Machine Learning-Driven Approach for a COVID-19 Warning System

Time Series analysis is being widely used in the fields of corporate sectors, medical sciences, weather forecasting, and stock prices to predict future values based on previously observed values. In this research, Time Series data of COVID-19 (SARS-CoV-2) obtained from a dataset obtained from worldometers has been to predict the prognosis of the virus in the forthcoming days. The dataset contains summed up values of the daily new cases, recovered cases, and deaths from 187 countries right from the day from which the first case was reported. After exploring and splitting the data into train and test sets to ensure the fidelity of the model, a Machine Learning model called Prophet which was introduced by Facebook has been used to train and test on the observed data. Finally, it has been used to draw probabilistic insights for the future on the new cases, recovered cases, and new deaths for the next month.

IRJET, 2020

Time Series analysis is being widely used in the fields of corporate sectors, medical sciences, weather forecasting, and stock prices to predict future values based on previously observed values. In this research, Time Series data of COVID-19 (SARS-CoV-2) obtained from a dataset obtained from worldometers has been to predict the prognosis of the virus in the forthcoming days. The dataset contains summed up values of the daily new cases, recovered cases, and deaths from 187 countries right from the day from which the first case was reported. After exploring and splitting the data into train and test sets to ensure the fidelity of the model, a Machine Learning model called Prophet which was introduced by Facebook has been used to train and test on the observed data. Finally, it has been used to draw probabilistic insights for the future on the new cases, recovered cases, and new deaths for the next month.

Malaysian Journal of Medicine and Health Sciences

Novel COVID-19 Coronavirus disease, namely SARS-CoV-2, is a global pandemic and has spread to more than 200 countries. The sudden rise in the number of cases is causing a tremendous effect on healthcare services worldwide. To assist strategies in containing its spread, machine learning (ML) has been employed to effectively track the daily infected and mortality cases as well as to predict the peak growth among the states or/and country-wise. The evidence of ML in tackling previous epidemics has encouraged researchers to reciprocate with this outbreak. In this paper, recent studies that apply various ML models in predicting and forecasting COVID-19 trends have been reviewed. The development in ML has significantly supported health experts with improved prediction and forecasting. By developing prediction models, the world can prepare and mitigate the spread and impact against COVID-19.

International Journal of Research Publication and Reviews

Coronavirus disease 2019 (COVID-19) is spreading rapidly; machine learning algorithms have been applied for a long time in many applications requiring the detection of adverse risk factors. The machine learning model proposed in this research paper uses three types of data, confirmed cases, recovered cases and deaths reported, the model can predict the spread of the virus in the next 20 days, and the data is time line series data and that is effective in predicting new cases of corona, death numbers and recovery.

Purpose: Coronavirus disease is an irresistible infection caused by the respiratory disease Coronavirus 2 (SARSCoV-2). It was ¦rst found in Wuhan, China, in December 2019, and has since spread universally, causing a constant pandemic. On June 3, 2020, 6.37 million cases were found in 188 countries and regions. Prevention is the only cure for this disease. A study was carried out on Coronavirous to observe the number of cases, deaths and recovery cases worldwide within a speci¦c time period of ¦ve months. Based on this data, this research paper will predict the future spread of this infectious disease in human society. Methods: In our study, the data set was taken from WHO "Data WHO Coronavirus Covid-19 cases and deaths-WHOCOVID-19-global-data". This dataset contains information about the observation date, provenance/state, country/region and latest updates. In this article, we implemented several forecasting techniques: naive method, simple average, moving average, single exponential smoothing, Holt linear trend method, Holt Winter method and ARIMA, for comparison, and how these methods improve the Root mean square error score. Results: The naive method is best suited as described over all other methods. In the ARIMA model, utilizing grid search, we recognized a lot of boundaries that delivered the best-¦t model for our time series data. By continuing the model, future predictions of death cases indicate that the number of deaths will increased by more than 600,000 by January 2020.

Applied Sciences

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

Bulletin of Electrical Engineering and Informatics, 2024

As coronavirus disease (Covid-19) it is a contagious disease that is spread by the SARS-CoV-2 virus, one of the most common causes of disease in humans. The disease was initially discovered in Wuhan, China, in 2019, and has now spread throughout the world, including Malaysia. A large number of people have lost their life partners and families because of this disease. Thus, in order for us to stop this epidemic spread, we have to implement social distance. The Covid-19 infection displays this type of behavior, which necessitates the development of mathematical and predictive modeling techniques capable of predicting possible disease patterns or trends, in order to assist the government and health authorities in predicting and preparing for potential outbreaks. The purpose of this paper is to provide an in-depth critique and analysis of the machine-learning approaches that have been implemented by researchers to predict Covid-19, based on existing research. As a result, future researchers will be able to use this paper as a valuable resource for their research related to the Covid-19 forecasting model.

Intelligent Automation & Soft Computing

COVID-19 is a novel virus that spreads in multiple chains from one person to the next. When a person is infected with this virus, they experience respiratory problems as well as rise in body temperature. Heavy breathlessness is the most severe sign of this COVID-19, which can lead to serious illness in some people. However, not everyone who has been infected with this virus will experience the same symptoms. Some people develop cold and cough, while others suffer from severe headaches and fatigue. This virus freezes the entire world as each country is fighting against COVID-19 and endures vaccination doses. Worldwide epidemic has been caused by this unusual virus. Several researchers use a variety of statistical methodologies to create models that examine the present stage of the pandemic and the losses incurred, as well as considered other factors that vary by location. The obtained statistical models depend on diverse aspects, and the studies are purely based on possible preferences, the pattern in which the virus spreads and infects people. Machine Learning classifiers such as Linear regression, Multi-Layer Perception and Vector Auto Regression are applied in this study to predict the various COVID-19 blowouts. The data comes from the COVID-19 data repository at Johns Hopkins University, and it focuses on the dissemination of different effect patterns of Covid-19 cases throughout Asian countries.

CEUR Workshop Proceedings, 2021

The epidemic COVID-19 has shaken the globe through its cruelty, and its spread rate continues to rise daily. This paper highlights the clinical stance in the COVID-19 research studies, where time-series statistical analysis has been performed by using Prophet Model. It is widely used to understand the trend of the current epidemic after 2 nd May 2020 with data at the worldwide state. The prophet model is an open-source model obtained by the data science panel on Facebook for performing predicting operations. It assists to make fast and accurate predictions for existing data samples. The Prophet model is simple to implement because its open authorized repository exists on GitHub. The time-series data analysis refers to the confirmed, recovered, and death rates for the time of 2 nd May 2021 to 17 th January 2022. The statistical validation strategy is followed by the implementation of a T-test on the evaluated time-series data. The expected data generated by the predictive model can be further used by the official authorities, medical departments of various countries. Moreover, the model is used to provide new graphical insights into past, present, and future trends.

Lahore Garrison University Research Journal of Computer Science and Information Technology

Covid-19 emerged as one of the most infectious diseases in the history of mankind, affecting nearly 250 million people all over the world in just a short period. The pandemic which started in China, has now spread all over the world, taking about 5 million lives globally. This has also severely affected the economies of countries and has proved to be a burden on health care systems. Due to these reasons, forecasting the spread of the disease has become critical so that concerned government authorities in countries can have the chance to mitigate the spread and plan health care resources efficiently and properly. This makes it more important to have a reliable forecast so that resources can be planned ahead of time. In the present work, linear regression is used for time forecasting the spread of Covid-19 in Pakistan. Statistical parameters and metrics have been used to evaluate and validate the model. The results show that linear regression results are highly reliable, time efficien...