Search

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.

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.

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: 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.

BackgroundWe report results of years 2 and 3 of consecutive cluster-randomized controlled trials of trivalent inactivated influenza vaccine (IIV3) in Senegal.MethodsWe cluster-randomized (1:1) 20 villages to annual vaccination with IIV3 or inactivated poliovirus vaccine (IPV) of age-eligible residents (6 months–10 years). The primary outcome was total vaccine effectiveness against laboratory-confirmed influenza illness (LCI) among age-eligible children (modified intention-to-treat population [mITT]). Secondary outcomes were indirect (herd protection) and population (overall community) vaccine effectiveness.ResultsWe vaccinated 74% of 12 408 age-eligible children in year 2 (June 2010–April 11) and 74% of 11 988 age-eligible children in year 3 (April 2011–December 2011) with study vaccines. Annual cumulative incidence of LCI was 4.7 (year 2) and 4.2 (year 3) per 100 mITT child vaccinees of IPV villages. In year 2, IIV3 matched circulating influenza strains. The total effectiveness was 52.8% (95% confidence interval [CI], 32.3–67.0), and the population effectiveness was 36.0% (95% CI, 10.2–54.4) against LCI caused by any influenza strain. The indirect effectiveness against LCI by A/H3N2 was 56.4% (95% CI, 39.0–68.9). In year 3, 74% of influenza detections were vaccine-mismatched to circulating B/Yamagata and 24% were vaccine-matched to circulating A/H3N2. The year 3 total effectiveness against LCI was −14.5% (95% CI, −81.2–27.6). Vaccine effectiveness varied by type/subtype of influenza in both years.ConclusionsIIV3 was variably effective against influenza illness in Senegalese children, with total and indirect vaccine effectiveness present during the year when all circulating strains matched the IIV3 formulation.Clinical Trials RegistrationNCT00893906.

Estimating population-level effects of a vaccine is challenging because there may be interference, i.e., the outcome of one individual may depend on the vaccination status of another individual. Partial interference occurs when individuals can be partitioned into groups such that interference occurs only within groups. In the absence of interference, inverse probability weighted (IPW) estimators are commonly used to draw inference about causal effects of an exposure or treatment. Tchetgen Tchetgen and VanderWeele (2012) proposed a modified IPW estimator for causal effects in the presence of partial interference. Motivated by a cholera vaccine study in Bangladesh, this paper considers an extension of the Tchetgen Tchetgen and VanderWeele IPW estimator to the setting where the outcome is subject to right censoring using inverse probability of censoring weights (IPCW). Censoring weights are estimated using proportional hazards frailty models. The large sample properties of the IPCW estimators are derived, and simulation studies are presented demonstrating the estimators’ performance in finite samples. The methods are then used to analyze data from the cholera vaccine study.

Summary Background Wuhan was the first epicentre of COVID-19 in the world, accounting for 80% of cases in China during the first wave. We aimed to assess household transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and risk factors associated with infectivity and susceptibility to infection in Wuhan. Methods This retrospective cohort study included the households of all laboratory-confirmed or clinically confirmed COVID-19 cases and laboratory-confirmed asymptomatic SARS-CoV-2 infections identified by the Wuhan Center for Disease Control and Prevention between Dec 2, 2019, and April 18, 2020. We defined households as groups of family members and close relatives who did not necessarily live at the same address and considered households that shared common contacts as epidemiologically linked. We used a statistical transmission model to estimate household secondary attack rates and to quantify risk factors associated with infectivity and susceptibility to infection, accounting for individual-level exposure history. We assessed how intervention policies affected the household reproductive number, defined as the mean number of household contacts a case can infect. Findings 27 101 households with 29 578 primary cases and 57 581 household contacts were identified. The secondary attack rate estimated with the transmission model was 15·6% (95% CI 15·2–16·0), assuming a mean incubation period of 5 days and a maximum infectious period of 22 days. Individuals aged 60 years or older were at a higher risk of infection with SARS-CoV-2 than all other age groups. Infants aged 0–1 years were significantly more likely to be infected than children aged 2–5 years (odds ratio [OR] 2·20, 95% CI 1·40–3·44) and children aged 6–12 years (1·53, 1·01–2·34). Given the same exposure time, children and adolescents younger than 20 years of age were more likely to infect others than were adults aged 60 years or older (1·58, 1·28–1·95). Asymptomatic individuals were much less likely to infect others than were symptomatic cases (0·21, 0·14–0·31). Symptomatic cases were more likely to infect others before symptom onset than after (1·42, 1·30–1·55). After mass isolation of cases, quarantine of household contacts, and restriction of movement policies were implemented, household reproductive numbers declined by 52% among primary cases (from 0·25 [95% CI 0·24–0·26] to 0·12 [0·10–0·13]) and by 63% among secondary cases (from 0·17 [0·16–0·18] to 0·063 [0·057–0·070]). Interpretation Within households, children and adolescents were less susceptible to SARS-CoV-2 infection but were more infectious than older individuals. Presymptomatic cases were more infectious and individuals with asymptomatic infection less infectious than symptomatic cases. These findings have implications for devising interventions for blocking household transmission of SARS-CoV-2, such as timely vaccination of eligible children once resources become available. Funding National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities, US National Institutes of Health, and US National Science Foundation.

The current pediatric vaccination program in England and Wales administers Live-Attenuated Influenza Vaccine (LAIV) to children ages 2-16 years old. Annual administration of LAIV to this age group is costly and poses substantial logistical issues. This study aims to evaluate the cost-effectiveness of prioritizing vaccination to age groups within the 2-16 year old age range to mitigate the operational and resource challenges of the current strategy. We performed economic evaluations comparing the influenza vaccination program from 1995-2013 to seven alternative strategies targeted at low risk individuals along the school age divisions Preschool (2-4 years old), Primary school (5-11 years old), and Secondary school (12-16 years old). These extensions are evaluated incrementally on the status quo scenario (vaccinating subgroups at high risk of influenza-related complications and individuals 65+ years old). Impact of vaccination was assessed using a transmission model from a previously published study and updated with new data. At all levels of coverage, all strategies had a 100% probability of being cost-effective at the current National Health Service threshold, £20,000/QALY gained. The incremental analysis demonstrated vaccinating Primary School children was the most cost-efficient strategy compared incrementally against others with an Incremental Cost-Effectiveness Ratio of £639 spent per QALY gained (Net Benefit: 404 M£ [155, 795]). When coverage was varied between 30%, 55%, and 70% strategies which included Primary school children had a higher probability of being cost-effective at lower willingness-to-pay levels. Although children were the vaccine target the majority of QALY gains occurred in the 25-44 years old and 65+ age groups. Influenza strain A/H3N2 incurred the greatest costs and QALYs lost regardless of which strategy was used. Improvement could be made to the current LAIV pediatric vaccination strategy by eliminating vaccination of 2-4 year olds and focusing on school-based delivery to Primary and Secondary school children in tandem.

