Slowing the spread: assessing early Covid-19 intervention effectiveness in São Paulo using socio-demographic and mobility data
<p><b>Background and Motivation:</b> The COVID-19 pandemic continues to have devastating health, social, and economic impacts around the world. Brazil ranks second globally in COVID-19 deaths, with spiraling growth after its first case in the Metropolitan Region of S ̃ao Paulo (MRS...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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2021
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| _version_ | 1797052293866061824 |
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| author | Yucel, S |
| author2 | Taylor, G |
| author_facet | Taylor, G Yucel, S |
| author_sort | Yucel, S |
| collection | OXFORD |
| description | <p><b>Background and Motivation:</b> The COVID-19 pandemic continues to have devastating health, social, and economic impacts around the world. Brazil ranks second globally in COVID-19 deaths, with spiraling growth after its first case in the Metropolitan Region of S ̃ao Paulo (MRSP), the largest urban agglomeration in South America. Lockdown interventions to reduce mobility are a leading tool for infection mitigation, and while effective on aggregate, their adherence is impacted by socio-demographic factors. A forward-looking model that evaluates the ability of lockdowns to slow disease spread through a socio-demographic lens can inform more equitable policy-making. </p>
<p><b>Methods:</b> Cellphone mobility data integrated with traditional commuting survey, census, and COVID-19 data from the MRSP are used to develop a novel effective distance-based model to measure the ability of lockdowns to slow disease spread. This Infection Delay Model (IDM) is applied to simulated epidemics across the MRSP. The first analysis clusters regions-at-risk that benefit similarly from lock-downs. The second analysis identifies outbreak regions where interventions lead to high and low slowdowns for the rest of the MRSP. Socio-spatial context provides meaningful and actionable interpretations of lockdown effectiveness. </p>
<p><b>Results:</b> For regions-at-risk, the clustering analysis reveals that socio-demographically vulnerable regions with lower centrality have greater delays to their first case by early lockdowns. The second analysis, observing each region as an initial source of infection, reveals that outbreaks beginning in socio-demographically secure regions with higher centrality are disproportionately slowed by lockdowns to the rest of the MRSP. </p>
<p><b>Conclusion:</b> A fundamental relationship exists between social inequality and the ability of lockdowns to slow infection spread. First cases in socio-demographically vulnerable regions can be most delayed by early lockdowns, conveying that rapid interventions can reduce health inequities. The outbreak analysis shows that lock-downs slow the spread more for outbreaks beginning in more secure regions. Increasing capacity of vulnerable regions to isolate can strengthen the MRSP’s resilience to future outbreaks.</p> |
| first_indexed | 2024-03-06T18:30:15Z |
| format | Thesis |
| id | oxford-uuid:096289a9-dae5-4440-90f1-bf722a8279a8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:30:15Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | oxford-uuid:096289a9-dae5-4440-90f1-bf722a8279a82022-03-26T09:18:10ZSlowing the spread: assessing early Covid-19 intervention effectiveness in São Paulo using socio-demographic and mobility dataThesishttp://purl.org/coar/resource_type/c_bdccuuid:096289a9-dae5-4440-90f1-bf722a8279a8InequalityEpidemiologyHuman MobilityDemographicsEnglishHyrax Deposit2021Yucel, STaylor, GCamargo, CPeixoto, PPereira, R<p><b>Background and Motivation:</b> The COVID-19 pandemic continues to have devastating health, social, and economic impacts around the world. Brazil ranks second globally in COVID-19 deaths, with spiraling growth after its first case in the Metropolitan Region of S ̃ao Paulo (MRSP), the largest urban agglomeration in South America. Lockdown interventions to reduce mobility are a leading tool for infection mitigation, and while effective on aggregate, their adherence is impacted by socio-demographic factors. A forward-looking model that evaluates the ability of lockdowns to slow disease spread through a socio-demographic lens can inform more equitable policy-making. </p> <p><b>Methods:</b> Cellphone mobility data integrated with traditional commuting survey, census, and COVID-19 data from the MRSP are used to develop a novel effective distance-based model to measure the ability of lockdowns to slow disease spread. This Infection Delay Model (IDM) is applied to simulated epidemics across the MRSP. The first analysis clusters regions-at-risk that benefit similarly from lock-downs. The second analysis identifies outbreak regions where interventions lead to high and low slowdowns for the rest of the MRSP. Socio-spatial context provides meaningful and actionable interpretations of lockdown effectiveness. </p> <p><b>Results:</b> For regions-at-risk, the clustering analysis reveals that socio-demographically vulnerable regions with lower centrality have greater delays to their first case by early lockdowns. The second analysis, observing each region as an initial source of infection, reveals that outbreaks beginning in socio-demographically secure regions with higher centrality are disproportionately slowed by lockdowns to the rest of the MRSP. </p> <p><b>Conclusion:</b> A fundamental relationship exists between social inequality and the ability of lockdowns to slow infection spread. First cases in socio-demographically vulnerable regions can be most delayed by early lockdowns, conveying that rapid interventions can reduce health inequities. The outbreak analysis shows that lock-downs slow the spread more for outbreaks beginning in more secure regions. Increasing capacity of vulnerable regions to isolate can strengthen the MRSP’s resilience to future outbreaks.</p> |
| spellingShingle | Inequality Epidemiology Human Mobility Demographics Yucel, S Slowing the spread: assessing early Covid-19 intervention effectiveness in São Paulo using socio-demographic and mobility data |
| title | Slowing the spread: assessing early Covid-19 intervention effectiveness in São Paulo using socio-demographic and mobility data |
| title_full | Slowing the spread: assessing early Covid-19 intervention effectiveness in São Paulo using socio-demographic and mobility data |
| title_fullStr | Slowing the spread: assessing early Covid-19 intervention effectiveness in São Paulo using socio-demographic and mobility data |
| title_full_unstemmed | Slowing the spread: assessing early Covid-19 intervention effectiveness in São Paulo using socio-demographic and mobility data |
| title_short | Slowing the spread: assessing early Covid-19 intervention effectiveness in São Paulo using socio-demographic and mobility data |
| title_sort | slowing the spread assessing early covid 19 intervention effectiveness in sao paulo using socio demographic and mobility data |
| topic | Inequality Epidemiology Human Mobility Demographics |
| work_keys_str_mv | AT yucels slowingthespreadassessingearlycovid19interventioneffectivenessinsaopaulousingsociodemographicandmobilitydata |