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10.1073/pnas.2010651117 http://scihub22266oqcxt.onion/10.1073/pnas.2010651117 32973089!7568285!32973089     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Proc+Natl+Acad+Sci+U+S+A 2020 ; 117 (41): 25904-25910 Nephropedia Template TP {{tp|p=32973089|t=2020. Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City |pdf=|usr=}}{{32973089}} gab.com Text Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=32973089 #FOAvip As the COVID-19 pandemic continues, formulating targeted policy interventions that are informed by differential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics will be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV-2 transmission that accounts for location-dependent distributions of age, household structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China; Lombardy, Italy; and New York City, United States. Using data on reported deaths to obtain a posterior distribution over unknown parameters, we infer differences in the progression of the epidemic in the three locations. We also examine the role of transmission due to particular age groups on total infections and deaths. The effect of limiting contacts by a particular age group varies by location, indicating that strategies to reduce transmission should be tailored based on population-specific demography and social structure. These findings highlight the role of between-population variation in formulating policy interventions. Across the three populations, though, we find that targeted "salutary sheltering" by 50% of a single age group may substantially curtail transmission when combined with the adoption of physical distancing measures by the rest of the population. Twit Text FOAVip Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=32973089 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32973089 #FOAvip English Wikipedia <ref>{{Cite pmid|32973089}}</ref> Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City #MMPMID32973089 Wilder B; Charpignon M; Killian JA; Ou HC; Mate A; Jabbari S; Perrault A; Desai AN; Tambe M; Majumder MS Proc Natl Acad Sci U S A 2020[Oct]; 117 (41): 25904-25910 PMID32973089show ga As the COVID-19 pandemic continues, formulating targeted policy interventions that are informed by differential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics will be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV-2 transmission that accounts for location-dependent distributions of age, household structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China; Lombardy, Italy; and New York City, United States. Using data on reported deaths to obtain a posterior distribution over unknown parameters, we infer differences in the progression of the epidemic in the three locations. We also examine the role of transmission due to particular age groups on total infections and deaths. The effect of limiting contacts by a particular age group varies by location, indicating that strategies to reduce transmission should be tailored based on population-specific demography and social structure. These findings highlight the role of between-population variation in formulating policy interventions. Across the three populations, though, we find that targeted "salutary sheltering" by 50% of a single age group may substantially curtail transmission when combined with the adoption of physical distancing measures by the rest of the population. |*Models, Statistical[MESH] |Betacoronavirus/physiology[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Communicable Disease Control/legislation & jurisprudence/methods[MESH] |Coronavirus Infections/epidemiology/pathology/*prevention & control/*transmission[MESH] |Humans[MESH] |Italy/epidemiology[MESH] |New York City/epidemiology[MESH] |Pandemics/*prevention & control[MESH] |Pneumonia, Viral/epidemiology/pathology/*prevention & control/*transmission[MESH]Modeling between-population variation in COVID-19 dynamics in Hubei, L(epmc).pdf DeepDyve Pubget Overpricing