From the Emory University Rollins School of Public Health, Atlanta, GA.
Yale University School of Public Health, New Haven, CT.
Epidemiology. 2022 Mar 1;33(2):217-227. doi: 10.1097/EDE.0000000000001452.
Recent evidence suggests transmission of Mycobacterium tuberculosis (Mtb) may be characterized by extreme individual heterogeneity in secondary cases (i.e., few cases account for the majority of transmission). Such heterogeneity implies outbreaks are rarer but more extensive and has profound implications in infectious disease control. However, discrete person-to-person transmission events in tuberculosis (TB) are often unobserved, precluding our ability to directly quantify individual heterogeneity in TB epidemiology.
We used a modified negative binomial branching process model to quantify the extent of individual heterogeneity using only observed transmission cluster size distribution data (i.e., the simple sum of all cases in a transmission chain) without knowledge of individual-level transmission events. The negative binomial parameter k quantifies the extent of individual heterogeneity (generally, indicates extensive heterogeneity, and as transmission becomes more homogenous). We validated the robustness of the inference procedure considering common limitations affecting cluster size data. Finally, we demonstrate the epidemiologic utility of this method by applying it to aggregate US molecular surveillance data from the US Centers for Disease Control and Prevention.
The cluster-based method reliably inferred k using TB transmission cluster data despite a high degree of bias introduced into the model. We found that the TB transmission in the United States was characterized by a high propensity for extensive outbreaks (; 95% confidence interval = 0.09, 0.10).
The proposed method can accurately quantify critical parameters that govern TB transmission using simple, more easily obtainable cluster data to improve our understanding of TB epidemiology.
最近的证据表明，结核分枝杆菌（Mtb）的传播可能具有极强的个体异质性，表现在继发感染病例中（即少数病例引发了大多数传播）。这种异质性意味着暴发的频率更低，但范围更广，这对传染病控制具有深远的影响。然而，结核病（TB）中离散的人际传播事件通常是不可观察的，这使得我们无法直接量化结核病流行病学中的个体异质性。
我们使用改进的负二项式分支过程模型，仅使用观察到的传播簇大小分布数据（即传播链中所有病例的简单总和），而无需了解个体水平的传播事件，来量化个体异质性的程度。负二项式参数 k 量化了个体异质性的程度（通常表示广泛的异质性，随着传播变得更加同质）。我们考虑了影响簇大小数据的常见限制，验证了推断程序的稳健性。最后，我们通过将该方法应用于美国疾病控制与预防中心的美国分子监测综合数据，展示了该方法在流行病学中的应用价值。
尽管模型中存在高度偏差，但基于簇的方法仍能可靠地推断 k。我们发现，美国的结核病传播具有广泛暴发的强烈倾向（; 95%置信区间= 0.09，0.10）。
该方法可以使用简单、更易获得的簇数据准确地量化控制结核病传播的关键参数，从而提高我们对结核病流行病学的认识。
