A Cluster-based Method to Quantify Individual Heterogeneity in Tuberculosis Transmission

Smith, Jonathan; Gandhi, Neel R.; Silk, Benjamin J.; Cohen, Ted; Lopman, Benjamin A.; Raz, Kala M.; Winglee, Kathryn; Kammerer, Steve; Benkeser, David; Kramer, Michael R.; Hill, Andrew

doi:10.1097/ede.0000000000001452

Cited by 5 publications

(6 citation statements)

References 47 publications

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“…We developed two primary models, an urban and rural model, based on if the participant’s primary residence was in the greater Gaborone district or Ghanzi district, respectively. Both models were based on a classical Galton-Watson branching process 10 , 21 , 23 . Briefly, each individual case in the population was assigned an associated individual reproductive number, denoted , drawn from a given probability distribution with mean 11 .…”

Section: Methodsmentioning

confidence: 99%

“…Understanding TB transmission dynamics is a notorious challenge given the marked variation in timing between infection and clinical TB disease. Limited evidence suggests TB transmission may be characterized by a high degree of individual heterogeneity, or differences in the number of secondary cases caused by each infectious individual 6 – 10 . Such heterogeneity implies outbreaks are rarer but more extensive, and has profound implications in infectious disease control 11 – 14 .…”

Section: Introductionmentioning

confidence: 99%

“…Epidemiologic models have long exploited this relationship and have used the distribution of cluster sizes to infer the average number of secondary cases per each individual (known generally as the reproduction number, ) in other infectious diseases 21 – 25 . Extending the use of transmission cluster distributions to the problem of quantifying individual heterogeneity in TB transmission may provide more complete insight into transmission dynamics in in high-burden settings 10 .…”

Section: Introductionmentioning

confidence: 99%

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Characterizing tuberculosis transmission dynamics in high-burden urban and rural settings

Smith

Oeltmann

Hill

et al. 2022

Sci Rep

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Mycobacterium tuberculosis transmission dynamics in high-burden settings are poorly understood. Growing evidence suggests transmission may be characterized by extensive individual heterogeneity in secondary cases (i.e., superspreading), yet the degree and influence of such heterogeneity is largely unknown and unmeasured in high burden-settings. We conducted a prospective, population-based molecular epidemiology study of TB transmission in both an urban and rural setting of Botswana, one of the highest TB burden countries in the world. We used these empirical data to fit two mathematical models (urban and rural) that jointly quantified both the effective reproductive number, $$R$$ R , and the propensity for superspreading in each population. We found both urban and rural populations were characterized by a high degree of individual heterogeneity, however such heterogeneity disproportionately impacted the rural population: 99% of secondary transmission was attributed to only 19% of infectious cases in the rural population compared to 60% in the urban population and the median number of incident cases until the first outbreak of 30 cases was only 32 for the rural model compared to 791 in the urban model. These findings suggest individual heterogeneity plays a critical role shaping local TB epidemiology within subpopulations.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterizing tuberculosis transmission dynamics in high-burden urban and rural settings

Smith

Oeltmann

Hill

et al. 2022

Sci Rep

Self Cite

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“…According to a systematic review, 21/26 studies quantifying SARS-CoV-2 superspreading reported significant over-dispersion in transmission and found that between 1% to 39% of individuals were responsible for 80% of secondary infections (with estimates for the negative binomial dispersion parameter k ranging from 0.01 to 0.72) [7]. Superspreading has also been recognized as a key feature of transmission dynamics for a wide variety of other infections including SARS [8,9], MERS [10], smallpox [8], ebola [11], tuberculosis [12][13][14], and HIV [15]. These dynamics highlight the limitations of summarizing transmission patterns using only average quantities, like the basic reproductive number, R 0 [16].…”

Section: Introductionmentioning

confidence: 99%

Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies

Anderson

Nande

Merenstein

et al. 2022

Preprint

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The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities with data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

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“…A formatting error occurred in the final probability distribution equation in the article entitled “A Cluster-based Method to Quantify Individual Heterogeneity in Tuberculosis Transmission,” in the March 2022 issue of Epidemiology . 1 The equation presented on page 218 omitted the gamma symbol false( Γ ) in both the equation and subsequent text. This error was only typographical in nature and did not affect any analysis, results, figures, or conclusions.…”

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confidence: 99%