Modelling COVID-19 contagion: risk assessment and targeted mitigation policies
We use a spatial epidemic model with demographic and geographical heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England. Our model emphasizes the role of variability of regional outcomes and heterogeneity across age groups and geographical locations, and provides a f...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Royal Society
2021
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| _version_ | 1826278972924624896 |
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| author | Cont, R Kotlicki, A Xu, R |
| author_facet | Cont, R Kotlicki, A Xu, R |
| author_sort | Cont, R |
| collection | OXFORD |
| description | We use a spatial epidemic model with demographic and geographical heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England. Our model emphasizes the role of variability of regional outcomes and heterogeneity across age groups and geographical locations, and provides a framework for assessing the impact of policies targeted towards subpopulations or regions. We define a concept of efficiency for comparative analysis of epidemic control policies and show targeted mitigation policies based on local monitoring to be more efficient than country-level or non-targeted measures. In particular, our results emphasize the importance of shielding vulnerable subpopulations and show that targeted policies based on local monitoring can considerably lower fatality forecasts and, in many cases, prevent the emergence of second waves which may occur under centralized policies. |
| first_indexed | 2024-03-06T23:51:54Z |
| format | Journal article |
| id | oxford-uuid:72e28873-9f3e-479e-92bf-c85e33e248ab |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:51:54Z |
| publishDate | 2021 |
| publisher | Royal Society |
| record_format | dspace |
| spelling | oxford-uuid:72e28873-9f3e-479e-92bf-c85e33e248ab2022-03-26T19:52:58ZModelling COVID-19 contagion: risk assessment and targeted mitigation policiesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:72e28873-9f3e-479e-92bf-c85e33e248abEnglishSymplectic ElementsRoyal Society2021Cont, RKotlicki, AXu, RWe use a spatial epidemic model with demographic and geographical heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England. Our model emphasizes the role of variability of regional outcomes and heterogeneity across age groups and geographical locations, and provides a framework for assessing the impact of policies targeted towards subpopulations or regions. We define a concept of efficiency for comparative analysis of epidemic control policies and show targeted mitigation policies based on local monitoring to be more efficient than country-level or non-targeted measures. In particular, our results emphasize the importance of shielding vulnerable subpopulations and show that targeted policies based on local monitoring can considerably lower fatality forecasts and, in many cases, prevent the emergence of second waves which may occur under centralized policies. |
| spellingShingle | Cont, R Kotlicki, A Xu, R Modelling COVID-19 contagion: risk assessment and targeted mitigation policies |
| title | Modelling COVID-19 contagion: risk assessment and targeted mitigation policies |
| title_full | Modelling COVID-19 contagion: risk assessment and targeted mitigation policies |
| title_fullStr | Modelling COVID-19 contagion: risk assessment and targeted mitigation policies |
| title_full_unstemmed | Modelling COVID-19 contagion: risk assessment and targeted mitigation policies |
| title_short | Modelling COVID-19 contagion: risk assessment and targeted mitigation policies |
| title_sort | modelling covid 19 contagion risk assessment and targeted mitigation policies |
| work_keys_str_mv | AT contr modellingcovid19contagionriskassessmentandtargetedmitigationpolicies AT kotlickia modellingcovid19contagionriskassessmentandtargetedmitigationpolicies AT xur modellingcovid19contagionriskassessmentandtargetedmitigationpolicies |