Predicting the potential nationwide distribution of the snail vector, Oncomelania hupensis quadrasi, in the Philippines using the MaxEnt algorithm.

Schistosomiasis remains a major public health concern affecting approximately 12 million people in the Philippines due to inadequate information about the disease and limited prevention and control efforts. Schistosoma japonicum, one of the causative agents of the disease, requires an amphibious snail Oncomelania hupensis quadrasi (O. h. quadrasi) to complete its life cycle. Using the geographical information system (GIS) and maximum entropy (MaxEnt) algorithm, this study aims to predict the potential high-risk habitats of O. h. quadrasi driven by environmental factors in the Philippines. Based on the bioclimatic determinants, a very high-performance model was generated (AUC = 0.907), with the mean temperature of the driest quarter (25.3%) contributing significantly to the prevalence of O. h. quadrasi. Also, the snail vector has a high focal distribution, preferring areas with a pronounced wet season and high precipitation throughout the year. However, the findings provided evidence for snail adaptation to different environmental conditions. High suitability of snail habitats was found in Quezon, Camarines Norte, Camarines Sur, Albay, Sorsogon, Northern Samar, Eastern Samar, Leyte, Bohol, Surigao del Norte, Surigao del Sur, Agusan del Norte, Davao del Norte, North Cotabato, Lanao del Norte, Misamis Occidental, and Zamboanga del Sur. Furthermore, snail habitat establishment includes natural and man-made waterlogged areas, with the progression of global warming and climate change predicted to be drivers of increasing schistosomiasis transmission zones in the country.

Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning.

Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collapse amidst 'Big Data', prompt interpretable machine-learning (iML) approaches. Predicting dengue incidence and mortality in the Philippines, a data-limited yet high-burden country, the mlr3 universe of R packages was used to build and optimize ML models based on remotely sensed provincial and dekadal 3 NDVI and 9 rainfall features from 2016 to 2020. Between two tasks, models differ across four random forest-based learners and two clustering strategies. Among 16 candidates, rfsrc-year-case and ranger-year-death significantly perform best for predicting dengue incidence and mortality, respectively. Therefore, temporal clustering yields the best models, reflective of dengue seasonality. The two best models were subjected to tripartite global exploratory model analyses, which encompass model-agnostic post-hoc methods such as Permutation Feature Importance (PFI) and Accumulated Local Effects (ALE). PFI reveals that the models differ in their important explanatory aspect, rainfall for rfsrc-year-case and NDVI for ranger-year-death, among which long-term average (lta) features are most relevant. Trend-wise, ALE reveals that average incidence predictions are positively associated with 'Rain.lta', reflective of dengue cases peaking during the wet season. In contrast, those for mortality are negatively associated with 'NDVI.lta', reflective of urban spaces driving dengue-related deaths. By technologically addressing the challenges of the human-animal-ecosystem interface, this study adheres to the One Digital Health paradigm operationalized under Sustainable Development Goals (SDGs). Leveraging data digitization and predictive modeling for epidemiological research paves SDG 3, which prioritizes holistic health and well-being.

Correlating Cases of Soil-Transmitted Helminths and Remote Sensing Covariates: A Case Study Utilizing 2015, 2017, and 2019 Metadata of Camarines Sur, Philippines.

INTRODUCTION: Soil-transmitted helminth (STH) is a group of helminths that are considered to be neglected tropical diseases (NTDs) and, globally, affect more than 1.5 to 2.6 million people yearly. Depending on the species, they can be acquired by ingesting embryonated eggs from contaminated matter or by skin penetration. Most species of STH are found in the tropics, such as the Philippines. Despite the Mass Drug Administration (MDA), the cases of STH infection continue to rise in the country. Surveys from the Research Institute of Tropical Medicine (RITM) indicate that a high prevalence of STH (Ascaris lumbricoides, Trichuris trichiura, and Necator americanus) was primarily observed in the provinces of the country, such as in Camarines Sur. OBJECTIVES: To correlate remote sensing covariates such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) - to STH-infected cases of the 37 municipalities of Camarines Sur. METHODOLOGY: The available public health record of STH cases from 2015 to 2019 were calculated using the Quantum Geographic Information System (QGIS)and correlated using Pearson Correlation Coefficient. RESULTS: The results showed that infection was higher in children than adults, and A. lumbricoides caused 60% of infection. No correlation of indices with infection cases during 2015 and 2017 was observed; however, 2019 showed a moderate strength (p = 0.037) in correlation. CONCLUSION: This indicates that infection relied not mainly on vegetation and urbanization but on additional environmental factors and non-environmental variables.

MaxEnt modeling of the potential risk of schistosomiasis in the Philippines using bioclimatic factors.

Schistosomiasis is a parasitic infection caused by Schistosoma japonicum. It remains a principal local health issue in the Philippines, demonstrating endemicity in 28 provinces and afflicting thousands of Filipino individuals annually. Despite this, no clear distribution maps for the disease have been comprehensively reported. Therefore, species distribution modeling (SDM) employing the MaxEnt algorithm and GIS application techniques was utilized to denote the potential risk of schistosomiasis in the country. With a high AUC score of 0.846, the SDM yielded a favorable and reliable correlative map illustrating a predicted schistosomal temporal distribution concentrated primarily on the country's eastern portion with a more pronounced wet than dry season. The precipitation of the driest quarter was determined to be the most significant contributing factor among the bioclimatic variables evaluated. This suggests a possible increase in adaptations concerning the rainfall and thermal tolerances of the parasites' vectors. Moreover, socioeconomic status between Philippine regions revealed an inverse proportion with the number of schistosomiasis cases. This study also discussed the potential role of climate change on the range shifts and the potential risk of parasite infection in the Philippines.

Speciation in progress? A phylogeographic study among populations of Hemitrichia serpula (Myxomycetes).

Myxomycetes (plasmodial slime molds, Amoebozoa) are often perceived as widely distributed, confounding to the "everything is everywhere" hypothesis. To test if gene flow within these spore-dispersed protists is restricted by geographical barriers, we chose the widespread but morphologically unmistakable species Hemitrichia serpula for a phylogeographic study. Partial sequences from nuclear ribosomal RNA genes (SSU) revealed 40 ribotypes among 135 specimens, belonging to three major clades. Each clade is dominated by specimens from a certain region and by one of two morphological varieties which can be differentiated by SEM micrographs. Partial sequences of the protein elongation factor 1 alpha (EF1A) showed each clade to possess a unique combination of SSU and EF1A genotypes. This pattern is best explained assuming the existence of several putative biospecies dominating in a particular geographical region. However, occasional mismatches between molecular data and morphological characters, but as well heterogeneous SSU and heterozygous EF1A sequences, point to ongoing speciation. Environmental niche models suggest that the putative biospecies are rather restricted by geographical barriers than by macroecological conditions. Like other protists, myxomycetes seem to follow the moderate endemicity hypothesis and are in active speciation, which is most likely shaped by limited gene flow and reproductive isolation.