A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data
Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time dur...
| Main Authors: | , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2024
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| _version_ | 1811139593089581056 |
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| author | Ogi-Gittins, I Hart, W Song, J Nash, R Polonsky, J Cori, A Hill, E Thompson, R |
| author_facet | Ogi-Gittins, I Hart, W Song, J Nash, R Polonsky, J Cori, A Hill, E Thompson, R |
| author_sort | Ogi-Gittins, I |
| collection | OXFORD |
| description | Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019–20 and 2022–23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks. |
| first_indexed | 2024-09-25T04:08:33Z |
| format | Journal article |
| id | oxford-uuid:2fa553d2-ba98-4e4f-8d44-07c81799c703 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:08:33Z |
| publishDate | 2024 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:2fa553d2-ba98-4e4f-8d44-07c81799c7032024-06-07T10:15:48ZA simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:2fa553d2-ba98-4e4f-8d44-07c81799c703EnglishSymplectic ElementsElsevier2024Ogi-Gittins, IHart, WSong, JNash, RPolonsky, JCori, AHill, EThompson, RTracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019–20 and 2022–23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks. |
| spellingShingle | Ogi-Gittins, I Hart, W Song, J Nash, R Polonsky, J Cori, A Hill, E Thompson, R A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data |
| title | A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data |
| title_full | A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data |
| title_fullStr | A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data |
| title_full_unstemmed | A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data |
| title_short | A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data |
| title_sort | simulation based approach for estimating the time dependent reproduction number from temporally aggregated disease incidence time series data |
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