Ecological Niche Model based on Maximum Entropy for mapping distribution of Bithynia siamensis goniomphalos, first intermediate host snail of Opisthorchis viverrini in Thailand

Spatial prediction of the probability of liver fluke infection in water resource within sub-basin using an optimized geographically-weighted regression model

Introduction

Infection with liver flukes (Opisthorchis viverrini) is partly attributed to their ability to thrive in sub-basin habitats, causing the intermediate host to remain within the watershed system throughout the year. It is crucial to conduct spatial monitoring of fluke infection at a small basin analysis scale as it helps in studying the spatial factors influencing these infections. The number of infected individuals was obtained from local authorities, converted into a percentage, and visually represented as raster data through a heat map. This approach generates continuous data with dependent variables.

Methods

The independent set comprises nine variables, including both vector and raster data, that establish a connection between the location of an infected person and their village. Design spatial units optimized for geo-weighted modeling by utilizing a clustering and overlay approach, thereby facilitating the optimal prediction of alternative models for infection.

Results and discussion

The Model-3 demonstrated the strongest correlation between the variables X5 (stream) and X7 (ndmi), which are associated with the percentage of infected individuals. The statistical analysis showed t-statistics values of -2.045 and 0.784, with corresponding p-values of 0.016 and 0.085. The RMSE was determined to be 2.571%, and the AUC was 0.659, providing support for these findings. Several alternative models were tested, and a generalized mathematical model was developed to incorporate the independent variables. This new model improved the accuracy of the GWR model by 5.75% and increased the R 2 value from 0.754 to 0.800. Additionally, spatial autocorrelation confirmed the difference in predictions between the modeled and actual infection values. This study demonstrates that when using GWR to create spatial models at the sub-basin level, it is possible to identify variables that are associated with liver fluke infection.

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.

Potential distribution of malaria vectors in Central Vietnam: A MaxEnt modeling approach

Background and Aim

In Central Vietnam, Anopheles dirus and Anopheles minimus are the primary malaria vector species. These Anopheles spp.' distribution and prevalence are determined by environmental, climatic, and socioeconomic conditions. This study aimed to predict the potential distribution of these two Anopheles spp. in this region.

Materials and Methods

This study was conducted in 15 Central Vietnamese provinces. From 2014 to 2018, we utilized An. dirus and An. minimus presence records. Proxy data from the Google Earth Engine platform for the study area, encompassing environmental, climatic, and socioeconomic factors. MaxEnt software predicted the potential environmental, climatic, and socioeconomic suitability of these two Anopheles spp. in Central Vietnam.

Results

The test area under the curve values for An. dirus and An. minimus MaxEnt models averaged 0.801 and 0.806, respectively, showing excellent performance. Minimum air temperature had the greatest impact on the distribution of both species. A negative correlation between precipitation and normalized difference water index influences the occurrence of An. dirus. In the temperature range of 13-19.5°C, An. minimus is most likely to be present, with nighttime light detrimentally influencing its distribution. The Central Highlands region is inhabited by both species, with some presence in North-Central and South-Central Coastal areas.

Conclusion

The importance of temperature in determining the presence of both species is emphasized by our findings, with subtle differences in the temperature-related factors shaping their distributions. The results highlight the need for focused malaria vector control and surveillance initiatives in the study area.

Temperature dependence of Opisthorchis felineus infection in the first intermediate host snail, Bithynia troschelii

Opisthorchiasis is one of the most serious trematodiases in Russia, where the world's largest focus is located in the Ob basin. Temperature is an important factor affecting the metabolism of cold-blooded animals. It determines the development of the causative agent of opisthorchiasis, Opisthorchis felineus, and the success of infection of an intermediate host, the snail Bithynia troschelii. In the present study, the effect of water temperature on the development of the liver fluke O. felineus in the host snail was assessed, as was the temperature threshold at which B. troschelii hibernation initiates. Adult uninfected B. troschelii individuals collected from natural bodies of water were infected with O. felineus and maintained at different temperatures of water (18-30 °C, intervals of 3 °C) in the laboratory. Each snail was fed with embryonated uterine eggs of O. felineus at 24 °C. O. felineus infection in snails was detected using polymerase chain reaction (PCR) using specific primers. The prevalence of O. felineus infection in B. troschelii depends on the water temperature in which the snails are maintained. The highest infection rate of 45.2 % ± 12.1 % was observed at 27 °C (p ≥ 0.1). The longest lifespan of infected and uninfected B. troschelii was recorded at water temperatures of 24 and 27 °C. The snails were more successfully infected at the beginning of the warm season. Among the infected individuals, the majority (up to 85 %) were large snails. Cercarial shedding was not detected in experimentally infected snails. Apparently, this is due to the natural physiological state of Bithynia snails during the autumn-winter diapause, when opisthorchiids development in snails stops. At 10 °C, complete hibernation of all B. troschelii snails was observed, and infection by the trematodes became impossible. The highest prevalence of infection was recorded at 27 °C, suggesting that during climate warming, an increase in opisthorchiid infection of snails may occur, which must be considered when epidemiological measures are planned.

