Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool.
The time-varying reproduction number (Rt) is an important measure of epidemic transmissibility that directly informs policy decisions and the optimisation of control measures. EpiEstim is a widely used opensource software tool that uses case incidence and the serial interval (SI, time between sympto...
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Public Library of Science (PLoS)
2023-08-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011439&type=printable |
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| author | Rebecca K Nash Samir Bhatt Anne Cori Pierre Nouvellet |
| author_facet | Rebecca K Nash Samir Bhatt Anne Cori Pierre Nouvellet |
| author_sort | Rebecca K Nash |
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| description | The time-varying reproduction number (Rt) is an important measure of epidemic transmissibility that directly informs policy decisions and the optimisation of control measures. EpiEstim is a widely used opensource software tool that uses case incidence and the serial interval (SI, time between symptoms in a case and their infector) to estimate Rt in real-time. The incidence and the SI distribution must be provided at the same temporal resolution, which can limit the applicability of EpiEstim and other similar methods, e.g. for contexts where the time window of incidence reporting is longer than the mean SI. In the EpiEstim R package, we implement an expectation-maximisation algorithm to reconstruct daily incidence from temporally aggregated data, from which Rt can then be estimated. We assess the validity of our method using an extensive simulation study and apply it to COVID-19 and influenza data. For all datasets, the influence of intra-weekly variability in reported data was mitigated by using aggregated weekly data. Rt estimated on weekly sliding windows using incidence reconstructed from weekly data was strongly correlated with estimates from the original daily data. The simulation study revealed that Rt was well estimated in all scenarios and regardless of the temporal aggregation of the data. In the presence of weekend effects, Rt estimates from reconstructed data were more successful at recovering the true value of Rt than those obtained from reported daily data. These results show that this novel method allows Rt to be successfully recovered from aggregated data using a simple approach with very few data requirements. Additionally, by removing administrative noise when daily incidence data are reconstructed, the accuracy of Rt estimates can be improved. |
| format | Article |
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| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2023-08-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS Computational Biology |
| spelling | doaj-art-7ace1aefb9b3465ba1fc856c2c2e7fc62025-08-20T02:22:26ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-08-01198e101143910.1371/journal.pcbi.1011439Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool.Rebecca K NashSamir BhattAnne CoriPierre NouvelletThe time-varying reproduction number (Rt) is an important measure of epidemic transmissibility that directly informs policy decisions and the optimisation of control measures. EpiEstim is a widely used opensource software tool that uses case incidence and the serial interval (SI, time between symptoms in a case and their infector) to estimate Rt in real-time. The incidence and the SI distribution must be provided at the same temporal resolution, which can limit the applicability of EpiEstim and other similar methods, e.g. for contexts where the time window of incidence reporting is longer than the mean SI. In the EpiEstim R package, we implement an expectation-maximisation algorithm to reconstruct daily incidence from temporally aggregated data, from which Rt can then be estimated. We assess the validity of our method using an extensive simulation study and apply it to COVID-19 and influenza data. For all datasets, the influence of intra-weekly variability in reported data was mitigated by using aggregated weekly data. Rt estimated on weekly sliding windows using incidence reconstructed from weekly data was strongly correlated with estimates from the original daily data. The simulation study revealed that Rt was well estimated in all scenarios and regardless of the temporal aggregation of the data. In the presence of weekend effects, Rt estimates from reconstructed data were more successful at recovering the true value of Rt than those obtained from reported daily data. These results show that this novel method allows Rt to be successfully recovered from aggregated data using a simple approach with very few data requirements. Additionally, by removing administrative noise when daily incidence data are reconstructed, the accuracy of Rt estimates can be improved.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011439&type=printable |
| spellingShingle | Rebecca K Nash Samir Bhatt Anne Cori Pierre Nouvellet Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool. PLoS Computational Biology |
| title | Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool. |
| title_full | Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool. |
| title_fullStr | Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool. |
| title_full_unstemmed | Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool. |
| title_short | Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool. |
| title_sort | estimating the epidemic reproduction number from temporally aggregated incidence data a statistical modelling approach and software tool |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011439&type=printable |
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