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Background Previously studied risk factors for rotavirus vaccine failure have not fully explained reduced rotavirus vaccine effectiveness in low-income settings. We assessed the relationship between histo-blood group antigen (HBGA) phenotypes and clinical rotavirus vaccine failure among children Methods Saliva was collected and tested for HBGA phenotype in children who received rotavirus vaccine. The association between secretor and Lewis phenotypes and rotavirus vaccine failure was examined overall and by infecting rotavirus genotype using conditional logistic regression in 218 rotavirus-positive cases with moderate-to-severe diarrhea and 297 matched healthy controls. Results Both nonsecretor and Lewis-negative phenotypes (null phenotypes) were associated with decreased rotavirus vaccine failure across all sites (matched odds ratio, 0.30 [95% confidence interval: 0.16–0.56] or 0.39 [0.25–0.62], respectively]. A similar decrease in risk against rotavirus vaccine failure among null HBGA phenotypes was observed for cases with P[8] and P[4] infection and their matched controls. While we found no statistically significant association between null HBGA phenotypes and vaccine failure among P[6] infections, the matched odds ratio point estimate for Lewis-negative individuals was >4. Conclusions Our study demonstrated a significant relationship between null HBGA phenotypes and decreased rotavirus vaccine failure in a population with P[8] as the most common infecting genotype. Further studies are needed in populations with a large burden of P[6] rotavirus diarrhea to understand the role of host genetics in reduced rotavirus vaccine effectiveness.

We developed an agent-based model using a trial emulation approach to quantify effect measure modification of spillover effects of pre-exposure prophylaxis (PrEP) for HIV among men who have sex with men (MSM) in the Atlanta-Sandy Springs-Roswell metropolitan area, Georgia. PrEP may impact not only the individual prescribed, but also their partners and beyond, known as spillover. We simulated a two-stage randomised trial with eligible components (≥3 agents with ≥1 HIV+ agent) first randomised to intervention or control (no PrEP). Within intervention components, agents were randomised to PrEP with coverage of 70%, providing insight into a high PrEP coverage strategy. We evaluated effect modification by component-level characteristics and estimated spillover effects on HIV incidence using an extension of randomisation-based estimators. We observed an attenuation of the spillover effect when agents were in components with a higher prevalence of either drug use or bridging potential (if an agent acts as a mediator between ≥2 connected groups of agents). The estimated spillover effects were larger in magnitude among components with either higher HIV prevalence or greater density (number of existing partnerships compared to all possible partnerships). Consideration of effect modification is important when evaluating the spillover of PrEP among MSM.

Peer-driven interventions can be effective in reducing HIV injection risk behaviors among people who inject drugs (PWID). We employed a causal mediation framework to examine the mediating role of recall of intervention knowledge in the relationship between a peer-driven intervention and subsequent self-reported HIV injection-related risk behavior among PWID in the HIV Prevention Trials Network (HPTN) 037 study. For each intervention network, the index participant received training at baseline to become a peer educator, while non-index participants and all participants in the control networks received only HIV testing and counseling; recall of intervention knowledge was measured at the 6-month visit for each participant, and each participant was followed to ascertain HIV injection-related risk behaviors at the 12-month visit. We used inverse probability weighting to fit marginal structural models to estimate the total effect (TE) and controlled direct effect (CDE) of the intervention on the outcome. The proportion eliminated (PE) by intervening to remove mediation by the recall of intervention knowledge was computed. There were 385 participants (47% in intervention networks) included in the analysis. The TE and CDE risk ratios for the intervention were 0.47 [95% confidence interval (CI): 0.28, 0.78] and 0.73 (95% CI: 0.26, 2.06) and the PE was 49%. Compared to participants in the control networks, the peer-driven intervention reduced the risk of HIV injection-related risk behavior by 53%. The mediating role of recall of intervention knowledge accounted for less than 50% of the total effect of the intervention, suggesting that other potential causal pathways between the intervention and the outcome, such as motivation and skill, self-efficacy, social norms and behavior modeling, should be considered in future studies.

