VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants.

BACKGROUND AND OBJECTIVE: The ever-mutating COVID-19 has infected billions of people worldwide and seriously affected the stability of human society and the world economic development. Therefore, it is essential to make long-term and short-term forecasts for COVID-19. However, the pandemic situation in different countries and regions may be dominated by different virus variants, and the transmission capacity of different virus variants diversifies. Therefore, there is a need to develop a predictive model that can incorporate mutational information to make reasonable predictions about the current pandemic situation. METHODS: This paper proposes a deep learning prediction framework, VOC-DL, based on Variants Of Concern (VOC). The framework uses slope feature method to process the time series dataset containing VOC variant information, and uses VOC-LSTM, VOC-GRU and VOC-BILSTM prediction models included in the framework to predict the daily newly confirmed cases. RESULTS: We analyzed daily newly confirmed cases in Italy, South Korea, Russia, Japan and India from April 14th, 2021 to July 3rd, 2021. The experimental results show that all VOC-DL models proposed in this paper can accurately predict the pandemic trend in the medium and long term, and VOC-LSTM model has the best prediction performance, with the highest average determination coefficient R2 of 96.83% in five nations' datasets. The overall prediction has robustness. CONCLUSIONS: The experimental results show that VOC-LSTM is the best predictor for such a series of data and has higher prediction accuracy in the long run. At the same time, our VOC-DL framework combining VOC variants has reference significance for predicting other variants in the future.

SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD.

COVID-19 is one of the biggest challenges that human beings have faced recently. Many researchers have proposed different prediction methods for establishing a virus transmission model and predicting the trend of COVID-19. Among them, the methods based on artificial intelligence are currently the most interesting and widely used. However, only using artificial intelligence methods for prediction cannot capture the time change pattern of the transmission of infectious diseases. To solve this problem, this paper proposes a COVID-19 prediction model based on time-dependent SIRVD by using deep learning. This model combines deep learning technology with the mathematical model of infectious diseases, and forecasts the parameters in the mathematical model of infectious diseases by fusing deep learning models such as LSTM and other time prediction methods. In the current situation of mass vaccination, we analyzed COVID-19 data from January 15, 2021, to May 27, 2021 in seven countries - India, Argentina, Brazil, South Korea, Russia, the United Kingdom, France, Germany, and Italy. The experimental results show that the prediction model not only has a 50% improvement in single-day predictions compared to pure deep learning methods, but also can be adapted to short- and medium-term predictions, which makes the overall prediction more interpretable and robust.

The Pulsatile Modification Improves Hemodynamics and Attenuates Inflammatory Responses in Extracorporeal Membrane Oxygenation.

BACKGROUND: COVID-19 is still a worldwide pandemic and extracorporeal membrane oxygenation (ECMO) is vital for extremely critical COVID-19 patients. Pulsatile flow impacts greatly on organ function and microcirculation, however, the effects of pulsatile flow on hemodynamics and inflammatory responses during ECMO are unknown. An in vivo study was launched aiming at comparing the two perfusion modes in ECMO. METHODS: Fourteen beagles were randomly allocated into two groups: the pulsatile group (n=7) and the non-pulsatile group (n=7). ECMO was conducted using the i-Cor system for 24 hours. Hemodynamic parameters including surplus hemodynamic energy (SHE), energy equivalent pressure (EEP), oxygenator pressure drop (OPD), and circuit pressure drop (CPD) were monitored. To assess inflammatory responses during ECMO, levels of tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), IL-6, IL-8, and transforming growth factor-ß1 (TGF-ß1) were measured. RESULTS: EEP and SHE were markedly higher in pulsatile circuits when compared with the conventional circuits. Between-group differences in both OPD and CPD reached statistical significance. Significant decreases in TNF-α were seen in animals treated with pulsatile flows at 2 hours, 12 hours, and 24 hours as well as a decrease in IL-1ß at 24 hours during ECMO. The TGF-ß1 levels were significantly higher in pulsatile circuits from 2 hours to 24 hours. The changes in IL-6 and IL-8 levels were insignificant. CONCLUSION: The modification of pulsatility in ECMO generates more hemodynamic energies and attenuates inflammatory responses as compared to the conventional non-pulsatile ECMO.