{
  "abstract": "Opisthorchis viverrini (OV) infection is a major public health problem in northeastern Thailand, where it causes cholangiocarcinoma. This study developed a spatial prediction model for OV infection risk in Sakon Nakhon Province by integrating Sentinel-2, Sentinel-3, and CHIRPS data with hexagonal grid optimization and ensemble machine learning. Eleven environmental indices were aggregated using a newly developed Hd index, which identified an optimal hexagonal grid size of d = 2,100 m. Three models-Random Forest, XGBoost, and Multi-scale Geographically Weighted Regression (MGWR)-were compared using R2, root mean square error, ROC-AUC at exceedance thresholds (1%, 2%, 3%), 5-fold cross-validation, and SHAP analysis. XGBoost outperformed the other models, achieving the highest test-set R2 (0.894) and AUC (0.955) at the >3% threshold, with stable cross-validated performance (mean R2 = 0.83). SHAP analysis identified GVMI as the most influential predictor (mean |SHAP| = 0.125), followed by MNDWI (0.090) and SPI-6 (0.070). Moderate moisture conditions and above-average rainfall increased risk, whereas deep open water reduced it. The resulting risk map supports targeted surveillance and resource allocation for OV control in endemic areas.",
  "abstract_shown": true,
  "affiliations": [
    "Research Unit of Geoinformatics for Spatial Management Department of Geoinformatics Faculty of Informatics Mahasarakham University Maha Sarakham Thailand.",
    "Program in Animal Science Faculty of Agricultural Technology Sakon Nakhon Rajabhat University Sakon Nakhon Thailand.",
    "Department of Parasitology Faculty of Medicine Khon Kaen University Khon Kaen Thailand.",
    "Department of Agriculture and Resources Faculty of Natural Resources and Agro-Industry Kasetsart University Sakon Nakhon Thailand.",
    "Department of Civil & Architectural Engineering & Mechanics University of Arizona Tucson AZ USA."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Patiwat Littidej",
      "orcid": "0000-0002-1024-547X"
    },
    {
      "affs": [
        1
      ],
      "name": "Benjamabhorn Pumhirunroj",
      "orcid": "0009-0009-6607-5594"
    },
    {
      "affs": [
        2
      ],
      "name": "Thidarut Boonmars"
    },
    {
      "affs": [
        3
      ],
      "name": "Atchara Artchayasawat"
    },
    {
      "affs": [
        0
      ],
      "name": "Nutchanat Buasri"
    },
    {
      "affs": [
        4
      ],
      "name": "Donald Slack"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1e503dxprkc4n.bib",
  "cited_by": 0,
  "doi": "10.1029/2026gh002245",
  "fulltext_url": "https://europepmc.org/articles/PMC13743928",
  "id": "1e503dxprkc4n",
  "issns": [
    "2471-1403"
  ],
  "landing_url": "https://europepmc.org/article/MED/42999529",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2405|Parasites and Host Interactions"
    ]
  ],
  "openalex": "W7225450805",
  "pdf_url": "https://europepmc.org/articles/PMC13743928?pdf=render",
  "pmcid": "PMC13743928",
  "pmid": "42999529",
  "published": "2026-11-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1e503dxprkc4n.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:34Z",
      "native_id": "MED:42999529",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42999529"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2405|Parasites and Host Interactions",
      "subfield": "microbiology"
    }
  ],
  "title": "Spatial Prediction of Opisthorchis viverrini Infection Risk Using Multi-Source Remote Sensing, Hexagonal Grid Optimization, and Ensemble Machine Learning: A Case Study From Northeastern Thailand.",
  "title_text": "Spatial Prediction of Opisthorchis viverrini Infection Risk Using Multi-Source Remote Sensing, Hexagonal Grid Optimization, and Ensemble Machine Learning: A Case Study From Northeastern Thailand.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:34Z",
  "url": "https://research.draconitic.com/paper/1e503dxprkc4n",
  "venue": "GeoHealth",
  "versions": []
}