Background Current urban vector control strategies have failed to contain dengue epidemics and to prevent the global expansion of Aedes-borne viruses (ABVs: dengue, chikungunya, Zika). Part of the challenge in sustaining effective ABV control emerges from the paucity of evidence regarding the epidemiological impact of any Aedes control method. A strategy for which there is limited epidemiological evidence is targeted indoor residual spraying (TIRS). TIRS is a modification of classic malaria indoor residual spraying that accounts for Aedes aegypti resting behavior by applying residual insecticides on exposed lower sections of walls ( Methods/design We are pursuing a two-arm, parallel, unblinded, cluster randomized controlled trial to quantify the overall efficacy of TIRS in reducing the burden of laboratory-confirmed ABV clinical disease (primary endpoint). The trial will be conducted in the city of Merida, Yucatan State, Mexico (population ~ 1million), where we will prospectively follow 4600 children aged 2–15 years at enrollment, distributed in 50 clusters of 5 × 5 city blocks each. Clusters will be randomly allocated (n = 25 per arm) using covariate-constrained randomization. A “fried egg” design will be followed, in which all blocks of the 5 × 5 cluster receive the intervention, but all sampling to evaluate the epidemiological and entomological endpoints will occur in the “yolk,” the center 3 × 3 city blocks of each cluster. TIRS will be implemented as a preventive application (~ 1–2 months prior to the beginning of the ABV season). Active monitoring for symptomatic ABV illness will occur through weekly household visits and enhanced surveillance. Annual sero-surveys will be performed after each transmission season and entomological evaluations of Ae. aegypti indoor abundance and ABV infection rates monthly during the period of active surveillance. Epidemiological and entomological evaluation will continue for up to three transmission seasons. Discussion The findings from this study will provide robust epidemiological evidence of the efficacy of TIRS in reducing ABV illness and infection. If efficacious, TIRS could drive a paradigm shift in Aedes control by considering Ae. aegypti behavior to guide residual insecticide applications and changing deployment to preemptive control (rather than in response to symptomatic cases), two major enhancements to existing practice. Trial registration ClinicalTrials.gov NCT04343521. Registered on 13 April 2020. The protocol also complies with the WHO International Clinical Trials Registry Platform (ICTRP) (Additional file 1). Primary sponsor National Institutes of Health, National Institute of Allergy and Infectious Diseases (NIH/NIAID).

We propose platform trials with outcome-adaptive randomization to efficiently select the most effective coronavirus disease 2019 (COVID-19) treatments. The global spread of severe acute respiratory syndrome coronavirus 2 infection is alarming in its geographic scope and in the number of associated deaths. There are currently no treatments proven to decrease mortality from COVID-19 further than what can be achieved through supportive care. Thus far, the choice of therapeutics has been limited to existing, repurposed medications. Given that some of the medications are perceived to have low toxicity, many have been embraced without evidence. Although remdesivir was recently found to shorten time to symptom resolution, evidence for survival benefit is inconclusive

Public Health Emergencies (PHEs) provide a complex and challenging environment for vaccine evaluation. Under the R&D Blueprint Plan of Action, the World Health Organization (WHO) has convened a group of experts to agree on standard procedures to rapidly evaluate experimental vaccines during PHEs while maintaining the highest scientific and ethical standards. The Blueprint priority diseases, selected for their likelihood to cause PHEs and the lack of adequate medical countermeasures,were used to frame our methodological discussions. Here, we outline major vaccine study designs to be used in PHEs and summarize high-level recommendations for their use in this setting. We recognize that the epidemiology and transmission dynamics of the Blueprint priority diseasesmay be highly uncertain and that the unique characteristics of the vaccines and outbreak settings may affect our study design. To address these challenges, our group underscores the need for novel, flexible,and responsive trial designs. We conclude that assignment to study groups using randomization is a key principle underlying rigorous study design and should be utilized except in exceptional circumstances. Advance planning for vaccine trial designs is critical for rapid and effective response to a PHE and to advance knowledge to address and mitigate future PHEs.

To rapidly evaluate the safety and efficacy of COVID-19 vaccine candidates, prioritizing vaccine trial sites in areas with high expected disease incidence can speed endpoint accrual and shorten trial duration. Mathematical and statistical forecast models can inform the process of site selection, integrating available data sources and facilitating comparisons across locations. We recommend the use of ensemble forecast modeling - combining projections from independent modeling groups - to guide investigators identifying suitable sites for COVID-19 vaccine efficacy trials. We describe an appropriate structure for this process, including minimum requirements, suggested output, and a user-friendly tool for displaying results. Importantly, we advise that this process be repeated regularly throughout the trial, to inform decisions about enrolling new participants at existing sites with waning incidence versus adding entirely new sites. These types of data-driven models can support the implementation of flexible efficacy trials tailored to the outbreak setting.

The regression discontinuity design (RDD), first proposed in the educational psychology literature and popularized in econometrics in the 1960s, has only recently been applied to epidemiologic research. A critical aim of infectious disease epidemiologists and global health researchers is to evaluate disease prevention and control strategies, including the impact of vaccines and vaccination programs. RDDs have very rarely been used in this context. This quasi-experimental approach using observational data is designed to quantify the effect of an intervention when eligibility for the intervention is based on a defined cutoff such as age or grade in school, making it ideally suited to estimating vaccine effects given that many vaccination programs and mass-vaccination campaigns define eligibility in this way. Here, we describe key features of RDDs in general, then specific scenarios, with examples, to illustrate that RDDs are an important tool for advancing our understanding of vaccine effects. We argue that epidemiologic researchers should consider RDDs when evaluating interventions designed to prevent and control diseases. This approach can address a wide range of research questions, especially those for which randomized clinical trials would present major challenges or be infeasible. Finally, we propose specific ways in which RDDs could advance future vaccine research.

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.

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.

Conducting trials of novel interventions during infectious disease emergencies, such as the ongoing Covid-19 pandemic, is increasingly recognized as important for determining the efficacy of potential vaccines and therapies. Clinical trials to evaluate investigational interventions are being implemented as part of the broader efforts to control the spread of an infectious disease and to improve patient outcomes. In such circumstances, however, it can be challenging to acquire the necessary evidence about the effects of the interventions to inform future patient care and public health planning, in part because of the unpredictable size, geographic location, and duration of outbreaks.

Outbreak to pandemic In response to global dispersion of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2), quarantine measures have been implemented around the world. To understand how travel and quarantine influence the dynamics of the spread of this novel human virus, Chinazzi et al. applied a global metapopulation disease transmission model to epidemiological data from China. They concluded that the travel quarantine introduced in Wuhan on 23 January 2020 only delayed epidemic progression by 3 to 5 days within China, but international travel restrictions did help to slow spread elsewhere in the world until mid-February. Their results suggest that early detection, hand washing, self-isolation, and household quarantine will likely be more effective than travel restrictions at mitigating this pandemic. Science , this issue p. 395

We extend the method proposed in a recent work by the Authors for trial-level general surrogate evaluation to allow combinations of biomarkers and provide a procedure for finding the "best" combination of biomarkers based on the absolute prediction error summary of surrogate quality. We use a nonparametric Bayesian model that allows us to select an optimal subset of biomarkers without having to consider a large number of explicit model specifications for that subset. This dramatically reduces the number of model comparisons needed. Given the model's flexibility, complex nonlinear relationships can be fit when enough data are available. We evaluate the operating characteristics of our proposed method in simulations designed to be similar to our motivating example. We use our method to compare and evaluate combinations of biomarkers as trial-level general surrogates for the pentavalent rotavirus vaccine RotaTeq™ (RV5) (Merck & Co, Inc, Kenilworth, New Jersey, USA), finding that the same single biomarker identified in our previously published analysis is likely the optimal subset.

