Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling
Abstract The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-rep...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-14979-0 |
| _version_ | 1811334356408467456 |
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| author | Adam Spannaus Theodore Papamarkou Samantha Erwin J. Blair Christian |
| author_facet | Adam Spannaus Theodore Papamarkou Samantha Erwin J. Blair Christian |
| author_sort | Adam Spannaus |
| collection | DOAJ |
| description | Abstract The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and 1-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported. |
| first_indexed | 2024-04-13T17:07:38Z |
| format | Article |
| id | doaj.art-f7665c90dff747d98c9c1cc554e6a59e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T17:07:38Z |
| publishDate | 2022-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f7665c90dff747d98c9c1cc554e6a59e2022-12-22T02:38:26ZengNature PortfolioScientific Reports2045-23222022-06-0112111210.1038/s41598-022-14979-0Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modellingAdam Spannaus0Theodore Papamarkou1Samantha Erwin2J. Blair Christian3Computational Sciences and Engineering Division, Oak Ridge National LaboratoryComputational Sciences and Engineering Division, Oak Ridge National LaboratoryPacific Northwest National LaboratoryComputational Sciences and Engineering Division, Oak Ridge National LaboratoryAbstract The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and 1-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.https://doi.org/10.1038/s41598-022-14979-0 |
| spellingShingle | Adam Spannaus Theodore Papamarkou Samantha Erwin J. Blair Christian Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling Scientific Reports |
| title | Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling |
| title_full | Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling |
| title_fullStr | Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling |
| title_full_unstemmed | Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling |
| title_short | Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling |
| title_sort | inferring the spread of covid 19 the role of time varying reporting rate in epidemiological modelling |
| url | https://doi.org/10.1038/s41598-022-14979-0 |
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