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Sadovska D, Ozere I, Pole I, Ķimsis J, Vaivode A, Vīksna A, Norvaiša I, Bogdanova I, Ulanova V, Čapligina V, Bandere D, Ranka R. Unraveling tuberculosis patient cluster transmission chains: integrating WGS-based network with clinical and epidemiological insights. Front Public Health 2024; 12:1378426. [PMID: 38832230 PMCID: PMC11144917 DOI: 10.3389/fpubh.2024.1378426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/07/2024] [Indexed: 06/05/2024] Open
Abstract
Background Tuberculosis remains a global health threat, and the World Health Organization reports a limited reduction in disease incidence rates, including both new and relapse cases. Therefore, studies targeting tuberculosis transmission chains and recurrent episodes are crucial for developing the most effective control measures. Herein, multiple tuberculosis clusters were retrospectively investigated by integrating patients' epidemiological and clinical information with median-joining networks recreated based on whole genome sequencing (WGS) data of Mycobacterium tuberculosis isolates. Methods Epidemiologically linked tuberculosis patient clusters were identified during the source case investigation for pediatric tuberculosis patients. Only M. tuberculosis isolate DNA samples with previously determined spoligotypes identical within clusters were subjected to WGS and further median-joining network recreation. Relevant clinical and epidemiological data were obtained from patient medical records. Results We investigated 18 clusters comprising 100 active tuberculosis patients 29 of whom were children at the time of diagnosis; nine patients experienced recurrent episodes. M. tuberculosis isolates of studied clusters belonged to Lineages 2 (sub-lineage 2.2.1) and 4 (sub-lineages 4.3.3, 4.1.2.1, 4.8, and 4.2.1), while sub-lineage 4.3.3 (LAM) was the most abundant. Isolates of six clusters were drug-resistant. Within clusters, the maximum genetic distance between closely related isolates was only 5-11 single nucleotide variants (SNVs). Recreated median-joining networks, integrated with patients' diagnoses, specimen collection dates, sputum smear microscopy, and epidemiological investigation results indicated transmission directions within clusters and long periods of latent infection. It also facilitated the identification of potential infection sources for pediatric patients and recurrent active tuberculosis episodes refuting the reactivation possibility despite the small genetic distance of ≤5 SNVs between isolates. However, unidentified active tuberculosis cases within the cluster, the variable mycobacterial mutation rate in dormant and active states, and low M. tuberculosis genetic variability inferred precise transmission chain delineation. In some cases, heterozygous SNVs with an allelic frequency of 10-73% proved valuable in identifying direct transmission events. Conclusion The complex approach of integrating tuberculosis cluster WGS-data-based median-joining networks with relevant epidemiological and clinical data proved valuable in delineating epidemiologically linked patient transmission chains and deciphering causes of recurrent tuberculosis episodes within clusters.
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Affiliation(s)
- Darja Sadovska
- Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Iveta Ozere
- Centre of Tuberculosis and Lung Diseases, Riga East University Hospital, Upeslejas, Latvia
- Department of Infectology, Riga Stradiņš University, Riga, Latvia
| | - Ilva Pole
- Centre of Tuberculosis and Lung Diseases, Riga East University Hospital, Upeslejas, Latvia
| | - Jānis Ķimsis
- Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Annija Vaivode
- Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Anda Vīksna
- Centre of Tuberculosis and Lung Diseases, Riga East University Hospital, Upeslejas, Latvia
- Department of Infectology, Riga Stradiņš University, Riga, Latvia
| | - Inga Norvaiša
- Centre of Tuberculosis and Lung Diseases, Riga East University Hospital, Upeslejas, Latvia
| | - Ineta Bogdanova
- Centre of Tuberculosis and Lung Diseases, Riga East University Hospital, Upeslejas, Latvia
| | - Viktorija Ulanova
- Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Valentīna Čapligina
- Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Dace Bandere
- Department of Pharmaceutical Chemistry, Riga Stradiņš University, Riga, Latvia
| | - Renāte Ranka
- Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre, Riga, Latvia
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Anderson TL, Nande A, Merenstein C, Raynor B, Oommen A, Kelly BJ, Levy MZ, Hill AL. Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies. Epidemics 2023; 44:100710. [PMID: 37556994 PMCID: PMC10594662 DOI: 10.1016/j.epidem.2023.100710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/17/2023] [Accepted: 07/18/2023] [Indexed: 08/11/2023] Open
Abstract
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 given 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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Affiliation(s)
- Thayer L Anderson
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Anjalika Nande
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Carter Merenstein
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Brinkley Raynor
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Anisha Oommen
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Brendan J Kelly
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America; Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America; Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Michael Z Levy
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Alison L Hill
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States of America; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America.
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Smith JP, Cohen T, Dowdy D, Shrestha S, Gandhi NR, Hill AN. Quantifying Mycobacterium tuberculosis Transmission Dynamics Across Global Settings: A Systematic Analysis. Am J Epidemiol 2023; 192:133-145. [PMID: 36227246 PMCID: PMC10144641 DOI: 10.1093/aje/kwac181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 07/23/2022] [Accepted: 10/10/2022] [Indexed: 01/11/2023] Open
Abstract
The degree to which individual heterogeneity in the production of secondary cases ("superspreading") affects tuberculosis (TB) transmission has not been systematically studied. We searched for population-based or surveillance studies in which whole genome sequencing was used to estimate TB transmission and in which the size distributions of putative TB transmission clusters were enumerated. We fitted cluster-size-distribution data to a negative binomial branching process model to jointly infer the transmission parameters $R$ (the reproduction number) and the dispersion parameter, $k$, which quantifies the propensity of superspreading in a population (generally, lower values of $k$ ($<1.0$) suggest increased heterogeneity). Of 4,796 citations identified in our initial search, 9 studies from 8 global settings met the inclusion criteria (n = 5 studies of all TB; n = 4 studies of drug-resistant TB). Estimated $R$ values (range, 0.10-0.73) were below 1.0, consistent with declining epidemics in the included settings; estimated $k$ values were well below 1.0 (range, 0.02-0.48), indicating the presence of substantial individual-level heterogeneity in transmission across all settings. We estimated that a minority of cases (range, 2%-31%) drive the majority (80%) of ongoing TB transmission at the population level. Identifying sources of heterogeneity and accounting for them in TB control may have a considerable impact on mitigating TB transmission.
