Efficient sequential Bayesian inference for state-space epidemic models using ensemble data assimilation

Dhorasso Temfack; Jason Wyse

doi:10.1371/journal.pcbi.1014301

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Open Access

Peer-reviewed

Research Article

Efficient sequential Bayesian inference for state-space epidemic models using ensemble data assimilation

Dhorasso Temfack ,

Roles Conceptualization, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

* E-mail: temfackd@tcd.ie

Affiliation School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland

https://orcid.org/0009-0007-0630-7504

⨯
Jason Wyse

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliation School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
⨯

Efficient sequential Bayesian inference for state-space epidemic models using ensemble data assimilation

Dhorasso Temfack,
Jason Wyse

Published: May 18, 2026
https://doi.org/10.1371/journal.pcbi.1014301

About the Authors

Dhorasso Temfack

Roles Conceptualization, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

* E-mail: temfackd@tcd.ie

Affiliation School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland

https://orcid.org/0009-0007-0630-7504

Jason Wyse

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliation School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland

Competing Interests

The authors have declared that no competing interests exist.

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Epidemiology
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Algorithms
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Infectious disease epidemiology
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Infectious disease modeling
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Kalman filter
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Nonlinear systems
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Monkeypox
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Public and occupational health
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