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An open-access database of infectious disease transmission trees to explore superspreader epidemiology [1]

['Juliana C. Taube', 'Department Of Mathematics', 'Bowdoin College', 'Brunswick', 'Maine', 'United States Of America', 'Paige B. Miller', 'Odum School Of Ecology', 'University Of Georgia', 'Athens']

Date: 2022-07

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.

Introduction

In the past 20 years, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks of Severe Acute Respiratory Syndrome Coronavirus 1 (SARS-CoV), Zika, Ebola, measles, and SARS-CoV-2 (Coronavirus Disease 2019 (COVID-19)). During outbreaks, public health officials conduct routine investigations to identify who infected whom and reconstruct the transmission tree. Transmission trees visualize transmission between cases as directed networks with nodes representing individuals and edges representing transmission from person to person. Transmission trees are typically reassembled by case-finding, contact-tracing, and detailed epidemiological interviews, followed sometimes by genome sequencing and/or probabilistic reconstruction, where the probability that one case infected another is calculated for each pair of cases [1,2]. These investigations are costly but valuable because transmission trees are information rich, including details about the settings of transmission and variation in number of secondary infections.

When published, transmission trees are shown and described in a variety of formats that makes them difficult to compare across outbreaks, let alone pathogens. Some are presented graphically using a number of different symbols and colors, or are buried in the text, making connections hard to piece together. The primary goal of this project was to create a standardized database of transmission trees that is easily accessible and analyzable. We hope that the OutbreakTrees database allows scientists and public health officials to take further advantage of outbreak investigations and their findings.

One phenomenon that is apparent in transmission trees is superspreading, which is important to the propagation patterns of several infectious diseases [3]. Lloyd-Smith and colleagues [3] quantitatively defined superspreaders as cases that cause more secondary infections than the 99th percentile of a Poisson(R 0 ) distribution, where R 0 is the basic reproductive number, or average number of secondary infections per case. Lloyd-Smith and colleagues [3] also conceptualized the offspring distribution (i.e., the number of infections caused by each infected individual) as a negative binomial distribution with dispersion parameter k and mean R. Large values of k denote little variation in number of secondary infections caused by each case, while small values of k (k<1) correspond to high heterogeneity in the offspring distribution. It was hypothesized that intermediate dispersion parameters between 0.1 and 1, depending on R, would give rise to the highest proportion of cases causing superspreading events [3].

Lloyd-Smith and colleagues’ theory on superspreading assumes stability of R and k over the course of an outbreak. In reality, most outbreaks are subject to control measures. These control measures, as well as changes in behavior, can reduce disease transmission and disperse the offspring distribution, thus leading to shifts in R and k from their pre-control values, as explored by [3]. Given information on the timing of control measures, parameter values can be compared before and after controls were imposed. In the absence of this information, we propose that a comparison of parameter values in the first versus second half of a transmission tree indicates the effect of control measures and behavior changes on a given transmission tree.

While previous work has characterized the biological and social factors that give rise to superspreading events [4], how superspreaders are generated (i.e., who spreads to superspreaders) is poorly understood. In 2020, Beldomenico [5] suggested that the generation of superspreaders may be linked to biological patterns in initial viral dosage: If individuals with unusually high viral shedding cause those they infect to also have high viral shedding, then cases infected by superspreaders may be disproportionately likely to be superspreaders themselves. Another possibility is that superspreaders may be more likely to engage in riskier behavior (such as attending large gatherings or not taking precautionary measures) making them more likely to infect others with similar behavior. This behavioral heterogeneity may be a larger contributor to superspreader generation than biological heterogeneity [6]. We investigate this issue using transmission tree data, hypothesizing that superspreaders will be more likely to be infected by other superspreaders than non-superspreading cases.

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[1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001685

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