Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges

Amani Alahmadi; Sarah Belet; Andrew Black; Deborah Cromer; Jennifer A. Flegg; Thomas House; Pavithra Jayasundara; Jonathan M. Keith; James M. McCaw; Robert Moss; Joshua V. Ross; Freya M. Shearer; Sai Thein Than Tun; James Walker; Lisa White; Jason M. Whyte; Ada W.C. Yan; Alexander E. Zarebski

doi:10.1016/j.epidem.2020.100393

Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges

Amani Alahmadi, Sarah Belet, Andrew Black, Deborah Cromer, Jennifer A. Flegg, Thomas House, Pavithra Jayasundara, Jonathan M. Keith, James M. McCaw, Robert Moss, Joshua V. Ross, Freya M. Shearer, Sai Thein Than Tun, James Walker, Lisa White, Jason M. Whyte, Ada W.C. Yan, Alexander E. Zarebski

School of Mathematics

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

31 Citations (Scopus)

Abstract

Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models’ usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.

Original language	English
Article number	100393
Number of pages	12
Journal	Epidemics
Volume	32
DOIs	https://doi.org/10.1016/j.epidem.2020.100393
Publication status	Published - Sept 2020

Keywords

Bayesian analysis
Computational methodology
Data challenges
Parameter identifiability
Policy and communication
Prior knowledge

UN SDGs

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

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10.1016/j.epidem.2020.100393Licence: CC BY-NC-ND

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Alahmadi, A., Belet, S., Black, A., Cromer, D., Flegg, J. A., House, T., Jayasundara, P., Keith, J. M., McCaw, J. M., Moss, R., Ross, J. V., Shearer, F. M., Tun, S. T. T., Walker, J., White, L., Whyte, J. M., Yan, A. W. C., & Zarebski, A. E. (2020). Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges. Epidemics, 32, Article 100393. https://doi.org/10.1016/j.epidem.2020.100393

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title = "Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges",

abstract = "Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models{\textquoteright} usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.",

keywords = "Bayesian analysis, Computational methodology, Data challenges, Parameter identifiability, Policy and communication, Prior knowledge",

author = "Amani Alahmadi and Sarah Belet and Andrew Black and Deborah Cromer and Flegg, {Jennifer A.} and Thomas House and Pavithra Jayasundara and Keith, {Jonathan M.} and McCaw, {James M.} and Robert Moss and Ross, {Joshua V.} and Shearer, {Freya M.} and Tun, {Sai Thein Than} and James Walker and Lisa White and Whyte, {Jason M.} and Yan, {Ada W.C.} and Zarebski, {Alexander E.}",

note = "Funding Information: We thank the mathematical research institute MATRIX in Australia where part of this research was performed. AEZ is supported by the Oxford Martin School Publisher Copyright: {\textcopyright} 2020 The Author(s)",

year = "2020",

month = sep,

doi = "10.1016/j.epidem.2020.100393",

language = "English",

volume = "32",

journal = "Epidemics",

issn = "1755-4365",

publisher = "Elsevier BV",

}

Alahmadi, A, Belet, S, Black, A, Cromer, D, Flegg, JA, House, T, Jayasundara, P, Keith, JM, McCaw, JM, Moss, R, Ross, JV, Shearer, FM, Tun, STT, Walker, J, White, L, Whyte, JM, Yan, AWC & Zarebski, AE 2020, 'Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges', Epidemics, vol. 32, 100393. https://doi.org/10.1016/j.epidem.2020.100393

TY - JOUR

T1 - Influencing public health policy with data-informed mathematical models of infectious diseases

T2 - Recent developments and new challenges

AU - Alahmadi, Amani

AU - Belet, Sarah

AU - Black, Andrew

AU - Cromer, Deborah

AU - Flegg, Jennifer A.

AU - House, Thomas

AU - Jayasundara, Pavithra

AU - Keith, Jonathan M.

AU - McCaw, James M.

AU - Moss, Robert

AU - Ross, Joshua V.

AU - Shearer, Freya M.

AU - Tun, Sai Thein Than

AU - Walker, James

AU - White, Lisa

AU - Whyte, Jason M.

AU - Yan, Ada W.C.

AU - Zarebski, Alexander E.

N1 - Funding Information: We thank the mathematical research institute MATRIX in Australia where part of this research was performed. AEZ is supported by the Oxford Martin School Publisher Copyright: © 2020 The Author(s)

PY - 2020/9

Y1 - 2020/9

N2 - Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models’ usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.

AB - Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models’ usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.

KW - Bayesian analysis

KW - Computational methodology

KW - Data challenges

KW - Parameter identifiability

KW - Policy and communication

KW - Prior knowledge

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JO - Epidemics

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Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges

Abstract

Keywords

UN SDGs

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ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights

Cite this

Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Projects

ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights

Cite this