Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave.

Considerable uncertainty surrounds the timeline of introductions and onsets of local transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) globally1-7. Although a limited number of SARS-CoV-2 introductions were reported in January and February 2020 (refs.8,9), the narrowness of the initial testing criteria, combined with a slow growth in testing capacity and porous travel screening10, left many countries vulnerable to unmitigated, cryptic transmission. Here we use a global metapopulation epidemic model to provide a mechanistic understanding of the early dispersal of infections and the temporal windows of the introduction of SARS-CoV-2 and onset of local transmission in Europe and the USA. We find that community transmission of SARS-CoV-2 was likely to have been present in several areas of Europe and the USA by January 2020, and estimate that by early March, only 1 to 4 in 100 SARS-CoV-2 infections were detected by surveillance systems. The modelling results highlight international travel as the key driver of the introduction of SARS-CoV-2, with possible introductions and transmission events as early as December 2019 to January 2020. We find a heterogeneous geographic distribution of cumulative infection attack rates by 4 July 2020, ranging from 0.78% to 15.2% across US states and 0.19% to 13.2% in European countries. Our approach complements phylogenetic analyses and other surveillance approaches and provides insights that can be used to design innovative, model-driven surveillance systems that guide enhanced testing and response strategies.

Evaluating targeted COVID-19 vaccination strategies with agent-based modeling.

We evaluate approaches to vaccine distribution using an agent-based model of human activity and COVID-19 transmission calibrated to detailed trends in cases, hospitalizations, deaths, seroprevalence, and vaccine breakthrough infections in Florida, USA. We compare the incremental effectiveness for four different distribution strategies at four different levels of vaccine supply, starting in late 2020 through early 2022. Our analysis indicates that the best strategy to reduce severe outcomes would be to actively target high disease-risk individuals. This was true in every scenario, although the advantage was greatest for the intermediate vaccine availability assumptions and relatively modest compared to a simple mass vaccination approach under high vaccine availability. Ring vaccination, while generally the most effective strategy for reducing infections, ultimately proved least effective at preventing deaths. We also consider using age group as a practical surrogate measure for actual disease-risk targeting; this approach also outperforms both simple mass distribution and ring vaccination. We find that quantitative effectiveness of a strategy depends on whether effectiveness is assessed after the alpha, delta, or omicron wave. However, these differences in absolute benefit for the strategies do not change the ranking of their performance at preventing severe outcomes across vaccine availability assumptions.

Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas.

Detailed characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City, NY and Seattle, WA metropolitan areas to build a detailed agent-based model of SARS-CoV-2 infection to estimate the where, when, and magnitude of transmission events during the pandemic's first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered superspreading events (SSEs). Although mass gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies.

SARS-CoV-2 Variants and Vaccines.

Viral variants of concern may emerge with dangerous resistance to the immunity generated by the current vaccines to prevent coronavirus disease 2019 (Covid-19). Moreover, if some variants of concern have increased transmissibility or virulence, the importance of efficient public health measures and vaccination programs will increase. The global response must be both timely and science based.

An introduction to the Marburg virus vaccine consortium, MARVAC.

The emergence of Marburg virus (MARV) in Guinea and Ghana triggered the assembly of the MARV vaccine "MARVAC" consortium representing leaders in the field of vaccine research and development aiming to facilitate a rapid response to this infectious disease threat. Here, we discuss current progress, challenges, and future directions for MARV vaccines.

Methods for the estimation of direct and indirect vaccination effects by combining data from individual- and cluster-randomized trials.

Both individually and cluster randomized study designs have been used for vaccine trials to assess the effects of vaccine on reducing the risk of disease or infection. The choice between individually and cluster randomized designs is often driven by the target estimand of interest (eg, direct versus total), statistical power, and, importantly, logistic feasibility. To combat emerging infectious disease threats, especially when the number of events from one single trial may not be adequate to obtain vaccine effect estimates with a desired level of precision, it may be necessary to combine information across multiple trials. In this article, we propose a model formulation to estimate the direct, indirect, total, and overall vaccine effects combining data from trials with two types of study designs: individual-randomization and cluster-randomization, based on a Cox proportional hazards model, where the hazard of infection depends on both vaccine status of the individual as well as the vaccine status of the other individuals in the same cluster. We illustrate the use of the proposed model and assess the potential efficiency gain from combining data from multiple trials, compared to using data from each individual trial alone, through two simulation studies, one of which is designed based on a cholera vaccine trial previously carried out in Matlab, Bangladesh.

