Estimates of the basic reproduction number for rubella using seroprevalence data and indicator-based approaches

Timos Papadopoulos; Emilia Vynnycky

doi:10.1371/journal.pcbi.1008858

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Open Access

Peer-reviewed

Research Article

Estimates of the basic reproduction number for rubella using seroprevalence data and indicator-based approaches

Timos Papadopoulos ,

Roles Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: Timos.papadopoulos@phe.gov.uk

Affiliations Modelling and Economics Unit, UK Health Security Agency, London, United Kingdom, Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom
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Emilia Vynnycky

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

Affiliations Modelling and Economics Unit, UK Health Security Agency, London, United Kingdom, TB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom, Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
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Estimates of the basic reproduction number for rubella using seroprevalence data and indicator-based approaches

Timos Papadopoulos,
Emilia Vynnycky

Published: March 3, 2022
https://doi.org/10.1371/journal.pcbi.1008858
See the preprint

About the Authors

Timos Papadopoulos

Roles Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: Timos.papadopoulos@phe.gov.uk

Affiliations Modelling and Economics Unit, UK Health Security Agency, London, United Kingdom, Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom

Emilia Vynnycky

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

Affiliations Modelling and Economics Unit, UK Health Security Agency, London, United Kingdom, TB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom, Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom

Competing Interests

The authors have declared that no competing interests exist.

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Machine learning
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Infectious disease epidemiology
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Linear regression analysis
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Rubella
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Mathematical functions
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Health economics
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Optimization
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Vaccination and immunization
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