PDF(Pubmed)
Abstract：
Mass vaccinations are crucial public health interventions for curbing infectious diseases. Canine rabies control relies on mass dog vaccination campaigns (MDVCs) that are held annually across the globe. Dog owners must bring their pets to fixed vaccination sites, but sometimes target coverage is not achieved due to low participation. Travel distance to vaccination sites is an important barrier to participation. We aimed to increase MDVC participation in silico by optimally placing fixed-point vaccination locations. We quantified participation probability based on walking distance to the nearest vaccination site using regression models fit to participation data collected over 4 years. We used computational recursive interchange techniques to optimally place fixed-point vaccination sites and compared predicted participation with these optimally placed vaccination sites to actual locations used in previous campaigns. Algorithms that minimized average walking distance or maximized expected participation provided the best solutions. Optimal vaccination placement is expected to increase participation by 7% and improve spatial evenness of coverage, resulting in fewer under-vaccinated pockets. However, unevenness in workload across sites remained. Our data-driven algorithm optimally places limited resources to increase overall vaccination participation and equity. Field evaluations are essential to assess effectiveness and evaluate potentially longer waiting queues resulting from increased participation.
摘要：
大规模疫苗接种是遏制传染病的关键公共卫生干预措施。犬狂犬病控制依赖于每年在全球举行的大规模犬疫苗接种运动（MDVC）。狗主人必须把他们的宠物带到固定的疫苗接种地点，但有时由于参与度低，目标覆盖率无法实现。到疫苗接种地点的旅行距离是参与的重要障碍。我们的目标是通过最佳地放置定点疫苗接种位置来增加MDVC在计算机上的参与。我们使用回归模型，根据步行距离到最近的疫苗接种地点量化参与概率，该回归模型适合4年收集的参与数据。我们使用计算递归互换技术来最佳地放置定点疫苗接种地点，并将预测的参与与这些最佳放置的疫苗接种地点与以前活动中使用的实际地点进行比较。最小化平均步行距离或最大化预期参与的算法提供了最佳解决方案。最佳的疫苗接种位置预计将增加7％的参与，并提高覆盖率的空间均匀性，导致更少的疫苗接种不足的口袋。然而,各站点的工作量仍然不均衡。我们的数据驱动算法以最佳方式放置有限的资源来增加总体疫苗接种参与和公平性。实地评估对于评估有效性和评估因参与增加而可能延长的等待队列至关重要。