Hypothesis testing and sample size considerations for the test-negative design.

The test-negative design (TND) is an observational study design to evaluate vaccine effectiveness (VE) that enrolls individuals receiving diagnostic testing for a target disease as part of routine care. VE is estimated as one minus the adjusted odds ratio of testing positive versus negative comparing vaccinated and unvaccinated patients. Although the TND is related to case-control studies, it is distinct in that the ratio of test-positive cases to test-negative controls is not typically pre-specified. For both types of studies, sparse cells are common when vaccines are highly effective. We consider the implications of these features on power for the TND. We use simulation studies to explore three hypothesis-testing procedures and associated sample size calculations for case-control and TND studies. These tests, all based on a simple logistic regression model, are a standard Wald test, a continuity-corrected Wald test, and a score test. The Wald test performs poorly in both case-control and TND when VE is high because the number of vaccinated test-positive cases can be low or zero. Continuity corrections help to stabilize the variance but induce bias. We observe superior performance with the score test as the variance is pooled under the null hypothesis of no group differences. We recommend using a score-based approach to design and analyze both case-control and TND. We propose a modification to the TND score sample size to account for additional variability in the ratio of controls over cases. This work enhances our understanding of the data generating mechanism in a test-negative design (TND) and how it is distinct from that of a case-control study due to its passive recruitment of controls.

Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants.

BACKGROUND: The serial interval is the period of time between symptom onset in the primary case and symptom onset in the secondary case. Understanding the serial interval is important for determining transmission dynamics of infectious diseases like COVID-19, including the reproduction number and secondary attack rates, which could influence control measures. Early meta-analyses of COVID-19 reported serial intervals of 5.2 days (95% CI: 4.9-5.5) for the original wild-type variant and 5.2 days (95% CI: 4.87-5.47) for Alpha variant. The serial interval has been shown to decrease over the course of an epidemic for other respiratory diseases, which may be due to accumulating viral mutations and implementation of more effective nonpharmaceutical interventions. We therefore aggregated the literature to estimate serial intervals for Delta and Omicron variants. METHODS: This study followed Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A systematic literature search was conducted of PubMed, Scopus, Cochrane Library, ScienceDirect, and preprint server medRxiv for articles published from April 4, 2021, through May 23, 2023. Search terms were: ("serial interval" or "generation time"), ("Omicron" or "Delta"), and ("SARS-CoV-2" or "COVID-19"). Meta-analyses were done for Delta and Omicron variants using a restricted maximum-likelihood estimator model with a random effect for each study. Pooled average estimates and 95% confidence intervals (95% CI) are reported. RESULTS: There were 46,648 primary/secondary case pairs included for the meta-analysis of Delta and 18,324 for Omicron. Mean serial interval for included studies ranged from 2.3-5.8 days for Delta and 2.1-4.8 days for Omicron. The pooled mean serial interval for Delta was 3.9 days (95% CI: 3.4-4.3) (20 studies) and Omicron was 3.2 days (95% CI: 2.9-3.5) (20 studies). Mean estimated serial interval for BA.1 was 3.3 days (95% CI: 2.8-3.7) (11 studies), BA.2 was 2.9 days (95% CI: 2.7-3.1) (six studies), and BA.5 was 2.3 days (95% CI: 1.6-3.1) (three studies). CONCLUSIONS: Serial interval estimates for Delta and Omicron were shorter than ancestral SARS-CoV-2 variants. More recent Omicron subvariants had even shorter serial intervals suggesting serial intervals may be shortening over time. This suggests more rapid transmission from one generation of cases to the next, consistent with the observed faster growth dynamic of these variants compared to their ancestors. Additional changes to the serial interval may occur as SARS-CoV-2 continues to circulate and evolve. Changes to population immunity (due to infection and/or vaccination) may further modify it.

Covariate-constrained randomization with cluster selection and substitution.

