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      An open-access database of infectious disease transmission trees to explore superspreader epidemiology

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      Journal: PLoS Biology
      Publisher: Public Library of Science

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          Abstract

          Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry.

          Abstract

          This study compiles and standardizes reported infectious disease transmission trees to analyze trends in superspreader frequency and generation; these data support theories that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events, and that superspreaders generate other superspreaders.

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          Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

          Qun Li, Xuhua Guan, Peng Wu … (2020)
          Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)
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            The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application

            Stephen A. Lauer, Kyra Grantz, Qifang Bi … (2020)
            Background: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. Objective: To estimate the length of the incubation period of COVID-19 and describe its public health implications. Design: Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. Setting: News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. Participants: Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. Measurements: Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. Results: There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. Limitation: Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. Conclusion: This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases. Primary Funding Source: U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.
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              Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study

              Qifang Bi, Yongsheng Wu, Shujiang Mei … (2020)
              Summary Background Rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, prompted heightened surveillance in Shenzhen, China. The resulting data provide a rare opportunity to measure key metrics of disease course, transmission, and the impact of control measures. Methods From Jan 14 to Feb 12, 2020, the Shenzhen Center for Disease Control and Prevention identified 391 SARS-CoV-2 cases and 1286 close contacts. We compared cases identified through symptomatic surveillance and contact tracing, and estimated the time from symptom onset to confirmation, isolation, and admission to hospital. We estimated metrics of disease transmission and analysed factors influencing transmission risk. Findings Cases were older than the general population (mean age 45 years) and balanced between males (n=187) and females (n=204). 356 (91%) of 391 cases had mild or moderate clinical severity at initial assessment. As of Feb 22, 2020, three cases had died and 225 had recovered (median time to recovery 21 days; 95% CI 20–22). Cases were isolated on average 4·6 days (95% CI 4·1–5·0) after developing symptoms; contact tracing reduced this by 1·9 days (95% CI 1·1–2·7). Household contacts and those travelling with a case were at higher risk of infection (odds ratio 6·27 [95% CI 1·49–26·33] for household contacts and 7·06 [1·43–34·91] for those travelling with a case) than other close contacts. The household secondary attack rate was 11·2% (95% CI 9·1–13·8), and children were as likely to be infected as adults (infection rate 7·4% in children <10 years vs population average of 6·6%). The observed reproductive number (R) was 0·4 (95% CI 0·3–0·5), with a mean serial interval of 6·3 days (95% CI 5·2–7·6). Interpretation Our data on cases as well as their infected and uninfected close contacts provide key insights into the epidemiology of SARS-CoV-2. This analysis shows that isolation and contact tracing reduce the time during which cases are infectious in the community, thereby reducing the R. The overall impact of isolation and contact tracing, however, is uncertain and highly dependent on the number of asymptomatic cases. Moreover, children are at a similar risk of infection to the general population, although less likely to have severe symptoms; hence they should be considered in analyses of transmission and control. Funding Emergency Response Program of Harbin Institute of Technology, Emergency Response Program of Peng Cheng Laboratory, US Centers for Disease Control and Prevention.
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                Author and article information

                Contributors
                Juliana C. Taube:
                ORCID: https://orcid.org/0000-0002-0100-7648
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Paige B. Miller: Role: Formal analysisRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                John M. Drake: Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – review & editing
                Steven Riley: Role: Academic Editor
                Journal
                Journal ID (nlm-ta): PLoS Biol
                Journal ID (iso-abbrev): PLoS Biol
                Journal ID (publisher-id): plos
                Title: PLoS Biology
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Print): 1544-9173
                ISSN (Electronic): 1545-7885
                Publication date (Electronic): 22 June 2022
                Publication date Collection: June 2022
                Publication date PMC-release: 22 June 2022
                Volume: 20
                Issue: 6
                Electronic Location Identifier: e3001685
                Affiliations
                [1 ] Department of Mathematics, Bowdoin College, Brunswick, Maine, United States of America
                [2 ] Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
                [3 ] Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
                Imperial College London, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                [¤]

                Current address: Department of Biology, Georgetown University, Washington, D.C., United States of America

                Author information
                https://orcid.org/0000-0002-0100-7648
                Article
                Publisher ID: PBIOLOGY-D-22-00521
                DOI: 10.1371/journal.pbio.3001685
                PMC ID: 9255728
                PubMed ID: 35731837
                SO-VID: 714a766b-0c73-44bc-82ab-48be07f6993d
                Copyright © © 2022 Taube et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 7 March 2022
                Date accepted : 23 May 2022
                Page count
                Figures: 5, Tables: 1, Pages: 17
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000153, Division of Biological Infrastructure;
                Award ID: DBI-1659683
                Award Recipient :
                ORCID: https://orcid.org/0000-0002-0100-7648
                Funded by: funder-id http://dx.doi.org/10.13039/100000153, Division of Biological Infrastructure;
                Award ID: DBI-1659683
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000082, Division of Graduate Education;
                Award ID: DGE-1545433
                Award Recipient :
                JCT and JMD were supported by the Population Biology of Infectious Diseases REU Site, National Science Foundation grant DBI-1659683 ( https://www.nsf.gov/awardsearch/showAward?AWD_ID=1659683). PBM was supported by National Science Foundation grant DGE-1545433 ( https://www.nsf.gov/awardsearch/showAward?AWD_ID=1545433&HistoricalAwards=false). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Methods and Resources
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Infectious Diseases
                Subject: Viral Diseases
                Subject: SARS
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Infectious Diseases
                Subject: Viral Diseases
                Subject: Covid 19
                Subject: Biology and Life Sciences
                Subject: Evolutionary Biology
                Subject: Evolutionary Systematics
                Subject: Phylogenetics
                Subject: Phylogenetic Analysis
                Subject: Biology and Life Sciences
                Subject: Taxonomy
                Subject: Evolutionary Systematics
                Subject: Phylogenetics
                Subject: Phylogenetic Analysis
                Subject: Computer and Information Sciences
                Subject: Data Management
                Subject: Taxonomy
                Subject: Evolutionary Systematics
                Subject: Phylogenetics
                Subject: Phylogenetic Analysis
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Infectious Diseases
                Subject: Respiratory Infections
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Respiratory Disorders
                Subject: Respiratory Infections
                Subject: Medicine and Health Sciences
                Subject: Pulmonology
                Subject: Respiratory Disorders
                Subject: Respiratory Infections
                Subject: Medicine and Health Sciences
                Subject: Epidemiology
                Subject: Biology and Life Sciences
                Subject: Physiology
                Subject: Plant Physiology
                Subject: Plant Respiration
                Subject: Biology and Life Sciences
                Subject: Plant Science
                Subject: Plant Physiology
                Subject: Plant Respiration
                Subject: Biology and Life Sciences
                Subject: Physiology
                Subject: Physiological Processes
                Subject: Respiration
                Subject: Plant Respiration
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Infectious Diseases
                Subject: Infectious Disease Control
                Subject: Medicine and Health Sciences
                Subject: Medical Conditions
                Subject: Infectious Diseases
                Custom metadata
                PLOS Publication Stage vor-update-to-uncorrected-proof
                Publication Update 2022-07-05
                Data Availability All tree data are available at https://outbreaktrees.ecology.uga.edu/. Intermediate data to reproduce these analyses are also available on Dryad at https://doi.org/10.5061/dryad.nk98sf7w7.

                ScienceOpen disciplines: Life sciences
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                ScienceOpen disciplines: Life sciences

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