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October 2, 2025American Journal of Tropical Medicine and HygieneOpen Access

Rethinking Machine-Learning Metrics in Schistosomiasis Control: Toward Predictive Equity, Field Readiness, and Biological Foresight

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Authors

NRNathkapach Kaewpitoon RattanapitoonPTPhatsakul ThitimahatthanukusolTMThirayu Meererksom

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Overview

This analysis rethinks metrics in schistosomiasis control, revealing gaps and suggesting improvements in predictive equity and field readiness.

Key Points

  • Rethinking machine-learning metrics enhances predictive equity in schistosomiasis control efforts.
  • Identifying gaps in current metrics may improve biological foresight for effective interventions.
  • Field readiness is crucial for successfully implementing machine-learning solutions in schistosomiasis control.
  • Adopting advanced metrics can lead to more equitable health outcomes in schistosomiasis management.

Cite This Study

Rattanapitoon et al. (2025) studied this question.

synapsesocial.com/papers/68de8eaeaa6cec72c69ea814https://doi.org/10.4269/ajtmh.25-0470a
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Also Consider

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  1. 1Response to “Rethinking Machine-Learning Metrics in Schistosomiasis Control: Toward Predictive Equity, Field Readiness, and Biological Foresight”2025
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  3. 3Socio-ecological heterogeneity and uncertainty in the elimination of human schistosomiasis2024 · 1 citations
  4. 4Economic Evaluation of Multi-Objective Schistosomiasis Control Through Systemic Causality: Theoretical Advances and Governance Implications2026
  5. 5One health at the last mile: Multi-scale predictors of Schistosoma japonicum infection in southwest china across two decades of control2026