Identifying the population susceptible to rubella in Japan, 2020: Fine-scale risk mapping.

BACKGROUND: Rubella remains a public health challenge in Japan, impeding the attainment of herd immunity. Despite vaccination efforts since 1976, persistent outbreaks reveal a susceptibility gap in male adults born before 1995. Seroepidemiological surveys are pivotal in evaluating population immunity and identifying at-risk groups. METHODS: This study aims to pinpoint high-risk areas for potential rubella outbreaks in Japan by merging seroepidemiological data from 2020 with population census information. Various data sources, including spatial demographic data, reported rubella and congenital rubella syndrome (CRS) cases, and traveler lodging statistics, were employed. Geospatial information for Japan's 230,300 small geographic areas was analyzed, and HI (hemagglutination inhibition) titers were examined by age and sex. Seroconversion was defined as an HI titer ≥ 1:32 or 1:16, indicating protective immunity. Geospatial maps illustrated the distribution of susceptible individuals per square kilometer, emphasizing high-risk urban areas like Tokyo and Osaka. Demographic shifts in the working-age population were assessed. RESULTS: Susceptible individuals cluster in densely populated urban centers, persisting despite demographic changes. The study highlights areas at risk of increased susceptibility, particularly with an HI titer cut-off of 1:16. Foreign travelers pose potential rubella importation risks as travel volume to Japan rises. To prevent epidemics and congenital rubella syndrome burden, achieving and sustaining herd immunity in high-risk areas is crucial. CONCLUSIONS: This study offers a comprehensive assessment of vulnerability in densely populated Japanese regions. Integrating population statistics with seroepidemiological data enhances our understanding of population immunity, guiding resource allocation for supplementary vaccination planning. To avert rubella epidemics, high-risk locations must bolster indirect protection through herd immunity, ultimately preventing congenital rubella syndrome.

Mapping the risk of exposure to Crimean-Congo haemorrhagic fever virus in the Iberian Peninsula using Eurasian wild boar (Sus scrofa) as a model.

Crimean-Congo haemorrhagic fever (CCHF) virus (CCHFV) is a tick-borne zoonotic pathogen that can cause a lethal haemorrhagic disease in humans. Although the virus appears to be endemically established in the Iberian Peninsula, CCHF is an emerging disease in Spain. Clinical signs of CCHFV infection are mainly manifested in humans, but the virus replicates in several animal species. Understanding the determinants of CCHFV exposure risk from animal models is essential to predicting high-risk exposure hotspots for public health action. With this objective in mind, we designed a cross-sectional study of Eurasian wild boar (Sus scrofa) in Spain and Portugal. The study analysed 5,291 sera collected between 2006 and 2022 from 90 wild boar populations with a specific double-antigen ELISA to estimate CCHFV serum prevalence and identify the main determinants of exposure probability. To do so, we statistically modelled exposure risk with host- and environment-related predictors and spatially projected it at a 10 × 10 km square resolution at the scale of the Iberian Peninsula to map foci of infection risk. Fifty-seven (63.3 %) of the 90 populations had at least one seropositive animal, with seroprevalence ranging from 0.0 to 88.2 %. Anti-CCHFV antibodies were found in 1,026 of 5,291 wild boar (19.4 %; 95 % confidence interval: 18.3-20.5 %), with highest exposure rates in southwestern Iberia. The most relevant predictors of virus exposure risk were wild boar abundance, local rainfall regime, shrub cover, winter air temperature and soil temperature variation. The spatial projection of the best-fit model identified high-risk foci as occurring in most of western and southwestern Iberia and identified recently confirmed risk foci in eastern Spain. The results of the study demonstrate that serological surveys of CCHFV vector hosts are a powerful, robust and highly informative tool for public health authorities to take action to prevent human cases of CCHF in enzootic and emergency settings.

Risky business: human-related data is lacking from Lyme disease risk models.

