Stemming cholera tides in Zimbabwe through mass vaccination.


Journal

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
ISSN: 1878-3511
Titre abrégé: Int J Infect Dis
Pays: Canada
ID NLM: 9610933

Informations de publication

Date de publication:
Jul 2020
Historique:
received: 21 02 2020
revised: 25 03 2020
accepted: 26 03 2020
pubmed: 7 5 2020
medline: 17 9 2020
entrez: 7 5 2020
Statut: ppublish

Résumé

In 2018, Zimbabwe declared another major cholera outbreak a decade after recording one of the worst cholera outbreaks in Africa. A mathematical model for cholera was used to estimate the magnitude of the cholera outbreak and vaccination coverage using cholera cases reported data. A Markov chain Monte Carlo method based on a Bayesian framework was used to fit the model in order to estimate the basic reproductive number and required vaccination coverage for cholera control. The results showed that the outbreak had a basic reproductive number of 1.82 (95% credible interval [CrI] 1.53-2.11) and required vaccination coverage of at least 58% (95% Crl 45-68%) to be contained using an oral cholera vaccine of 78% efficacy. Sensitivity analysis demonstrated that a vaccine with at least 55% efficacy was sufficient to contain the outbreak but at higher coverage of 75% (95% Crl 58-88%). However, high-efficacy vaccines would greatly reduce the required coverage, with 100% efficacy vaccine reducing coverage to 45% (95% Crl 35-53%). These findings reinforce the crucial need for oral cholera vaccines to control cholera in Zimbabwe, considering that the decay of water reticulation and sewerage infrastructure is unlikely to be effectively addressed in the coming years.

Sections du résumé

BACKGROUND BACKGROUND
In 2018, Zimbabwe declared another major cholera outbreak a decade after recording one of the worst cholera outbreaks in Africa.
METHODS METHODS
A mathematical model for cholera was used to estimate the magnitude of the cholera outbreak and vaccination coverage using cholera cases reported data. A Markov chain Monte Carlo method based on a Bayesian framework was used to fit the model in order to estimate the basic reproductive number and required vaccination coverage for cholera control.
RESULTS RESULTS
The results showed that the outbreak had a basic reproductive number of 1.82 (95% credible interval [CrI] 1.53-2.11) and required vaccination coverage of at least 58% (95% Crl 45-68%) to be contained using an oral cholera vaccine of 78% efficacy. Sensitivity analysis demonstrated that a vaccine with at least 55% efficacy was sufficient to contain the outbreak but at higher coverage of 75% (95% Crl 58-88%). However, high-efficacy vaccines would greatly reduce the required coverage, with 100% efficacy vaccine reducing coverage to 45% (95% Crl 35-53%).
CONCLUSIONS CONCLUSIONS
These findings reinforce the crucial need for oral cholera vaccines to control cholera in Zimbabwe, considering that the decay of water reticulation and sewerage infrastructure is unlikely to be effectively addressed in the coming years.

Identifiants

pubmed: 32371191
pii: S1201-9712(20)30210-1
doi: 10.1016/j.ijid.2020.03.077
pii:
doi:

Substances chimiques

Cholera Vaccines 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

222-227

Subventions

Organisme : NIAID NIH HHS
ID : R01 AI097405
Pays : United States

Informations de copyright

Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Auteurs

Zindoga Mukandavire (Z)

Centre for Data Science, Coventry University, UK; School of Computing, Electronics and Mathematics, Coventry University, UK. Electronic address: zindoga.mukandavire@coventry.ac.uk.

Portia Manangazira (P)

Ministry of Health and Child Care, Harare, Zimbabwe.

Farai Nyabadza (F)

Department of Mathematics and Applied Mathematics, University of Johannesburg, South Africa.

Diego F Cuadros (DF)

Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH, USA; Health Geography and Disease Modeling Laboratory, University of Cincinnati, Cincinnati, OH, USA.

Godfrey Musuka (G)

ICAP at Columbia University, Harare, Zimbabwe.

J Glenn Morris (JG)

Emerging Pathogens Institute, University of Florida, Gainesville, USA.

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