Rapid response modeling of SARS-CoV-2 transmission
What can modelers learn from recent history to help prepare for the next pandemic? The COVID-19 pandemic has cemented the role of mechanistic infectious disease models as drivers of the scientific, public, and policy discourse during infectious disease emergencies. On page 596 of this issue, Pulliam...
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| Veröffentlicht in: | Science (American Association for the Advancement of Science) 2022-05, Vol.376 (6593), p.579-580 |
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| creator | Zelner, Jon Eisenberg, Marisa |
| description | What can modelers learn from recent history to help prepare for the next pandemic?
The COVID-19 pandemic has cemented the role of mechanistic infectious disease models as drivers of the scientific, public, and policy discourse during infectious disease emergencies. On page 596 of this issue, Pulliam
et al.
(
1
) add to these contributions through their use of a mechanistic model to document the high rate of reinfection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant in South Africa among people previously infected by the initial wild-type strain or the Alpha, Beta, or Delta variants. This work provides another example of how rapid-response modeling has facilitated the testing of key hypotheses and assumptions with unprecedented speed and near-immediate public health impact. |
| doi_str_mv | 10.1126/science.abp9498 |
| format | Article |
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The COVID-19 pandemic has cemented the role of mechanistic infectious disease models as drivers of the scientific, public, and policy discourse during infectious disease emergencies. On page 596 of this issue, Pulliam
et al.
(
1
) add to these contributions through their use of a mechanistic model to document the high rate of reinfection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant in South Africa among people previously infected by the initial wild-type strain or the Alpha, Beta, or Delta variants. This work provides another example of how rapid-response modeling has facilitated the testing of key hypotheses and assumptions with unprecedented speed and near-immediate public health impact.</description><identifier>ISSN: 0036-8075</identifier><identifier>EISSN: 1095-9203</identifier><identifier>DOI: 10.1126/science.abp9498</identifier><identifier>PMID: 35511985</identifier><language>eng</language><publisher>United States: The American Association for the Advancement of Science</publisher><subject>Coronaviruses ; COVID-19 ; Humans ; Infectious diseases ; Modelling ; Pandemics ; Pandemics - prevention & control ; Public health ; Respiratory diseases ; SARS-CoV-2 ; Severe acute respiratory syndrome ; Severe acute respiratory syndrome coronavirus 2 ; Viral diseases</subject><ispartof>Science (American Association for the Advancement of Science), 2022-05, Vol.376 (6593), p.579-580</ispartof><rights>Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c366t-36842390a6c45f7675f54343b5d15d85dff8e582d1fe4f925ffdf6c1a7ef6dee3</citedby><cites>FETCH-LOGICAL-c366t-36842390a6c45f7675f54343b5d15d85dff8e582d1fe4f925ffdf6c1a7ef6dee3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,2871,2872,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35511985$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zelner, Jon</creatorcontrib><creatorcontrib>Eisenberg, Marisa</creatorcontrib><title>Rapid response modeling of SARS-CoV-2 transmission</title><title>Science (American Association for the Advancement of Science)</title><addtitle>Science</addtitle><description>What can modelers learn from recent history to help prepare for the next pandemic?
The COVID-19 pandemic has cemented the role of mechanistic infectious disease models as drivers of the scientific, public, and policy discourse during infectious disease emergencies. On page 596 of this issue, Pulliam
et al.
(
1
) add to these contributions through their use of a mechanistic model to document the high rate of reinfection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant in South Africa among people previously infected by the initial wild-type strain or the Alpha, Beta, or Delta variants. 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The COVID-19 pandemic has cemented the role of mechanistic infectious disease models as drivers of the scientific, public, and policy discourse during infectious disease emergencies. On page 596 of this issue, Pulliam
et al.
(
1
) add to these contributions through their use of a mechanistic model to document the high rate of reinfection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant in South Africa among people previously infected by the initial wild-type strain or the Alpha, Beta, or Delta variants. This work provides another example of how rapid-response modeling has facilitated the testing of key hypotheses and assumptions with unprecedented speed and near-immediate public health impact.</abstract><cop>United States</cop><pub>The American Association for the Advancement of Science</pub><pmid>35511985</pmid><doi>10.1126/science.abp9498</doi><tpages>2</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
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| ispartof | Science (American Association for the Advancement of Science), 2022-05, Vol.376 (6593), p.579-580 |
| issn | 0036-8075 1095-9203 |
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| source | MEDLINE; Science Magazine |
| subjects | Coronaviruses COVID-19 Humans Infectious diseases Modelling Pandemics Pandemics - prevention & control Public health Respiratory diseases SARS-CoV-2 Severe acute respiratory syndrome Severe acute respiratory syndrome coronavirus 2 Viral diseases |
| title | Rapid response modeling of SARS-CoV-2 transmission |
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