Space-time covid-19 Bayesian SIR modeling in South Carolina.
The Covid-19 pandemic has spread across the world since the beginning of 2020. Many regions have experienced its effects. The state of South Carolina in the USA has seen cases since early March 2020 and a primary peak in early April 2020. A lockdown was imposed on April 6th but lifting of restrictio...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0242777 |
| _version_ | 1819134724735500288 |
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| author | Andrew B Lawson Joanne Kim |
| author_facet | Andrew B Lawson Joanne Kim |
| author_sort | Andrew B Lawson |
| collection | DOAJ |
| description | The Covid-19 pandemic has spread across the world since the beginning of 2020. Many regions have experienced its effects. The state of South Carolina in the USA has seen cases since early March 2020 and a primary peak in early April 2020. A lockdown was imposed on April 6th but lifting of restrictions started on April 24th. The daily case and death data as reported by NCHS (deaths) via the New York Times GitHUB repository have been analyzed and approaches to modeling of the data are presented. Prediction is also considered and the role of asymptomatic transmission is assessed as a latent unobserved effect. Two different time periods are examined and one step prediction is provided. The results suggest that both socio-economic disadvantage, asymptomatic transmission and spatial confounding are important ingredients in any model pertaining to county level case dynamics. |
| first_indexed | 2024-12-22T10:07:45Z |
| format | Article |
| id | doaj.art-3fd2c11edd904a2ab0d7d9e39c7c9c43 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T10:07:45Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-3fd2c11edd904a2ab0d7d9e39c7c9c432022-12-21T18:29:56ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01163e024277710.1371/journal.pone.0242777Space-time covid-19 Bayesian SIR modeling in South Carolina.Andrew B LawsonJoanne KimThe Covid-19 pandemic has spread across the world since the beginning of 2020. Many regions have experienced its effects. The state of South Carolina in the USA has seen cases since early March 2020 and a primary peak in early April 2020. A lockdown was imposed on April 6th but lifting of restrictions started on April 24th. The daily case and death data as reported by NCHS (deaths) via the New York Times GitHUB repository have been analyzed and approaches to modeling of the data are presented. Prediction is also considered and the role of asymptomatic transmission is assessed as a latent unobserved effect. Two different time periods are examined and one step prediction is provided. The results suggest that both socio-economic disadvantage, asymptomatic transmission and spatial confounding are important ingredients in any model pertaining to county level case dynamics.https://doi.org/10.1371/journal.pone.0242777 |
| spellingShingle | Andrew B Lawson Joanne Kim Space-time covid-19 Bayesian SIR modeling in South Carolina. PLoS ONE |
| title | Space-time covid-19 Bayesian SIR modeling in South Carolina. |
| title_full | Space-time covid-19 Bayesian SIR modeling in South Carolina. |
| title_fullStr | Space-time covid-19 Bayesian SIR modeling in South Carolina. |
| title_full_unstemmed | Space-time covid-19 Bayesian SIR modeling in South Carolina. |
| title_short | Space-time covid-19 Bayesian SIR modeling in South Carolina. |
| title_sort | space time covid 19 bayesian sir modeling in south carolina |
| url | https://doi.org/10.1371/journal.pone.0242777 |
| work_keys_str_mv | AT andrewblawson spacetimecovid19bayesiansirmodelinginsouthcarolina AT joannekim spacetimecovid19bayesiansirmodelinginsouthcarolina |