Evidence map›Paper›PMID 40669598›Full record

ArticleJournal of advanced research2026

Crafting an innovative one health-aligned machine learning framework for neglected tropical diseases elimination.

Ziyu Zhao, Liying Wang, Robert Bergquist, Lu Liu, Nakul Chitnis, Lars Kamber, Gong Cheng, Xiaonong Zhou, Guojing Yang

Abstract read
In one paragraph

Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ziyu ZhaoNHC Key Laboratory of Tropical Disease Control, School of Tropical Medicine, the First Affiliated Hospital, Hainan Medical University, Haikou, Hainan 571199, China.
Liying WangNHC Key Laboratory of Tropical Disease Control, School of Tropical Medicine, the First Affiliated Hospital, Hainan Medical University, Haikou, Hainan 571199, China.
Robert BergquistIngerod, Brastad, Formerly at the UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases, Sweden.
Lu LiuNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, Shanghai, China.
Nakul ChitnisSwiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland.
Lars KamberSwiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland.
Gong ChengTsinghua University-Peking University Joint Center for Life Sciences, School of Medicine, Tsinghua University, Beijing, China.
Xiaonong ZhouNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention/Chinese Center for Tropical Research, NHC Key Laboratory On Parasite and Vector Biology, Shanghai, China; One Health Center, Shanghai Jiao Tong University-The University of Edinburgh, School of Global Health, Chinese Center for Tropical Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Hainan Center for Tropical Diseases Research/Subcenter of National Center for Tropical Diseases Research, Haikou, China.
Guojing YangNHC Key Laboratory of Tropical Disease Control, School of Tropical Medicine, the First Affiliated Hospital, Hainan Medical University, Haikou, Hainan 571199, China. Electronic address: guojingyang@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionStrategic investment in new interventions is crucial for controlling and eliminating NTDs. However, selecting the optimal intervention combination is challenging, especially with limited resources and urgent disease transmission. Hybrid models offer a flexible framework for simulating intervention scenarios, but their use in NTDs control remains underexplored.

objectivesTo propose a novel One Health-driven framework to guide and accelerate the elimination of NTDs.

methodsThe proposed simulation framework consists of three parts: the disease dynamics model (the white box), the emulation process (the black box with supervised machine learning methods), and their joint contribution to optimizing the intervention strategy (the grey box). We demonstrate how this optimized strategy supports the development of new disease intervention tools using clonorchiasis as an example.

resultsThe comparison of several simulators consistently indicated that Gaussian process is the best machine learning core function. A disease model emulator with high-precision prediction ability was developed through extensive training. Optimizing intervention measures showed that chemotherapy was most effective, but long-term repeated chemotherapy carries the risk of drug resistance and economic burden. Fish vaccination addresses this contradiction. After five years of application, fish vaccination alone (99% efficacy) reduced prevalence by 34.16%, while the combination of chemotherapy and fish vaccination reduced it by 86.24%. The combination of chemotherapy and fish vaccination, both with 85.74% coverage, achieved all health goals in six scenarios.

conclusionsIn resource-limited settings, eliminating clonorchiasis requires quantitative consideration of intervention strategies and cost-effectiveness. The combination of chemotherapy and fish vaccination has shown long-term benefits and is expected to help eliminate the disease. This innovative framework provides a theoretical basis and technological pipeline for research and development (R&D) investment, as well as the development of new intervention measures and/or products in countries with limited resources.

Indexed as

Disease EradicationMachine LearningNeglected DiseasesTropical MedicineAnimalsComputer SimulationHumansEliminationIntervention optimizationMachine learningNeglected Tropical DiseasesOne HealthTransmission dynamics model

Identifiers

PMID40669598
PMCPMC13001167

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.