BACKGROUND Distance sampling methods are widely used in ecology to estimate and map the abundance of animal and plant populations from spatial survey data. The key underlying concept in distance sampling is the detection function, the probability of detecting the occurrence of an event as a function of its distance from the observer, as well as other covariates that may influence detection. In epidemiology, the burden and distribution of infectious disease is often inferred from cases that are reported at clinics and hospitals. In areas with few public health facilities and low accessibility, the probability of detecting a case is also a function of the distance between an infected person and the "observer" (e.g. a health centre). While the problem of distance-related under-reporting is acknowledged in public health; there are few quantitative methods for assessing and correcting for this bias when mapping disease incidence. Here, we develop a modified version of distance sampling for prediction of infectious disease incidence by relaxing some of the framework's fundamental assumptions. We illustrate the utility of this approach using as our example malaria distribution in rural Burkina Faso, where there is a large population at risk but relatively low accessibility of health facilities. RESULTS The modified distance-sampling framework was used to predict the probability of reporting malaria infection at 8 rural clinics, based on road-travel distances from villages. The rate at which reporting probability dropped with distance varied between clinics, depending on road and clinic positions. The probability of case detection was estimated as 0.3-1 in the immediate vicinity of the clinic, dropping to 0.1-0.6 at a travel distance of 10 km, and effectively zero at distances > 30-40 km. CONCLUSIONS To enhance the method's strategic impact, we provide an interactive mapping tool (as a self-contained R Shiny app) that can be used by non-specialists to interrogate model outputs and visualize how the overall probability of under-reporting and the catchment area of each clinic is influenced by changing the number and spatial allocation of health centres.

中文翻译：

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流行病学的距离采样：一种用于根据临床数据估计病例报告不足的交互式工具。

背景技术距离采样方法被广泛用于生态学中，以根据空间调查数据估计和绘制动植物种群的数量。距离采样中的关键基础概念是检测功能，根据距观察者的距离来检测事件发生的概率以及可能影响检测的其他协变量。在流行病学中，经常从诊所和医院报告的病例中推断出传染病的负担和分布。在公共卫生设施少，可及性差的地区，发现病例的机率还取决于被感染者与“观察者”（例如保健中心）之间的距离的函数。虽然与距离有关的报告不足的问题已在公共卫生中得到认可；在绘制疾病发生率时，很少有定量方法可以评估和纠正这种偏差。在这里，我们通过放宽框架的一些基本假设，开发了距离采样的改进版本，以预测传染病的发病率。我们以布基纳法索农村地区的疟疾分布为例来说明这种方法的效用，那里人口众多，但医疗机构的可及性相对较低。结果改进的距离采样框架用于根据距村庄的公路旅行距离来预测在8个农村诊所报告疟疾感染的可能性。诊所之间的距离，报告概率下降的速率取决于道路和诊所的位置。案件发现的可能性估计为0。在诊所附近的3-1处，在10公里的行进距离处下降到0.1-0.6，在距离> 30-40公里处有效地为零。结论为了增强该方法的战略影响，我们提供了一个交互式地图绘制工具（作为自包含的R Shiny应用程序），非专业人员可以使用该工具来询问模型输出并可视化报告不足的总体概率和集水区每个诊所的医院数量都会受到卫生中心数量和空间分配的影响。