The recent outbreak of COVID-19 in different states of India has major concerns for all administrative departments of the government and general public. The Pandemic has been tested positive in 1287945 individuals with 817209 recovered and 30601 succumbed to the disease. The first case of the novel coronavirus was detected in India on 30 January 2020. There was a lockdown imposed by the Government of India from 24 March 2020 and ended on 31 May 2020. A forecast in no lockdown scenario would help us to track the further progress of the disease and make sufficient data available in order to plan the future of hospital facilities, pharmaceutical investment etc.

Forecasting of COVID-19 in India Using ARIMA Model

Abstract The unprecedented and overwhelming SARS-CoV-2 virus and COVID-19 disease significantly challenged our way of life, society and the economy. Many questions emerge, a critical one being how to quantify the challenges, realities, intervention effect and influence of the pandemic. With the massive effort that has been in relation to modeling COVID-19, what COVID-19 issues have been modeled? What and how well have epidemiology, AI, data science, machine learning, deep learning, mathematics and social science characterized the COVID-19 epidemic? what are the gaps and opportunities of quantifying the pandemic? Such questions involve a wide body of knowledge and literature, which are unclear but important for present and future health crisis quantification. Here, we provide a comprehensive review of the challenges, tasks, methods, progress, gaps and opportunities in relation to modeling COVID-19 processes, data, mitigation and impact. With a research landscape of COVID-19 modeling, we further categorize, summarize, compare and discuss the related methods and the progress which has been made in modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and medical treatments, non-pharmaceutical interventions and their effects, drug and vaccine development, psychological, economic and social influence and impact, and misinformation, etc. The review shows how modeling methods such as mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, AI and data science, in particular shallow and deep machine learning, simulation modeling, social science methods and hybrid modeling have addressed the COVID-19 challenges, what gaps exist and what research directions can be followed for a better future. 

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COVID-19 Modeling: A Review

The SARS-CoV-2 virus and COVID-19 disease have posed unprecedented and overwhelming demand, challenges and opportunities to domain, model and data driven modeling. This paper provides a comprehensive review of the challenges, tasks, methods, progress, gaps and opportunities in relation to modeling COVID-19 problems, data and objectives. It constructs a research landscape of COVID-19 modeling tasks and methods, and further categorizes, summarizes, compares and discusses the related methods and progress of modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and medical treatments, non-pharmaceutical interventions and their effects, drug and vaccine development, psychological, economic and social influence and impact, and misinformation, etc. The modeling methods involve mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, AI and data science in particular shallow and deep machine learning, simulation modeling, social science methods, and hybrid modeling.

The goal of this research is to analyze data on vaccinations, vaccination administration, and forecasting vaccination rates on a country-by-country basis for the general public, policymakers, vaccine manufacturers, national governments, and international governments to better understand the current state of COVID-19 vaccination. In this study, two public datasets were used: the Johns Hopkins University coronavirus 2019 dataset and Our World in Data - Coronavirus Pandemic dataset. With datasets, two approaches have been used: visual data analysis for COVID-19 vaccine administration and the autoregressive integrated moving average (ARIMA) model for forecasting vaccination rates. The findings confirm that Oxford/AstraZeneca is the top vaccine used across the globe with 26.54%, the United States is the top in vaccination, with 277,290,173, India is the top in number of daily vaccinations with 3.659357M, and in total vaccinations per hundred people, the United States has the highest count with 82.91, among the top five countries. It is also estimated that the vaccination rate in the United States will reach almost 60%, while India, Brazil, France, and Turkey will reach about 15%, 28%, 60%, and 23%, respectively, in the following 50 days beginning 20 May 2021. This exploratory study of COVID-19 vaccination data was carried out to effectively show the current state of COVID-19 vaccine administration and to anticipate vaccination rates in the United States, India, Brazil, France, and Turkey. 

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An exploratory analysis of COVID-19 vaccination administration

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A local outbreak of initially unknown cause pneumonia was detected in Wuhan (Hubei, China) in December 2019 and a novel coronavirus, the severe acute respiratory syndrome coronavirus 2, was quickly found to be causing it. Since then, the epidemic has spread to all of China's mainland provinces as well as 58 other countries and territories, with more than 87,137 confirmed cases around the globe, including 79,968 from China, 7169 from other countries as of 1 March 2020, as stated by the World Health Organization in the COVID-19 situation report-41. In response to this current public health emergency, this study done a statistical analysis and visualized reported cases of coronavirus disease 2019 (COVID-19) based on the open data collection provided by Johns Hopkins University. Where the location and number of confirmed infected cases have been shown, there have also been deaths, recovered cases and comparisons of the growth rates between the Globe countries. This was intended to provide researchers, public health officials and the general public with exposure to the epidemic.

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K. Saritha

Papers

Statistical analysis and visualization of the potential cases of pandemic coronavirus