A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics.
Although some methods for estimating the instantaneous reproductive number during epidemics have been developed, the existing frameworks usually require information on the distribution of the serial interval and/or additional contact tracing data. However, in the case of outbreaks of emerging infect...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1011021 |
| _version_ | 1797831714515451904 |
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| author | Chenxi Dai Dongsheng Zhou Bo Gao Kaifa Wang |
| author_facet | Chenxi Dai Dongsheng Zhou Bo Gao Kaifa Wang |
| author_sort | Chenxi Dai |
| collection | DOAJ |
| description | Although some methods for estimating the instantaneous reproductive number during epidemics have been developed, the existing frameworks usually require information on the distribution of the serial interval and/or additional contact tracing data. However, in the case of outbreaks of emerging infectious diseases with an unknown natural history or undetermined characteristics, the serial interval and/or contact tracing data are often not available, resulting in inaccurate estimates for this quantity. In the present study, a new framework was specifically designed for joint estimates of the instantaneous reproductive number and serial interval. Concretely, a likelihood function for the two quantities was first introduced. Then, the instantaneous reproductive number and the serial interval were modeled parametrically as a function of time using the interpolation method and a known traditional distribution, respectively. Using the Bayesian information criterion and the Markov Chain Monte Carlo method, we ultimately obtained their estimates and distribution. The simulation study revealed that our estimates of the two quantities were consistent with the ground truth. Seven data sets of historical epidemics were considered and further verified the robust performance of our method. Therefore, to some extent, even if we know only the daily incidence, our method can accurately estimate the instantaneous reproductive number and serial interval to provide crucial information for policymakers to design appropriate prevention and control interventions during epidemics. |
| first_indexed | 2024-04-09T13:56:09Z |
| format | Article |
| id | doaj.art-dfce098c38174567885dbdd55995a89a |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-09T13:56:09Z |
| publishDate | 2023-03-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS Computational Biology |
| spelling | doaj.art-dfce098c38174567885dbdd55995a89a2023-05-08T05:31:18ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-03-01193e101102110.1371/journal.pcbi.1011021A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics.Chenxi DaiDongsheng ZhouBo GaoKaifa WangAlthough some methods for estimating the instantaneous reproductive number during epidemics have been developed, the existing frameworks usually require information on the distribution of the serial interval and/or additional contact tracing data. However, in the case of outbreaks of emerging infectious diseases with an unknown natural history or undetermined characteristics, the serial interval and/or contact tracing data are often not available, resulting in inaccurate estimates for this quantity. In the present study, a new framework was specifically designed for joint estimates of the instantaneous reproductive number and serial interval. Concretely, a likelihood function for the two quantities was first introduced. Then, the instantaneous reproductive number and the serial interval were modeled parametrically as a function of time using the interpolation method and a known traditional distribution, respectively. Using the Bayesian information criterion and the Markov Chain Monte Carlo method, we ultimately obtained their estimates and distribution. The simulation study revealed that our estimates of the two quantities were consistent with the ground truth. Seven data sets of historical epidemics were considered and further verified the robust performance of our method. Therefore, to some extent, even if we know only the daily incidence, our method can accurately estimate the instantaneous reproductive number and serial interval to provide crucial information for policymakers to design appropriate prevention and control interventions during epidemics.https://doi.org/10.1371/journal.pcbi.1011021 |
| spellingShingle | Chenxi Dai Dongsheng Zhou Bo Gao Kaifa Wang A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics. PLoS Computational Biology |
| title | A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics. |
| title_full | A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics. |
| title_fullStr | A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics. |
| title_full_unstemmed | A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics. |
| title_short | A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics. |
| title_sort | new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics |
| url | https://doi.org/10.1371/journal.pcbi.1011021 |
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