Abstract
Abstract Heterogeneity in the number of secondary tuberculosis (TB) cases per source case, the effective reproductive number, R, is important in modelling prevention strategies' impact on incidence. We estimated mean R (Rm) and calculate the dispersion parameter of this distribution, k, using surveillance and genotyping data for U.S. cases during 2009–2018. We modelled transmission assuming cases in a cluster have matching genotypes and share characteristics related to geography, temporal proximity (i.e. serial interval) and time since U.S. arrival among non-U.S.-born persons. Complete data were available for 55 330/85 958 cases. Varying the serial interval and geographic proximity used to derive clusters, we consistently estimated Rm<1.0 and k < 0.08; the low value of k indicates a small number of source cases produce a disproportionate number of secondary cases. U.S. TB reproductive number has a highly skewed distribution, indicating a minority of source cases disproportionately contribute to transmission.
Highlights
10 million people became sick with tuberculosis (TB) in 2019 [1]
We consistently estimated low values of k, indicating substantial heterogeneity and overdispersion in Rm. These findings suggest a minority of source cases disproportionately contribute to TB transmission in the United States
A systematic review identified inconsistent TB serial interval and R estimates, which might be due to the diverse assumptions applied to parameters such as the transmission rate [12]
Summary
10 million people became sick with tuberculosis (TB) in 2019 [1]. In order to achieve the Sustainable Development Goal of ending the TB epidemic by 2030 [2], substantial reductions in TB incidence will be required. Understanding which control strategies are most effective at reducing TB incidence is of paramount importance to meet this goal. Mathematical models are useful in determining the efficacy of prevention strategies at reducing disease incidence. The validity of these models relies on the values and distributions of input parameters, such as the effective reproductive number (R), the number of secondary cases of disease an individual source case produces. Mathematical models are often encoded with the assumptions that all individuals are infectious, which is questionable based on outbreak reports and molecular and spatial analyses [3,4,5]. In the context of infectious disease transmission, smaller values of k (
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