Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission
Accurately estimating the effectiveness of stay-at-home orders (SHOs) on reducing social contact and disease spread is crucial for mitigating pandemics. Leveraging individual-level location data for 10 million smartphones, we observe that by April 30th---when nine in ten Americans were under a SHO--...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Chen, M. Keith Zhuo, Yilin de la Fuente, Malena Rohla, Ryne Long, Elisa F |
| description | Accurately estimating the effectiveness of stay-at-home orders (SHOs) on
reducing social contact and disease spread is crucial for mitigating pandemics.
Leveraging individual-level location data for 10 million smartphones, we
observe that by April 30th---when nine in ten Americans were under a
SHO---daily movement had fallen 70% from pre-COVID levels. One-quarter of this
decline is causally attributable to SHOs, with wide demographic differences in
compliance, most notably by political affiliation. Likely Trump voters reduce
movement by 9% following a local SHO, compared to a 21% reduction among their
Clinton-voting neighbors, who face similar exposure risks and identical
government orders. Linking social distancing behavior with an epidemic model,
we estimate that reductions in movement have causally reduced SARS-CoV-2
transmission rates by 49%. |
| doi_str_mv | 10.48550/arxiv.2005.05469 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2005_05469</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2005_05469</sourcerecordid><originalsourceid>FETCH-LOGICAL-a679-c4ad48e878e682957b316d3895c20d2e4ea4bdb92bf07ed26b207a91349fe2743</originalsourceid><addsrcrecordid>eNotj8tqwzAURLXJoqT9gK6qH5ArX72XQaRNIRCoTbfmKpLBEMdBckvz93XTrmYxzGEOIY81r6RVij9j_h6-KuBcVVxJ7e6I9_hZ8ES3ZR5GnIfpTKeeNjNeGc5sN42JHnJMudClaTbvDfPTBwPaZjyXcShlWdyTVY-nkh7-c03al23rd2x_eH3zmz1DbRw7SozSJmts0hacMkHUOgrr1BF4hCQTyhCDg9BzkyLoANygq4V0fQIjxZo8_WFvFt0lL4fztfu16W424gcaCUNY</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission</title><source>arXiv.org</source><creator>Chen, M. Keith ; Zhuo, Yilin ; de la Fuente, Malena ; Rohla, Ryne ; Long, Elisa F</creator><creatorcontrib>Chen, M. Keith ; Zhuo, Yilin ; de la Fuente, Malena ; Rohla, Ryne ; Long, Elisa F</creatorcontrib><description>Accurately estimating the effectiveness of stay-at-home orders (SHOs) on
reducing social contact and disease spread is crucial for mitigating pandemics.
Leveraging individual-level location data for 10 million smartphones, we
observe that by April 30th---when nine in ten Americans were under a
SHO---daily movement had fallen 70% from pre-COVID levels. One-quarter of this
decline is causally attributable to SHOs, with wide demographic differences in
compliance, most notably by political affiliation. Likely Trump voters reduce
movement by 9% following a local SHO, compared to a 21% reduction among their
Clinton-voting neighbors, who face similar exposure risks and identical
government orders. Linking social distancing behavior with an epidemic model,
we estimate that reductions in movement have causally reduced SARS-CoV-2
transmission rates by 49%.</description><identifier>DOI: 10.48550/arxiv.2005.05469</identifier><language>eng</language><subject>Computer Science - Social and Information Networks ; Physics - Physics and Society ; Quantitative Biology - Populations and Evolution ; Quantitative Finance - Economics</subject><creationdate>2020-05</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2005.05469$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2005.05469$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, M. Keith</creatorcontrib><creatorcontrib>Zhuo, Yilin</creatorcontrib><creatorcontrib>de la Fuente, Malena</creatorcontrib><creatorcontrib>Rohla, Ryne</creatorcontrib><creatorcontrib>Long, Elisa F</creatorcontrib><title>Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission</title><description>Accurately estimating the effectiveness of stay-at-home orders (SHOs) on
reducing social contact and disease spread is crucial for mitigating pandemics.
