fluEvidenceSynthesis: An R package for evidence synthesis based analysis of epidemiological outbreaks

Edwin van Leeuwen; Petra Klepac; Dominic Thorrington; Richard Pebody; Marc Baguelin

doi:10.1371/journal.pcbi.1005838

fluEvidenceSynthesis: An R package for evidence synthesis based analysis of epidemiological outbreaks

Edwin van Leeuwen^*, Petra Klepac, Dominic Thorrington, Richard Pebody, Marc Baguelin

^*Corresponding author for this work

Modelling Economics Unit

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

Abstract

Public health related decisions often have to balance the cost of intervention strategies with the benefit of the reduction in disease burden. While the cost can often be inferred, forward modelling of the effect of different intervention options is complicated and disease specific. Here we introduce a package that is aimed to simplify this process. The package allows one to infer parameters using a Bayesian approach, perform forward modelling of the likely results of the proposed intervention and finally perform cost effectiveness analysis of the results. The package is based on a method previously used in the UK to inform vaccination strategies for influenza, with extensions to make it easily adaptable to other diseases and data sources.

Original language	English
Article number	e1005838
Journal	PLoS Computational Biology
Volume	13
Issue number	11
DOIs	https://doi.org/10.1371/journal.pcbi.1005838
Publication status	Published - Nov 2017

Bibliographical note

Funding Information:
EvL, PK and MB were funded by the National Institute for Health Research (NIHR) Health Protection Research Units (HPRU) in Respiratory Infections (EvL, Imperial College London), Modelling Methodology (PK, Imperial College London) and Immunisation (MB, London School of Hygiene and Tropical Medicine) in partnership with Public Health England (PHE). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England http://www.nihr.ac.uk/about-us/how-we-are-managed/our-structure/research/health-protection-research-units.htm. EvL also acknowledges funding from the UK Medical Research Council (Project MR/J008761/1): https://www.mrc.ac.uk. DT was funded by the I-MOVE+ (Integrated Monitoring of Vaccines in Europe) project that received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement #634446: https://ec.europa.eu/programmes/horizon2020/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We would like to thank the R Epidemics Consortium (RECON) for guidelines and advice on development of R-packages for analysing epidemics. The research was performed in partnership with Public Health England (PHE). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England.

Publisher Copyright:
© 2017 van Leeuwen et al.

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abstract = "Public health related decisions often have to balance the cost of intervention strategies with the benefit of the reduction in disease burden. While the cost can often be inferred, forward modelling of the effect of different intervention options is complicated and disease specific. Here we introduce a package that is aimed to simplify this process. The package allows one to infer parameters using a Bayesian approach, perform forward modelling of the likely results of the proposed intervention and finally perform cost effectiveness analysis of the results. The package is based on a method previously used in the UK to inform vaccination strategies for influenza, with extensions to make it easily adaptable to other diseases and data sources.",

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note = "Funding Information: EvL, PK and MB were funded by the National Institute for Health Research (NIHR) Health Protection Research Units (HPRU) in Respiratory Infections (EvL, Imperial College London), Modelling Methodology (PK, Imperial College London) and Immunisation (MB, London School of Hygiene and Tropical Medicine) in partnership with Public Health England (PHE). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England http://www.nihr.ac.uk/about-us/how-we-are-managed/our-structure/research/health-protection-research-units.htm. EvL also acknowledges funding from the UK Medical Research Council (Project MR/J008761/1): https://www.mrc.ac.uk. DT was funded by the I-MOVE+ (Integrated Monitoring of Vaccines in Europe) project that received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement #634446: https://ec.europa.eu/programmes/horizon2020/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We would like to thank the R Epidemics Consortium (RECON) for guidelines and advice on development of R-packages for analysing epidemics. The research was performed in partnership with Public Health England (PHE). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England. Publisher Copyright: {\textcopyright} 2017 van Leeuwen et al.",

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fluEvidenceSynthesis: An R package for evidence synthesis based analysis of epidemiological outbreaks

Abstract

Bibliographical note

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