We chose the above nations because of the large numbers of positive cases here. In this paper, we report our results based on a comprehensive data analysis of nine major countries-China, USA, Italy, France, Spain, Germany, South Korea, Japan and India.
We will detail our results in the next section.
We observed that along with us, several other researchers have also reported power-law growth after the exponential regime (Ziff and Ziff 2020 Komarova and Wodarz 2020 Manchein et al. ( 2020) argued that I( t) or total death count could be modelled using the error function. In another interesting of analysis of COVID-19 epidemic, Schüttler et al. There are several other models designed to understand the spread of COVID-19 (Labadin et al. In addition, there are many epidemic models that are inspired by population growth models (Daley and Gani 2001 Wu et al. ( 2013) simulated how a disease could spread within a network with different mixing styles, and showed that the average epidemic size and speed depend critically on network parameters. To account for spatio-temporal behavour, Min et al. ( 2020) constructed a India-specific model for devising intervention strategies they focussed on four metros-Delhi, Mumbai, Kolkata, and Bengaluru-along with intercity connectivity. ( 2020) performed feasibility studies of controlling COVID-19 epidemic by isolation. ( 2020) studied the effects of travel restrictions on the spread of COVID-19 in China and in the world, and Hellewell et al. Their predictions are in good agreement with the present data. ( 2020) constructed a seven-variable model (including quarantined and death variables) for epidemic spread in China and predicted that the daily count of exposed and infectious individuals will be negligible by March 30, 2020. The saturation or flattening of the curve in China is attributed to strong lockdowns.įor COVID-19 epidemic, some of the new models have managed to provide good forecasts that appears to match with the data. Note that quarantines and lockdowns help in suppressing the maximum number of infected individuals such steps are critical for the epidemic management with limited public health resources. Hence, modelling COVD-19 requires more complex models of epidemiology, including features of quarantine, lockdowns, stochasticity, interactions among population pockets, etc.
In addition, many infected individuals, called asymptomatic carriers, who show mild or no symptoms of infection have contributed significantly to the spread of the epidemic unwittingly (Li et al. SARS-CoV-2 is an extremely contagious virus. The SIR model has been generalized to SEIR model that includes exposed individuals, E, who are infected but not yet infectious (Bjørnstad 2018 Daley and Gani 2001). The variable R represents the removed individuals who have either recovered or died. Here, the variables S and I describe respectively the numbers of susceptible and infected individuals. Kermack and McKendrick ( 1927) constructed one of the first models, called SIR model, for epidemic evolution.
This feature can be used as an important diagnostic for flattening of the epidemic curve.Įpidemiologists have made various models for understanding and forecasting epidemics. The data show that countries that have achieved flattening of the epidemic curve exhibit power law growth before saturation. In this paper, we analyze the publicly available data set given in the WorldOMeter ( 2020). Hence, modeling and forecast of this epidemic is of critical importance. As of May 9, 2020, it has infected approximately 4 million humans, killed more than 0.27 million individuals, and has brought most of the world to a standstill in lockdowns (WorldOMeter 2020 Johns Hopkins University 2020). COVID-19 pandemic has caused major disruptions in the world.
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