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.

Homeless shelter residents and staff may be at higher risk of SARS-CoV-2 infection. However, SARS-CoV-2 infection estimates in this population have been reliant on cross-sectional or outbreak investigation data. We conducted routine surveillance and outbreak testing in 23 homeless shelters in King County, Washington to estimate the occurrence of laboratory-confirmed SARS-CoV-2 infection and risk factors during 1/1/2020-5/31/2021. Symptom surveys and nasal swabs were collected for SARS-CoV-2 testing by RT-PCR for residents aged 3 months and older and staff. We collected 12,915 specimens from 2,930 unique participants. We identified 4.74 (95% CI 4.00-5.58) SARS-CoV-2 infections per 100 individuals (residents: 4.96, 95% CI 4.12-5.91; staff: 3.86, 95% CI 2.43-5.79). Most infections were asymptomatic at time of detection (74%) and detected during routine surveillance (73%). Outbreak testing yielded higher test positivity compared to routine surveillance (2.7% vs. 0.9%). Among those infected, residents were less likely to report symptoms than staff. Participants who were vaccinated against seasonal influenza and were current smokers had lower odds of having an infection detected. Active surveillance that includes SARS-CoV-2 testing of all persons is essential in ascertaining the true burden of SARS-CoV-2 infections among residents and staff of congregate settings.

After an outbreak of meningococcal B (MenB) disease at a university, we surveyed students regarding their vaccination status 2 months and 20 months after campus-led vaccination campaigns and compared students’ self-report to vaccination records. Nearly all participants accurately reported the number of vaccine doses at both visits. Among those who received two doses of the vaccine, accurate recall of the timing of MenB vaccination was 85.7% (95% CI: 82.7–88.6) in the short term and 62.9% (95% CI: 56.0–69.8) in the long term. After the outbreak, only one-third reported feeling ‘very confident’ in their MenB disease and vaccine knowledge. Our findings suggest that the validity of self-reported vaccination status among university students in an outbreak setting is high, but that if the duration of protection is unknown and additional doses of vaccine may be needed, documented vaccination records may be preferred over self-report to assess timing of vaccine receipt.

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.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated disease, coronavirus disease 2019 (COVID-19), has caused a devastating pandemic worldwide. Here, we explain basic concepts underlying the transition from an epidemic to an endemic state, where a pathogen is stably maintained in a population. We discuss how the number of infections and the severity of disease change in the transition from the epidemic to the endemic phase and consider the implications of this transition in the context of COVID-19.

Access to COVID-19 vaccines on the global scale has been drastically impacted by structural socio-economic inequities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower middle and low income countries (LMIC) sampled from all WHO regions. We focus on the first critical months of vaccine distribution and administration, exploring counterfactual scenarios where we assume the same per capita daily vaccination rate reported in selected high income countries. We estimate that, in this high vaccine availability scenario, more than 50% of deaths (min-max range: [56% − 99%]) that occurred in the analyzed countries could have been averted. We further consider a scenario where LMIC had similarly early access to vaccine doses as high income countries; even without increasing the number of doses, we estimate an important fraction of deaths (min-max range: [7% − 73%]) could have been averted. In the absence of equitable allocation, the model suggests that considerable additional non-pharmaceutical interventions would have been required to offset the lack of vaccines (min-max range: [15% − 75%]). Overall, our results quantify the negative impacts of vaccines inequities and call for amplified global efforts to provide better access to vaccine programs in low and lower middle income countries.

