Biology subjects

Halloran, M. E.

Publications and source records attributed to Halloran, M. E..

6 recordsLinked to original sources

bioRxiv2018

Successes and failures of the live-attenuated influenza vaccine, can we do better?

Live-attenuated vaccines are usually highly effective against many acute viral infections. However, the effectiveness of the live attenuated influenza vaccine (LAIV) can vary widely, ranging from 0% effectiveness in some studies done in the United States to 50% in studies done in Europe. The reasons for these discrepancies remain largely unclear. In this paper we use mathematical models to explore how the efficacy of LAIV is affected by the degree of mismatch with the currently circulating influenza strain and interference with pre-existing immunity. The model incorporates two key antigenic distances - the distance between pre-existing immunity and the currently circulating strain as well as the LAIV strain. Our models show that a LAIV that is matched with the currently circulating strain is likely to have only modest efficacy. Our results suggest that the efficacy of the vaccine would be increased (optimized) if, rather than being matched to the circulating strain, it is antigenically slightly further from pre-existing immunity compared with the circulating strain. The models also suggest two regimes in which LAIV that is matched to circulating strains may provide effective protection. The first is in children before they have built immunity from circulating strains. The second is in response to novel strains (such as antigenic shifts) which are at substantial antigenic distance from previously circulating strains. Our models provide an explanation for the variation in vaccine effectiveness, both between children and adults as well as between studies of vaccine effectiveness observed during the 2014-15 influenza season in different countries.\n\nSignificance StatementThe live-attenuated influenza vaccine, in principle provides an important intervention for the control of both seasonal and pandemic influenza. However vaccine effectiveness studies have found seemingly contradictory results with effectiveness ranging from 0 to 50%. Based on mathematical models we suggest that a major factor responsible for the variable efficacy of the vaccine is negative interference - where pre-existing immunity precludes the vaccine from working. Our models suggest that there are broad regimes for which LAIV will fail to be immunogenic, but also allow us to make suggestions for the choice of vaccine strain that will allow optimization of protective immunity in different scenarios.

epidemiology↗

bioRxiv2018

Designing a Study of Correlates of Risk for Ebola Vaccination

The 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 outbreak situations has become difficult. Establishing correlates of vaccine protection would be useful in helping vaccine candidates become licensed. In this note, we explore power and sample calculations to study potential correlates of risk (protection) during an Ebola vaccination campaign in an outbreak situation under a number of 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 under certain assumptions. In the May - July 2018 Ebola outbreak in DRC, over 3000 individuals were vaccinated, with no reported cases in vaccinated individuals. To be feasible, this type of study need Ebola endpoints in vaccinated individuals.

epidemiology↗

bioRxiv2018

Quantifying the risk of local Zika virus transmission in the continental US during the 2015-2016 ZIKV epidemic

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.\n\nMethodWe 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.\n\nResultsWe estimate the risk of ZIKV introduction and local transmission at the county level and at the 0.025{degrees} x 0.025{degrees} 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.\n\nConclusionsThis 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.

epidemiology↗

bioRxiv2018

Considerations for the design of vaccine efficacy trials during public health emergencies

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.\n\nOne 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.

epidemiology↗

bioRxiv2017

Simulations for Designing and Interpreting Intervention Trials in Infectious Diseases

Here we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly in emerging infectious disease, that more accurately reflects the dynamics of the transmission process. Interventions in infectious diseases can have indirect effects on those not receiving the intervention as well as direct effects on those receiving the intervention. Combinations of interventions can have complex interactions at the population level. These often cannot be adequately addressed with standard study designs and analytic methods. Simulations can help to accurately represent transmission dynamics in an increasingly complex world which is critical for proper trial design and interpretation. Some ethical aspects of a trial can also be quantified using simulations. After a trial has been conducted, simulations can be used to explore possible explanations for the observed effects. A great deal 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.

epidemiology↗

bioRxiv2017

Preliminary results of models to predict areas in the Americas with increased likelihood of Zika virus transmission in 2017.

Numerous Zika virus vaccines are being developed. However, identifying sites to evaluate the efficacy of a Zika virus vaccine is challenging due to the general decrease in Zika virus activity. We compare results from three different modeling approaches to estimate areas that may have increased relative risk of Zika virus transmission during 2017. The analysis focused on eight priority countries (i.e., Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Mexico, Panama, and Peru). The models projected low incidence rates during 2017 for all locations in the priority countries but identified several subnational areas that may have increased relative risk of Zika virus transmission in 2017. Given the projected low incidence of disease, the total number of participants, number of study sites, or duration of study follow-up may need to be increased to meet the efficacy study endpoints.

epidemiology↗