Outbreak analytics: A developing data science for informing the response to emerging pathogens

Jonathan A. Polonsky; Amrish Baidjoe; Zhian N. Kamvar; Anne Cori; Kara Durski; William Edmunds; Rosalind M. Eggo; Sebastian Funk; Laurent Kaiser; Patrick Keating; Olivier Le Polain De Waroux; Michael Marks; Paula Moraga; Oliver Morgan; Pierre Nouvellet; Ruwan Ratnayake; Chrissy H. Roberts; Jimmy Whitworth; Thibaut Jombart

doi:10.1098/rstb.2018.0276

Outbreak analytics: A developing data science for informing the response to emerging pathogens

Jonathan A. Polonsky, Amrish Baidjoe, Zhian N. Kamvar, Anne Cori, Kara Durski, William Edmunds, Rosalind M. Eggo, Sebastian Funk, Laurent Kaiser, Patrick Keating, Olivier Le Polain De Waroux, Michael Marks, Paula Moraga, Oliver Morgan, Pierre Nouvellet, Ruwan Ratnayake, Chrissy H. Roberts, Jimmy Whitworth, Thibaut Jombart^*

^*Corresponding author for this work

Public Health England

Research output: Contribution to journal › Review article › peer-review

75 Citations (Scopus)

Abstract

Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Original language	English
Article number	20180276
Journal	Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Volume	374
Issue number	1776
DOIs	https://doi.org/10.1098/rstb.2018.0276
Publication status	Published - 2019

Bibliographical note

Funding Information:
This paper was supported with funding from the Global Challenges Research Fund (GCRF) for the project ‘RECAP—research capacity building and knowledge generation to support preparedness and response to humanitarian crises and epidemics' managed through RCUK and ESRC (ES/P010873/1). P.K., O.L.P., J.W. and T.J. receive support from the UK Public Health Rapid Support Team, which is funded by the United Kingdom Department of Health and Social Care. We acknowledge the National Institute for Health Research—Health Protection Research Unit for Modelling Methodology (T.J.) for funding. M.M., C.H.R., receive funding through the National Institute for Health Research (PR-OD-1017-20001). R.M.E. acknowledges funding from an HDR UK Innovation Fellowship (grant no. MR/S003975/1). A.C. thanks the Medical Research Council for funding. S.F. was supported by the Wellcome Trust (210758/Z/18/Z). The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.

Funding Information:
This paper was supported with funding from the Global Challenges Research Fund (GCRF) for the project 'RECAP-research capacity building and knowledge generation to support preparedness and response to humanitarian crises and epidemics' managed through RCUK and ESRC (ES/P010873/1). P.K., O.L.P., J.W. and T.J. receive support from the UK Public Health Rapid Support Team, which is funded by the United Kingdom Department of Health and Social Care. We acknowledge the National Institute for Health Research-Health Protection Research Unit for Modelling Methodology (T.J.) for funding. M.M., C.H.R., receive funding through the National Institute for Health Research (PR-OD-1017-20001). R.M.E. acknowledges funding from an HDR UK Innovation Fellowship (grant no. MR/ S003975/1). A.C. thanks the Medical Research Council for funding. S.F. was supported by the Wellcome Trust (210758/Z/18/Z). The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.

Publisher Copyright:
© 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License

Keywords

Epidemics
Infectious
Methods
Pipeline
Software
Tools

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1098/rstb.2018.0276

Cite this

Polonsky, J. A., Baidjoe, A., Kamvar, Z. N., Cori, A., Durski, K., Edmunds, W., Eggo, R. M., Funk, S., Kaiser, L., Keating, P., Le Polain De Waroux, O., Marks, M., Moraga, P., Morgan, O., Nouvellet, P., Ratnayake, R., Roberts, C. H., Whitworth, J., & Jombart, T. (2019). Outbreak analytics: A developing data science for informing the response to emerging pathogens. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 374(1776), [20180276]. https://doi.org/10.1098/rstb.2018.0276

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abstract = "Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.",