BACKGROUND: An ongoing cluster-randomized trial for the prevention of arboviral diseases utilizes covariate-constrained randomization to balance two treatment arms across four specified covariates and geographic sector. Each cluster is within a census tract of the city of Mérida, Mexico, and there were 133 eligible tracts from which to select 50. As some selected clusters may have been subsequently found unsuitable in the field, we desired a strategy to substitute new clusters while maintaining covariate balance. METHODS: We developed an algorithm that successfully identified a subset of clusters that maximized the average minimum pairwise distance between clusters in order to reduce contamination and balanced the specified covariates both before and after substitutions were made. SIMULATIONS: Simulations were performed to explore some limitations of this algorithm. The number of selected clusters and eligible clusters were varied along with the method of selecting the final allocation pattern. CONCLUSION: The algorithm is presented here as a series of optional steps that can be added to the standard covariate-constrained randomization process in order to achieve spatial dispersion, cluster subsampling, and cluster substitution. Simulation results indicate that these extensions can be used without loss of statistical validity, given a sufficient number of clusters included in the trial.

Designing effective control of dengue with combined interventions.

Viruses transmitted by Aedes mosquitoes, such as dengue, Zika, and chikungunya, have expanding ranges and seem unabated by current vector control programs. Effective control of these pathogens likely requires integrated approaches. We evaluated dengue management options in an endemic setting that combine novel vector control and vaccination using an agent-based model for Yucatán, Mexico, fit to 37 y of data. Our intervention models are informed by targeted indoor residual spraying (TIRS) experiments; trial outcomes and World Health Organization (WHO) testing guidance for the only licensed dengue vaccine, CYD-TDV; and preliminary results for in-development vaccines. We evaluated several implementation options, including varying coverage levels; staggered introductions; and a one-time, large-scale vaccination campaign. We found that CYD-TDV and TIRS interfere: while the combination outperforms either alone, performance is lower than estimated from their separate benefits. The conventional model hypothesized for in-development vaccines, however, performs synergistically with TIRS, amplifying effectiveness well beyond their independent impacts. If the preliminary performance by either of the in-development vaccines is upheld, a one-time, large-scale campaign followed by routine vaccination alongside aggressive new vector control could enable short-term elimination, with nearly all cases avoided for a decade despite continuous dengue reintroductions. If elimination is impracticable due to resource limitations, less ambitious implementations of this combination still produce amplified, longer-lasting effectiveness over single-approach interventions.

The ring vaccination trial design for the estimation of vaccine efficacy and effectiveness during infectious disease outbreaks.

The ring vaccination trial is a recently developed approach for evaluating the efficacy and effectiveness of vaccines, modeled after the surveillance and containment strategy of ring vaccination. Contacts and contacts of contacts of a newly identified disease case form a ring, and these rings are randomized as part of a cluster-randomized trial or with individual randomization within rings. Key advantages of the design include its flexibility to follow the epidemic as it progresses and the targeting of high-risk participants to increase power. We describe the application of the design to estimate the efficacy and effectiveness of an Ebola vaccine during the 2014-2016 West African Ebola epidemic. The design has several notable statistical features. Because vaccination occurs around the time of exposure, the design is particularly sensitive to the choice of per protocol analysis period. If incidence wanes before the per protocol analysis period begins (due to a slow-acting vaccine or a fast-moving pathogen), power can be substantially reduced. Mathematical modeling is valuable for exploring the suitability of the approach in different disease settings. Another statistical feature is zero inflation, which can occur if the chain of transmission does not take off within a ring. In the application to Ebola, the majority of rings had zero subsequent cases. The ring vaccination trial can be extended in several ways, including the definition of rings (e.g. contact-based, spatial, and occupational). The design will be valuable in settings where the spatio-temporal spread of the pathogen is highly focused and unpredictable.

A platform trial design for preventive vaccines against Marburg virus and other emerging infectious disease threats.

