Biology subjects

Marc Lipsitch

Publications and source records attributed to Marc Lipsitch.

4 recordsLinked to original sources

bioRxiv2016

Using simulation to aid trial design: ring-vaccination trials

BackgroundThe 2014-5 West African Ebola epidemic highlights the need for rigorous, rapid clinical trials under difficult circumstances. Challenges include temporally and spatially patchy transmission, and the responsibility to deliver public health interventions during a randomized trial. An innovative design such as ring vaccination with an immediate arm and a delayed arm can address these issues, but complex trials raise complex analysis issues.\n\nMethods and FindingsWe present a stochastic, compartmental model for a ring vaccination trial of a vaccine for an Ebola-like disease. After identification of an index case, a ring of primary contacts is recruited and either vaccinated immediately or after a delay of 21 days. The primary outcome of the trial is effectiveness calculated from cumulative incidence in the two arms, counting cases only from a pre-specified window in which the immediate arm is assumed to be fully protected and the delayed arm is not protected. The results of simulating the trial are used to calculate the sample size necessary for 80% power and the estimates of effectiveness are reported under a variety of assumptions regarding the trial design and implementation.\n\nThe three key components of sample size calculations - attack rate in controls, estimate of incidence difference between the arms, and intracluster correlation coefficient - are dependent on trial design and implementation in a way that can be quantitatively predicted by the model. Under baseline parameter assumptions, we found that a total of 8,900 study participants were needed to achieve 80% power to detect a difference in attack rate between the two arms, whereas a standard approach with the same parameters returns a necessary sample size of 7,100 individuals. Such a study would on average return a vaccine effectiveness estimate of 69.81%, with average 95% confidence interval (41.2%, 84.2%).\n\nWe found that for this design the necessary sample size and estimated effectiveness are sensitive to properties of the vaccine - in particular, pre-exposure and post-exposure efficacy; to two setting-specific parameters over which investigators have little control - rate of infections from outside the ring and overall attack rate in the controls; and to three parameters that are determined by the study design - the time window in which cases are counted, intensity of case-detection and administrative delay in vaccinating individuals.\n\nThis approach replaces assumptions about parameters in the trial with assumptions about disease dynamics and vaccine characteristics at the individual level.\n\nConclusionsIncorporating simulation into the trial design process can improve robustness of sample size calculations. Simulation can identify optimal values for study design parameters that can be controlled. For this specific trial design, vaccine effectiveness depends on properties of the ring vaccination design and on the measurement window, as well as the epidemiologic setting. Rejecting the null likely indicates one or more types of vaccine efficacy at the individual level, but the magnitude of the effect will vary across settings.

Epidemiology↗

bioRxiv2016

Population effect of influenza vaccination under co-circulation of non-vaccine variants and the case for a multi-strain A/H3N2 vaccine component

Some past epidemics of different influenza (sub)types (particularly A/H3N2) in the US saw co-circulation of vaccine-type and variant strains. There is evidence that natural infection with one influenza (sub)type offers short-term protection against infection with another influenza (sub)type (henceforth, cross-immunity). This suggests that such cross-immunity for strains within a (sub)type is expected to be strong. Therefore, while vaccination effective against one strain may reduce transmission of that strain, this may also lead to a reduction of the ability of the vaccine-type strain to suppress spread of a variant strain. It remains unclear what the joint effect of vaccination and cross-immunity is for co-circulating influenza strains, and what is the potential benefit of a bivalent vaccine that protects against both strains.\n\nWe simulated co-circulation of vaccine-type and variant strains under a variety of scenarios. In each scenario, we considered the case when the vaccine efficacy against the variant strain is lower than the efficacy against the vaccine-type strain (monovalent vaccine), as well the case when vaccine is equally efficacious against both strains (bivalent vaccine).\n\nAdministration of a bivalent vaccine results in a significant reduction in the overall incidence of infection compared to administration of a monovalent vaccine, even with lower coverage by the bivalent vaccine. Additionally, we found that the stronger is the degree of cross-immunity, the less beneficial is the increase in coverage levels for the monovalent vaccine, and the more beneficial is the introduction of the bivalent vaccine.\n\nOur work exhibits the limitations of influenza vaccines that have low efficacy against non-vaccine strains, and demonstrates the benefits of vaccines that offer good protection against multiple influenza strains. The results elucidate the need for guarding against the potential co-circulation of non-vaccine strains for an influenza (sub)type, at least during select seasons, possibly through inclusion of multiple strains within a (sub)type (particularly A/H3N2) in a vaccine.