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note = "Funding Information: This paper was supported with funding from the Global Challenges Research Fund (GCRF) for the project {\textquoteleft}RECAP—research capacity building and knowledge generation to support preparedness and response to humanitarian crises and epidemics' managed through RCUK and ESRC (ES/P010873/1). P.K., O.L.P., J.W. and T.J. receive support from the UK Public Health Rapid Support Team, which is funded by the United Kingdom Department of Health and Social Care. We acknowledge the National Institute for Health Research—Health Protection Research Unit for Modelling Methodology (T.J.) for funding. M.M., C.H.R., receive funding through the National Institute for Health Research (PR-OD-1017-20001). R.M.E. acknowledges funding from an HDR UK Innovation Fellowship (grant no. MR/S003975/1). A.C. thanks the Medical Research Council for funding. S.F. was supported by the Wellcome Trust (210758/Z/18/Z). The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. Funding Information: This paper was supported with funding from the Global Challenges Research Fund (GCRF) for the project 'RECAP-research capacity building and knowledge generation to support preparedness and response to humanitarian crises and epidemics' managed through RCUK and ESRC (ES/P010873/1). P.K., O.L.P., J.W. and T.J. receive support from the UK Public Health Rapid Support Team, which is funded by the United Kingdom Department of Health and Social Care. We acknowledge the National Institute for Health Research-Health Protection Research Unit for Modelling Methodology (T.J.) for funding. M.M., C.H.R., receive funding through the National Institute for Health Research (PR-OD-1017-20001). R.M.E. acknowledges funding from an HDR UK Innovation Fellowship (grant no. MR/ S003975/1). A.C. thanks the Medical Research Council for funding. S.F. was supported by the Wellcome Trust (210758/Z/18/Z). The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. Publisher Copyright: {\textcopyright} 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License",

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language = "English",

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Polonsky, JA, Baidjoe, A, Kamvar, ZN, Cori, A, Durski, K, Edmunds, W, Eggo, RM, Funk, S, Kaiser, L, Keating, P, Le Polain De Waroux, O, Marks, M, Moraga, P, Morgan, O, Nouvellet, P, Ratnayake, R, Roberts, CH, Whitworth, J & Jombart, T 2019, 'Outbreak analytics: A developing data science for informing the response to emerging pathogens', Philosophical transactions of the Royal Society of London. Series B, Biological sciences, vol. 374, no. 1776, 20180276. https://doi.org/10.1098/rstb.2018.0276

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T1 - Outbreak analytics

T2 - A developing data science for informing the response to emerging pathogens

AU - Polonsky, Jonathan A.

AU - Baidjoe, Amrish

AU - Kamvar, Zhian N.

AU - Cori, Anne

AU - Durski, Kara

AU - Edmunds, William

AU - Eggo, Rosalind M.

AU - Funk, Sebastian

AU - Kaiser, Laurent

AU - Keating, Patrick

AU - Le Polain De Waroux, Olivier

AU - Marks, Michael

AU - Moraga, Paula

AU - Morgan, Oliver

AU - Nouvellet, Pierre

AU - Ratnayake, Ruwan

AU - Roberts, Chrissy H.

AU - Whitworth, Jimmy

AU - Jombart, Thibaut

N1 - Funding Information: This paper was supported with funding from the Global Challenges Research Fund (GCRF) for the project ‘RECAP—research capacity building and knowledge generation to support preparedness and response to humanitarian crises and epidemics' managed through RCUK and ESRC (ES/P010873/1). P.K., O.L.P., J.W. and T.J. receive support from the UK Public Health Rapid Support Team, which is funded by the United Kingdom Department of Health and Social Care. We acknowledge the National Institute for Health Research—Health Protection Research Unit for Modelling Methodology (T.J.) for funding. M.M., C.H.R., receive funding through the National Institute for Health Research (PR-OD-1017-20001). R.M.E. acknowledges funding from an HDR UK Innovation Fellowship (grant no. MR/S003975/1). A.C. thanks the Medical Research Council for funding. S.F. was supported by the Wellcome Trust (210758/Z/18/Z). The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. Funding Information: This paper was supported with funding from the Global Challenges Research Fund (GCRF) for the project 'RECAP-research capacity building and knowledge generation to support preparedness and response to humanitarian crises and epidemics' managed through RCUK and ESRC (ES/P010873/1). P.K., O.L.P., J.W. and T.J. receive support from the UK Public Health Rapid Support Team, which is funded by the United Kingdom Department of Health and Social Care. We acknowledge the National Institute for Health Research-Health Protection Research Unit for Modelling Methodology (T.J.) for funding. M.M., C.H.R., receive funding through the National Institute for Health Research (PR-OD-1017-20001). R.M.E. acknowledges funding from an HDR UK Innovation Fellowship (grant no. MR/ S003975/1). A.C. thanks the Medical Research Council for funding. S.F. was supported by the Wellcome Trust (210758/Z/18/Z). The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. Publisher Copyright: © 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License

PY - 2019

Y1 - 2019

N2 - Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

AB - Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

KW - Epidemics

KW - Infectious

KW - Methods

KW - Pipeline

KW - Software

KW - Tools

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DO - 10.1098/rstb.2018.0276

M3 - Review article

C2 - 31104603

AN - SCOPUS:85065403501

SN - 0962-8436

VL - 374

JO - Philosophical transactions of the Royal Society of London. Series B, Biological sciences

JF - Philosophical transactions of the Royal Society of London. Series B, Biological sciences

IS - 1776

M1 - 20180276

ER -

Outbreak analytics: A developing data science for informing the response to emerging pathogens

Abstract

Bibliographical note

Keywords

UN SDGs

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