BACKGROUND: The threat of a possible Marburg virus disease outbreak in Central and Western Africa is growing. While no Marburg virus vaccines are currently available for use, several candidates are in the pipeline. Building on knowledge and experiences in the designs of vaccine efficacy trials against other pathogens, including SARS-CoV-2, we develop designs of randomized Phase 3 vaccine efficacy trials for Marburg virus vaccines. METHODS: A core protocol approach will be used, allowing multiple vaccine candidates to be tested against controls. The primary objective of the trial will be to evaluate the effect of each vaccine on the rate of virologically confirmed Marburg virus disease, although Marburg infection assessed via seroconversion could be the primary objective in some cases. The overall trial design will be a mixture of individually and cluster-randomized designs, with individual randomization done whenever possible. Clusters will consist of either contacts and contacts of contacts of index cases, that is, ring vaccination, or other transmission units. RESULTS: The primary efficacy endpoint will be analysed as a time-to-event outcome. A vaccine will be considered successful if its estimated efficacy is greater than 50% and has sufficient precision to rule out that true efficacy is less than 30%. This will require approximately 150 total endpoints, that is, cases of confirmed Marburg virus disease, per vaccine/comparator combination. Interim analyses will be conducted after 50 and after 100 events. Statistical analysis of the trial will be blended across the different types of designs. Under the assumption of a 6-month attack rate of 1% of the participants in the placebo arm for both the individually and cluster-randomized populations, the most likely sample size is about 20,000 participants per arm. CONCLUSION: This event-driven design takes into the account the potentially sporadic spread of Marburg virus. The proposed trial design may be applicable for other pathogens against which effective vaccines are not yet available.

COVID-19 vaccine trials: The potential for "hybrid" analyses.

BACKGROUND: Although several COVID-19 vaccines have been found to be effective in rigorous evaluation and have emerging availability in parts of the world, their supply will be inadequate to meet international needs for a considerable period of time. There also will be continued interest in vaccines that are more effective or have improved scalability to facilitate mass vaccination campaigns. Ongoing clinical testing of new vaccines also will be needed as variant strains continue to emerge that may elude some aspects of immunity induced by current vaccines. Randomized clinical trials meaningfully enhance the efficiency and reliability of such clinical testing. In clinical settings with limited or no access to known effective vaccines, placebo-controlled randomized trials of new vaccines remain a preferred approach to maximize the reliability, efficiency and interpretability of results. When emerging availability of licensed vaccines makes it no longer possible to use a placebo control, randomized active comparator non-inferiority trials may enable reliable insights. METHODS: In this article, "hybrid" methods are proposed to address settings where, during the conduct of a placebo-controlled trial, a judgment is made to replace the placebo arm by a licensed COVID-19 vaccine due to emerging availability of effective vaccines in regions participating in that trial. These hybrid methods are based on proposed statistics that aggregate evidence to formally test as well as to estimate the efficacy of the experimental vaccine, by combining placebo-controlled data during the first period of trial conduct with active-controlled data during the second period. RESULTS: Application of the proposed methods is illustrated in two important scenarios where the active control vaccine would become available in regions engaging in the experimental vaccine's placebo-controlled trial: in the first, the active comparator's vaccine efficacy would have been established to be 50%-70% for the 4- to 6-month duration of follow-up of its placebo-controlled trial; in the second, the active comparator's vaccine efficacy would have been established to be 90%-95% during that duration. These two scenarios approximate what has been seen with adenovirus vaccines or mRNA vaccines, respectively, assuming the early estimates of vaccine efficacy for those vaccines would hold over longer-term follow-up. CONCLUSION: The proposed hybrid methods could readily play an important role in the near future in the design, conduct and analysis of randomized clinical trials performed to address the need for multiple additional vaccines reliably established to be safe and have worthwhile efficacy in reducing the risk of symptomatic disease from SARS-CoV-2 infections.

COVID-19 vaccine trials: The use of active controls and non-inferiority studies.

