Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data.
For the long term control of an infectious disease such as COVID-19, it is crucial to identify the most likely individuals to become infected and the role that differences in demographic characteristics play in the observed patterns of infection. As high-volume surveillance winds down, testing data...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011611&type=printable |
| _version_ | 1827400166467960832 |
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| author | Anthony J Wood Aeron R Sanchez Paul R Bessell Rebecca Wightman Rowland R Kao |
| author_facet | Anthony J Wood Aeron R Sanchez Paul R Bessell Rebecca Wightman Rowland R Kao |
| author_sort | Anthony J Wood |
| collection | DOAJ |
| description | For the long term control of an infectious disease such as COVID-19, it is crucial to identify the most likely individuals to become infected and the role that differences in demographic characteristics play in the observed patterns of infection. As high-volume surveillance winds down, testing data from earlier periods are invaluable for studying risk factors for infection in detail. Observed changes in time during these periods may then inform how stable the pattern will be in the long term. To this end we analyse the distribution of cases of COVID-19 across Scotland in 2021, where the location (census areas of order 500-1,000 residents) and reporting date of cases are known. We consider over 450,000 individually recorded cases, in two infection waves triggered by different lineages: B.1.1.529 ("Omicron") and B.1.617.2 ("Delta"). We use random forests, informed by measures of geography, demography, testing and vaccination. We show that the distributions are only adequately explained when considering multiple explanatory variables, implying that case heterogeneity arose from a combination of individual behaviour, immunity, and testing frequency. Despite differences in virus lineage, time of year, and interventions in place, we find the risk factors remained broadly consistent between the two waves. Many of the observed smaller differences could be reasonably explained by changes in control measures. |
| first_indexed | 2024-03-08T19:58:01Z |
| format | Article |
| id | doaj.art-4e133414409943d08b29e024549d1933 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-08T19:58:01Z |
| publishDate | 2023-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-4e133414409943d08b29e024549d19332023-12-24T05:31:41ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-11-011911e101161110.1371/journal.pcbi.1011611Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data.Anthony J WoodAeron R SanchezPaul R BessellRebecca WightmanRowland R KaoFor the long term control of an infectious disease such as COVID-19, it is crucial to identify the most likely individuals to become infected and the role that differences in demographic characteristics play in the observed patterns of infection. As high-volume surveillance winds down, testing data from earlier periods are invaluable for studying risk factors for infection in detail. Observed changes in time during these periods may then inform how stable the pattern will be in the long term. To this end we analyse the distribution of cases of COVID-19 across Scotland in 2021, where the location (census areas of order 500-1,000 residents) and reporting date of cases are known. We consider over 450,000 individually recorded cases, in two infection waves triggered by different lineages: B.1.1.529 ("Omicron") and B.1.617.2 ("Delta"). We use random forests, informed by measures of geography, demography, testing and vaccination. We show that the distributions are only adequately explained when considering multiple explanatory variables, implying that case heterogeneity arose from a combination of individual behaviour, immunity, and testing frequency. Despite differences in virus lineage, time of year, and interventions in place, we find the risk factors remained broadly consistent between the two waves. Many of the observed smaller differences could be reasonably explained by changes in control measures.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011611&type=printable |
| spellingShingle | Anthony J Wood Aeron R Sanchez Paul R Bessell Rebecca Wightman Rowland R Kao Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data. PLoS Computational Biology |
| title | Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data. |
| title_full | Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data. |
| title_fullStr | Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data. |
| title_full_unstemmed | Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data. |
| title_short | Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data. |
| title_sort | assessing the importance of demographic risk factors across two waves of sars cov 2 using fine scale case data |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011611&type=printable |
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