Recent large-scale chikungunya virus (CHIKV) and Zika virus epidemics in the Americas pose a growing public health threat. Given that mosquito bite prevention and vector control are the main prevention methods available to reduce transmission of these viruses, we assessed adherence to these methods in the United States Virgin Islands (USVI). We interviewed 334 USVI residents between December 2014 and February 2015 to measure differences in mosquito prevention practices by gender, income, presence of CHIKV symptoms, and age. Only 27% (91/334) of participants reported having an air conditioner, and of the 91 with air-conditioners, 18 (20%) reported never using it. Annual household income > $50,000 was associated with owning and using an air conditioner (41%; 95% confidence interval [CI]: 28–53% compared with annual household income ≤ $50,000: 17%; 95% CI: 12–22%). The majority of participants reported the presence of vegetation in their yard or near their home (79%; 265) and a cistern on their property (78%; 259). Only 52 (16%) participants reported wearing mosquito repellent more than once per week. Although the majority (80%; 268) of participants reported having screens on all of their windows and doors, most (82%; 273) of those interviewed still reported seeing mosquitoes in their homes. Given the uniformly low adherence to individual- and household-level mosquito bite prevention measures in the USVI, these findings emphasize the need for improved public health messaging and investment in therapeutic and vaccine research to mitigate vector-borne disease outbreaks.

Dengue transmission in Mexico has become a major public health problem. Few epidemiological studies have examined the seroprevalence of dengue in Mexico, and recent estimates are needed to better understand dengue transmission dynamics. We conducted a dengue seroprevalence survey among 1,668 individuals including all age groups in three urban settings in Yucatan, Mexico. Children (< 19 years old) were selected randomly from schools. The adults (≥ 19 years old) were selected from healthcare facilities. Participants were asked to provide a venous blood sample and to answer a brief questionnaire with demographic information. Previous exposure to dengue was determined using indirect immunoglobulin G enzyme-linked immunosorbent assay. The overall seroprevalence was 73.6%. The age-specific seroprevalence increased with age, going from 51.4% (95% confidence interval [CI] = 45.0–57.9%) in children ≤ 8 years to 72% (95% CI = 66.3–77.2%) in the 9- to 14-years old. The highest seroprevalence was 83.4% (95% CI = 77–82.2%) in adults greater than 50 years. The seroprevalence in Merida was 68.6% (95% CI = 65–72%), in Progreso 68.7% (95% CI = 64.2–72.8%), and in Ticul 85.3% (95% CI = 81.9–88.3%). Ticul had the highest seroprevalence in all age groups. Logistic regression analysis showed that age and city of residence were associated with greater risk of prior dengue exposure. The results highlight the level of past exposure to dengue virus including young children. Similar studies should be conducted elsewhere in Mexico and other endemic countries to better understand the transmission dynamics of dengue.

Background A recombinant, live-attenuated, tetravalent dengue vaccine (CYD-TDV) was licensed for children aged ≥9 years in a few countries, but the dependence of vaccine efficacy on baseline immunity status and age groups has not been fully characterized. Methods Combining the 2 phase 3 trials CYD14 and CYD15, we estimated the vaccine efficacy for each of the 4 serotypes of dengue virus (DENV), as well as all serotypes combined, simultaneously stratified by baseline immunity status and age group, while accounting for uncertainty in the baseline immunity status of subjects. Results Baseline seropositive subjects showed high efficacy for all serotypes: 70.2% (95% confidence interval [CI], 57.4%-80.1%) for dengue serotype 1 (DENV-1), 67.9% (95% CI, 49.9%-82.0%) for DENV-2, 77.5% (95% CI, 64.3%-90.2%) for DENV-3, 89.9% (95% CI, 79.8%-99.9%) for DENV-4, and 75.4% (95% CI, 68.3%-81.6%) overall. In contrast, baseline seronegative subjects showed moderate efficacy against DENV-4 (51.2% [95% CI, 20.0%-72.8%]) but no significant efficacy against other serotypes. Among seropositive children, the overall efficacy tended to increase with age: 35.9% (95% CI, -7.6% to 69.3%) for children ≤5 years old, 65.6% (95% CI, 40.3%-84.2%) for those 6-8 years old, 73.4% (95% CI, 62.6%-82.1%) for those 9-11 years old, and 80.6% (95% CI, 72.9%-87.3%) for those 12 years or older. Conclusions The CYD-TDV vaccine was highly efficacious for all dengue serotypes among children aged >5 years who have acquired baseline immunity from previous exposure. Increasing vaccine efficacy with age was not fully explained by increasing prevalence of baseline immunity with age.

Vaccination in populations can have several kinds of effects. Establishing that vaccination produces population-level effects beyond the direct effects in the vaccinated individuals can have important consequences for public health policy. Formal methods have been developed for study designs and analysis that can estimate the different effects of vaccination. However, implementing field studies to evaluate the different effects of vaccination can be expensive, of limited generalizability, or unethical. It would be advantageous to use routinely collected data to estimate the different effects of vaccination. We consider how different types of data are needed to estimate different effects of vaccination. The examples include rotavirus vaccination of young children, influenza vaccination of elderly adults, and a targeted influenza vaccination campaign in schools. Directions for future research are discussed. Copyright © 2017 John Wiley & Sons, Ltd.

After the 2014–2015 outbreak of chikungunya virus in the US Virgin Islands, we compared the prevalence of persistent arthralgia among case-patients and controls. Prevalence was higher in case-patients than controls 6 and 12 months after disease onset. Continued vaccine research to prevent acute illness and long-term sequelae is essential.

Motivated by the rapid spread of a novel coronavirus (2019-nCoV) in Mainland China, we use a global metapopulation disease transmission model to project the impact of both domestic and international travel limitations on the national and international spread of the epidemic. The model is calibrated on the evidence of internationally imported cases before the implementation of the travel quarantine of Wuhan. By assuming a generation time of 7.5 days, the reproduction number is estimated to be 2.4 [90% CI 2.2-2.6]. The median estimate for number of cases before the travel ban implementation on January 23, 2020 is 58,956 [90% CI 40,759 - 87,471] in Wuhan and 3,491 [90% CI 1,924 - 7,360] in other locations in Mainland China. The model shows that as of January 23, most Chinese cities had already received a considerable number of infected cases, and the travel quarantine delays the overall epidemic progression by only 3 to 5 days. The travel quarantine has a more marked effect at the international scale, where we estimate the number of case importations to be reduced by 80% until the end of February. Modeling results also indicate that sustained 90% travel restrictions to and from Mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.

Significance Using realistic simulation of dengue and plausible control programs in the state of Yucatán, Mexico, we show that vaccination programs can have amplifying or interfering dynamic effects when combined with expanded vector control effort. These results should inform planning in regions considering an integrated dengue control program, particularly if using CYD-TDV, which we found to underperform when combined with increasing vector control., 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.

Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases., The growing need for realism in addressing complex public health questions calls for accurate models of the human contact patterns that govern disease transmission. Here, the authors generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features.

Summary Background Cholera was introduced into Haiti in 2010. Since then, more than 820 000 cases and nearly 10 000 deaths have been reported. Oral cholera vaccine (OCV) is safe and effective, but has not been seen as a primary tool for cholera elimination due to a limited period of protection and constrained supplies. Regionally, epidemic cholera is contained to the island of Hispaniola, and the lowest numbers of cases since the epidemic began were reported in 2019. Hence, Haiti may represent a unique opportunity to eliminate cholera with OCV. Methods In this modelling study, we assessed the probability of elimination, time to elimination, and percentage of cases averted with OCV campaign scenarios in Haiti through simulations from four modelling teams. For a 10-year period from January 19, 2019, to Jan 13, 2029, we compared a no vaccination scenario with five OCV campaign scenarios that differed in geographical scope, coverage, and rollout duration. Teams used weekly department-level reports of suspected cholera cases from the Haiti Ministry of Public Health and Population to calibrate the models and used common vaccine-related assumptions, but other model features were determined independently. Findings Among campaigns with the same vaccination coverage (70% fully vaccinated), the median probability of elimination after 5 years was 0–18% for no vaccination, 0–33% for 2-year campaigns focused in the two departments with the highest historical incidence, 0–72% for three-department campaigns, and 35–100% for nationwide campaigns. Two-department campaigns averted a median of 12–58% of infections, three-department campaigns averted 29–80% of infections, and national campaigns averted 58–95% of infections. Extending the national campaign to a 5-year rollout (compared to a 2-year rollout), reduced the probability of elimination to 0–95% and the proportion of cases averted to 37–86%. Interpretation Models suggest that the probability of achieving zero transmission of Vibrio cholerae in Haiti with current methods of control is low, and that bolder action is needed to promote elimination of cholera from the region. Large-scale cholera vaccination campaigns in Haiti would offer the opportunity to synchronise nationwide immunity, providing near-term population protection while improvements to water and sanitation promote long-term cholera elimination. Funding Bill & Melinda Gates Foundation, Global Good Fund, Institute for Disease Modeling, Swiss National Science Foundation, and US National Institutes of Health.

Preexposure prophylaxis (PrEP) for prevention of human immunodeficiency virus (HIV) infection may benefit not only the person who uses it but also their uninfected sexual risk contacts. We developed an agent-based model using a novel trial emulation approach to quantify disseminated effects of PrEP use among men who have sex with men in Atlanta, Georgia, from 2015 to 2017. Model components (subsets of agents connected through partnerships in a sexual network but not sharing partnerships with any other agents) were first randomized to an intervention coverage level or the control group; then, within intervention components, eligible agents were randomized to receive or not receive PrEP. We calculated direct and disseminated (indirect) effects using randomization-based estimators and report corresponding 95% simulation intervals across scenarios ranging from 10% coverage in the intervention components to 90% coverage. A population of 11,245 agents was simulated, with an average of 1,551 components identified. When comparing agents randomized to no PrEP in 70% coverage components with control agents, there was a 15% disseminated risk reduction in HIV incidence (risk ratio = 0.85, 95% simulation interval: 0.65, 1.05). Persons not on PrEP may receive a protective benefit by being in a sexual network with higher PrEP coverage. Agent-based models are useful for evaluating possible direct and disseminated effects of HIV prevention modalities in sexual networks.

SummaryBackgroundCholera was introduced into Haiti in 2010. Since, there have been over 820,000 reported cases and nearly 10,000 deaths. The year 2019 has seen the lowest reported number of cases since the epidemic began. Oral cholera vaccine (OCV) is safe and effective, but has generally not been seen as a primary tool for cholera elimination due to a limited period of protection and constrained supplies. Regionally, epidemic cholera is contained to the island of Hispaniola. Hence, Haiti may represent a unique opportunity to eliminate cholera by use of OCV.MethodsWe assess the probability of elimination and the potential health impact of OCV use in Haiti by leveraging simulations from four independent modeling teams. For a 10-year projection period, we compared the impact of five vaccination campaign scenarios, differing in geographic scope, vaccination coverage, and rollout duration to a status quo scenario without vaccination. Teams used common calibration data and assumptions for vaccine efficacy and vaccination scenarios, but all other model features and assumptions were determined independently.FindingsA two-department OCV campaign proposed in Haiti’s national plan for elimination had less than 50% probability of elimination across models, and only ambitious, nationwide campaigns had a high probability of reaching this goal. Despite their low probability of elimination, two-department campaigns averted a median of 13-58% of infections across models over the five years after the start of vaccination campaigns; a nationwide campaign implemented at the same coverage and rollout duration averted a median of 58-95% of infections across models.InterpretationDespite recent declines in cholera cases in Haiti, bold action is needed to promote elimination of cholera from the region. Large-scale cholera vaccination campaigns in Haiti offer the opportunity to synchronize nationwide immunity, providing near-term protection to the population while improvements to water and sanitation infrastructure create an environment favorable to long-term cholera elimination.Research in ContextEvidence before this studyWe searched PubMed without language or date restrictions on October 4, 2019 for all records matching (“cholera*” AND “Haiti” AND (“vaccin*” OR “elim*”)) in any field and added one known article on the probability of elimination of cholera that was not indexed by PubMed to our review. Of 94 results, four articles were not about the cholera outbreak in Haiti or the use of cholera vaccination, and 34 were not original research articles. Fourteen articles presented research on cholera biology or cholera vaccine biology, either through discussion of Vibrio cholerae genetics, immunogenicity of oral cholera vaccine (OCV), or prospective vaccine candidate antigens. Twenty articles assessed OCV vaccine effectiveness, evaluated OCV campaign implementation or attitudes and knowledge about cholera control, or presented lessons learned on outbreak response and policy as a result of the Haiti cholera outbreak. Seven articles were about general cholera outbreak epidemiology in Haiti, and six articles were related to cholera transmission models outside our research scope.Of the nine remaining articles, five examined the impact of potential OCV campaigns at an early time point when Haiti’s cholera outbreak still exhibited epidemic dynamics, and one other projected the impact of the OCV campaigns planned after Hurricane Matthew in 2016. Two of the articles considered prospects for cholera elimination in Haiti in 2013 and 2014 and found that further targeted interventions were needed. One final study from 2017 modeled the possibility for OCV campaigns to eliminate cholera transmission in the Ouest department within a few years.Added value of this studyPrevious assessments of the impact of OCV use in Haiti occurred during early points of the outbreak when OCV campaigns were unlikely to lead to cholera elimination. Our study projects cholera transmission in Haiti with multiple years of more recent data, and directly examines prospect of cholera elimination in the status quo and under various mass OCV campaign scenarios. In bringing together results from multiple modeling teams, our study provides robust evidence about the current state of cholera transmission across Haiti and the potential impact of multiple mass OCV campaign scenarios.Implications of all of the available evidenceWhile 2019 has seen the lowest number of cholera cases in Haiti since the outbreak began, model simulations suggest that it may be possible for cholera transmission to persist without additional cholera control interventions.While a single two-department vaccination campaign may avert roughly 13-58% of infections with V. cholerae over a five year period, only a nationwide campaign led to a high probability of cholera elimination. Ambitious nationwide vaccination campaigns may break the cycle of endemic cholera transmission in Haiti as long-term improvements to water and sanitation infrastructure, which will limit the effects of potential re-introductions of Vibrio cholerae, are being made.