BACKGROUND: Recently emerging results from a few placebo-controlled randomized trials of COVID-19 vaccines revealed estimates of 62%-95% relative reductions in risk of virologically confirmed symptomatic COVID-19 disease, over approximately 2-month average follow-up period. Additional safe and effective COVID-19 vaccines are needed in a timely manner to adequately address the pandemic on an international scale. Such safe and effective vaccines would be especially appealing for international deployment if they also have favorable stability, supply, and potential for implementation in mass vaccination campaigns. Randomized trials provide particularly reliable insights about vaccine efficacy and safety. While enhanced efficiency and interpretability can be obtained from placebo-controlled trials, in settings where their conduct is no longer possible, randomized non-inferiority trials may enable obtaining reliable evaluations of experimental vaccines through direct comparison with active comparator vaccines established to have worthwhile efficacy. METHODS: The usual objective of non-inferiority trials is to reliably assess whether the efficacy of an experimental vaccine is not unacceptably worse than that of an active control vaccine previously established to be effective, likely in a placebo-controlled trial. This is formally achieved by ruling out a non-inferiority margin identified to be the minimum threshold for what would constitute an unacceptable loss of efficacy. This article not only investigates non-inferiority margins, denoted by δ, that address the usual objective of determining whether the experimental vaccine is "at least similarly effective to" the active comparator vaccine in the non-inferiority trial, but also develops non-inferiority margins, denoted by δo, intended to address the worldwide need for multiple safe and effective vaccines by satisfying the less stringent requirement that the experimental vaccine be "at least similarly effective to" an active comparator vaccine having efficacy that satisfies the widely accepted World Health Organization-Food and Drug Administration criteria for "worthwhile" vaccine efficacy. RESULTS: Using the margin δ enables non-inferiority trials to reliably evaluate experimental vaccines that truly are similarly effective to an active comparator vaccine having any level of "worthwhile" efficacy. When active comparator vaccines have efficacy in the range of 50%-70%, non-inferiority trials designed to use the margin δo have appealing properties, especially for experimental vaccines having true efficacy of approximately 60%. CONCLUSION: Non-inferiority trials using the proposed margins may enable reliable randomized evaluations of efficacy and safety of experimental COVID-19 vaccines. Such trials often require approximately two- to three-fold the person-years follow-up than a placebo-controlled trial. This could be achieved, without substantive increases in sample size, by increasing the average duration of follow-up from 2 months to approximately 4-6 months, assuming efficacy of the active comparator vaccine has been reliably evaluated over that longer duration.

Using simulated infectious disease outbreaks to inform site selection and sample size for individually randomized vaccine trials during an ongoing epidemic.

BACKGROUND: Novel strategies are needed to make vaccine efficacy trials more robust given uncertain epidemiology of infectious disease outbreaks, such as arboviruses like Zika. Spatially resolved mathematical and statistical models can help investigators identify sites at highest risk of future transmission and prioritize these for inclusion in trials. Models can also characterize uncertainty in whether transmission will occur at a site, and how nearby or connected sites may have correlated outcomes. A structure is needed for how trials can use models to address key design questions, including how to prioritize sites, the optimal number of sites, and how to allocate participants across sites. METHODS: We illustrate the added value of models using the motivating example of Zika vaccine trial planning during the 2015-2017 Zika epidemic. We used a stochastic, spatially resolved, transmission model (the Global Epidemic and Mobility model) to simulate epidemics and site-level incidence at 100 high-risk sites in the Americas. We considered several strategies for prioritizing sites (average site-level incidence of infection across epidemics, median incidence, probability of exceeding 1% incidence), selecting the number of sites, and allocating sample size across sites (equal enrollment, proportional to average incidence, proportional to rank). To evaluate each design, we stochastically simulated trials in each hypothetical epidemic by drawing observed cases from site-level incidence data. RESULTS: When constraining overall trial size, the optimal number of sites represents a balance between prioritizing highest-risk sites and having enough sites to reduce the chance of observing too few endpoints. The optimal number of sites remained roughly constant regardless of the targeted number of events, although it is necessary to increase the sample size to achieve the desired power. Though different ranking strategies returned different site orders, they performed similarly with respect to trial power. Instead of enrolling participants equally from each site, investigators can allocate participants proportional to projected incidence, though this did not provide an advantage in our example because the top sites had similar risk profiles. Sites from the same geographic region may have similar outcomes, so optimal combinations of sites may be geographically dispersed, even when these are not the highest ranked sites. CONCLUSION: Mathematical and statistical models may assist in designing successful vaccination trials by capturing uncertainty and correlation in future transmission. Although many factors affect site selection, such as logistical feasibility, models can help investigators optimize site selection and the number and size of participating sites. Although our study focused on trial design for an emerging arbovirus, a similar approach can be made for any infectious disease with the appropriate model for the particular disease.

Temporal Confounding in the Test-Negative Design.