Used as a communicative tool for risk management, risk maps provide a service to the public, conveying information that can raise risk awareness and encourage mitigation. Several studies have utilized risk maps to determine risks associated with the distribution of Borrelia burgdorferi, the causal agent of Lyme disease in North America and Europe, as this zoonotic disease can lead to severe symptoms. This literature review focused on the use of risk maps to model distributions of B. burgdorferi and its vector, the blacklegged tick (Ixodes scapularis), in North America to compare variables used to predict these spatial models. Data were compiled from the existing literature to determine which ecological, environmental, and anthropic (i.e., human focused) variables past research has considered influential to the risk level for Lyme disease. The frequency of these variables was examined and analyzed via a non-metric multidimensional scaling analysis to compare different map elements that may categorize the risk models performed. Environmental variables were found to be the most frequently used in risk spatial models, particularly temperature. It was found that there was a significantly dissimilar distribution of variables used within map elements across studies: Map Type, Map Distributions, and Map Scale. Within these map elements, few anthropic variables were considered, particularly in studies that modeled future risk, despite the objective of these models directly or indirectly focusing on public health intervention. Without including human-related factors considering these variables within risk map models, it is difficult to determine how reliable these risk maps truly are. Future researchers may be persuaded to improve disease risk models by taking this into consideration.

Distribution and antibiotic resistance of vibrio population in an urbanized tropical lake-the Vembanad-in the southwest coast of India.

Among the diverse Vibrio spp. autochthonous to coastal ecosystems, V. cholerae, V. fluvialis, V. vulnificus and V. parahaemolyticus are pathogenic to humans. Increasing sea-surface temperature, sea-level rise and water-related disasters associated with climate change have been shown to influence the proliferation of these bacteria and change their geographic distribution. We investigated the spatio-temporal distribution of Vibrio spp. in a tropical lake for 1 year at a 20-day interval. The abundance of Vibrio spp. was much higher during the south-west monsoon in 2018, when the lake experienced a once-in-a-century flood. The distribution of Vibrio spp. was influenced by salinity (r = 0.3, p < 0.001), phosphate (r = 0.18, p < 0.01) and nitrite (r = 0.16, p < 0.02) in the water. We isolated 470 colonies of Vibrio-like organisms and 341 could be revived further and identified using 16S rRNA gene sequencing. Functional annotations showed that all the 16 Vibrio spp. found in the lake could grow in association with animals. More than 60% of the isolates had multiple antibiotic resistance (MAR) index greater than 0.5. All isolates were resistant to erythromycin and cefepime. The proliferation of multiple antibiotic-resistant Vibrio spp. is a threat to human health. Our observations suggest that the presence of a diverse range of Vibrio spp. is favoured by the low-saline conditions brought about by heavy precipitation. Furthermore, infections caused by contact with Vibrio-contaminated waters may be difficult to cure due to their multiple antibiotic resistances. Therefore, continuous monitoring of bacterial pollution in the lakes is essential, as is the generation of risk maps of vibrio-infested waters to avoid public contact with contaminated waters and associated disease outbreaks.

West Nile virus in the Iberian Peninsula: using equine cases to identify high-risk areas for humans.

BackgroundWest Nile virus (WNV) is a flavivirus with an enzootic cycle between birds and mosquitoes; humans and horses are incidental dead-end hosts. In 2020, the largest outbreak of West Nile virus infection in the Iberian Peninsula occurred, with 141 clusters in horses and 77 human cases.AimWe analysed which drivers influence spillover from the cycle to humans and equines and identified areas at risk for WNV transmission.MethodsBased on data on WNV cases in horses and humans in 2020 in Portugal and Spain, we developed logistic regression models using environmental and anthropic variables to highlight risk areas. Models were adapted to a high-resolution risk map.ResultsCases of WNV in horses could be used as indicators of viral activity and thus predict cases in humans. The risk map of horses was able to define high-risk areas for previous cases in humans and equines in Portugal and Spain, as well as predict human and horse cases in the transmission seasons of 2021 and 2022. We found that the spatial patterns of the favourable areas for outbreaks correspond to the main hydrographic basins of the Iberian Peninsula, jointly affecting Portugal and Spain.ConclusionA risk map highlighting the risk areas for potential future cases could be cost-effective as a means of promoting preventive measures to decrease incidence of WNV infection in Europe, based on a One Health surveillance approach.

Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk.

Flood risk assessment is a key step in flood management and mitigation, and flood risk maps provide a quantitative measure of flood risk. Therefore, integration of deep learning - an updated version of machine learning techniques - and multi-criteria decision making (MCDM) models can generate high-resolution flood risk maps. In this study, a novel integrated approach has been developed based on multiplicative long short-term memory (mLSTM) deep learning models and an MCDM ensemble model to map flood risk in the Minab-Shamil plain, southern Iran. A flood hazard map generated by the mLSTM model is based on nine critical features selected by GrootCV (distance to the river, vegetation cover, variables extracted from DEM (digital elevation model) and river density) and a flood inventory map (70% and 30% data were randomly selected as training and test datasets, respectively). The values of all criteria used to assess model accuracy performance (except Cohens kappa for train dataset = 86, and for test dataset = 84) achieved values greater than 90, which indicates that the mLSTM model performed very well for the generation of a spatial flood hazard map. According to the spatial flood hazard map produced by mLSTM, the very low, low, moderate, high and very high classes cover 26%, 35.3%, 20.5%, 11.2% and 7% of the total area, respectively. Flood vulnerability maps were produced by the combinative distance-based assessment (CODAS), the evaluation based on distance from average solution (EDAS), and the multi-objective optimization on the basis of simple ratio analysis (MOOSRA), and then validated by Spearman's rank correlation coefficients (SRC). Based on the SRC, the three models CODAS, EDAS, and MOOSRA showed high-ranking correlations with each other, and all three models were then used in the ensemble process. According to the CODAS-EDAS-MOOSRA ensemble model, 21.5%, 34.2%, 23.7%, 13%, and 7.6% of the total area were classified as having a very low to very high flood vulnerability, respectively. Finally, a flood risk map was generated by the combination of flood hazard and vulnerability maps produced by the mLSTM and MCDM ensemble model. According to the flood risk map, 27.4%, 34.3%, 14.8%, 15.7%, and 7.8% of the total area were classified as having a very low, low, moderate, high, and very high flood risk, respectively. Overall, the integration of mLSTM and the MCDM ensemble is a promising tool for generating precise flood risk maps and provides a useful reference for flood risk management.

Quantifying the influence of climate, host and change in land-use patterns on occurrence of Crimean Congo Hemorrhagic Fever (CCHF) and development of spatial risk map for India.

Crimean Congo Hemorrhagic Fever (CCHF), is an emerging zoonosis globally and in India. The present study focused on identifying the risk factors for occurrence of CCHF in the Indian state of Gujarat and development of risk map for India. The past CCHF outbreaks in India were collated for the analyses. Influence of land use change and climatic factors in determining the occurrence of CCHF in Gujarat was assessed using Bayesian spatial models. Change in maximum temperature in affected districts was analysed to identify the significant change points over 110 years. Risk map was developed for Gujarat using Bayesian Additive Regression Trees (BART) model with remotely sensed environmental variables and host (livestock and human) factors. We found the change in land use patterns and maximum temperature in affected districts to be contributing to the occurrence of CCHF in Gujarat. Spatial risk map developed using CCHF occurrence data for Gujarat identified density of buffalo, minimum land surface temperature and elevation as risk determinants. Further, spatial risk map for the occurrence of CCHF in India was developed using selected variables. Overall, we found that combination of factors such as change in land-use patterns, maximum temperature, buffalo density, day time minimum land surface temperature and elevation led to the emergence and further spread of the disease in India. Mitigation measures for CCHF in India could be designed considering disease epidemiology and initiation of surveillance strategies based on the risk map developed in this study.

