Assessing the impact of aggregating disease stage data in model predictions of human African trypanosomiasis transmission and control activities in Bandundu province (DRC)
Assessing the impact of aggregating disease stage data in model predictions of human African trypanosomiasis transmission and control activities in Bandundu province (DRC)
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DOI：
10.1371/journal.pntd.0007976
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发表时间：
2020-01-01
影响因子：
3.8
通讯作者：
Chitnis, Nakul
中科院分区：
文献类型：
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作者：
Castano, Maria Soledad;Ndeffo-Mbah, Martial L.;Chitnis, Nakul
自进入世纪以来，国际社会在消除冈比亚人非洲锥虫（HAT）方面取得了巨大进展。主要依靠筛查和治疗活动的消除方案也建立了丰富的HAT流行病学数据库。用这些数据校准的数学模型可以帮助填补我们对HAT传播动态的理解中存在的空白，包括关键的运筹学问题，例如是否需要将病媒控制与当前的干预战略相结合来实现HAT消除。在这里，我们探索，通过集成的模型和模拟研究，包括或不包括疾病阶段的数据，或使用更更新的数据集如何影响模型预测未来的控制策略。
Since the turn of the century, the global community has made great progress towards the elimination of gambiense human African trypanosomiasis (HAT). Elimination programs, primarily relying on screening and treatment campaigns, have also created a rich database of HAT epidemiology. Mathematical models calibrated with these data can help to fill remaining gaps in our understanding of HAT transmission dynamics, including key operational research questions such as whether integrating vector control with current intervention strategies is needed to achieve HAT elimination. Here we explore, via an ensemble of models and simulation studies, how including or not disease stage data, or using more updated data sets affect model predictions of future control strategies.