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10.1126/scitranslmed.abc1126 http://scihub22266oqcxt.onion/10.1126/scitranslmed.abc1126 C7319260!7319260 !32571980     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32571980 &cmd=llinks): Failed to open stream: HTTP request failed! 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Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States |pdf=|usr=}}{{32571980 }} gab.com Text Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States JO: Sci Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=32571980 #FOAvip Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections to date has relied heavily on reverse transcription polymerase chain reaction testing. However, limited test availability, high false-negative rates, and the existence of asymptomatic or subclinical infections have resulted in an undercounting of the true prevalence of SARS-CoV-2. Here, we show how influenza-like illness (ILI) outpatient surveillance data can be used to estimate the prevalence of SARS-CoV-2. We found a surge of non-influenza ILI above the seasonal average in March 2020 and showed that this surge correlated with coronavirus disease 2019 (COVID-19) case counts across states. If one-third of patients infected with SARS-CoV-2 in the United States sought care, this ILI surge would have corresponded to more than 8.7 million new SARS-CoV-2 infections across the United States during the 3-week period from 8 to 28 March 2020. Combining excess ILI counts with the date of onset of community transmission in the United States, we also show that the early epidemic in the United States was unlikely to have been doubling slower than every 4 days. Together, these results suggest a conceptual model for the COVID-19 epidemic in the United States characterized by rapid spread across the United States with more than 80% infected individuals remaining undetected. We emphasize the importance of testing these findings with seroprevalence data and discuss the broader potential to use syndromic surveillance for early detection and understanding of emerging infectious diseases. Twit Text FOAVip Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States ????Sci Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=32571980 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32571980 #FOAvip English Wikipedia <ref>{{Cite pmid|32571980 }}</ref> Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States #MMPMID32571980 Silverman JD ; Hupert N ; Washburne AD Sci Transl Med 2020[Jul]; 12 (554 ): ä PMID32571980 show ga Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections to date has relied heavily on reverse transcription polymerase chain reaction testing. However, limited test availability, high false-negative rates, and the existence of asymptomatic or subclinical infections have resulted in an undercounting of the true prevalence of SARS-CoV-2. Here, we show how influenza-like illness (ILI) outpatient surveillance data can be used to estimate the prevalence of SARS-CoV-2. We found a surge of non-influenza ILI above the seasonal average in March 2020 and showed that this surge correlated with coronavirus disease 2019 (COVID-19) case counts across states. If one-third of patients infected with SARS-CoV-2 in the United States sought care, this ILI surge would have corresponded to more than 8.7 million new SARS-CoV-2 infections across the United States during the 3-week period from 8 to 28 March 2020. Combining excess ILI counts with the date of onset of community transmission in the United States, we also show that the early epidemic in the United States was unlikely to have been doubling slower than every 4 days. Together, these results suggest a conceptual model for the COVID-19 epidemic in the United States characterized by rapid spread across the United States with more than 80% infected individuals remaining undetected. We emphasize the importance of testing these findings with seroprevalence data and discuss the broader potential to use syndromic surveillance for early detection and understanding of emerging infectious diseases. |*Population Surveillance [MESH] |Betacoronavirus/*physiology [MESH] |COVID-19 [MESH] |Coronavirus Infections/*epidemiology/mortality [MESH] |Humans [MESH] |Influenza, Human/*epidemiology [MESH] |Pandemics [MESH] |Patient Acceptance of Health Care [MESH] |Pneumonia, Viral/*epidemiology/mortality [MESH] |Prevalence [MESH] |SARS-CoV-2 [MESH] |Syndrome [MESH]Using influenza surveillance networks to estimate state-specific preva(epmc).pdf DeepDyve Pubget Overpricing