A predictive risk map for human leptospirosis guiding further investigations in brown rats and surface water.

Leptospirosis is a zoonosis caused by the spirochete Leptospira spp. It is often not clear why certain areas appear to be hotspots for human leptospirosis. Therefore, a predictive risk map for the Netherlands was developed and assessed, based on a random forest model for human leptospirosis incidence levels with various environmental factors and rat density as variables. Next, it was tested whether misclassifications of the risk map could be explained by the prevalence of Leptospira spp. in brown rats. Three recreational areas were chosen, and rats (≥25/location) were tested for Leptospira spp. Concurrently, it was investigated whether Leptospira spp. prevalence in brown rats was associated with Leptospira DNA concentration in surface water, to explore the usability of this parameter in future studies. Approximately 1 L of surface water sample was collected from 10 sites and was tested for Leptospira spp. Although the model predicted the locations of patients relatively well, this study showed that the prevalence of Leptospira spp. infection in rats may be an explaining variable that could improve the predictive model performance. Surface water samples were all negative, even if they had been taken at sites with a high Leptospira spp. prevalence in rats.

Temporal modelling of Lymnaea natalensis (Krauss, 1848) in tropical aquatic habitats.

Lymnaea natalensis is the only snail intermediate host of Fasciola gigantica, the causative agent of fascioliasis, in Nigeria. The species also serves as intermediate host for many other African trematode species of medical and veterinary importance, and it is found throughout the country. However, there is no detailed information on the factors that influence its distribution and seasonal abundance in the tropical aquatic habitats in Nigeria. This study used the geographic information system and remotely sensed data to develop models for predicting the distribution of L. natalensis in South-Western Nigeria. Both land surface temperature (LST) and normalised difference vegetation index (NDVI) were extracted from Landsat satellite imagery; other variables (slope and elevation) were extracted from a digital elevation model (DEM) while rainfall data were retrieved from the European Meteorology Research Programme (EMRP). These environmental variables were integrated into a geographic information system (GIS) to predict suitable habitats of L. natalensis using exploratory regression. A total of 1410 L. natalensis snails were collected vis-à-vis 22 sampling sites. Built-up areas recorded more L. natalensis compared with farmlands. There was no significant difference in the abundance of snails with season (p  0.05). The regression models showed that rainfall, NDVI, and slope were predictors of L. natalensis distribution. The habitats suitable for L. natalensis were central areas, while areas to the north and south were not suitable for L. natalensis.Contribution: The predictive risk models of L. natalensis in the study will be useful in mapping other areas where the snail sampling could not be conducted.

Human anthrax in India in recent times: A systematic review & risk mapping.

The disease anthrax occurs generally in herbivores and the causative organism (Bacillus anthracis) infects humans who come in contact with infected animals or their products. The persistence of anthrax spores for decades and its lethality contribute to its biowarfare potential. We conducted this systematic review along with risk mapping to investigate the spatio-temporal distribution, clinico-epidemiological, socio-behavioural and programmatic issues pertaining to anthrax in India over the last two decades. Peer reviewed quantitative and qualitative studies and grey literature comprising weekly reports of the 'Integrated Disease Surveillance Program' (IDSP), were accessed for extracting data. IDSP data were used for geo-referencing of the villages of anthrax cases; Pseudo-absence was generated to fit a Bayesian Additive Regression Trees (BART) model to develop anthrax risk map. The case fatality rate of cutaneous anthrax ranged from 2% to 38%, while the gastrointestinal and inhalational types were 100% fatal. Our synthesis revealed that human anthrax outbreaks in India were clustered around the eastern coastal regions. The states of Odisha, West Bengal, Andhra Pradesh and Jharkhand reported maximum number of outbreaks. Odisha reported a maximum number of 439 human anthrax cases since 2009, of which Koraput district contributed to 200 cases (46%). While handling or consumption of infected animal product were proximal drivers of these events, poverty, lack of awareness, traditional beliefs and local practices served as facilitatory factors. Other structural determinants were wild life-livestock interface, historical forest loss, soil pH, soil-water balance, organic carbon content, temperature, rainfall and humidity. The programmatic issues identified through this review were lack of active surveillance, non-availability of diagnostic facility at the periphery, delayed reporting, absence of routine livestock vaccination and lack of adequate veterinary services. Interventions based on One-health approach in the country merit immediate policy and program attention; high risk zones for anthrax identified during present investigation, should be prioritized.

