Modelling COVID-19 contagion: risk assessment and targeted mitigation policies
<p>We use a spatial epidemic model with demographic and geographic heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England.</p> <p>Our model emphasises the role of variability of regional outcomes and heterogeneity across age groups and geographic lo...
| Main Authors: | , , |
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| Format: | Internet publication |
| Language: | English |
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2020
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| _version_ | 1797093715719749632 |
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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 | <p>We use a spatial epidemic model with demographic and geographic heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England.</p>
<p>Our model emphasises the role of variability of regional outcomes and heterogeneity across age groups and geographic locations, and provides a framework for assessing the impact of policies targeted towards sub-populations 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 emphasise the importance of shielding vulnerable sub-populations 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 centralised policies.</p> |
| first_indexed | 2024-03-07T04:04:14Z |
| format | Internet publication |
| id | oxford-uuid:c598e7e8-e06c-4a4d-bb37-381446d8f018 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T04:04:14Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | oxford-uuid:c598e7e8-e06c-4a4d-bb37-381446d8f0182022-03-27T06:32:15ZModelling COVID-19 contagion: risk assessment and targeted mitigation policiesInternet publicationhttp://purl.org/coar/resource_type/c_7ad9uuid:c598e7e8-e06c-4a4d-bb37-381446d8f018EnglishSymplectic Elements2020Cont, RKotlicki, AXu, R<p>We use a spatial epidemic model with demographic and geographic heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England.</p> <p>Our model emphasises the role of variability of regional outcomes and heterogeneity across age groups and geographic locations, and provides a framework for assessing the impact of policies targeted towards sub-populations 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 emphasise the importance of shielding vulnerable sub-populations 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 centralised policies.</p> |
| 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 |