Leveraging individual-level location data for 10 million smartphones, we
observe that by April 30th---when nine in ten Americans were under a
SHO---daily movement had fallen 70% from pre-COVID levels. One-quarter of this
decline is causally attributable to SHOs, with wide demographic differences in
compliance, most notably by political affiliation. Likely Trump voters reduce
movement by 9% following a local SHO, compared to a 21% reduction among their
Clinton-voting neighbors, who face similar exposure risks and identical
government orders. Linking social distancing behavior with an epidemic model,
we estimate that reductions in movement have causally reduced SARS-CoV-2
transmission rates by 49%.</description><subject>Computer Science - Social and Information Networks</subject><subject>Physics - Physics and Society</subject><subject>Quantitative Biology - Populations and Evolution</subject><subject>Quantitative Finance - Economics</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tqwzAURLXJoqT9gK6qH5ArX72XQaRNIRCoTbfmKpLBEMdBckvz93XTrmYxzGEOIY81r6RVij9j_h6-KuBcVVxJ7e6I9_hZ8ES3ZR5GnIfpTKeeNjNeGc5sN42JHnJMudClaTbvDfPTBwPaZjyXcShlWdyTVY-nkh7-c03al23rd2x_eH3zmz1DbRw7SozSJmts0hacMkHUOgrr1BF4hCQTyhCDg9BzkyLoANygq4V0fQIjxZo8_WFvFt0lL4fztfu16W424gcaCUNY</recordid><startdate>20200511</startdate><enddate>20200511</enddate><creator>Chen, M. Keith</creator><creator>Zhuo, Yilin</creator><creator>de la Fuente, Malena</creator><creator>Rohla, Ryne</creator><creator>Long, Elisa F</creator><scope>ADEOX</scope><scope>AKY</scope><scope>ALC</scope><scope>GOX</scope></search><sort><creationdate>20200511</creationdate><title>Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission</title><author>Chen, M. Keith ; Zhuo, Yilin ; de la Fuente, Malena ; Rohla, Ryne ; Long, Elisa F</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a679-c4ad48e878e682957b316d3895c20d2e4ea4bdb92bf07ed26b207a91349fe2743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Social and Information Networks</topic><topic>Physics - Physics and Society</topic><topic>Quantitative Biology - Populations and Evolution</topic><topic>Quantitative Finance - Economics</topic><toplevel>online_resources</toplevel><creatorcontrib>Chen, M. Keith</creatorcontrib><creatorcontrib>Zhuo, Yilin</creatorcontrib><creatorcontrib>de la Fuente, Malena</creatorcontrib><creatorcontrib>Rohla, Ryne</creatorcontrib><creatorcontrib>Long, Elisa F</creatorcontrib><collection>arXiv Economics</collection><collection>arXiv Computer Science</collection><collection>arXiv Quantitative Biology</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chen, M. Keith</au><au>Zhuo, Yilin</au><au>de la Fuente, Malena</au><au>Rohla, Ryne</au><au>Long, Elisa F</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission</atitle><date>2020-05-11</date><risdate>2020</risdate><abstract>Accurately estimating the effectiveness of stay-at-home orders (SHOs) on
reducing social contact and disease spread is crucial for mitigating pandemics.
Leveraging individual-level location data for 10 million smartphones, we
observe that by April 30th---when nine in ten Americans were under a
SHO---daily movement had fallen 70% from pre-COVID levels. One-quarter of this
decline is causally attributable to SHOs, with wide demographic differences in
compliance, most notably by political affiliation. Likely Trump voters reduce
movement by 9% following a local SHO, compared to a 21% reduction among their
Clinton-voting neighbors, who face similar exposure risks and identical
government orders. Linking social distancing behavior with an epidemic model,
we estimate that reductions in movement have causally reduced SARS-CoV-2
transmission rates by 49%.</abstract><doi>10.48550/arxiv.2005.05469</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2005.05469 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2005_05469 |
| source | arXiv.org |
| subjects | Computer Science - Social and Information Networks Physics - Physics and Society Quantitative Biology - Populations and Evolution Quantitative Finance - Economics |
| title | Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T04%3A03%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Causal%20Estimation%20of%20Stay-at-Home%20Orders%20on%20SARS-CoV-2%20Transmission&rft.au=Chen,%20M.%20Keith&rft.date=2020-05-11&rft_id=info:doi/10.48550/arxiv.2005.05469&rft_dat=%3Carxiv_GOX%3E2005_05469%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |