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.

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.

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.

A workflow for low-cost automated image analysis of myxomycete spore numbers, size and shape.

Measuring spore size is a standard method for the description of fungal taxa, but in manual microscopic analyses the number of spores that can be measured and information on their morphological traits are typically limited. To overcome this weakness we present a method to analyze the size and shape of large numbers of spherical bodies, such as spores or pollen, by using inexpensive equipment. A spore suspension mounted on a slide is treated with a low-cost, high-vibration device to distribute spores uniformly in a single layer without overlap. Subsequently, 10,000 to 50,000 objects per slide are measured by automated image analysis. The workflow involves (1) slide preparation, (2) automated image acquisition by light microscopy, (3) filtering to separate high-density clusters, (4) image segmentation by applying a machine learning software, Waikato Environment for Knowledge Analysis (WEKA), and (5) statistical evaluation of the results. The technique produced consistent results and compared favorably with manual measurements in terms of precision. Moreover, measuring spore size distribution yields information not obtained by manual microscopic analyses, as shown for the myxomycete Physarum albescens. The exact size distribution of spores revealed irregularities in spore formation resulting from the influence of environmental conditions on spore maturation. A comparison of the spore size distribution within and between sporocarp colonies showed large environmental and likely genetic variation. In addition, the comparison identified specimens with spores roughly twice the normal size. The successful implementation of the presented method for analyzing myxomycete spores also suggests potential for other applications.

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.