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10.4103/ijph.IJPH_502_20 http://scihub22266oqcxt.onion/10.4103/ijph.IJPH_502_20 32496253!ä!32496253 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32496253&cmd=llinks): Failed to open stream: HTTP request failed! 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Distribution and growth rate of COVID-19 outbreak in Tamil Nadu: A log-linear regression approach |pdf=|usr=}}{{32496253}} gab.com Text Distribution and growth rate of COVID-19 outbreak in Tamil Nadu: A log-linear regression approach JO: Indian J Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32496253 #FOAvip BACKGROUND: Most of the countries are affected with the pandemic outbreak of the coronavirus infection. Understanding the severity and distribution in various regions will help in planning the controlling measures. OBJECTIVES: The objective was to assess the distribution and growth rate of COVID-19 infection in Tamil Nadu, India. METHODS: The data on the number of infections of COVID-19 have been obtained from the media reports released by the Government of Tamil Nadu. The data contain information on the incidence of the disease for the first 41 days of the outbreak started on March 7, 2020. Log-linear model has been used to estimate the progression of the COVID-19 infection in Tamil Nadu. Separate models were employed to model the growth rate and decay rate of the disease. Spatial Poisson regression was used to identify the high-risk areas in the state. RESULTS: : The models estimated the doubling time for the number of cases in growth phase as 3.96 (95% confidence interval [CI]: 2.70, 9.42) days and halving time in the decay phase as 12.08 (95% CI: 6.79, 54.78) days. The estimated median reproduction numbers were 1.88 (min = 1.09, max = 2.51) and 0.76 (min = 0.56, max = 0.99) in the growth and decay phases, respectively. The spatial Poisson regression identified 11 districts as high risk. CONCLUSION: The results indicate that the outbreak is showing decay in the number of infections of the disease which highlights the effectiveness of controlling measures. Twit Text FOAVip Distribution and growth rate of COVID-19 outbreak in Tamil Nadu: A log-linear regression approach ????Indian J Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32496253 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32496253 #FOAvip English Wikipedia <ref>{{Cite pmid|32496253}}</ref> Distribution and growth rate of COVID-19 outbreak in Tamil Nadu: A log-linear regression approach #MMPMID32496253 Bhaskar A; Ponnuraja C; Srinivasan R; Padmanaban S Indian J Public Health 2020[Jun]; 64 (Supplement): S188-S191 PMID32496253show ga BACKGROUND: Most of the countries are affected with the pandemic outbreak of the coronavirus infection. Understanding the severity and distribution in various regions will help in planning the controlling measures. OBJECTIVES: The objective was to assess the distribution and growth rate of COVID-19 infection in Tamil Nadu, India. METHODS: The data on the number of infections of COVID-19 have been obtained from the media reports released by the Government of Tamil Nadu. The data contain information on the incidence of the disease for the first 41 days of the outbreak started on March 7, 2020. Log-linear model has been used to estimate the progression of the COVID-19 infection in Tamil Nadu. Separate models were employed to model the growth rate and decay rate of the disease. Spatial Poisson regression was used to identify the high-risk areas in the state. RESULTS: : The models estimated the doubling time for the number of cases in growth phase as 3.96 (95% confidence interval [CI]: 2.70, 9.42) days and halving time in the decay phase as 12.08 (95% CI: 6.79, 54.78) days. The estimated median reproduction numbers were 1.88 (min = 1.09, max = 2.51) and 0.76 (min = 0.56, max = 0.99) in the growth and decay phases, respectively. The spatial Poisson regression identified 11 districts as high risk. CONCLUSION: The results indicate that the outbreak is showing decay in the number of infections of the disease which highlights the effectiveness of controlling measures. |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology[MESH] |Data Interpretation, Statistical[MESH] |Humans[MESH] |Incidence[MESH] |India/epidemiology[MESH] |Linear Models[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH] |SARS-CoV-2[MESH]Distribution and growth rate of COVID-19 outbreak in Tamil Nadu: A log(epmc).pdf DeepDyve Pubget Overpricing