Background Colombia was the second most affected country during the American Zika virus (ZIKV) epidemic, with over 109,000 reported cases. Despite the scale of the outbreak, limited genomic sequence data were available from Colombia. We sought to sequence additional samples and use genomic epidemiology to describe ZIKV dynamics in Colombia. Methods We sequenced ZIKV genomes directly from clinical diagnostic specimens and infected Aedes aegypti samples selected to cover the temporal and geographic breadth of the Colombian outbreak. We performed phylogeographic analysis of these genomes, along with other publicly-available ZIKV genomes from the Americas, to estimate the frequency and timing of ZIKV introductions to Colombia. Results We attempted PCR amplification on 184 samples; 19 samples amplified sufficiently to perform sequencing. Of these, 8 samples yielded sequences with at least 50% coverage. Our phylogeographic reconstruction indicates two separate introductions of ZIKV to Colombia, one of which was previously unrecognized. We find that ZIKV was first introduced to Colombia in February 2015 (95%CI: Jan 2015 – Apr 2015), corresponding to 5 to 8 months of cryptic ZIKV transmission prior to confirmation in September 2015. Despite the presence of multiple introductions, we find that the majority of Colombian ZIKV diversity descends from a single introduction. We find evidence for movement of ZIKV from Colombia into bordering countries, including Peru, Ecuador, Panama, and Venezuela. Conclusions Similarly to genomic epidemiological studies of ZIKV dynamics in other countries, we find that ZIKV circulated cryptically in Colombia. More accurately dating when ZIKV was circulating refines our definition of the population at risk. Additionally, our finding that the majority of ZIKV transmission within Colombia was attributable to transmission between individuals, rather than repeated travel-related importations, indicates that improved detection and control might have succeeded in limiting the scale of the outbreak within Colombia.

Chikungunya virus (CHIKV), an alphavirus that causes fever and severe polyarthralgia, swept through the Americas in 2014 with almost 2 million suspected or confirmed cases reported by April 2016. In this study, we estimate the direct medical costs, cost of lost wages due to absenteeism, and years lived with disability (YLD) associated with the 2014–2015 CHIKV outbreak in the U.S. Virgin Islands (USVI). For this analysis, we used surveillance data from the USVI Department of Health, medical cost data from three public hospitals in USVI, and data from two studies of laboratory-positive cases up to 12 months post illness. On average, employed case-patients missed 9 days of work in the 12 months following their disease onset, which resulted in an estimated cost of $15.5 million. Estimated direct healthcare costs were $2.9 million for the first 2 months and $0.6 million for 3–12 months following the outbreak. The total estimated cost associated with the outbreak ranged from $14.8 to $33.4 million (approximately 1% of gross domestic product), depending on the proportion of the population infected with symptomatic disease, degree of underreporting, and proportion of cases who were employed. The estimated YLDs associated with long-term sequelae from the CHIKV outbreak in the USVI ranged from 599–1,322. These findings highlight the significant economic burden of the recent CHIKV outbreak in the USVI and will aid policy-makers in making informed decisions about prevention and control measures for inevitable, future CHIKV outbreaks., Author summary Chikungunya, a virus carried and transmitted by mosquitoes, causes fever, headache, and severe joint pain in humans that often resolves within 7–10 days. However, a proportion of cases, up to 79% in some outbreaks, report persistent joint pain and chronic inflammatory rheumatism, resulting in decreased quality of life for months to years following initial infection. In 2014, chikungunya virus swept through the Americas, resulting in almost 2 million suspected or confirmed cases reported by April 2016. Previous studies have noted the large resource burden from chikungunya outbreaks, including high healthcare costs, lost wages due to absenteeism, and decreased quality of life for months following infection. Our work aimed to estimate the direct medical costs, cost of lost productivity due to absenteeism, and years lived with disability associated with the chikungunya outbreak in the U.S. Virgin Islands. This information may aid policy-makers in making informed decisions about prevention and control measures for inevitable, future chikungunya outbreaks.

The understanding of immunological interactions among the four dengue virus (DENV) serotypes and their epidemiological implications is often hampered by the lack of individual-level infection history. Using a statistical framework that infers full infection history, we analyze a prospective pediatric cohort in Nicaragua to characterize how infection history modulates the risks of DENV infection and subsequent clinical disease. After controlling for age, one prior infection is associated with 54% lower, while two or more are associated with 91% higher, risk of a new infection, compared to DENV-naive children. Children >8 years old have 55% and 120% higher risks of infection and subsequent disease, respectively, than their younger peers. Among children with ≥1 prior infection, intermediate antibody titers increase, whereas high titers lower, the risk of subsequent infection, compared with undetectable titers. Such complex dependency needs to be considered in the design of dengue vaccines and vaccination strategies., Lack of knowledge of individual infection history hinders understanding of immunological interactions among DENV serotypes. Here, the authors introduce a framework to infer the relationship between unobserved infection history and subsequent infection and disease risk, and find complex dependencies.

One hundred years ago Sir Ronald Ross published his treatise on a general Theory of Happenings. Dependent happenings are those in which the frequency depends on the number already affected. When there is dependency of events, interventions can have different types of effects. Interventions such as vaccination can have direct protective effects for the person receiving the treatment, as well as indirect/spillover effects for others in the population. Causal inference is a framework for carefully defining the causal effect of a treatment, exposure, or policy, and then determining conditions under which such effects can be estimated from the observed data. We consider here scenarios in which the potential outcomes of an individual can depend on the treatment of other individuals in the population, known as causal inference with interference. Much of the research so far has assumed the population is divided into groups or clusters, and individuals can interfere with others within their clusters but not across clusters. Recent developments have assumed more general forms of interference. We review some of the different types of effects that have been defined for dependent happenings, particularly using the methods of causal inference with interference. Many of the methods are applicable across disciplines, such as infectious diseases, social sciences, and economics.

Influenza A virus subtype H5N1 has been a public health concern for almost 20years due to its potential ability to become transmissible among humans. Phase I and II clinical trials have assessed safety, reactogenicity and immunogenicity of inactivated influenza A/H5N1 virus vaccines. A shortage of vaccine is likely to occur during the first months of a pandemic. Hence, determining whether to give one dose to more people or two doses to fewer people to best protect the population is essential. We use hemagglutination-inhibition antibody titers as an immune correlate for avian influenza vaccines. Using an established relationship to obtain a theoretical vaccine efficacy from immunogenicity data from thirteen arms of six phase I and phase II clinical trials of inactivated influenza A/H5N1 virus vaccines, we assessed: (1) the proportion of theoretical vaccine efficacy achieved after a single dose (defined as primary response level), and (2) whether theoretical efficacy increases after a second dose, with and without adjuvant. Participants receiving vaccine with AS03 adjuvant had higher primary response levels (range: 0.48-0.57) compared to participants receiving vaccine with MF59 adjuvant (range: 0.32-0.47), with no observed trends in primary response levels by antigen dosage. After the first and second doses, vaccine with AS03 at dosage levels 3.75, 7.5 and 15mcg had the highest estimated theoretical vaccine efficacy: Dose (1) 45% (95% CI: 36-57%), 53% (95% CI: 42-63%) and 55% (95% CI: 44-64%), respectively and Dose (2) 93% (95% CI: 89-96%), 97% (95% CI: 95-98%) and 97% (95% CI: 96-100%), respectively. On average, the estimated theoretical vaccine efficacy of lower dose adjuvanted vaccines (AS03 and MF59) was 17% higher than that of higher dose unadjuvanted vaccines, suggesting that including an adjuvant is dose-sparing. These data indicate adjuvanted inactivated influenza A/H5N1 virus vaccine produces high theoretical efficacy after two doses to protect individuals against a potential avian influenza pandemic.

