Understanding community level influences on the prevalence of SARS-CoV-2 infection in England: new insights from comparison over time and space
Understanding and monitoring the major influences on SARS-CoV-2 prevalence is essential to inform policy making and devise appropriate packages of non-pharmaceutical interventions. Through evaluating community level influences on the prevalence of SARS-CoV-2 infection and their spatio-temporal varia...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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The Royal Society
2023-09-01
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| Series: | Royal Society Open Science |
| Subjects: | |
| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.221001 |
| _version_ | 1797687558685065216 |
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| author | Chaitanya Joshi Arif Ali Thomas ÓConnor Li Chen Kaveh Jahanshahi |
| author_facet | Chaitanya Joshi Arif Ali Thomas ÓConnor Li Chen Kaveh Jahanshahi |
| author_sort | Chaitanya Joshi |
| collection | DOAJ |
| description | Understanding and monitoring the major influences on SARS-CoV-2 prevalence is essential to inform policy making and devise appropriate packages of non-pharmaceutical interventions. Through evaluating community level influences on the prevalence of SARS-CoV-2 infection and their spatio-temporal variations in England, this study aims to provide some insights into the most important risk parameters. We used spatial clusters developed in Jahanshahi and Jin (2021 Transportation 48, 1329–1359 (doi:10.1007/s11116-020-10098-9)) as geographical areas with distinct land use and travel patterns. We also segmented our data by time periods to control for changes in policies or development of the disease over the course of the pandemic. We then used multivariate linear regression to identify influences driving infections within the clusters and to compare the variations of those between the clusters. Our findings demonstrate the key roles that workplace and commuting modes have had on some of the sections of the working population after accounting for several interrelated influences including mobility and vaccination. We found communities of workers in care homes and warehouses and to a lesser extent textile and ready meal industries and those who rely more on public transport for commuting tend to carry a higher risk of infection across all residential area types and time periods. |
| first_indexed | 2024-03-12T01:19:36Z |
| format | Article |
| id | doaj.art-7cd2d23055cb46ffb2c8180a33b9efac |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-03-12T01:19:36Z |
| publishDate | 2023-09-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-7cd2d23055cb46ffb2c8180a33b9efac2023-09-13T07:05:23ZengThe Royal SocietyRoyal Society Open Science2054-57032023-09-0110910.1098/rsos.221001Understanding community level influences on the prevalence of SARS-CoV-2 infection in England: new insights from comparison over time and spaceChaitanya Joshi0Arif Ali1Thomas ÓConnor2Li Chen3Kaveh Jahanshahi4Data Science Campus, Office for National Statistics, Newport, UKData Science Campus, Office for National Statistics, Newport, UKData Science Campus, Office for National Statistics, Newport, UKData Science Campus, Office for National Statistics, Newport, UKData Science Campus, Office for National Statistics, Newport, UKUnderstanding and monitoring the major influences on SARS-CoV-2 prevalence is essential to inform policy making and devise appropriate packages of non-pharmaceutical interventions. Through evaluating community level influences on the prevalence of SARS-CoV-2 infection and their spatio-temporal variations in England, this study aims to provide some insights into the most important risk parameters. We used spatial clusters developed in Jahanshahi and Jin (2021 Transportation 48, 1329–1359 (doi:10.1007/s11116-020-10098-9)) as geographical areas with distinct land use and travel patterns. We also segmented our data by time periods to control for changes in policies or development of the disease over the course of the pandemic. We then used multivariate linear regression to identify influences driving infections within the clusters and to compare the variations of those between the clusters. Our findings demonstrate the key roles that workplace and commuting modes have had on some of the sections of the working population after accounting for several interrelated influences including mobility and vaccination. We found communities of workers in care homes and warehouses and to a lesser extent textile and ready meal industries and those who rely more on public transport for commuting tend to carry a higher risk of infection across all residential area types and time periods.https://royalsocietypublishing.org/doi/10.1098/rsos.221001communitylevelinfluencesCOVID-19prevalencesEngland |
| spellingShingle | Chaitanya Joshi Arif Ali Thomas ÓConnor Li Chen Kaveh Jahanshahi Understanding community level influences on the prevalence of SARS-CoV-2 infection in England: new insights from comparison over time and space Royal Society Open Science community level influences COVID-19 prevalences England |
| title | Understanding community level influences on the prevalence of SARS-CoV-2 infection in England: new insights from comparison over time and space |
| title_full | Understanding community level influences on the prevalence of SARS-CoV-2 infection in England: new insights from comparison over time and space |
| title_fullStr | Understanding community level influences on the prevalence of SARS-CoV-2 infection in England: new insights from comparison over time and space |
| title_full_unstemmed | Understanding community level influences on the prevalence of SARS-CoV-2 infection in England: new insights from comparison over time and space |
| title_short | Understanding community level influences on the prevalence of SARS-CoV-2 infection in England: new insights from comparison over time and space |
| title_sort | understanding community level influences on the prevalence of sars cov 2 infection in england new insights from comparison over time and space |
| topic | community level influences COVID-19 prevalences England |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.221001 |
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