Epidemiology↗

bioRxiv2016

Fractional Dosing of Yellow Fever Vaccine to Extend Supply: A Modeling Study

BackgroundThe ongoing yellow fever (YF) epidemic in Angola strains the global vaccine supply, prompting WHO to adopt dose sparing for its vaccination campaign in Kinshasa in July-August 2016. Although a 5-fold fractional-dose vaccine is similar to standard-dose vaccine in safety and immunogenicity, efficacy is untested. There is an urgent need to ensure the robustness of fractional-dose vaccination by elucidating the conditions under which dose fractionation would reduce transmission.\n\nMethodsWe estimate the effective reproductive number for YF in Angola using disease natural history and case report data. With simple mathematical models of YF transmission, we calculate the infection attack rate (IAR, the proportion of population infected over the course of an epidemic) under varying levels of transmissibility and five-fold fractional-dose vaccine efficacy for two vaccination scenarios: (i) random vaccination in a hypothetical population that is completely susceptible; (ii) the Kinshasa vaccination campaign in July-August 2016 with different age cutoff for fractional-dose vaccines.\n\nFindingsWe estimate the effective reproductive number early in the Angola outbreak was between 5{middle dot}2 and 7{middle dot}1. If vaccine action is all-or-nothing (i.e. a proportion VE of vaccinees receives complete and the remainder receive no protection), n-fold fractionation can dramatically reduce IAR as long as efficacy VE exceeds 1/n. This benefit threshold becomes more stringent if vaccine action is leaky (i.e. the susceptibility of each vaccinee is reduced by a factor that is equal to the vaccine efficacy VE). The age cutoff for fractional-dose vaccines chosen by the WHO for the Kinshasa vaccination campaign (namely, 2 years) provides the largest reduction in IAR if the efficacy of five-fold fractional-dose vaccines exceeds 20%.\n\nInterpretationDose fractionation is a very effective strategy for reducing infection attack rate that would be robust with a large margin for error in case fractional-dose VE is lower than expected.\n\nFundingNIH-MIDAS, HMRF-Hong Kong

Epidemiology↗

bioRxiv2015

Shared genomic variants: identification of transmission routes using pathogen deep sequence data

Sequencing pathogen samples during a communicable disease outbreak is becoming an increasingly common procedure in epidemiological investigations. Identifying who infected whom sheds considerable light on transmission patterns, high-risk settings and subpopulations, and infection control effectiveness. Genomic data shed new light on transmission dynamics, and can be used to identify clusters of individuals likely to be linked by direct transmission. However, identification of individual routes of infection via single genome samples typically remains uncertain. Here, we investigate the potential of deep sequence data to provide greater resolution on transmission routes, via the identification of shared genomic variants. We assess several easily implemented methods to identify transmission routes using both shared variants and genetic distance, demonstrating that shared variants can provide considerable additional information in most scenarios. While shared variant approaches identify relatively few links in the presence of a small transmission bottleneck, these links are highly confident. Furthermore, we proposed hybrid approach additionally incorporating phylogenetic distance to provide greater resolution. We apply our methods to data collected during the 2014 Ebola outbreak, identifying several likely routes of transmission. Our study highlights the power of pathogen deep sequence data as a component of outbreak investigation and epidemiological analyses.

Genomics↗