Modeling potential distribution and above-ground biomass of Scots pine (Pinus sylvestris L.) forests in the Inner Anatolian Region, Türkiye

Scots pine (Pinus sylvestris L.) holds a substantial position as a tree species designated for biomass energy within European forests, covering a significant part of Türkiye's forests. We used the machine learning technique, namely, maximum entropy (MaxEnt), to estimate the suitable areas for Scots pine and to investigate its potential future distribution under various climate change scenarios in Inner Anatolian Region, Türkiye. The distribution data of Scots pine was utilized, and a set of 20 variables was chosen from spectral, topographic, and bioclimatic datasets to train the MaxEnt model. A map depicting the potential distribution of Scots pine in the area was generated, and alterations in its spatial distribution under SSP2-4.5 and SSP5-8.5 climate change scenarios were predicted. The results showed that the most effective factors for the distribution of Scots pine in the region were normalized difference vegetation index (NDVI), Red band of the imagery, and Bio19 variables, and the contribution percentages were 45.6%, 18.5%, and 18.1%, respectively. Current conditions have indicated that 81.11% of the region is not suitable for Scots pine. Highly suitable areas for Scots pine constituted 0.88% of the total area in the east and southeast parts of the region. Considering the SSP2-4.5 and SSP5-8.5 scenarios, it has been determined that there may be a partial increase in highly suitable areas. The above-ground biomass (AGB) data generated based on potential distribution areas were predicted between 0.04 and 168.76 t ha-1, and the areas with dense biomass over 120 t ha-1 were identified in the west, north, and northeast parts of the region. While actual AGB of Scots pine was 6.92 MT, its potential AGB was estimated 125.93 MT in total area. The difference may well be attributed to the wide potential distribution of Scots pine stands in the area apart from the current forest lands. Nevertheless, this research contributes to the holistic management of forests and provides substantial values for formulating well-suited silvicultural interventions, developing sustainable forest management strategies, and furthering research aimed at estimating biomass reserves.

Risk transboundary transmission areas and driving factors of brucellosis along the borders between China and Mongolia

OBJECTIVE

Brucellosis is a common and neglected zoonotic infectious disease worldwide caused by Brucella. However, transboundary transmissions among countries, particularly those with high incidences, are seldom investigated. In the present study, by taking China and Mongolia as examples, we aim to identify transboundary transmission risk and driving factors of brucellosis along borders.

METHODS

167 brucellosis outbreak locations along the border between China and Mongolia were collected. Wildlife distribution and cross-border activities were mapped. Maximum entropy approach modeling was conducted to predict the potential risk of prevalence of brucellosis with meteorological factors, geographical environment, economic development, living habits et al. The accuracy of the models was assessed by the area under the receiver operating characteristic (ROC) curve (AUC), Kappa test, and correctly classified instances (CCI).

RESULTS

The spatial model performed excellent predictive performance with the predictor variables of soils, pastures, goat density, mean precipitation of the wettest month, temperature seasonality, and population density, which with the contribution and permutation important in 27.2 %, 31.9; 23.3 %, 6.8; 18.0 %, 17.2; 11.2 %, 18.1; 10. 3 %, 15.2; 10.0 %, 10.8. The calculated AUC, SD, Kappa, and CCI are 0.870, 0.001, 0.882, and 0.883, respectively. The distribution map of brucellosis showed high-risk areas along the borders.

CONCLUSIONS

Our study identified high-risk areas and the driving effect of brucellosis along the borders between China and Mongolia. Moreover, there is the possibility of cross-border wildlife activities in high-risk areas, which increases the risk of cross-border brucellosis transmission. The funding provides clues for cooperative prevention and control of brucellosis by reducing transboundary transmission.

