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

Bhaskar, Adhin; C, George Priya Doss; Ramalingam, Srinivasan; Padmanaban, Srinivasan

doi:10.4103/ijph.ijph_502_20

Cited by 12 publications

(11 citation statements)

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“…The subsequent reduction toward the end of April can be attributed to aggressive testing, contact tracing, and isolation measures implemented in the urban area of Jodhpur during that month. Our R 0 estimate for the first month (1.61) was slightly higher than the national estimate of 1.47 and lower than the estimate from the state of Tamil Nadu (1.88) in India during the same period of March to April 2020 [ 9 , 13 ]. District level R 0 estimates are more likely to show pronounced fluctuations than state or national estimates, as the latter are aggregated across a wide range of epidemiological settings.…”

Section: Discussioncontrasting

confidence: 75%

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Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study

Saurabh¹,

Verma²,

Gautam³

et al. 2020

JMIR Public Health Surveill

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Background On March 9, 2020, the first COVID-19 case was reported in Jodhpur, Rajasthan, in the northwestern part of India. Understanding the epidemiology of COVID-19 at a local level is becoming increasingly important to guide measures to control the pandemic. Objective The aim of this study was to estimate the serial interval and basic reproduction number (R0) to understand the transmission dynamics of the COVID-19 outbreak at a district level. We used standard mathematical modeling approaches to assess the utility of these factors in determining the effectiveness of COVID-19 responses and projecting the size of the epidemic. Methods Contact tracing of individuals infected with SARS-CoV-2 was performed to obtain the serial intervals. The median and 95th percentile values of the SARS-CoV-2 serial interval were obtained from the best fits with the weibull, log-normal, log-logistic, gamma, and generalized gamma distributions. Aggregate and instantaneous R0 values were derived with different methods using the EarlyR and EpiEstim packages in R software. Results The median and 95th percentile values of the serial interval were 5.23 days (95% CI 4.72-5.79) and 13.20 days (95% CI 10.90-18.18), respectively. R0 during the first 30 days of the outbreak was 1.62 (95% CI 1.07-2.17), which subsequently decreased to 1.15 (95% CI 1.09-1.21). The peak instantaneous R0 values obtained using a Poisson process developed by Jombert et al were 6.53 (95% CI 2.12-13.38) and 3.43 (95% CI 1.71-5.74) for sliding time windows of 7 and 14 days, respectively. The peak R0 values obtained using the method by Wallinga and Teunis were 2.96 (95% CI 2.52-3.36) and 2.92 (95% CI 2.65-3.22) for sliding time windows of 7 and 14 days, respectively. R0 values of 1.21 (95% CI 1.09-1.34) and 1.12 (95% CI 1.03-1.21) for the 7- and 14-day sliding time windows, respectively, were obtained on July 6, 2020, using method by Jombert et al. Using the method by Wallinga and Teunis, values of 0.32 (95% CI 0.27-0.36) and 0.61 (95% CI 0.58-0.63) were obtained for the 7- and 14-day sliding time windows, respectively. The projection of cases over the next month was 2131 (95% CI 1799-2462). Reductions of transmission by 25% and 50% corresponding to reasonable and aggressive control measures could lead to 58.7% and 84.0% reductions in epidemic size, respectively. Conclusions The projected transmission reductions indicate that strengthening control measures could lead to proportionate reductions of the size of the COVID-19 epidemic. Time-dependent instantaneous R0 estimation based on the process by Jombart et al was found to be better suited for guiding COVID-19 response at the district level than overall R0 or instantaneous R0 estimation by the Wallinga and Teunis method. A data-driven approach at the local level is proposed to be useful in guiding public health strategy and surge capacity planning.

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Section: Discussioncontrasting

confidence: 75%

“…Current mathematical modeling approaches for epidemiological understanding of COVID-19 in India are based on aggregate data reported at the national and state levels [ 7 - 13 ]. Very often, conclusions based on large-scale data are not appropriate for designing interventions at the local level.…”

Section: Introductionmentioning

confidence: 99%

Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study

Saurabh¹,

Verma²,

Gautam³

et al. 2020

JMIR Public Health Surveill

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“…A medida que el SARS-CoV-2 (COVID-19) se propaga en México se van conociendo más características de su dinámica de contagios entre las entidades federativas. La determinación del patrón de infecciones en el espacio y tiempo ayuda a entender cómo ocurre la propagación y la forma en que se transmite en medio de las acciones de control establecidas por los sistemas de vigilancia epidemiológica y, en consecuencia, auxilia a redefinir las estrategias de intervención para disminuir el impacto en la salud de las poblaciones (Bhaskar et al, 2020;Cuartas et al, 2020;Medeiros et al, 2020;Parr, 2020). Este texto tiene como objetivo analizar el patrón espacio-temporal de propagación del COVID-19 en los municipios de la entidad fronteriza de Baja California entre la semana epidemiológica 10 y la 31 por medio de la utilización de las metodologías de Dinámica de Sistemas (sd, por sus siglas en inglés) y Sistemas de Información Geográfica (sig).…”

Section: Introductionunclassified

Análisis del patrón espacio-temporal de transmisión del COVID-19 por municipios de Baja California

Rodríguez

Flores

Cervantes³

2021

REF

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El texto analiza del patrón espacio-temporal de propagación del COVID-19 en los municipios de Baja California desde la semana epidemiológica 10 hasta la 31 con base en las metodologías de Dinámica de Sistemas (ds) y Sistemas de Información Geográfica (sig). Se usa el modelo susceptibles, infectados y recuperados (sir) epidémico a fin de modelar los factores críticos de contagio de la pandemia —tasa de infección y tasa de recuperación de la infección— con datos de la Dirección General de Epidemiología de la Secretaría de Salud disponibles el día 6 de junio de 2020. El patrón epidemiológico tiende a concentrase espacialmente en Mexicali, Tijuana y Tecate, ciudades que albergan a los trabajadores transfronterizos entre las Californias. Además, presenta una dinámica temporal cambiante hacia los municipios de Ensenada y Playas de Rosarito que son el destino de mayor demanda de los turistas californianos.

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“…Agent based models have provided useful insights, at the level of full cities, into mitigation methods and the effectiveness of non-pharmaceutical interventions [12]. Related references which model COVID-19 in India are [10,[13][14][15][16][17][18][19][20][21][22][23][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. These models are very largely compartmental models of varying degrees of complexity [37].…”

Section: Introductionmentioning

confidence: 99%

The INDSCI-SIM model for COVID-19 in India

Hazra

Pujari

Shekatkar

et al. 2021

Preprint

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Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. The INDSCI-SIM model is a 9-component, age-stratified, contact-structured compartmental model for COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions, an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient across the country. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05 - 0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 40% of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.

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

Cited by 12 publications

References 1 publication

Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study

Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study

Análisis del patrón espacio-temporal de transmisión del COVID-19 por municipios de Baja California

The INDSCI-SIM model for COVID-19 in India

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