Strategic 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.
To propose a novel One Health-driven framework to guide and accelerate the elimination of NTDs.
The 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.
The 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.
In 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.
BT - Journal of Advanced Research DO - 10.1016/j.jare.2025.07.023 LA - eng M3 - Research Article N2 -Strategic 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.
To propose a novel One Health-driven framework to guide and accelerate the elimination of NTDs.
The 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.
The 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.
In 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.
PB - Elsevier BV PY - 2025 T2 - Journal of Advanced Research TI - Crafting an innovative one health-aligned machine learning framework for neglected tropical diseases elimination UR - https://www.sciencedirect.com/science/article/pii/S209012322500548X SN - 2090-1232 ER -