Application of airborne geophysical survey data in a logistic regression model to improve the predictive power of geogenic radon maps. A case study in Castleisland, County Kerry, Ireland.

In this study, a novel methodology was investigated to improve the spatial resolution and predictive power of geogenic radon maps. The data inputs comprise indoor radon measurements and seven geogenic factors including geological data (i.e. bedrock and Quaternary geology, aquifer type and soil permeability) and airborne geophysical parameters (i.e. magnetic field strength, gamma-ray radiation and electromagnetic resistivity). The methodology was tested in Castleisland southwest Ireland, a radon-prone area identified based on the results of previous indoor radon surveys. The developed model was capable of justifying almost 75 % of the variation in geogenic radon potential. It was found that the attributes with the greatest statistical significance were equivalent uranium content (EqU) and soil permeability. A new radon potential map was produced at a higher spatial resolution compared with the original map, which did not include geophysical parameter data. In the final step, the activity of radon in soil gas was measured at 87 sites, and the correlation between the observed soil gas radon and geophysical properties was evaluated. The results indicate that any model using only geophysical data cannot accurately predict soil radon activity and that geological information should be integrated to achieve a successful prediction model. Furthermore, we found that EqU is a better indicator for predicting indoor radon potential than the measured soil radon concentrations.

Modeling potential risk areas of Orthohantavirus transmission in Northwestern Argentina using an ecological niche approach.

BACKGROUND: Hantavirus Pulmonary Syndrome (HPS) is a rodent-borne zoonosis in the Americas, with up to 50% mortality rates. In Argentina, the Northwestern endemic area presents half of the annually notified HPS cases in the country, transmitted by at least three rodent species recognized as reservoirs of Orthohantavirus. The potential distribution of reservoir species based on ecological niche models (ENM) can be a useful tool to establish risk areas for zoonotic diseases. Our main aim was to generate an Orthohantavirus risk transmission map based on ENM of the reservoir species in northwest Argentina (NWA), to compare this map with the distribution of HPS cases; and to explore the possible effect of climatic and environmental variables on the spatial variation of the infection risk. METHODS: Using the reservoir geographic occurrence data, climatic/environmental variables, and the maximum entropy method, we created models of potential geographic distribution for each reservoir in NWA. We explored the overlap of the HPS cases with the reservoir-based risk map and a deforestation map. Then, we calculated the human population at risk using a census radius layer and a comparison of the environmental variables' latitudinal variation with the distribution of HPS risk. RESULTS: We obtained a single best model for each reservoir. The temperature, rainfall, and vegetation cover contributed the most to the models. In total, 945 HPS cases were recorded, of which 97,85% were in the highest risk areas. We estimated that 18% of the NWA population was at risk and 78% of the cases occurred less than 10 km from deforestation. The highest niche overlap was between Calomys fecundus and Oligoryzomys chacoensis. CONCLUSIONS: This study identifies potential risk areas for HPS transmission based on climatic and environmental factors that determine the distribution of the reservoirs and Orthohantavirus transmission in NWA. This can be used by public health authorities as a tool to generate preventive and control measures for HPS in NWA.

Parametrization identification and characterization of the radon priority areas for indoor radon risk management.

