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ORIGINAL ARTICLE
Year : 2020  |  Volume : 64  |  Issue : 6  |  Page : 188-191

Distribution and growth rate of COVID-19 outbreak in Tamil Nadu: A log-linear regression approach


1 Scientist B, Department of Statistics, National Institute for Research in Tuberculosis, Chennai, Tamil Nadu, India
2 Scientist E, Department of Statistics, National Institute for Research in Tuberculosis, Chennai, Tamil Nadu, India
3 Senior Technical Officer, Department of Statistics, National Institute for Research in Tuberculosis, Chennai, Tamil Nadu, India

Correspondence Address:
Adhin Bhaskar
Department of Statistics, National Institute for Research in Tuberculosis, #1, Mayor Sathiyamoorthy Road, Chetpet, Chennai - 600 031, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijph.IJPH_502_20

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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.


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