Application of α-Sutte model in epidemic prediction—based on software R

  Objective  To introduce the principle and method ofα-Sutte model, establish a α-Sutte model by using software R, compare the fitting and prediction effects of theα-Sutte model and multiple seasonal autoregressive integrated moving average model, SARIMA model and provides reference for the application of theα-Sutte model in epidemic prediction.

  Methods  The daily cumulative number of reported cases of COVID-19 from India, the United States, Italy, Brazil, Russia, and South Africa from January 1, 2020 to July 16, 2021 were collected. Based on the time of the first reported case, the data reported by June 16, 2021 were used as training data, and the data reported from June 17, 2021 to July 16, 2021 were used as test data. According to the calculation formula of theα-Sutte model, the fitting and prediction functionα-Sutte() was written by software R. The training data was used to train theα-Sutte model and the SARIMA model. Two models were established to predict the number of daily reported cases of COVID-19 from June 17, 2021 to July 16, 2021. The fitted value was compared with the training data, the predicted value was compared with the test data, and the Mean Absolute Percentage Error (MAPE) was used to evaluate the model fitting and prediction effect.

  Results  The optimal SARIMA models established by India, the United States, Italy, Brazil, Russia and South Africa were SARIMA(5, 2, 2) SARIMA(0, 2, 2), SARIMA(2, 2, 2), SARIMA(3, 2, 2), SARIMA(0, 2, 1) and SARIMA(4, 2, 3) respectively. The MAPE fitted by theα-Sutte and SARIMA models in India, the United States, Italy, Brazil, Russia, and South Africa were 1.32%, 1.34%, 0.89%, 1.65%, 0.99%, 0.99% and 1.51%, 1.59%, 0.89%, 1.67%, 1.03%, 1.13% respectively. The MAPE predicted by theα-Sutte and SARIMA models in 6 countries were 0.81%, 0.09%, 0.13%, 1.58%, 1.73%, 3.77% and 0.09%, 0.09%, 0.18%, 1.13%, 1.83%, 3.43% respectively.

  Conclusion  The principle and modeling of theα-Sutte model are simple. Theα-Sutte model established by software R has high fitting and prediction accuracy, and it is worth to promote in disease surveillance.