An intermediate response measure that accurately predicts efficacy in a new setting can reduce trial cost and time to product licensure. In this article, we define a trial level general surrogate, which is an intermediate response that can be used to accurately predict efficacy in a new setting. Methods for evaluating general surrogates have been developed previously. Many methods in the literature use trial level intermediate responses for prediction. However, all existing methods focus on surrogate evaluation and prediction in new settings, rather than comparison of candidate general surrogates, and few formalize the use of cross validation to quantify the expected prediction error. Our proposed method uses Bayesian non-parametric modeling and cross-validation to estimate the absolute prediction error for use in evaluating and comparing candidate trial level general surrogates. Simulations show that our method performs well across a variety of scenarios. We use our method to evaluate and to compare candidate trial level general surrogates in several multi-national trials of a pentavalent rotavirus vaccine. We identify at least one immune measure that has potential value as a trial level general surrogate and use it to predict efficacy in a new trial where the clinical outcome was not measured.

Sierra Leone is the most severely affected country by an unprecedented outbreak of Ebola virus disease (EVD) in West Africa. Although successfully contained, the transmission dynamics of EVD and the impact of interventions in the country remain unclear. We established a database of confirmed and suspected EVD cases from May 2014 to September 2015 in Sierra Leone and mapped the spatiotemporal distribution of cases at the chiefdom level. A Poisson transmission model revealed that the transmissibility at the chiefdom level, estimated as the average number of secondary infections caused by a patient per week, was reduced by 43% [95% confidence interval (CI): 30%, 52%] after October 2014, when the strategic plan of the United Nations Mission for Emergency Ebola Response was initiated, and by 65% (95% CI: 57%, 71%) after the end of December 2014, when 100% case isolation and safe burials were essentially achieved, both compared with before October 2014. Population density, proximity to Ebola treatment centers, cropland coverage, and atmospheric temperature were associated with EVD transmission. The household secondary attack rate (SAR) was estimated to be 0.059 (95% CI: 0.050, 0.070) for the overall outbreak. The household SAR was reduced by 82%, from 0.093 to 0.017, after the nationwide campaign to achieve 100% case isolation and safe burials had been conducted. This study provides a complete overview of the transmission dynamics of the 2014-2015 EVD outbreak in Sierra Leone at both chiefdom and household levels. The interventions implemented in Sierra Leone seem effective in containing the epidemic, particularly in interrupting household transmission.

Background The population effects of influenza vaccination in children have not been extensively studied, especially in tropical, developing countries. In rural Senegal, we assessed the total (primary objective) and indirect effectiveness of a trivalent inactivated influenza vaccine (IIV3). Methods In this double-blind, cluster-randomized trial, villages were randomly allocated (1:1) for the high-coverage vaccination of children aged 6 months through 10 years with either the 2008–09 northern hemisphere IIV3 or an inactivated polio vaccine (IPV). Vaccinees were monitored for serious adverse events. All village residents, vaccinated and unvaccinated, were monitored for signs and symptoms of influenza illness using weekly home visits and surveillance in designated clinics. The primary outcome was all laboratory-confirmed symptomatic influenza. Results Between 23 May and 11 July 2009, 20 villages were randomized, and 66.5% of age-eligible children were enrolled (3918 in IIV3 villages and 3848 in IPV villages). Follow-up continued until 28 May 2010. There were 4 unrelated serious adverse events identified. Among vaccinees, the total effectiveness against illness caused by the seasonal influenza virus (presumed to all be drifted A/H3N2, based on antigenic characterization data) circulating at high rates among children was 43.6% (95% confidence interval [CI] 18.6–60.9%). The indirect effectiveness against seasonal A/H3N2 was 15.4% (95% CI -22.0 to 41.3%). The total effectiveness against illness caused by the pandemic influenza virus (A/H1N1pdm09) was -52.1% (95% CI -177.2 to 16.6%). Conclusions IIV3 provided statistically significant, moderate protection to children in Senegal against circulating, pre-2010 seasonal influenza strains, but not against A/H1N1pdm09, which was not included in the vaccine. No indirect effects were measured. Further study in low-resource populations is warranted. Clinical Trials Registration NCT00893906.

Background The implementation of vector control interventions and potential introduction new tools requires baseline data to evaluate their direct and indirect effects. The objective of the study is to present the seroprevalence of dengue infection in a cohort of children 0 to 15 years old followed during 2015 to 2016, the risk factors and the role of enhanced surveillance strategies in three urban sites (Merida, Ticul and Progreso) in Yucatan, Mexico. Methods A cohort of school children and their family members was randomly selected in three urban areas with different demographic, social conditions and levels of transmission. We included results from 1,844 children aged 0 to 15 years. Serum samples were tested for IgG, NS1 and IgM. Enhanced surveillance strategies were established in schools (absenteeism) and cohort families (toll-free number). Results Seroprevalence in children 0 to 15 years old was 46.8 (CI 95% 44.1–49.6) with no difference by sex except in Ticul. Prevalence increased with age and was significantly lower in 0 to 5 years old (26.9%, 95% CI:18.4–35.4) compared with 6 to 8 years old (43.9%, 95% CI:40.1–47.7) and 9 to 15 years old (61.4%, 95% CI:58.0–64.8). Sharing the domestic space with other families increased the risk 1.7 times over the individual families that own or rented their house, while risk was significantly higher when kitchen and bathroom were outside. Complete protection with screens in doors and windows decreased risk of infection. Seroprevalence was significantly higher in the medium and high risk areas. Conclusions The prevalence of antibodies in children 0 to 15 years in three urban settings in the state of Yucatan describe the high exposure and the heterogenous transmission of dengue virus by risk areas and between schools in the study sites. The enhanced surveillance strategy was useful to improve detection of dengue cases with the coincident transmission of chikungunya and Zika viruses., Author summary Dengue is a major public health problem in Latin America. Its transmission is highly heterogeneous, and its burden varies by geographic region, age group affected, serotype and other factors. While surveillance of dengue in the region has improved, several limitations remain, including under detection, misdiagnosis and the complexity of controlling a vector that has adapted to human dwellings in tropical and subtropical urban contexts. Prospective studies have become crucial to understand the transmission of dengue in urban environments and assess the impact of control strategies, such as the introduction of a dengue vaccine or additional vector control interventions. Our findings provide epidemiological data regarding the serological profile and risk factors for dengue infections in a cohort of children 0 to 15 years old in an endemic state in Mexico and confirmed the high exposure in these age groups. Likewise, enhanced and passive surveillance of cases gave us the opportunity to measure the behavior of dengue activity during chikungunya and Zika viruses’ arrival, which we believe will contribute to improve the design of surveillance and control strategies.