The aim of work is to contribute to the development of methodologies concerning the selection and characterisation of radon priority areas. The selection of areas was based on risk from indoor radon exposure, expressed in terms of number of expected deaths per year. Radon data come from a survey carried out in the Lazio Region (Italy) and consist of 5297 indoor concentration measurements. Population data were also used. Data showed that dwellings with concentrations above 300 Bq/m3, taken as reference level (RL), are not confined to specific areas, but rather spread out over the territory. An absolute risk model has been chosen to predict annual deaths on a regular grid of cells 2kmx2km sized. The analysis showed that 21.7% of the territory is completely uninhabited and that another 13.9% presents a marginal risk, quantifiable in total as less than one expected death per year. The remaining territory is of interest to identify the areas where dwellings with a concentration higher than the RL would be located. It was found that: such dwellings occur with different percentage in all the cells; exposed people varies from a few to almost 2000 per cell; indoor radon risk from exposure above RL is dominated by the number of exposed people and amounts to 106 deaths per year; the number of cells where a such risk is low is far greater than where the risk is high. These findings led to restrict RPA to the smallest set of cells that retained 85% of risk, i.e. 90 expected deaths per year. This percentage has been subjectively set because the technical and economic information required for its optimal calculation was not available. Based on this assumption, the RPA were identified by applying a threshold of 43 to the number of exposed people in each cell, in order to reach 85% of risk. The other main characteristics, also expressed as percentages of the corresponding totals within the area of interest, were found to be: extension 31.5% and exposed people 84%.

Planning for wolf-livestock coexistence: landscape context predicts livestock depredation risk in agricultural landscapes.

Extensive pastoral livestock systems in Central Europe provide multiple ecosystem services and support biodiversity in agricultural landscapes but their viability is challenged by livestock depredation (LD) associated with the recovery of wolf populations. Variation in the spatial distribution of LD depends on a suite of factors, most of which are unavailable at the appropriate scales. To assess if LD patterns can be predicted sufficiently with land use data alone at the scale of one federal state in Germany, we employed a machine-learning-supported resource selection approach. The model used LD monitoring data, and publicly available land use data to describe the landscape configuration at LD and control sites (resolution 4 km * 4 km). We used SHapley Additive exPlanations to assess the importance and effects of landscape configuration and cross-validation to evaluate the model performance. Our model predicted the spatial distribution of LD events with a mean accuracy of 74%. The most influential land use features included grassland, farmland and forest. The risk of livestock depredation was high if these three landscape features co-occurred with a specific proportion. A high share of grassland, combined with a moderate proportion of forest and farmland, increased LD risk. We then used the model to predict the LD risk in five regions; the resulting risk maps showed high congruence with observed LD events. While of correlative nature and lacking specific information on wolf and livestock distribution and husbandry practices, our pragmatic modelling approach can guide spatial prioritisation of damage prevention or mitigation practices to improve livestock-wolf coexistence in agricultural landscapes.

The spatial-temporal risk profiling of Clonorchis sinensis infection over 50 years implies the effectiveness of control programs in South Korea: a geostatistical modeling study.

Background: Over the past 50 years, two national control programs on Clonorchis sinensis infection have been conducted in South Korea. Spatial-temporal profiles of infection risk provide useful information on assessing the effectiveness of the programs and planning spatial-targeted control strategies. Methods: Advanced Bayesian geostatistical joint models with spatial-temporal random effects were developed to analyze disease data collecting by a systematic review with potential influencing factors, and to handle issues of preferential sampling and data heterogeneities. Changes of the infection risk were analyzed. Findings: We presented the first spatial-temporal risk maps of C. sinensis infection at 5 × 5 km2 resolution from 1970 to 2020 in South Korea. Moderate-to-high risk areas were shrunk, but temporal variances were shown in different areas. The population-adjusted estimated prevalence across the country was 5.99% (95% BCI: 5.09-7.01%) in 1970, when the first national deworming campaign began. It declined to 3.95% (95% BCI: 2.88-3.95%) in 1995, when the campaign suspended, and increased to 4.73% (95% BCI: 4.00-5.42%) in 2004, just before the Clonorchiasis Eradication Program (CEP). The population-adjusted prevalence was estimated at 2.77% (95% BCI: 1.67-4.34%) in 2020, 15 years after CEP started, corresponding to 1.42 (95% BCI: 0.85-2.23) million infected people. Interpretation: The first nationwide campaign and the CEP showed effectiveness on control of C. sinensis infection. Moderate-to-high risk areas identified by risk maps should be prioritized for control and intervention. Funding: The National Natural Science Foundation of China (project no. 82073665) and the Natural Science Foundation of Guangdong Province (project no. 2022A1515010042).

