Estimation of Reproduction Number (Ro) and Early Prediction of 2019 Novel Coronavirus Disease (COVID-19) Outbreak in India Using Statistical Computing Approach

Kanagarathinam, Karthick; Sekar, K. R.

doi:10.4178/epih.e2020028

Cited by 18 publications

(11 citation statements)

References 13 publications

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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: 76%

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: 76%

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

View full text Add to dashboard Cite

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“…Table I summarises a few of the recently developed prediction and forecasting models. Most of the models are based on the SEIR model and its extended version, like symptomatic infectious, asymptomatic infectious, quarantined, hospitalized, recovered, dead model (SEIDIuQHRD) [36][37][38]. Machine learning and deep learning models are also used for prediction and forecasting [39][40][41] .…”

Section: Recent Study On Covid-19mentioning

confidence: 99%

Analysis and predictions of spread, recovery, and death caused by COVID-19 in India

Kumari

Kumar

Poonia

et al. 2021

Big Data Min. Anal.

118

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The novel coronavirus outbreak was first reported in late December 2019 and more than 7 million people were infected with this disease and over 0.40 million worldwide lost their lives. The first case was diagnosed on 30 January 2020 in India and the figure crossed 0.24 million as of 6 June 2020. This paper presents a detailed study of recently developed forecasting models and predicts the number of confirmed, recovered, and death cases in India caused by COVID-19. The correlation coefficients and multiple linear regression applied for prediction and autocorrelation and autoregression have been used to improve the accuracy. The predicted number of cases shows a good agreement with 0.9992 R-squared score to the actual values. The finding suggests that lockdown and social distancing are two important factors that can help to suppress the increasing spread rate of COVID-19.

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“…Modeling and simulation techniques are alternative methods to make accurate predictions. 16,17 These techniques can be categorized into two categories: Agent-Based Simulation (ABS) and Discrete-Event Simulation (DES). The ABS focuses on modeling autonomous agents that interact with each other in complex systems, while DES focuses on modeling systems' processes.…”

Section: Forecasting and Prediction Techniquesmentioning

confidence: 99%

The contribution of reduced COVID-19 test time in controlling the spread of the disease: A simulation-based approach

Frichi

Kacem

Jawab

et al. 2021

J. Public Health Afr.

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The novel coronavirus COVID-19 has known a large spread over the globe threatening human health. Recommendations from WHO and specialists insist on testing on a mass scale. However, health systems do not have enough resources. The current process requires the isolation of testees in the hospitals’ isolation rooms for several hours until the test results are revealed, limiting hospitals’ capacities to test large numbers of cases. The aim of this paper was to estimate the impact of reducing the COVID-19 test time on controlling the pandemic spread, through increasing hospitals’ capacities to test on a mass scale. First, a discrete-event simulation was used to model and simulate the COVID-19 testing process in Morocco. Second, a mathematical model was developed to demonstrate the effect of accurate identification of infected cases on controlling the disease’s spread. Simulation results showed that hospitals’ testing capacities could be increased six times if the test duration fell from 10 hours to 10 minutes. The reduction of test time would increase testing capacities, which help to identify all the infected cases. In contrast, the simulation results indicated that if the infected population is not accurately identified and no precautionary measures are taken, the virus will continue to spread until it reaches the total population. Reducing test time is a vital component of the response to the COVID-19 pandemic. It is essential for the effective implementation of policies to contain the virus.

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Estimation of Reproduction Number (Ro) and Early Prediction of 2019 Novel Coronavirus Disease (COVID-19) Outbreak in India Using Statistical Computing Approach

Cited by 18 publications

References 13 publications

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

Analysis and predictions of spread, recovery, and death caused by COVID-19 in India

The contribution of reduced COVID-19 test time in controlling the spread of the disease: A simulation-based approach

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