Background Local mosquito-borne Zika virus (ZIKV) transmission has been reported in two counties in the contiguous United States (US), prompting the issuance of travel, prevention, and testing guidance across the contiguous US. Large uncertainty, however, surrounds the quantification of the actual risk of ZIKV introduction and autochthonous transmission across different areas of the US. Methods We present a framework for the projection of ZIKV autochthonous transmission in the contiguous US during the 2015–2016 epidemic using a data-driven stochastic and spatial epidemic model accounting for seasonal, environmental, and detailed population data. The model generates an ensemble of travel-related case counts and simulates their potential to have triggered local transmission at the individual level in the 2015–2016 ZIKV epidemic. Results We estimate the risk of ZIKV introduction and local transmission at the county level and at the 0.025° × 0.025° cell level across the contiguous US. We provide a risk measure based on the probability of observing local transmission in a specific location during a ZIKV epidemic modeled after the epidemic observed during the years 2015–2016. The high spatial and temporal resolution of the model allows us to generate statistical estimates of the number of ZIKV introductions leading to local transmission in each location. We find that the risk was spatially heterogeneously distributed and concentrated in a few specific areas that account for less than 1% of the contiguous US population. Locations in Texas and Florida that have actually experienced local ZIKV transmission were among the places at highest risk according to our results. We also provide an analysis of the key determinants for local transmission and identify the key introduction routes and their contributions to ZIKV transmission in the contiguous US. Conclusions This framework provides quantitative risk estimates, fully captures the stochasticity of ZIKV introduction events, and is not biased by the under-ascertainment of cases due to asymptomatic cases. It provides general information on key risk determinants and data with potential uses in defining public health recommendations and guidance about ZIKV risk in the US. Electronic supplementary material The online version of this article (10.1186/s12916-018-1185-5) contains supplementary material, which is available to authorized users.

Background Interventions in infectious diseases can have both direct effects on individuals who receive the intervention as well as indirect effects in the population. In addition, intervention combinations can have complex interactions at the population level, which are often difficult to adequately assess with standard study designs and analytical methods. Discussion Herein, we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly with regard to emerging infectious diseases, one that more accurately reflects the dynamics of the transmission process. In an increasingly complex world, simulations can explicitly represent transmission dynamics, which are critical for proper trial design and interpretation. Certain ethical aspects of a trial can also be quantified using simulations. Further, after a trial has been conducted, simulations can be used to explore the possible explanations for the observed effects. Conclusion Much is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods, and the conduct of clinical trials.

Background Norovirus is a leading cause of outbreaks of acute infectious gastroenteritis worldwide, yet its transmissibility within households and associated risk factors remain unknown in developing countries. Methods Household, demographic, and clinical data were collected from a semi-urban area in south China where an outbreak occurred in the winter of 2014. Using a Bayesian modeling framework, we assessed the transmissibility and potential risk modifiers in both urban and rural households. Results In urban apartment buildings, the secondary attack rates were 84% (95% credible interval [CI] = 60%, 96%) among households of size two and 29% (95% CI = 9.6%, 53%) in larger households. In the rural village, secondary attack rate estimates were lower than the urban setting, 13% (0.51%, 54%) for households of size two and 7.3% (0.38%, 27%) for larger households. Males were 31% (95% CI = 3%, 50%) less susceptible to the disease than female. Water disinfection with chlorine was estimated to reduce environmental risk of infection by 60% (95% CI = 26%, 82%), and case isolation was estimated to reduce person-to-person transmission by 65% (95% CI = 15%, 93%). Nausea and vomiting were not associated with household transmission. Conclusions Norovirus is highly contagious within households, in particular in small households in urban communities. Our results suggest that water disinfection and case isolation are associated with reduction of outbreaks in resource-limited communities.

BackgroundLocal mosquito-borne Zika virus (ZIKV) transmission has been reported in two counties of the continental United State (US), prompting the issuance of travel, prevention, and testing guidance across the continental US. Large uncertainty, however, surrounds the quantification of the actual risk of ZIKV introduction and autochthonous transmission across different areas of the US.MethodWe present a framework for the projection of ZIKV autochthonous transmission in the continental US during the 2015-2016 epidemic, using a data-driven stochastic and spatial epidemic model accounting for seasonal, environmental and detailed population data. The model generates an ensemble of travel-related case counts and simulate their potential to trigger local transmission at individual level.ResultsWe estimate the risk of ZIKV introduction and local transmission at the county level and at the 0.025° x 0.025° cell level across the continental US. We provide a risk measure based on the probability of observing local transmission in a specific location during a ZIKV epidemic modeled after the one observed during the years 2015-2016. The high spatial and temporal resolutions of the model allow us to generate statistical estimates of the number of ZIKV introductions leading to local transmission in each location. We find that the risk is spatially heterogeneously distributed and concentrated in a few specific areas that account for less than 1% of the continental US population. Locations in Texas and Florida that have actually experienced local ZIKV transmission are among the places at highest risk according to our results. We also provide an analysis of the key determinants for local transmission, and identify the key introduction routes and their contributions to ZIKV spread in the continental US.ConclusionsThis framework provides quantitative risk estimates, fully captures the stochas-ticity of ZIKV introduction events, and is not biased by the under-ascertainment of cases due to asymptomatic infections. It provides general information on key risk determinants and data with potential uses in defining public health recommendations and guidance about ZIKV risk in the US.

Background Historically, mosquito control programs successfully helped contain malaria and yellow fever, but recent efforts have been unable to halt the spread of dengue, chikungunya, or Zika, all transmitted by Aedes mosquitoes. Using a dengue transmission model and results from indoor residual spraying (IRS) field experiments, we investigated how IRS-like campaign scenarios could effectively control dengue in an endemic setting. Methods and findings In our model, we found that high levels of household coverage (75% treated once per year), applied proactively before the typical dengue season could reduce symptomatic infections by 89.7% (median of 1000 simulations; interquartile range [IQR]:[83.0%, 94.8%]) in year one and 78.2% (IQR: [71.2%, 88.0%]) cumulatively over the first five years of an annual program. Lower coverage had correspondingly lower effectiveness, as did reactive campaigns. Though less effective than preventative campaigns, reactive and even post-epidemic interventions retain some effectiveness; these campaigns disrupt inter-seasonal transmission, highlighting an off-season control opportunity. Regardless, none of the campaign scenarios maintain their initial effectiveness beyond two seasons, instead stabilizing at much lower levels of benefit: in year 20, median effectiveness was only 27.3% (IQR: [-21.3%, 56.6%]). Furthermore, simply ceasing an initially successful program exposes a population with lowered herd immunity to the same historical threat, and we observed outbreaks more than four-fold larger than pre-intervention outbreaks. These results do not take into account evolving insecticide resistance, thus long-term effectiveness may be lower if new, efficacious insecticides are not developed. Conclusions Using a detailed agent-based dengue transmission model for Yucatán State, Mexico, we predict that high coverage indoor residual spraying (IRS) interventions can largely eliminate transmission for a few years, when applied a few months before the typical seasonal epidemic peak. However, vector control succeeds by preventing infections, which precludes natural immunization. Thus, as a population benefits from mosquito control, it gradually loses naturally acquired herd immunity, and the control effectiveness declines; this occurs across all of our modeled scenarios, and is consistent with other empirical work. Long term control that maintains early effectiveness would require some combination of increasing investment, complementary interventions such as vaccination, and control programs across a broad region to diminish risk of importation., Author summary Using realistic simulation of dengue in the state of Yucatán, Mexico, we show high coverage indoor residual spraying (IRS) interventions can largely eliminate transmission for a few years, when applied proactively. However, initial success relies on population-level immunity, which declines with reduced infection rates, so simulated IRS campaigns stabilize at much lower effectiveness than initially observed. Moreover, if a campaign then suddenly stops, the model predicts large outbreaks until population immunity recovers. These results suggest that mosquito control could enable elimination in endemic settings, but that natural infections must be replaced, e.g. with vaccination, to achieve that end. Regardless, early campaign years’ performance cannot be assumed representative of longterm benefit, and campaign cost estimates must account for increasing population susceptibility.