Integrating Citizen Scientist Data into the Surveillance System for Avian Influenza Virus, Taiwan.

The continuing circulation and reassortment with low-pathogenicity avian influenza Gs/Gd (goose/Guangdong/1996)-like avian influenza viruses (AIVs) has caused huge economic losses and raised public health concerns over the zoonotic potential. Virologic surveillance of wild birds has been suggested as part of a global AIV surveillance system. However, underreporting and biased selection of sampling sites has rendered gaining information about the transmission and evolution of highly pathogenic AIV problematic. We explored the use of the Citizen Scientist eBird database to elucidate the dynamic distribution of wild birds in Taiwan and their potential for AIV exchange with domestic poultry. Through the 2-stage analytical framework, we associated nonignorable risk with 10 species of wild birds with >100 significant positive results. We generated a risk map, which served as the guide for highly pathogenic AIV surveillance. Our methodologic blueprint has the potential to be incorporated into the global AIV surveillance system of wild birds.

Detection of Schistosoma mekongi DNA in Human Stool and Intermediate Host Snail Neotricula aperta via Loop-Mediated Isothermal Amplification Assay in Lao PDR.

Schistosomiasis mekongi infection represents a public health concern in Laos and Cambodia. While both countries have made significant progress in disease control over the past few decades, eradication has not yet been achieved. Recently, several studies reported the application of loop-mediated isothermal amplification (LAMP) for detecting Schistosoma DNA in low-transmission settings. The objective of this study was to develop a LAMP assay for Schistosoma mekongi using a simple DNA extraction method. In particular, we evaluated the utility of the LAMP assay for detecting S. mekongi DNA in human stool and snail samples in endemic areas in Laos. We then used the LAMP assay results to develop a risk map for monitoring schistosomiasis mekongi and preventing epidemics. A total of 272 stool samples were collected from villagers on Khon Island in the southern part of Laos in 2016. DNA for LAMP assays was extracted via the hot-alkaline method. Following the Kato-Katz method, we determined that 0.4% (1/272) of the stool samples were positive for S. mekongi eggs, as opposed to 2.9% (8/272) for S. mekongi DNA based on the LAMP assays. Snail samples (n = 11,762) were annually collected along the riverside of Khon Island from 2016 to 2018. DNA was extracted from pooled snails as per the hot-alkaline method. The LAMP assay indicated that the prevalence of S. mekongi in snails was 0.26% in 2016, 0.08% in 2017, and less than 0.03% in 2018. Based on the LAMP assay results, a risk map for schistosomiasis with kernel density estimation was created, and the distribution of positive individuals and snails was consistent. In a subsequent survey of residents, schistosomiasis prevalence among villagers with latrines at home was lower than that among villagers without latrines. This is the first study to develop and evaluate a LAMP assay for S. mekongi detection in stools and snails. Our findings indicate that the LAMP assay is an effective method for monitoring pathogen prevalence and creating risk maps for schistosomiasis.

Is Asian tiger mosquito (Aedes albopictus) going to become homodynamic in Southern Europe in the next decades due to climate change?

