine CMIP6 climate models and climate scenarios ranging from strong to low mitigation measures at the Southeast Asian scale and for the next 80 y. METHODS: The process-based model described, through a system of ordinary differential equations, the variations of mosquito densities in 10 compartments, corresponding to 10 different stages of mosquito life cycle, in response to temperature and precipitation variations. Local field data were used to validate model outputs. RESULTS: We show that both species densities will globally increase due to future temperature increases. In Southeast Asia by the end of the century, Ae. aegypti densities are expected to increase from 25% with climate mitigation measures to 46% without; Ae. albopictus densities are expected to increase from 13%-21%, respectively. However, we find spatially contrasted responses at the seasonal scales with a significant decrease in Ae. albopictus densities in lowlands during summer in the future. DISCUSSION: These results contrast with previous results, which brings new insight on the future impacts of climate change on Aedes densities. Major sources of uncertainties, such as mosquito model parametrization and climate model uncertainties, were addressed to explore the limits of such modeling.

Background Progress in malaria elimination has been hindered by recent changes in mosquito behaviour and increased insecticide resistance in response to traditional vector control measures, such as indoor residual spraying and long-lasting insecticidal nets. There is, therefore, increasing interest in the use of larval source management (LSM) to supplement current insecticide-based interventions. However, LSM implementation requires the characterization of larval habitats at fine spatial and temporal scales to ensure interventions are well-placed and well-timed. Remotely sensed optical imagery captured via drones or satellites offers one way to monitor larval habitats remotely, but its use at large spatio-temporal scales has important limitations. Methods A method using radar imagery is proposed to monitor flooding dynamics in individual rice fields, a primary larval habitat, over very large geographic areas relevant to national malaria control programmes aiming to implement LSM at scale. This is demonstrated for a 3971 km 2 malaria-endemic district in Madagascar with over 17,000 rice fields. Rice field mapping on OpenStreetMap was combined with Sentinel-1 satellite imagery (radar, 10 m) from 2016 to 2022 to train a classification model of radar backscatter to identify rice fields with vegetated and open water, resulting in a time-series of weekly flooding dynamics for thousands of rice fields. Results From these time-series, over a dozen indicators useful for LSM implementation, such as the timing and frequency of flooding seasons, were obtained for each rice field. These monitoring tools were integrated into an interactive GIS dashboard for operational use by vector control programmes, with results available at multiple scales (district, sub-district, rice field) relevant for different phases of LSM intervention (e.g. prioritization of sites, implementation, follow-up). Conclusions Scale-up of these methods could enable wider implementation of evidence-based LSM interventions and reduce malaria burdens in contexts where irrigated agriculture is a major transmission driver.

Outdoor and early biting by mosquitoes challenge the efficacy of bed nets and indoor residual spraying against malaria in the Greater Mekong Subregion. The objective of this study was to assess the efficacy of outdoor residual spraying (ORS) for malaria vector-control in this region. A cluster randomized controlled trial was conducted between July 2018 and April 2019 in twelve villages in Karen (Kayin) state, Myanmar. Villages were randomly assigned to receive either a single round of ORS with a capsule suspension of lambda-cyhalothrin for two days in October or no intervention (six villages per group). The primary endpoint was the biting rate of malaria mosquitoes assessed with human-landing catch and cow-baited trap collection methods, and was analyzed with a Bayesian multi-level model. In the intervention villages, the proportion of households located within the sprayed area ranged between 42 and 100% and the application rate ranged between 63 and 559 g of active ingredient per hectare. At baseline, the median of Anopheles biting rate estimates in the twelve villages was 2 bites per person per night (inter-quartile range [IQR] 0-5, range 0-48) indoors, 6 bites per person per night (IQR 2-16, range 0-342) outdoors and 206 bites per cow per night (IQR 83-380, range 19-1149) in the cow-baited trap. In intention-to-treat analysis, it was estimated that ORS reduced biting rate by 72% (95% confidence interval [CI] 63-79) from Month 0 to Month 3 and by 79% (95% CI 62-88) from Month 4 to Month 6, considering control villages as the reference. In conclusion, ORS rapidly reduces the biting rates of malaria mosquitoes in a Southeast Asian setting where the vectors bite mostly outdoors and at a time when people are not protected by mosquito bed nets.