Response to Zika virus (ZIKV) invasion in Brazil lagged a year from its estimated February 2014 introduction, and was triggered by the occurrence of severe congenital malformations. Dengue (DENV) and chikungunya (CHIKV) invasions tend to show similar response lags. We analyzed geo-coded symptomatic case reports from the city of Merida, Mexico, with the goal of assessing the utility of historical DENV data to infer CHIKV and ZIKV introduction and propagation. About 42% of the 40,028 DENV cases reported during 2008–2015 clustered in 27% of the city, and these clustering areas were where the first CHIKV and ZIKV cases were reported in 2015 and 2016, respectively. Furthermore, the three viruses had significant agreement in their spatio-temporal distribution (Kendall W>0.63; p, Author summary Over the past decades, Aedes-borne viruses (dengue, chikungunya, Zika) have become a major source of morbidity within urban areas. Worldwide, public health response to these viruses is reactive to the occurrence of symptomatic cases (a small proportion of all infections). Here we used geocoded passive surveillance data to determine if areas of historically persistent dengue transmission fuel the introduction and propagation of other Aedes-borne viruses. This article provides quantitative evidence of the strong spatio-temporal overlap that occurs between dengue, chikungunya and Zika, all transmitted by Aedes aegypti mosquitoes in the city. Additionally, it emphasizes the value of analyzing long-term geo-coded passive surveillance information to help identify areas for prioritizing surveillance and control. Findings from this article open a window for considering historical DENV data to make predictions of likely sources of invasion for other emerging Aedes-borne viruses, as well as to the consideration of spatially-targeted approaches for delivery of vector control and surveillance. Arbovirus control in complex urban environments can greatly benefit from exploiting existing spatial information for better delivery of interventions.

Factors affecting our ability to control an Ebola outbreak include transmissibility of the virus and the proportion of transmissions occurring asymptomatically. We performed a meta-analysis of Ebola household secondary attack rate (SAR), disaggregating by type of exposure (direct contact, no direct contact, nursing care, direct contact but no nursing care). The estimated overall household SAR is 12.5% (95% confidence interval [CI], 8.6%-16.3%). Transmission was driven by direct contact, with little transmission occurring in its absence (SAR, 0.8% [95% CI, 0%-2.3%]). The greatest risk factor was the provision of nursing care (SAR, 47.9% [95% CI, 23.3%-72.6%]). There was evidence of a decline in household SAR for direct contact between 1976 and 2014 (P = .018). We estimate that 27.1% (95% CI, 14.5%-39.6%) of Ebola infections are asymptomatic. Our findings suggest that surveillance and containment measures should be effective for controlling Ebola.

The chikungunya virus (CHIKV) epidemic in the Americas is of significant public health importance due to the lack of effective control and prevention strategies, severe disease morbidity among susceptible populations, and potential for persistent arthralgia and long-term impaired physical functionality. Using surveillance data of suspected CHIKV cases, we describe the first reported outbreak in the U.S. Virgin Islands. CHIKV incidence was highest among individuals aged 55-64 years (13.1 cases per 1,000 population) and lowest among individuals aged 0-14 years (1.8 cases per 1,000 population). Incidence was higher among women compared to men (6.6 and 5.0 cases per 1,000 population, respectively). More than half of reported laboratory-positive cases experienced fever lasting 2-7 days, chills/rigor, myalgia, anorexia, and headache. No clinical symptoms apart from the suspected case definition of fever ≥ 38°C and arthralgia were significantly associated with being a reported laboratory-positive case. These results contribute to our knowledge of demographic risk factors and clinical manifestations of CHIKV disease and may aid in mitigating future CHIKV outbreaks in the Caribbean.

Avian influenza A (H7N9), emerged in China in April 2013, sparking fears of a new, highly pathogenic, influenza pandemic. In addition, avian influenza A (H5N1) continues to circulate and remains a threat. Currently, influenza H7N9 vaccines are being tested to be stockpiled along with H5N1 vaccines. These vaccines require two doses, 21 days apart, for maximal protection. We developed a mathematical model to evaluate two possible strategies for allocating limited vaccine supplies: a one-dose strategy, where a larger number of people are vaccinated with a single dose, or a two-dose strategy, where half as many people are vaccinated with two doses. We prove that there is a threshold in the level of protection obtained after the first dose, below which vaccinating with two doses results in a lower illness attack rate than with the one-dose strategy; but above the threshold, the one-dose strategy would be better. For reactive vaccination, we show that the optimal use of vaccine depends on several parameters, with the most important one being the level of protection obtained after the first dose. We describe how these vaccine dosing strategies can be integrated into effective pandemic control plans.

Public Health Emergencies (PHEs) provide a complex and challenging environment for vaccine evaluation. Under the R&D Blueprint Plan of Action, the World Health Organization (WHO) has convened a group of experts to agree on standard procedures to rapidly evaluate experimental vaccines during PHEs while maintaining the highest scientific and ethical standards. The Blueprint priority diseases, selected for their likelihood to cause PHEs and the lack of adequate medical countermeasures, were used to frame our methodological discussions. Here, we outline major vaccine study designs to be used in PHEs and summarize high-level recommendations for their use in this setting. We recognize that the epidemiology and transmission dynamics of the Blueprint priority diseases may be highly uncertain and that the unique characteristics of the vaccines and outbreak settings may affect our study design. To address these challenges, our group underscores the need for novel, flexible, and responsive trial designs. We conclude that assignment to study groups using randomization is a key principle underlying rigorous study design and should be utilized except in exceptional circumstances. Advance planning for vaccine trial designs is critical for rapid and effective response to a PHE and to advance knowledge to address and mitigate future PHEs.One Sentence SummaryAs part of the WHO research and development Blueprint for action to prevent epidemics, we describe key considerations for the design and analysis of trials and studies to evaluate experimental vaccines during public health emergencies.