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Coronavirus disease 2019 (COVID-19), which causes severe respiratory illness, has become a pandemic. The World Health Organization has declared it a public health crisis of international concern. We developed a susceptible, exposed, infected, recovered (SEIR) model for COVID-19 to show the importance of estimating the reproduction number (R_{0}). This work is focused on predicting the COVID-19 outbreak in its early stage in India based on an estimation of R_{0}. The developed model will help policymakers to take active measures prior to the further spread of COVID-19. Data on daily newly infected cases in India from March 2, 2020 to April 2, 2020 were to estimate R_{0} using the earlyR package. The maximum-likelihood approach was used to analyze the distribution of R_{0} values, and the bootstrap strategy was applied for resampling to identify the most likely R_{0} value. We estimated the median value of R_{0} to be 1.471 (95% confidence interval [CI], 1.351 to 1.592) and predicted that the new case count may reach 39,382 (95% CI, 34,300 to 47,351) in 30 days.

Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide, with 896,450 confirmed total new cases and 45,526 deaths globally as of April 2, 2020 [_{0}) can be estimated statistically or empirically. In this work, we used the earlyR (_{0} and predict the trajectory of the outbreak.

SEIR models can be used to predict the number of people infected based on R_{0}. We have given a SEIR model in this study to demonstrate the importance of estimating R_{0} [

- The population growth of the region/country is exponential, and the COVID-19 epidemic is occurring in a sufficiently short period

- Infected individuals are assumed not to give birth

- Recovered individuals acquire permanent immunity with a probability

With S referring to susceptible individuals, E to susceptible individuals that become exposed at time t-τ, I to individuals who are infected, and R to those who have recovered from COVID-19, the resulting differential equations are:

Where μ is the per capita death rate due to causes other than the disease, γ is the rate of contact (or) transmission rate (or) infection rate, α is the recovery rate, and

At any instant,

R_{0} is defined as,

This constant is extremely important in characterizing the spread of COVID-19. It reflects how many people contract the disease from an infectious individual. In general, If R_{0}> 1, secondary infections will occur and the disease is spreading throughout the population. According to WHO information as of January 23, 2020, the R_{0} of COVID-19 lies between 1.4 and 2.5. R_{0} may vary considerably for different infectious diseases, but also for the same disease in different populations [

All the data shown in _{0}. A higher R_{0} indicates a higher likelihood of new infections.

The transmissibility of COVID-19 in India was evaluated using the earlyR package. It was assumed that interventions so far have had a minimal impact on COVID-19 transmission in India. The model used herein is a simplified version of the model introduced by Cori et al. [_{0}. We assumed that the mean and SD were 4.7 days and 2.9 days, respectively, based on existing research [_{0}. The bootstrap strategy was applied for re-sampling 1,000 times to obtain likely R_{0} values. The R package projection was used to predict the cumulative daily incidence [

Where V (t-k) the vector of the probability mass function and X_{k} is is the real-time incidence at time k. The forecasting model depended on the present incidence and serial interval distributions. The projections were based on resampling and probability computations. The statistical analysis and model development were done using R version 3.6.3 (

The analysis in the article is based on data which is open to public. The article does not require the ethical committee approval.

_{0} of COVID-19 in India. We estimated the ML value of R_{0} as 1.471 (95% CI, 1.351 to 1.592) for COVID-19 in the early stage in India. _{0} values using the bootstrap strategy with 1,000 likely samples.

We computed that the cumulative number of new cases may reach 39,382 (95% CI, 34,300 to 47,351) in the next 30 days. The R_{0} data were estimated based on the existing COVID-19 data from March 2, 2020 to April 2, 2020. The Indian government has already announced a nationwide lockdown. As per the WHO information on January 23, 2020, the R_{0} of COVID-19 lies between 1.4 and 2.5. Our estimation indicates that for India, the median R_{0} value of 1.471 (95% CI, 1.351 to 1.592) is in the lower range. However, various studies have indicated that precisely estimating R_{0} is challenging, because R_{0} depends on environmental conditions, demography, and the modeling method. In our method, the accuracy of R_{0} depended on the premise that all cases of COVID-19 in India were identified in the study period. If the same scenario continues, we predict that the cumulative number of new cases may reach 39,382 (95% CI, 34,300 to 47,351) in next 30 days. We believe that our forecasting numbers may help in various aspects, such as developing the required medical infrastructure and focusing efforts on mitigating the economic impact of the pandemic. Our findings were derived based on a limited time frame, and the results may change after the occurrence of a considerable number of additional cases. The R_{0} value corresponding to the spread of COVID-19 can be controlled by strictly following social distancing in daily life, wearing masks, frequent hand-washing with soap or sanitizers, quarantining infected people, identifying cases using rapid diagnostic methods, and so on.

We estimated the median value of R_{0} to be 1.471 (95% CI, 1.351 to 1.592) and predicted that the cumulative number of new cases may reach 39,382 (95% CI, 34,300 to 47,351) in the next 30 days. The predicted size largely depends on changes in R_{0}. Effective measures against COVID-19 will help to reduce R_{0}. The presence of numerous unidentified cases in the study period may result uncertainties in the estimated value of R_{0} used in the developed forecasting model.

The authors have no conflicts of interest to declare for this study.

None.

Conceptualization: KK. Data curation: KS. Formal analysis: KS. Funding acquisition: None. Methodology: KK. Writing – original draft: KK. Writing – review & editing: KK, KS.

None.

Actual daily incidence of coronavirus disease 2019 in India.

Maximum-likelihood value of reproduction number (R_{0}).

Sample of likely values of reproduction number (R_{0}).

Global spread of infections.

Predicted cumulative new cases in the next 30 days.

Actual coronavirus disease 2019 daily new confirmed cases in India

Date in 2020 | New confirmed cases (n) | Date in 2020 | New confirmed cases (N) |
---|---|---|---|

Mar 2 | 2 | Mar 18 | 14 |

Mar 3 | 1 | Mar 19 | 22 |

Mar 4 | 22 | Mar 20 | 50 |

Mar 5 | 2 | Mar 21 | 60 |

Mar 6 | 1 | Mar 22 | 77 |

Mar 7 | 3 | Mar 23 | 74 |

Mar 8 | 5 | Mar 24 | 85 |

Mar 9 | 5 | Mar 25 | 87 |

Mar 10 | 6 | Mar 26 | 88 |

Mar 11 | 10 | Mar 27 | 140 |

Mar 12 | 13 | Mar 28 | 84 |

Mar 13 | 8 | Mar 29 | 106 |

Mar 14 | 16 | Mar 30 | 227 |

Mar 15 | 10 | Mar 31 | 146 |

Mar 16 | 11 | Apr 1 | 437 |

Mar 17 | 19 | Apr 2 | 235 |