Brand, Samuel, Munywoki, Patrick K., Walumbe, David, Keeling, Matthew James and Nokes, D. James (2019) Data for Reducing RSV hospitalisation in a lower-income country by vaccinating mothers-to-be and their households. [Dataset]

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Abstract

Respiratory syncytial virus is the leading cause of lower respiratory tract infection among infants. RSV is a priority for vaccine development. In this study, we investigate the potential effectiveness of a two-vaccine strategy aimed at mothers-to-be, thereby boosting maternally acquired antibodies of infants, and their household cohabitants, further cocooning infants against infection. We use a dynamic RSV transmission model which captures transmission both within households and communities, adapted to the changing demographics and RSV seasonality of a low-income country. Model parameters were inferred from past RSV hospitalisations, and forecasts made over a 10-year horizon. We find that a 50% reduction in RSV hospitalisations is possible if the maternal vaccine effectiveness can achieve 75 days of additional protection for newborns combined with a 75% coverage of their birth household co-inhabitants (∼7.5% population coverage).

Item Type:	Dataset
Subjects:	Q Science > QM Human anatomy R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine R Medicine > RG Gynecology and obstetrics
Divisions:	Faculty of Medicine > Warwick Medical School
Type of Data:	Experimental data
Library of Congress Subject Headings (LCSH):	Respiratory syncytial virus, Vaccines, Pregnancy -- Immunological aspects
Publisher:	Harvard Dataverse
Official Date:	5 April 2019
Dates:	Date Event 5 April 2019 Published
Status:	Not Peer Reviewed
Publication Status:	Published
Media of Output:	.jld
Access rights to Published version:	Open Access
Copyright Holders:	University of Warwick
Description:	Data record consists of 3 data files in .jld format, and an accompanying readme file. These dataset(s) contain RSV hospitalization data, individual type distributions, and household type distributions. We developed a mathematical model for simulating RSV transmission amongst households in Kilifi county. The model was parameterized using anonymised datasets generated from the Kilifi Demographic and Health surveillance system (KDHSS), and, daily reports of confirmed RSV hospitalisations at Kilifi county hospital (KCH). The datasets derived from the underlying KDHSS dataset were generated by filtering for people alive and living in Kilifi county on the 1st Jan 2000, 2001, … , 2017. Each person was described by an age category, the number of members of her household, and whether the household contained an under-one year old. Each household was described by the number of over-one year olds and under-one year olds. These data sets, and metadata such as the start and end times of each age category were stored as vectors of arrays in .jld format.
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID 102975 Wellcome Trust http://dx.doi.org/10.13039/100010269
Related URLs:	Related item in WRAP Other
Contributors:	Contribution Name Contributor ID Contact Person Brand, Samuel 61211

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