Background Test-negative design studies for evaluating influenza vaccine effectiveness (VE) enroll patients with acute respiratory infection. Enrollment typically occurs before influenza status is determined, resulting in over-enrollment of influenza-negative patients. With availability of rapid and accurate molecular clinical testing, influenza status could be ascertained before enrollment, thus improving study efficiency. We estimate potential biases in VE when using clinical testing. Methods We simulate data assuming 60% vaccinated, 25% of those vaccinated are influenza positive, and VE of 50%. We show the effect on VE in 5 scenarios. Results Vaccine effectiveness is affected only when clinical testing preferentially targets patients based on both vaccination and influenza status. Vaccine effectiveness is overestimated by 10% if nontesting occurs in 39% of vaccinated influenza-positive patients and 24% of others. VE is also overestimated by 10% if nontesting occurs in 8% of unvaccinated influenza-positive patients and 27% of others. Vaccine effectiveness is underestimated by 10% if nontesting occurs in 32% of unvaccinated influenza-negative patients and 18% of others. Conclusions Although differential clinical testing by vaccine receipt and influenza positivity may produce errors in estimated VE, bias in testing would have to be substantial and overall proportion of patients tested would have to be small to result in a meaningful difference in VE.

Purpose The aim of this study was to measure the effect of a multicomponent human papillomavirus (HPV) vaccine promotion campaign on adolescent HPV vaccine uptake at school-based health centers (SBHCs) in Seattle, WA. Methods Youth-led HPV vaccine promotion campaigns were introduced in 2016 in 13 schools with SBHCs in Seattle. Five other schools with SBHCs served as controls. Vaccination records for students were obtained from the Washington Immunization Information System from September 2012 to August 2018. We compared increase in HPV vaccine uptake in SBHCs between 1) intervention and control schools, and 2) pre- and post-intervention periods in intervention schools using generalized estimating equations. Results HPV vaccine uptake was high at baseline among students that use SBHCs for vaccines and has steadily increased between 2012 and 2018. Implementing the promotion campaign resulted in 14% higher (95% Confidence Interval (CI): 1%, 30%) HPV vaccine uptake in intervention SBHCs compared to control SBHCs, adjusting for time and confounders. Comparing pre-and post-intervention periods in intervention SBHCs, HPV vaccine uptake was 14% higher (95% CI: −4%, 35%) in the post-intervention period. SBHCs that received more active intervention activities saw 9% higher (95% CI: 1%, 21%) vaccine uptake compared to those that received passive intervention. Conclusion The vaccination promotion program implemented in a school-based setting resulted in higher HPV vaccine uptake in the post-intervention period compared to pre-intervention period, but this increase was not statistically significant. Even so, schools that received more intervention activities for longer periods of time had higher HPV vaccine uptake.

This study estimates the overall effect of two influenza vaccination programs consecutively administered in a cluster-randomized trial in western Senegal over the course of two influenza seasons from 2009 to 2011. We apply cutting-edge methodology combining social contact data with infection data to reduce bias in estimation arising from contamination between clusters. Our time-varying estimates reveal a reduction in seasonal influenza from the intervention and a non-significant increase in H1N1 pandemic influenza. We estimate an additive change in overall cumulative incidence (which was 6.13% in the control arm) of -0.68 percentage points during Year 1 of the study (95% CI: −2.53, 1.18). When H1N1 pandemic infections were excluded from analysis, the estimated change was −1.45 percentage points and was significant (95% CI, −2.81, −0.08). Because cross-cluster contamination was low (0–3% of contacts for most villages), an estimator assuming no contamination was only slightly attenuated (−0.65 percentage points). These findings are encouraging for studies carefully designed to minimize spillover. Further work is needed to estimate contamination – and its effect on estimation – in a variety of settings.

The new coronavirus disease 2019 (COVID-19) has required the implementation of severe mobility restrictions and social distancing measures worldwide. While these measures have been proven effective in abating the epidemic in several countries, it is important to estimate the effectiveness of testing and tracing strategies to avoid a potential second wave of the COVID-19 epidemic. We integrate highly detailed (anonymized, privacy-enhanced) mobility data from mobile devices, with census and demographic data to build a detailed agent-based model to describe the transmission dynamics of SARS-CoV-2 in the Boston metropolitan area. We find that enforcing strict social distancing followed by a policy based on a robust level of testing, contact-tracing and household quarantine, could keep the disease at a level that does not exceed the capacity of the health care system. Assuming the identification of 50% of the symptomatic infections, and the tracing of 40% of their contacts and households, which corresponds to about 9% of individuals quarantined, the ensuing reduction in transmission allows the reopening of economic activities while attaining a manageable impact on the health care system. Our results show that a response system based on enhanced testing and contact tracing can play a major role in relaxing social distancing interventions in the absence of herd immunity against SARS-CoV-2.