Predicting Suitable Areas for African Swine Fever Outbreaks in Wild Boars in South Korea and Their Implications for Managing High-Risk Pig Farms

African swine fever (ASF) is a highly contagious disease affecting domestic pigs and wild boars, with no effective vaccine or treatment available. In South Korea, extensive measures have been implemented to prevent ASF transmission between wild boars and ASF spillover from wild boars to pig farm sectors, including the search for ASF-infected carcasses in mountainous forests and the installation of fences across wide areas of these forests. To determine the priority search range for infected carcasses and establish pig farm-centered quarantine measures, it is necessary to predict the specific path of ASF outbreaks in wild boars and identify pig farms at high risk of ASF spillover from wild boars. Here, we aimed to predict suitable areas and geographical paths for ASF outbreaks in wild boars using the MaxEnt model and shortest-path betweenness centrality analysis. The analysis identified a high frequency of ASF outbreaks in areas with a suitability value ≥0.4 on the suitability map and in areas within a 1.8 km range from the path on the shortest-path map, indicating these areas were high-risk zones for ASF outbreaks. Among the 5063 pig farms analyzed, 37 were in the high-risk zone on the suitability map, 499 were in the high-risk zone on the shortest-path map, and 9 were in both risk zones. Of the 51 pig farm sectors with a dense distribution of pig farms (kernel density ≥ 8), 25 sectors were in contact with or partially overlapped the high risk zone on the suitability map, 18 sectors were located within the high risk zone on the shortest-path map, and 14 sectors were located within both risk zones. These findings aided in determining the priority range for searches for wild boar carcasses and enabled the establishment of preemptive ASF prevention measures around the pig farming sectors that are at risk of ASF spillover from wild boars.

Promoting Effects of the Exercise Behavioral Ecological Model on Physical Activity Behaviors of Students

Objectives: In this study, we explored the promoting effects of the Exercise Behavioral Ecological Model on the physical activity behaviors of middle school students, and relevant risk factors for physical inactivity. Methods: A total of 291 middle school students (junior and senior high school students) were enrolled as the research participants. The sedentary behavior of middle school students was assessed using the Adolescent Sedentary Behavior Scale. We used the Exercise Behavioral Ecological Model to influence the exercise behaviors of middle school students from the perspectives of environment, physiology, and psychology. Then the independent risk factors for physical inactivity behaviors of middle school students were analyzed by means of multivariate logistic regression analysis. Results: The change strategy, positive effect on decision-making balance, and self- efficacy scores were higher and the negative effect on decision-making balance score was lower than those before intervention (p<.05). Multivariate logistic regression analysis denoted that grade (senior high school students), sedentary time (> 4 hours), daily TV watching time (>2 hours) and change stage (pre-contemplation stage) were associated risk factors for physical inactivity among middle school students (p <.05). Conclusions: The Exercise Behavioral Ecological Model can facilitate physical activity and reduce sedentary behavior.

Predicting the habitat suitability of Schistosoma intermediate host Bulinus truncatus, its predatory aquatic insect Odonata nymph, and the associated aquatic plant Ceratophyllum demersum using MaxEnt

Schistosomiasis is one of the most important parasitic diseases in tropical and subtropical areas. Its prevalence is associated with the distribution of freshwater snails, which are their intermediate hosts. Thus, control of freshwater snails is the solution to reduce the transmission of this disease. This will be achieved by understanding the relationship between the snails and their habitats including natural enemies and associated aquatic plants as well as the factors affecting their distribution. In this study, Maximum Entropy model (MaxEnt) was used for mapping and predicting the possible geographic distribution of Bulinus truncatus snail (the intermediate host of Schistosoma haematobium), Odonata nymph (predatory aquatic insect), and Ceratophyllum demersum (the associated aquatic plant) in Egypt based on topographic and climatic factors. The models of the investigated species were evaluated using the area under receiver operating characteristic curve. The results showed that the potential risk areas were along the banks of the Nile River and its irrigation canals. In addition, the MaxEnt models revealed some similarities in the distribution pattern of the vector, the predator, and the aquatic plant. It is obvious that the predictive distribution range of B. truncatus was affected by altitude, precipitation seasonality, isothermality, and mean temperature of warmest quarter. The presence of B. truncatus decreases with the increase of altitude and precipitation seasonality values. It could be concluded that the MaxEnt model could help introducing a predictive risk map for Schistosoma haematobium prevalence and performing better management strategies for schistosomiasis.

