F.I. Proctor Foundation, USA; Department of Ophthalmology, USA; Department of Epidemiology and Biostatistics, USA; Global Health Sciences, University of California San Francisco, San Francisco, CA, USA.
F.I. Proctor Foundation, USA.
Epidemics. 2018 Sep;24:21-25. doi: 10.1016/j.epidem.2018.02.002. Epub 2018 Feb 14.
Mathematical models predict that the community-level incidence of a controlled infectious disease across a region approaches a geometric distribution. This could hold over larger regions, if new cases remain proportional to existing cases. Leprosy has been disappearing for centuries, making an excellent candidate for testing this hypothesis. Here, we show the annual new case detection rate of leprosy in Indian districts to be consistent with a geometric distribution. For 2008-2013, goodness-of-fit testing was unable to exclude the geometric, and the shape parameter of the best fit negative binomial distribution was close to unity (0.95, 95% CI 0.87-1.03). Ramifications include that a district-level cross-sectional survey may reveal whether an infectious disease is headed towards elimination, that apparent outliers are expected and not necessarily representative of program failure, and that proportion 1/e of a small geographical unit may not meet a control threshold even when a larger area has.
数学模型预测,在一个区域内,受控制的传染病的社区级发病率接近几何分布。如果新病例与现有病例保持比例,那么这可能适用于更大的区域。麻风病已经消失了几个世纪,是检验这一假设的绝佳候选。在这里,我们发现印度各地区的麻风病新发病例检出率与几何分布一致。对于 2008-2013 年的数据,拟合优度检验无法排除几何分布,最佳拟合负二项分布的形状参数接近 1(0.95,95%CI 0.87-1.03)。这意味着,一个地区的横断面调查可能会揭示一种传染病是否正在走向消除,预期会出现明显的异常值,而不一定代表项目失败,即使一个较大的区域已经达到,一个小地理单元的比例 1/e 也可能不会达到控制阈值。