Cluster-randomized trials are often conducted to assess vaccine effects. Defining estimands of interest before conducting a trial is integral to the alignment between a study’s objectives and the data to be collected and analyzed. This paper considers estimands and estimators for overall, indirect, and total vaccine effects in trials, where clusters of individuals are randomized to vaccine or control. The scenario is considered where individuals self-select whether to participate in the trial, and the outcome of interest is measured on all individuals in each cluster. Unlike the overall, indirect, and total effects, the direct effect of vaccination is shown in general not to be estimable without further assumptions, such as no unmeasured confounding. An illustrative example motivated by a cluster-randomized typhoid vaccine trial is provided.

How much do COVID-19 vaccines reduce transmission? The answer is a moving target

We previously reported a household secondary attack rate (SAR) for SARS-CoV-2 of 18.9% through June 17, 2021. To examine how emerging variants and increased vaccination have affected transmission rates, we searched PubMed from June 18, 2021, through January 7, 2022. Meta-analyses used generalized linear mixed models to obtain SAR estimates and 95%CI, disaggregated by several covariates. SARs were used to estimate vaccine effectiveness based on the transmission probability for susceptibility (VES,p), infectiousness (VEI,p), and total vaccine effectiveness (VET,p). Household SAR for 27 studies with midpoints in 2021 was 35.8% (95%CI, 30.6%−41.3%), compared to 15.7% (95%CI, 13.3%−18.4%) for 62 studies with midpoints through April 2020. Household SARs were 38.0% (95%CI, 36.0%−40.0%), 30.8% (95%CI, 23.5%−39.3%), and 22.5% (95%CI, 18.6%−26.8%) for Alpha, Delta, and Beta, respectively. VEI,p, VES,p, and VET,p were 56.6% (95%CI, 28.7%−73.6%), 70.3% (95%CI, 59.3%−78.4%), and 86.8% (95%CI, 76.7%−92.5%) for full vaccination, and 27.5% (95%CI, −6.4%−50.7%), 43.9% (95%CI, 21.8%−59.7%), and 59.9% (95%CI, 34.4%−75.5%) for partial vaccination, respectively. Household contacts exposed to Alpha or Delta are at increased risk of infection compared to the original wild-type strain. Vaccination reduced susceptibility to infection and transmission to others., Summary: Household secondary attack rates (SARs) were higher for Alpha and Delta variants than previous estimates. SARs were higher to unvaccinated contacts than to partially or fully vaccinated contacts and were higher from unvaccinated index cases than from fully vaccinated index cases.

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. Y.M. thanks M. Clarin for help with the design of Fig. 1. A.A. and Y.M. acknowledge support from Banco Santander (Santander-UZ 2020/0274) and Intesa Sanpaolo Innovation Center. N.E.D., M.E.H., I.M.L., and A.V. acknowledge support from NIH/National Institute of Allergy and Infectious Diseases (NIAID) R56-AI148284. A.P.y.P., M.A., M.L., M.C. and A.V. acknowledge support from COVID Supplement CDC-HHS-6U01IP001137-01. M.C. and A.V. acknowledge support from Google Cloud Healthcare and Life Sciences Solu-tions via the Google Cloud Platform research credits program. A.V. acknowledges support from the McGovern Foundation and the Chleck Foundation. Y.M. acknowledges support by the Government of Aragón through Grant E36-20R, and by Ministerio de Ciencia e Innovación/Agencia Española de Investigación (MCIN/AEI/10.13039/501100011033) through Grant PID2020-115800GB-I00. E.M. acknowledges support by Ministerio de Ciencia e Innovación/Agencia Española de Investigación (MCIN/AEI/10.13039/50110011033) through grants FIS2016-78904-C3-3-P and PID2019-106811GB-C32.

