Motivated by the rapid spread of coronavirus disease 2019 (COVID-19) in mainland China, we use a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic. The model is calibrated on the basis of internationally reported cases and shows that, at the start of the travel ban from Wuhan on 23 January 2020, most Chinese cities had already received many infected travelers. The travel quarantine of Wuhan delayed the overall epidemic progression by only 3 to 5 days in mainland China but had a more marked effect on the international scale, where case importations were reduced by nearly 80% until mid-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.
A maximum likelihood procedure is given for estimating houshold and community transmission parameters from observed influenza infection data. The estimator for the household transmission probability is an improvement over the classical secondary attack rate calculations because it factors out community-acquired infections from true secondary infections. The mathematical model used does not require the specification of infection onset times and, therefore, can be used with serologic data which detect asymptomatic infections. Infection data were derived by seroiogy and virus isolation from the Tecumseh Respiratory Iliness Study and the Seattle Flu Study for the years 1975–1979. Included were seasons of influenza B and influenza A subtypes H1N1 and H3N2. The transmission characteristics of influenza B and influenza A(H3N2) and A(H1N1) outbreaks during this period are compared. Influenza A(H1N1), A(H3N2) and infiuenza B are found to be in descending order both in terms of ease of spread in the household and intensity of the epidemic in the community. Children are found to be the main introducers of influenza into households. The degree of estimation error from the misclassification of infected and susceptible individuals is illustrated with a stochastic simulation model. This model simulates the expected number of detected infections at different levels of sensitivity and specificity for the serologic tests used. Other sources of estimation error, such as deviation from the model assumption of uniform community exposure and the possible presence of superspreaders, are also discussed.
Abstract A deterministic model for the transmission of an acute infectious disease in a heterogeneous, nonrandomly mixing population is developed. This model facilitates the estimation of transmission probabilities from the observed attack rates. If some of the members of the population are vaccinated, then the vaccine efficacy (VE), defined as the relative reduction in the transmission probability due to vaccination, can be estimated. We provide several estimators of VE, depending on the amount of information available on the mixing pattern and on the action of the vaccine. We show that if vaccinated persons increase the frequency of their contacts with infectious persons, then estimators ignoring this change in behavior may substantially underestimate the VE.
ABSTRACT Background A global shift to bivalent mRNA vaccines is ongoing to counterbalance diminishing monovalent vaccine effectiveness (VE) due to the evolution of SARS-CoV-2 variants, yet substantial variation in the bivalent VE exists across studies and a complete picture is lacking. Methods We searched papers evaluating SARS-CoV-2 bivalent mRNA vaccines on PubMed, Web of Science, Cochrane Library, Google Scholar, Embase, Scopus, bioRxiv, and medRxiv published from September 1st, 2022, to November 8th, 2023. Pooled VE against Omicron-associated infection and severe events was estimated in reference to unvaccinated, ≥2 monovalent doses, and ≥3 monovalent doses. Results From 630 citations identified, 28 studies were included, involving 55,393,303 individuals. Bivalent boosters demonstrated superior protection against symptomatic or any infection compared to unvaccinated, ≥2 monovalent doses, and ≥3 monovalent doses, with corresponding relative VE estimated as 53.5% (95% CI: - 22.2-82.3%), 30.8% (95% CI: 22.5-38.2%), and 28.4% (95% CI: 10.2-42.9%) for all ages, and 22.5% (95% CI: 16.8-39.8%), 31.4% (95% CI: 27.7-35.0%), and 30.6% (95% CI: -13.2-57.5%) for adults ≥60 years old. Pooled bivalent VE estimates against severe events were higher, 72.9% (95% CI: 60.5-82.4%), 57.6% (95% CI: 42.4-68.8%), and 62.1% (95% CI: 54.6-68.3%) for all ages, and 72.0% (95% CI: 51.4-83.9%), 63.4% (95% CI: 41.0-77.3%), and 60.7% (95% CI: 52.4-67.6%) for adults ≥60 years old, compared to unvaccinated, ≥2 monovalent doses, and ≥3 monovalent doses, respectively. Conclusions Bivalent boosters demonstrated higher VE against severe outcomes than monovalent boosters across age groups, highlighting the critical need for improving vaccine coverage, especially among the vulnerable older subpopulation.
Highly pathogenic avian influenza A (subtype H5N1) is threatening to cause a human pandemic of potentially devastating proportions. We used a stochastic influenza simulation model for rural Southeast Asia to investigate the effectiveness of targeted antiviral prophylaxis, quarantine, and pre-vaccination in containing an emerging influenza strain at the source. If the basic reproductive number ( R 0 ) was below 1.60, our simulations showed that a prepared response with targeted antivirals would have a high probability of containing the disease. In that case, an antiviral agent stockpile on the order of 100,000 to 1 million courses for treatment and prophylaxis would be sufficient. If pre-vaccination occurred, then targeted antiviral prophylaxis could be effective for containing strains with an R 0 as high as 2.1. Combinations of targeted antiviral prophylaxis, pre-vaccination, and quarantine could contain strains with an R 0 as high as 2.4.
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.
Abstract Conducting vaccine efficacy trials during outbreaks of emerging pathogens poses particular challenges. The ‘Ebola ça suffit’ trial in Guinea used a novel ring vaccination cluster randomized design to target populations at highest risk of infection. Another key feature of the trial was the use of a delayed vaccination arm as a comparator, in which clusters were randomized to immediate vaccination or vaccination 21 days later. This approach, chosen to improve ethical acceptability of the trial, complicates the statistical analysis as participants in the comparison arm are eventually protected by vaccine. Furthermore, for infectious diseases, we observe time of illness onset and not time of infection, and we may not know the time required for the vaccinee to develop a protective immune response. As a result, including events observed shortly after vaccination may bias the per protocol estimate of vaccine efficacy. We provide a framework for approximating the bias and power of any given per protocol analysis period as functions of the background infection hazard rate, disease incubation period, and vaccine immune response. We use this framework to provide recommendations for designing standard vaccine efficacy trials and trials with a delayed vaccination comparator. Briefly, narrower analysis periods within the correct window can minimize or eliminate bias but may suffer from reduced power. Designs should be reasonably robust to misspecification of the incubation period and time to develop a vaccine immune response.
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