<abstract>
<p>Starting February 2020, COVID-19 was confirmed in 11,946 people worldwide, with a mortality rate of almost 2%. A significant number of epidemic diseases consisting of human Coronavirus display patterns. In this study, with the benefit of data analytic, we develop regression models and a Susceptible-Infected-Recovered (SIR) model for the contagion to compare the performance of models to predict the number of cases. First, we implement a good understanding of data and perform Exploratory Data Analysis (EDA). Then, we derive parameters of the model from the available data corresponding to the top 4 regions based on the history of infections and the most infected people as of the end of August 2020. Then models are compared, and we recommend further research.</p>
</abstract>
COVID-19 has surged in the United States since January 2020. Since then, social distancing and lockdown have helped many people to avoid infectious diseases. However, this did not help the upswing of the number of cases after the lockdown was finished. Modeling the infectious disease can help the health care providers and governors to plan ahead for obtain the needed resources. In this manner, precise short-term determining of the number of cases can be imperative to the healthcare system. Many models have been used since the pandemic has started. In this paper we will compare couple of time series models like Simple Moving Average, Exponentially Weighted Moving Average, Holt-Winters Double Exponential Smoothing Additive, ARIMA, and SARIMA. Two models that have been used to predict the number of cases are ARIMA and SARIMA. A grid search has been implemented to select the best combination of the parameters for both models. Results show that in the case of modeling, the Holt-Winters Double Exponential model outperforms Exponentially Weighted Moving Average and Simple Moving Average while forecasting ARIMA outperforms SARIMA.
Starting February 2020, COVID-19 was confirmed in 11,946 people worldwide, with a mortality rate of almost 2%.
A significant number of epidemic diseases including human Coronavirus display patterns. In this study with the benefit of data analytic, we develop regression models and a Susceptible-Infected-Recovered (SIR) model for the contagion to compare the performance of models to predict number of cases. first, we implement a good understanding of data and perform Exploratory Data Analysis (EDA). Then, we derive the parameters of the model from the available data corresponding to the top 4 regions based on the history of infections and the most infected people as of the end of August 2020. Then models are compared and further research are introduced.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.