Outbreaks of emerging pathogens pose unique methodological and practical challenges for the design, implementation, and evaluation of vaccine efficacy trials. Lessons learned from COVID-19 highlight the need for innovative and flexible study design and application to quickly identify promising candidate vaccines. Trial design strategies should be tailored to the dynamics of the specific pathogen, location of the outbreak, and vaccine prototypes, within the regional socioeconomic constraints. Mathematical and statistical models can assist investigators in designing infectious disease clinical trials. We introduce key challenges for planning, evaluating, and modelling vaccine efficacy trials for emerging pathogens.

Dengue virus serotypes 1-4 (DENV1-4) and Zika virus (ZIKV) are mosquito-borne flaviviruses that induce both virus-specific and broadly-reactive antibodies. A first DENV infection is thought to induce antibodies that wane over two years to titers that can subsequently enhance severe dengue disease, causing large dengue epidemics. Secondary DENV infection with a different serotype is thought to induce stable, cross-serotype protective antibodies. Low dengue disease incidence following the recent Zika pandemic led to the hypothesis that ZIKV infection is also transiently cross-protective. We investigated antibody kinetics in 4189 children up to 11 years after one and multiple DENV and ZIKV infections in longitudinal cohorts in Nicaragua. We used a DENV inhibition enzyme linked immunosorbent assay (iELISA), which measures antibodies associated with protection against dengue and Zika disease and with enhancement of dengue disease severity. Surprisingly, we found that overall DENV iELISA titers stabilized by 8 months post-primary DENV infection to a half-life longer than a human life and waned, although gradually, after secondary DENV infection. Similarly, DENV iELISA titers were stable or rose after primary ZIKV infection but declined in individuals with histories of DENV and ZIKV infection. In contrast, kinetics of anti-ZIKV antibodies post-ZIKV infection were similar regardless of prior DENV immunity. We observed heterogeneity in DENV iELISA titer, suggesting that individual antibody titer setpoint, rather than waning, is important for future dengue disease risk. Together, these findings change our understanding of anti-flavivirus antibody kinetics and have implications for measuring vaccine efficacy and for predicting future dengue and Zika outbreaks.

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.

Background The effectiveness of the live-attenuated influenza vaccine (LAIV) can vary widely, ranging from 0% to 50%. The reasons for these discrepancies remain largely unclear. Methods We use mathematical models to explore how the efficacy of LAIV is affected by the degree of mismatch with the currently circulating influenza strain and interference with pre-existing immunity. The models incorporate 3 key antigenic distances: the distances between the vaccine strain, pre-existing immunity, and the challenge strain. Results Our models show that an LAIV that is matched with the currently circulating strain is likely to have only modest efficacy. Our results suggest that the efficacy of the vaccine would be increased (optimized) if, rather than being matched to the circulating strain, it is antigenically slightly further from pre-existing immunity than the circulating strain. The models also suggest 2 regimes in which LAIV that is matched to circulating strains may be protective: in children before they have built immunity to circulating strains and in response to novel strains (such as antigenic shifts) which are at substantial antigenic distance from previously circulating strains. We provide an explanation for the variation in vaccine effectiveness between studies and countries of vaccine effectiveness observed during the 2014–2015 influenza season. Conclusions LAIV is offered to children across the world; however, its effectiveness significantly varies between studies. Here, we propose a mechanistic explanation to understand these differences. We further propose a way to select the LAIV strain that would have a higher chance of being protective., Live-attenuated influenza vaccine (LAIV) studies have found effectiveness ranging from 0–50%. Using mathematical models we show that this can be the result of negative interference and LAIV will not be immunogenic for broad regimes. We make suggestions for optimally choosing vaccine strains.

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.