The Asian tiger mosquito, Aedes albopictus, competent vector of several arboviruses, poses significant impact on human health worldwide. Although global warming is a driver of A . albopictus range expansion, few studies focused on its effects on homodynamicity (i.e. the ability to breed all-year-round), a key factor of vectorial capacity and a primary condition for an Aedes-borne disease to become endemic in temperate areas. Data from a 4-year monitoring network set in Central Italy and records from weather stations were used to assess winter adult activity and weekly minimum temperatures. Winter oviposition occurred in 38 localities with a seasonal mean photoperiod of 9.7 : 14.3 (L : D) h. Positive collections (87) occurred with an average minimum temperature of the two and three weeks before sampling of approximately 4°C. According to these evidences and considering the climate projections of three global climate models and three shared socio-economic pathways for the next three 20-year periods (from 2021 to 2080), the minimum temperature of January will increase enough to allow an all-year-round oviposition of A . albopictus in several areas of the Mediterranean Basin. Due to vector homodynamicity, Aedes-borne diseases could become endemic in Southern Europe by the end of the twenty-first century, worsening the burden on human health.

Predicting the potential distribution of the cattle fever tick Rhipicephalus annulatus (Acari: Ixodidae) using ecological niche modeling.

Rhipicephalus annulatus is a tick species of veterinary importance due to its potential to transmit babesiosis to cattle. This species has a Holarctic distribution with some Afrotropical records and is one-host species of veterinary importance. This study was carried out from September 2021 to February 2022 at 6 Egyptian collection sites, and a total of 1150 cattle were scanned randomly to collect ticks. A total of 1095 tick specimens were collected and identified as R. annulatus using taxonomic keys. Males were found on all parts of the cattle except the head and around the eyes, but females were found on all parts; in addition, the highest number of specimens was gathered from the udder, (neck and chest), and belly. Maximum entropy (MaxEnt) modeling was used to predict the potential global distribution of R. annulatus. The MaxEnt model performed better than random with an average test area under the curve (AUC) value of 0.96, and model predictions were significantly better than random and gave (AUC) ratios above the null expectations in the partial receiver operating characteristic (pROC) analyses (P < 0.001). Based on correlation analyses, a set of 9 variables was selected for species from 15 bioclimatic and 5 normalized difference vegetation index (NDVI) variables. The study showed that the current distribution of R. annulatus is estimated to occur across Asia, Africa, Europe, South America, and North America. Annual mean temperature (Bio1) and median NDVI had the highest effect on the distribution of this species. The environmentally suitable habitat for R. annulatus sharply increased with increasing annual mean temperature (Bio1). These results can be used for making effective control planning decisions in areas suitable to this vector of many diseases worldwide.

Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy process.

In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria. We selected Quang Binh province of Vietnam as a case study and collected available data, including 696 flooding locations of historical flooding events in 2007, 2010, 2016, and 2020; and flood influencing factors of elevation, slope, curvature, flow direction, flow accumulation, distance from river, river density, land cover, geology, and rainfall. These data were used to construct training and testing datasets. The susceptibility models were validated and compared using statistical techniques. An integrated flood risk assessment framework was proposed to incorporate flood hazard (flood susceptibility), flood exposure (distance from river, land use, population density, and rainfall), and flood vulnerability (poverty rate, number of freshwater stations, road density, number of schools, and healthcare facilities). Model validation suggested that deep learning has the best performance of AUC = 0.984 compared with other ensemble models of MultiBoostAB Ensemble (0.958), Random SubSpace Ensemble (0.962), and credal decision tree (AUC = 0.918). The final flood risk map shows 5075 ha (0.63%) in extremely high risk, 47,955 ha (5.95%) in high-risk, 40,460 ha (5.02%) in medium risk, 431,908 ha (53.55%) in low risk areas, and 281,127 ha (34.86%) in very low risk. The present study highlights that the integration of ML models and AHP is a promising framework for mapping flood risks in flood-prone areas.