Short-Term Prediction Methodology of COVID-19 Infection in South Korea

The purpose of this study is to predict the short-term trend of the COVID-19 pandemic and give insights into effective response strategies. Based on the basic SIR model, a compartment method for modeling the course of an epidemic, the short-term infection change ratio md, is derived. The number of infected people can be predicted using this ratio. We calculated different md values on a weekly basis. As we tested different combinations of md, the prediction from the combination of md based on a week and md based on 4 weeks was found to be statistically reliable. According to our regression analysis, our approach has an explanatory power of 96%. However, this method could only predict 1 week ahead of current data. Thus, we use LSTM, a deep learning method applied for time series data, to forecast the trend 4 weeks ahead. The forecasted trends show that the number of infected people in South Korea will reach its peak a week after the writing of this work and start to gradually decline after that.

Data mining of coronavirus: SARS-CoV-2, SARS-CoV and MERS-CoV.

OBJECTIVE: In this study we compare the amino acid and codon sequence of SARS-CoV-2, SARS-CoV and MERS-CoV using different statistics programs to understand their characteristics. Specifically, we are interested in how differences in the amino acid and codon sequence can lead to different incubation periods and outbreak periods. Our initial question was to compare SARS-CoV-2 to different viruses in the coronavirus family using BLAST program of NCBI and machine learning algorithms. RESULTS: The result of experiments using BLAST, Apriori and Decision Tree has shown that SARS-CoV-2 had high similarity with SARS-CoV while having comparably low similarity with MERS-CoV. We decided to compare the codons of SARS-CoV-2 and MERS-CoV to see the difference. Though the viruses are very alike according to BLAST and Apriori experiments, SVM proved that they can be effectively classified using non-linear kernels. Decision Tree experiment proved several remarkable properties of SARS-CoV-2 amino acid sequence that cannot be found in MERS-CoV amino acid sequence. The consequential purpose of this paper is to minimize the damage on humanity from SARS-CoV-2. Hence, further studies can be focused on the comparison of SARS-CoV-2 virus with other viruses that also can be transmitted during latent periods.