Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models.

Globally, since the outbreak of the Omicron variant in November 2021, the number of confirmed cases of COVID-19 has continued to increase, posing a tremendous challenge to the prevention and control of this infectious disease in many countries. The global daily confirmed cases of COVID-19 between November 1, 2021, and February 17, 2022, were used as a database for modeling, and the ARIMA, MLR, and Prophet models were developed and compared. The prediction performance was evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The study showed that ARIMA (7, 1, 0) was the optimum model, and the MAE, MAPE, and RMSE values were lower than those of the MLR and Prophet models in terms of fitting performance and forecasting performance. The ARIMA model had superior prediction performance compared to the MLR and Prophet models. In real-world research, an appropriate prediction model should be selected based on the characteristics of the data and the sample size, which is essential for obtaining more accurate predictions of infectious disease incidence.

The research of SARIMA model for prediction of hepatitis B in mainland China.

ABSTRACT: Hepatitis B virus infection is a major global public health concern. This study explored the epidemic characteristics and tendency of hepatitis B in 31 provinces of mainland China, constructed a SARIMA model for prediction, and provided corresponding preventive measures.Monthly hepatitis B case data from mainland China from 2013 to 2020 were obtained from the website of the National Health Commission of the People's Republic of China. Monthly data from 2013 to 2020 were used to build the SARIMA model and data from 2021 were used to test the model.Between 2013 and 2020, 9,177,313 hepatitis B cases were reported in mainland China. SARIMA(1,0,0)(0,1,1)12 was the optimal model and its residual was white noise. It was used to predict the number of hepatitis B cases from January to December 2021, and the predicted values for 2021 were within the 95% confidence interval.This study suggests that the SARIMA model simulated well based on epidemiological trends of hepatitis B in mainland China. The SARIMA model is a feasible tool for monitoring hepatitis B virus infections in mainland China.

The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China.

BACKGROUND AND OBJECTIVE: Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population's health but also affects economic and social development. It requires an accurate prediction analysis to help to make policymakers with early warning and provide effective precautionary measures. In this study, ARIMA, GM(1,1), and LSTM models were constructed and compared, respectively. The results showed that the LSTM was the optimal model, which can be achieved satisfactory performance for TB cases predictions in mainland China. METHODS: The data of tuberculosis cases in mainland China were extracted from the National Health Commission of the People's Republic of China website. According to the TB data characteristics and the sample requirements, we created the ARIMA, GM(1,1), and LSTM models, which can make predictions for the prevalence trend of TB. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were applied to evaluate the effects of model fitting predicting accuracy. RESULTS: There were 3,021,995 tuberculosis cases in mainland China from January 2018 to December 2020. And the overall TB cases in mainland China take on a downtrend trend. We established ARIMA, GM(1,1), and LSTM models, respectively. The optimal ARIMA model is the ARIMA (0,1,0) × (0,1,0)12. The equation for GM(1,1) model was X(k+1) = -10057053.55e(-0.01k) + 10153178.55 the Mean square deviation ratio C value was 0.49, and the Small probability of error P was 0.94. LSTM model consists of an input layer, a hidden layer and an output layer, the parameters of epochs, learning rating are 60, 0.01, respectively. The MAE, RMSE, and MAPE values of LSTM model were smaller than that of GM(1,1) and ARIMA models. CONCLUSIONS: Our findings showed that the LSTM model was the optimal model, which has a higher accuracy performance than that of ARIMA and GM (1,1) models. Its prediction results can act as a predictive tool for TB prevention measures in mainland China.

Genomic characterization and phylogenetic evolution of the SARS-CoV-2.

 The coronavirus disease 2019 (COVID-19) starting on 12 December 2019 in Wuhan, China, caused 7,885,123 cases including 431,835 deaths by 14 Jun 2020 all over the world. Here we report the genomic characterization and phylogenetic evolution of coronavirus SARS-CoV-2 causing COVID-19. The SARS-CoV-2 and other coronavirus genomes were obtained from GISAID and GenBank. The genomes were annotated and potential genetic recombination was investigated. Phylogenetic analysis was conducted and used to determine the evolutionary history of the virus and to elucidate the origin of the virus. The analysis had revealed that SARS-CoV-2 possessed a similar genomic organization to bat-SARS-like-CoV collected in China. The genome sequences of SARS-CoV-2 were very similar, showing 99.6-100% sequence identity. Notably, SARS-CoV-2 was closely related (with 88% identity) to bat-SARS-like coronavirus, but was more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%). Phylogenetic tree of the complete viral genome showed that the virus clustered with bat SARS-like coronavirus. The results of the similarity between SARS-CoV-2 and related viruses did not identify any potential genomic recombination events. Therefore, it seems that the SARS-CoV-2 might be originally hosted by bats, and might have been transmitted to humans via intermediate hosts of currently unknown wild animal(s). Finally, based on the wide spread of SARS-CoV in their natural reservoirs, future studies should focus more on surveillance of coronaviruses, and measures against the domestication and consumption of wild animals should be implemented. Keywords: coronavirus; SARS coronavirus; SARS-CoV-2; genomic characterization; phylogenetic evolution.