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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.

Historically, emerging and re-emerging infectious diseases have caused large, deadly, and expensive multi-national 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 of 383 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 outbreak size, average number of secondary infections, the dispersion parameter, and the number of superspreaders. We demonstrated the potential utility of the database through short analyses addressing questions about superspreader epidemiology for a variety of diseases, including COVID-19. First, we compared the frequency and contribution of superspreaders to onward transmission across diseases. COVID-19 outbreaks had significantly fewer superspreaders than outbreaks of SARS and MERS and a dispersion parameter between that of SARS and MERS. Across diseases the presence of more superspreaders was associated with greater outbreak size. Second, we further examined how early spread impacts tree size. Generally, trees sparked by a superspreader had larger outbreak sizes than those trees not sparked by a superspreader, and this trend was significant for COVID-19 trees. Third, we investigated patterns in how superspreaders are infected. Across trees with more than one superspreader, we found support for the theory that superspreaders generate other superspreaders, even when controlling for number of secondary infections. In sum, our findings put the role of superspreading to COVID-19 transmission in perspective with that of SARS and MERS and suggest an avenue for further research on the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry.Author SummaryPublic health investigations often aim to identify who infected whom, or the transmission tree, during outbreaks of infectious diseases. These investigations tend to be resource intensive but valuable as they contain epidemiological information, including the average number of infections caused by each individual and the variation in this number. To date, there remains no standardized format nor comprehensive database of infectious disease transmission trees. To fill this gap, we standardized and compiled more than 350 published transmission trees for 16 directly-transmitted diseases into a database that is publicly available. In this paper, we give an overview of the database construction process, as well as a demonstration of the types of questions that the database can be used to answer related to superspreader epidemiology. For example, we show that COVID-19 outbreaks have fewer superspreaders than outbreaks of SARS and MERS. We also find support for the theory that superspreaders generate other superspreaders. In the future, this database can be used to answer other outstanding questions in the field of epidemiology.

Background Face mask wearing has been identified as an effective strategy to prevent the transmission of SARS-CoV-2, yet mask mandates were never imposed nationally in the United States. This decision resulted in a patchwork of local policies and varying compliance, potentially generating heterogeneities in the local trajectories of COVID-19 in the United States. Although numerous studies have investigated the patterns and predictors of masking behavior nationally, most suffer from survey biases and none have been able to characterize mask wearing at fine spatial scales across the United States through different phases of the pandemic. Objective Urgently needed is a debiased spatiotemporal characterization of mask-wearing behavior in the United States. This information is critical to further assess the effectiveness of masking, evaluate the drivers of transmission at different time points during the pandemic, and guide future public health decisions through, for example, forecasting disease surges. Methods We analyzed spatiotemporal masking patterns in over 8 million behavioral survey responses from across the United States, starting in September 2020 through May 2021. We adjusted for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debiased self-reported masking estimates using bias measures derived by comparing vaccination data from the same survey to official records at the county level. Lastly, we evaluated whether individuals’ perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data. Results We found that county-level masking behavior was spatially heterogeneous along an urban-rural gradient, with mask wearing peaking in winter 2021 and declining sharply through May 2021. Our results identified regions where targeted public health efforts could have been most effective and suggest that individuals’ frequency of mask wearing may be influenced by national guidance and disease prevalence. We validated our bias correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of a small sample size and representation. Self-reported behavior estimates were especially prone to social desirability and nonresponse biases, and our findings demonstrated that these biases can be reduced if individuals are asked to report on community rather than self behaviors. Conclusions Our work highlights the importance of characterizing public health behaviors at fine spatiotemporal scales to capture heterogeneities that may drive outbreak trajectories. Our findings also emphasize the need for a standardized approach to incorporating behavioral big data into public health response efforts. Even large surveys are prone to bias; thus, we advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of health behaviors. Finally, we invite the public health and behavioral research communities to use our publicly available estimates to consider how bias-corrected behavioral estimates may improve our understanding of protective behaviors during crises and their impact on disease dynamics.

Background: An improved understanding of the clinico-epidemiology of bronchiolitis hospitalizations, a clinical surrogate of respiratory syncytial virus (RSV) disease, is critical to inform public health strategies for mitigating the in-patient burden of bronchiolitis in early life. Methods: A retrospective chart review was conducted of all bronchiolitis first admissions ( N = 295) to the Children’s Hospital at Dartmouth-Hitchcock, CHaD, between 1 November 2010 and 31 October 2017 using the relevant International Classification of Diseases (ICD)-9 and ICD-10 codes for this illness. Abstracted data included laboratory confirmation of RSV infection, severity of illness, duration of hospitalization, age at admission in days, weight at admission, prematurity, siblings, and relevant medical pre-existing conditions. Results: Admissions for bronchiolitis were strongly associated with age of the child, the calendar month of an infant’s birth, and the presence of older children in the family. Medical risk factors associated with admission included premature birth and underlying cardiopulmonary disease. Conclusion: The very early age of hospitalization emphasizes the high penetration of RSV in the community, by implication the limited protection afforded by maternal antibody, and the complexity of protecting infants from this infection. Plain Language Summary Although risks for respiratory syncytial virus (RSV)/bronchiolitis hospitalization are well described, few studies have examined, with precision, the age-related frequency and severity of RSV/bronchiolitis. We also explore the implications of RSV clinico-epidemiology for our understanding of the pathogenesis of the disease and development of optimal approaches to prevention.