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10.12688/f1000research.23496.2 http://scihub22266oqcxt.onion/10.12688/f1000research.23496.2 32528664!7262570!32528664     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 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 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       F1000Res 2020 ; 9 (ä): 315 Nephropedia Template TP {{tp|p=32528664|t=2020. SARS-CoV-2 epidemic in India: epidemiological features and in silico analysis of the effect of interventions |pdf=|usr=}}{{32528664}} gab.com Text SARS-CoV-2 epidemic in India: epidemiological features and in silico analysis of the effect of interventions JO: F1000Res http://www.kidney.de/pget.php?&tw=1&pmid=32528664 #FOAvip Background: After SARS-CoV-2 set foot in India, the Government took a number of steps to limit the spread of the virus in the country. This included restricted testing, isolation, contact tracing and quarantine, and enforcement of a nation-wide lockdown starting 25 March 2020. The objectives of this study were to i) describe the age, gender distribution, and mortality among COVID-19 patients identified till 14 April 2020 and predict the range of contact rate; and ii) predict the number of COVID-19 infections after 40 days of lockdown. Methods: We used a cross-sectional descriptive design for the first objective and a susceptible-infected-removed model for in silico predictions. We collected data from government-controlled and crowdsourced websites. Results: Studying age and gender parameters of 1161 Indian COVID-19 patients, the median age was 38 years (IQR, 27-52) with 20-39 year-old males being the most affected group. The number of affected patients were 854 (73.6%) men and 307 (26.4%) women. If the current contact rate continues (0.25-27), India may have 110460 to 220575 infected persons at the end of 40 days lockdown. Conclusion: The disease is majorly affecting a younger age group in India. Interventions have been helpful in preventing the worst-case scenario in India but will be unable to prevent the spike in the number of cases. Twit Text FOAVip SARS-CoV-2 epidemic in India: epidemiological features and in silico analysis of the effect of interventions ????F1000Res http://www.kidney.de/pget.php?&tw=1&pmid=32528664 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32528664 #FOAvip English Wikipedia <ref>{{Cite pmid|32528664}}</ref> SARS-CoV-2 epidemic in India: epidemiological features and in silico analysis of the effect of interventions #MMPMID32528664 Mazumder A; Arora M; Bharadiya V; Berry P; Agarwal M; Behera P; Shewade HD; Lohiya A; Gupta M; Rao A; Parameswaran GG F1000Res 2020[]; 9 (ä): 315 PMID32528664show ga Background: After SARS-CoV-2 set foot in India, the Government took a number of steps to limit the spread of the virus in the country. This included restricted testing, isolation, contact tracing and quarantine, and enforcement of a nation-wide lockdown starting 25 March 2020. The objectives of this study were to i) describe the age, gender distribution, and mortality among COVID-19 patients identified till 14 April 2020 and predict the range of contact rate; and ii) predict the number of COVID-19 infections after 40 days of lockdown. Methods: We used a cross-sectional descriptive design for the first objective and a susceptible-infected-removed model for in silico predictions. We collected data from government-controlled and crowdsourced websites. Results: Studying age and gender parameters of 1161 Indian COVID-19 patients, the median age was 38 years (IQR, 27-52) with 20-39 year-old males being the most affected group. The number of affected patients were 854 (73.6%) men and 307 (26.4%) women. If the current contact rate continues (0.25-27), India may have 110460 to 220575 infected persons at the end of 40 days lockdown. Conclusion: The disease is majorly affecting a younger age group in India. Interventions have been helpful in preventing the worst-case scenario in India but will be unable to prevent the spike in the number of cases. |*Coronavirus Infections/epidemiology/mortality[MESH] |*Pneumonia, Viral/epidemiology/mortality[MESH] |Adult[MESH] |Age Distribution[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Communicable Disease Control[MESH] |Cross-Sectional Studies[MESH] |Female[MESH] |Humans[MESH] |India/epidemiology[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |SARS-CoV-2[MESH] |Sex Distribution[MESH]SARS-CoV-2 epidemic in India: epidemiological features and in silico a(epmc).pdf DeepDyve Pubget Overpricing