In the test-negative design, routine testing at health-care facilities is leveraged to estimate the effectiveness of an intervention such as a vaccine. The odds of vaccination for individuals who test positive for a target pathogen is compared with the odds of vaccination for individuals who test negative for that pathogen, adjusting for key confounders. The design is rapidly growing in popularity, but many open questions remain about its properties. In this paper, we examine temporal confounding by generalizing derivations to allow for time-varying vaccine status, including out-of-season controls, and open populations. We confirm that calendar time is an important confounder when vaccine status varies during the study. We demonstrate that, where time is not a confounder, including out-of-season controls can improve precision. We generalize these results to open populations. We use our theoretical findings to interpret 3 recent papers utilizing the test-negative design. Through careful examination of the theoretical properties of this study design, we provide key insights that can directly inform the implementation and analysis of future test-negative studies.

Designing a Study of Correlates of Risk for Ebola Vaccination.

The recombinant vesicular stomatitis virus (rVSV) Ebola vaccine was shown to be very efficacious in a novel ring vaccination trial in Guinea. However, no correlates of vaccine protection have been established for Ebola vaccines. Several Ebola vaccine candidates are available, but conducting randomized trials of additional candidates in outbreaks is difficult. Establishing correlates of vaccine protection is essential. Here we explore power and sample-size calculations to evaluate potential correlates of risk during an Ebola vaccination campaign in an outbreak. The method requires that a blood draw be made at a predetermined time after vaccination. The statistical analysis estimates the relative risk of the Ebola endpoint occurring from after the blood draw through to the end of follow-up, contrasting vaccine recipients with different values of the immune response marker. The analysis can be done assuming a trichotomous or continuous marker. Under certain assumptions, at an overall vaccine efficacy of 75%, 50 Ebola endpoints in the vaccinees provided good power. At an overall vaccine efficacy of 90%, 20 Ebola endpoints gave good power. Power was highest when more vaccinees were in the high- and low-responder groups versus the middle group and when vaccine efficacy differed the most between the high- and low-responder groups.

Spread of Zika virus in the Americas.

We use a data-driven global stochastic epidemic model to analyze the spread of the Zika virus (ZIKV) in the Americas. The model has high spatial and temporal resolution and integrates real-world demographic, human mobility, socioeconomic, temperature, and vector density data. We estimate that the first introduction of ZIKV to Brazil likely occurred between August 2013 and April 2014 (90% credible interval). We provide simulated epidemic profiles of incident ZIKV infections for several countries in the Americas through February 2017. The ZIKV epidemic is characterized by slow growth and high spatial and seasonal heterogeneity, attributable to the dynamics of the mosquito vector and to the characteristics and mobility of the human populations. We project the expected timing and number of pregnancies infected with ZIKV during the first trimester and provide estimates of microcephaly cases assuming different levels of risk as reported in empirical retrospective studies. Our approach represents a modeling effort aimed at understanding the potential magnitude and timing of the ZIKV epidemic and it can be potentially used as a template for the analysis of future mosquito-borne epidemics.

Determinants of Transmission Risk During the Late Stage of the West African Ebola Epidemic.

Understanding risk factors for Ebola transmission is key for effective prediction and design of interventions. We used data on 860 cases in 129 chains of transmission from the latter half of the 2013-2016 Ebola epidemic in Guinea. Using negative binomial regression, we determined characteristics associated with the number of secondary cases resulting from each infected individual. We found that attending an Ebola treatment unit was associated with a 38% decrease in secondary cases (incidence rate ratio (IRR) = 0.62, 95% confidence interval (CI): 0.38, 0.99) among individuals that did not survive. Unsafe burial was associated with a higher number of secondary cases (IRR = 1.82, 95% CI: 1.10, 3.02). The average number of secondary cases was higher for the first generation of a transmission chain (mean = 1.77) compared with subsequent generations (mean = 0.70). Children were least likely to transmit (IRR = 0.35, 95% CI: 0.21, 0.57) compared with adults, whereas older adults were associated with higher numbers of secondary cases. Men were less likely to transmit than women (IRR = 0.71, 95% CI: 0.55, 0.93). This detailed surveillance data set provided an invaluable insight into transmission routes and risks. Our analysis highlights the key role that age, receiving treatment, and safe burial played in the spread of EVD.