Current status of human liver fluke infections in the Greater Mekong Subregion

The Greater Mekong Subregion (GMS) is a trans-national region of the Mekong River basin in Southeast Asia comprising Cambodia, the People's Republic of China (specifically Yunnan Province and Guangxi Zhuang Autonomous Region), Lao People's Democratic Republic (Lao PDR), Myanmar, Thailand, and Vietnam home to more than 340 million people or almost 4.5% of the global population. Human liver fluke infections caused by Opisthorchis viverrini and Clonorchis sinensis have been major public health problems in this region for decades. Opisthorchiasis caused by O. viverrini is prevalent in Thailand, Lao PDR, Cambodia and central-southern Vietnam with more than 12 million people infected. Clonorchiasis caused by C. sinensis is endemic in northern Vietnam and Guangxi with estimated 3.5 -5 million infected. The infections can cause several liver and biliary diseases such as cholangitis, periductal fibrosis, gallstones, and cholangiocarcinoma (CCA), a fatal bile duct cancer. Key determinants of the geographical distribution differences of the two liver fluke species are snail species and geographic barriers. Main risk behaviour of the infections is the culture of eating raw fish "the raw attitude" of people in the GMS, especially the Tai/Dai/Thai/Laos ethnic groups, the major population in the GMS. Over the past 20 years, there is a big change in prevalence of the infections. Opisthorchiasis has long been endemic, particularly in northern and northeastern Thailand and Lao PDR with over 8-10 million cases estimated. However, after several rounds of national campaign against opisthorchiasis using integrated control approach in Thailand over the past three decades, the prevalence of O. viverrini infection has reduced from over 15% in 1996 to 2.2% in 2019. High prevalence of O. viverrini infection continues in Lao PDR and central Vietnam. Emerging high prevalence, up to a maximum of 47.5%, has been noted in Cambodia during the past 10 years possibly due to more studies being conducted rather than increasing prevalence. O. viverrini infection has now also been reported in Lower Myanmar in recent years. Clonorchiasis has been known in northern Vietnam and southern China for a long time. Several surveys have reported infections in Guangxi in the last 10 years, and until now liver fluke infected cases have not been reported in Yunnan. Overall, nowadays, there is a shift in high risk areas for GMS liver fluke infection from northeastern Thailand to Lao PDR, Cambodia, Vietnam, Myanmar, and Guangxi P.R. China. Urgent systematic disease mapping and integrated liver fluke control using One Health approaches should be implemented nationwide in GMS countries.

Opisthorchis viverrini and Strongyloides stercoralis mono- and co-infections: Bayesian geostatistical analysis in an endemic area, Thailand

Parasitic infections caused by Opisthorchis viverrini and Strongyloides stercoralis remain a major public health threat in the Greater Mekong Sub-region. An understanding of climate and other environmental influences on the geographical distribution and emergence of parasitic diseases is a crucial step to guide targeted control and prevention programs. A parasitological survey was conducted from 2008 to 2013 and included 12,554 individuals (age between 20 and 60 years) from 142 villages in five districts in Khon Kaen Province, Thailand. Geographical information systems, remote sensing technologies and a Bayesian geostatistical framework were used to develop models for O. viverrini and S. stercoralis mono- and co-infections in areas where both parasites are known to co-occur. The results indicate that male sex, increased age, altitude, precipitation, and land surface temperature have influenced the infection rate and geographical distribution of mono- and co-infections of O. viverrini and S. stercoralis in this area. Males were 6.69 times (95% CrI: 5.26-8.58) more likely to have O. viverrini - S. stercoralis co-infection. We observed that O. viverrini and S. stercoralis mono-infections display distinct spatial pattern, while co-infection is predicted in the center and southeast of the study area. The observed spatial clustering of O. viverrini and S. stercoralis provides valuable information for the spatial targeting of prevention interventions in this area.

Genetic structure and geographical variation of Bithynia siamensis goniomphalos sensu lato (Gastropoda: Bithyniidae), the snail intermediate host of Opisthorchis viverrini sensu lato (Digenea: Opisthorchiidae) in the Lower Mekong Basin revealed by mitochondrial DNA sequences