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Affiliation(s)
- Jonathan P Smith
- Correspondence to Dr. Jonathan Smith, Yale School of Public Health, Yale University, 60 College Street, New Haven, CT 06510 (e-mail: )
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Shrestha S, Winglee K, Hill AN, Shaw T, Smith JP, Kammerer JS, Silk BJ, Marks SM, Dowdy D. Model-based Analysis of Tuberculosis Genotype Clusters in the United States Reveals High Degree of Heterogeneity in Transmission and State-level Differences Across California, Florida, New York, and Texas. Clin Infect Dis 2022; 75:1433-1441. [PMID: 35143641 PMCID: PMC9412192 DOI: 10.1093/cid/ciac121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Reductions in tuberculosis (TB) transmission have been instrumental in lowering TB incidence in the United States. Sustaining and augmenting these reductions are key public health priorities. METHODS We fit mechanistic transmission models to distributions of genotype clusters of TB cases reported to the Centers for Disease Control and Prevention during 2012-2016 in the United States and separately in California, Florida, New York, and Texas. We estimated the mean number of secondary cases generated per infectious case (R0) and individual-level heterogeneity in R0 at state and national levels and assessed how different definitions of clustering affected these estimates. RESULTS In clusters of genotypically linked TB cases that occurred within a state over a 5-year period (reference scenario), the estimated R0 was 0.29 (95% confidence interval [CI], .28-.31) in the United States. Transmission was highly heterogeneous; 0.24% of simulated cases with individual R0 >10 generated 19% of all recent secondary transmissions. R0 estimate was 0.16 (95% CI, .15-.17) when a cluster was defined as cases occurring within the same county over a 3-year period. Transmission varied across states: estimated R0s were 0.34 (95% CI, .3-.4) in California, 0.28 (95% CI, .24-.36) in Florida, 0.19 (95% CI, .15-.27) in New York, and 0.38 (95% CI, .33-.46) in Texas. CONCLUSIONS TB transmission in the United States is characterized by pronounced heterogeneity at the individual and state levels. Improving detection of transmission clusters through incorporation of whole-genome sequencing and identifying the drivers of this heterogeneity will be essential to reducing TB transmission.
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Affiliation(s)
- Sourya Shrestha
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Kathryn Winglee
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Andrew N Hill
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Tambi Shaw
- California Department of Public Health, Richmond, California, USA
| | - Jonathan P Smith
- Department of Policy and Administration, Yale University, New Haven, Connecticut, USA
| | - J Steve Kammerer
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Benjamin J Silk
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Suzanne M Marks
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - David Dowdy
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
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Characterizing tuberculosis transmission dynamics in high-burden urban and rural settings. Sci Rep 2022; 12:6780. [PMID: 35474076 PMCID: PMC9042872 DOI: 10.1038/s41598-022-10488-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 04/06/2022] [Indexed: 12/23/2022] Open
Abstract
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, \documentclass[12pt]{minimal}
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\begin{document}$$R$$\end{document}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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Smith JP, Gandhi NR, Silk BJ, Cohen T, Lopman B, Raz K, Winglee K, Kammerer S, Benkeser D, Kramer MR, Hill AN. A Cluster-based Method to Quantify Individual Heterogeneity in Tuberculosis Transmission. Epidemiology 2022; 33:217-227. [PMID: 34907974 PMCID: PMC8886690 DOI: 10.1097/ede.0000000000001452] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND 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. METHODS 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. RESULTS 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). CONCLUSIONS 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.
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Affiliation(s)
- Jonathan P. Smith
- Emory University Rollins School of Public Health, Atlanta, GA
- Yale University School of Public Health, New Haven, CT
| | - Neel R. Gandhi
- Emory University Rollins School of Public Health, Atlanta, GA
| | - Benjamin J. Silk
- United States Centers for Disease Control and Prevention, Atlanta, GA
| | - Ted Cohen
- Yale University School of Public Health, New Haven, CT
| | - Benjamin Lopman
- Emory University Rollins School of Public Health, Atlanta, GA
| | - Kala Raz
- United States Centers for Disease Control and Prevention, Atlanta, GA
| | - Kathryn Winglee
- United States Centers for Disease Control and Prevention, Atlanta, GA
| | - Steve Kammerer
- United States Centers for Disease Control and Prevention, Atlanta, GA
| | - David Benkeser
- Emory University Rollins School of Public Health, Atlanta, GA
| | | | - Andrew N. Hill
- United States Centers for Disease Control and Prevention, Atlanta, GA
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Quantifying superspreading for COVID-19 using Poisson mixture distributions. Sci Rep 2021; 11:14107. [PMID: 34238978 PMCID: PMC8266910 DOI: 10.1038/s41598-021-93578-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 06/22/2021] [Indexed: 12/23/2022] Open
Abstract
The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, [Formula: see text], while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution.
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