Studying the progress of COVID-19 outbreak in India using SIRD model

Abstract

We explore a standard epidemiological model, known as the SIRD model, to study the COVID-19 infection in India, and a few other countries around the world. We use (a) the stable cumulative infection of various countries and (b) the number of infection versus the tests carried out to evaluate the model. The time-dependent infection rate is set in the model to obtain the best fit with the available data. The model is simulated aiming to project the probable features of the infection in India, various Indian states, and other countries. India imposed an early lockdown to contain the infection that can be treated by its healthcare system. We find that with the current infection rate and containment measures, the total active infection in India would be maximum at the end of June or beginning of July 2020. With proper containment measures in the infected zones and social distancing, the infection is expected to fall considerably from August. If the containment measures are relaxed before the arrival of the peak infection, more people from the susceptible population will fall sick as the infection is expected to see a three-fold rise at the peak. If the relaxation is given a month after the peak infection, a second peak with a moderate infection will follow. However, a gradual relaxation of the lockdown started well ahead of the peak infection, leads to a nearly two-fold increase of the peak infection with no second peak. The model is further extended to incorporate the infection arising from the population showing no symptoms. The preliminary finding suggests that random testing needs to be carried out within the asymptomatic population to contain the spread of the disease. Our model provides a semi-quantitative overview of the progression of COVID-19 in India, with model projections reasonably replicating the current progress. The projection of the model is highly sensitive to the choice of the parameters and the available data. Besides, since the pandemic is an ongoing dynamic phenomenon, the reported results are subjected to regular updates in consonance with the acquired real data.

WITHDRAWN: A new model for epidemic prediction: COVID-19 in kingdom saudi arabia case study

“…The prediction result in India is compared with other countries like USA, Spain, South Korea and Germany. However, the final result will not be accurate because their will be many folks infected by the virus without any symptoms and the different nature of each of the society [14] . Also, Luisa Ferrari et al, have used the SIRD for predicting the COVID-19 spread over the regions in Italy [15] .…”

Analyses and Forecast for COVID-19 epidemic in India

“…Also the average incubation period is estimated to be 5.2 in a Wuhan based study in [15] as notified in Worldometer. We have taken a range of 5-7 days, for mean incubation period motivated by these studies and Indian studies [16,17]. The other important parameter for modelling the epidemic is to find the period of infectiousness.…”

Dynamics of COVID-19 transmission in Dhaka and Chittagong: Two business hubs of Bangladesh

“…Time-dependent effective reproduction number, R e, is chosen at different phases of the outbreak. For instance, based on the real data, the R e was set in the range 2.50 ≤ R e ≤ 3.80 at the beginning of the outbreak similar to another study, 23 whereas we assume a gradual attenuation (0.95 ≤ R e ≤ 3.8) during flexible lockdown for the next 150 days (1st August- 30th December). We postulate that a high transmission level will be substantially modified once the lockdown is relaxed.…”