The freshwater snail Bithynia siamensis goniomphalos sensu lato is widely distributed in the Lower Mekong Basin where it acts as the first intermediate host of the liver fluke Opisthorchis viverrini, a group 1 carcinogen causing cholangiocarcinoma. This study explores the genetic structure and geographical variation of B. s. goniomphalos from eight previously studied catchments and eight new catchments. These catchments belong to five previously studied catchment systems and one new catchment system (Tonlesap) in the Lower Mekong Basin. Two new catchment systems, Prachin Buri and Bang Pakong from eastern and central Thailand, respectively, were also examined. We collected 289 specimens of B. s. goniomphalos from 15 previously studied localities and 18 new localities in Thailand, Lao PDR (People's Democratic Republic), and Cambodia. The mitochondrial cytochrome c oxidase subunit 1 and 16S ribosomal DNA sequences were used to determine genetic variation. Classification of haplotypes specified 100 at the cox1 locus and 15 at the rrnL locus. Comparison between 16 catchment populations found significant genetic differences (Ф_ST) between all populations. The phylogenetic tree and haplotype network analyses classified B. s. goniomphalos into three evolutionary lineages (lineage I-III). Lineage I contained B. s. goniomphalos from the Mekong, Chi, Mun, Prachin Buri and Bang Pakong catchments in Thailand, including the Nam Ngum catchment in Lao PDR. Lineage II contained all specimens from the Tonlesap catchment, whereas lineage III contained specimens from the Mekong and Sea Bang Heang catchments in Thailand and Lao PDR, respectively. Interestingly, Bithynia siamensis siamensis was placed between lineages I and II of B. s. goniomphalos. This study supports the hypothesis that B. s. goniomphalos is a species complex containing at least three distinct evolutionary lineages in the Lower Mekong Basin, and that comprehensive molecular genetic analyses need to be conducted to further our understanding of the evolutionary and systematic relationships of these Bithynia snail taxa.

Bayesian spatial analysis of cholangiocarcinoma in Northeast Thailand

Cholangiocarcinoma (CCA) is a malignant neoplasm of the biliary tract. Thailand reports the highest incidence of CCA in the world. The aim of this study was to map the distribution of CCA and identify spatial disease clusters in Northeast Thailand. Individual-level data of patients with histopathologically confirmed CCA, aggregated at the sub-district level, were obtained from the Cholangiocarcinoma Screening and Care Program (CASCAP) between February 2013 and December 2017. For analysis a multivariate Zero-inflated, Poisson (ZIP) regression model was developed. This model incorporated a conditional autoregressive (CAR) prior structure, with posterior parameters estimated using Bayesian Markov chain Monte Carlo (MCMC) simulation with Gibbs sampling. Covariates included in the models were age, sex, normalized vegetation index (NDVI), and distance to water body. There was a total of 1,299 cases out of 358,981 participants. CCA incidence increased 2.94 fold (95% credible interval [CrI] 2.62–3.31) in patients >60 years as compared to ≤60 years. Males were 2.53 fold (95% CrI: 2.24–2.85) more likely to have CCA when compared to females. CCA decreased with a 1 unit increase of NDVI (Relative Risk =0.06; 95% CrI: 0.01–0.63). When posterior means were mapped spatial clustering was evident after accounting for the model covariates. Age, sex and environmental variables were associated with an increase in the incidence of CCA. When these covariates were included in models the maps of the posterior means of the spatially structured random effects demonstrated evidence of spatial clustering.

Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

Support vector machine (SVM) and maximum entropy (MaxEnt) machine learning techniques are well suited to model the habitat suitability of species. In this study, SVM and MaxEnt models were developed to predict the habitat suitability of Juniperus spp. in the Southern Zagros Mountains of Iran. In recent decades, drought extension and climate alteration have led to extensive changes in the geographical occurrence of this species and its growth and regeneration are extremely limited in this area. This study evaluated the habitat suitability of Juniperus through spatial modeling and predicts appropriate regions for future cultivation and resource conservation. We modeled the natural habitat of Juniperus for an area of 700 ha in Sepidan Area in the Fars province using (1) data regarding the presence of the species (295 samples) collected through field surveys and GPS, (2) habitat soil information and indices derived from 60 soil samples collected in the study area, and (3) climatic and topographic datasets collected from various sources. In total, 15 conditioning factors were used for this spatial modeling approach. Receiver operator characteristic (ROC) curves were applied to estimate the accuracy of the habitat suitability models produced by the SVM and MaxEnt techniques. Results indicated logical and similar area under the curve (AUC)-ROC values for the SVM (0.735) and MaxEnt (0.728) models. Both the SVM and MaxEnt methods revealed a significant relationship between the Juniperus spp. distribution and conditioning factors. Environmental factors played a vital role in evaluating the presence of Juniperus sp. as Max and Min temperatures and annual mean rainfall were the three most important factors for habitat suitability in the study area. Finally, an area with high and very high suitability for the future cultivation of Juniperus sp. and for landscape conservation was suggested based on the SVM model.