Prediction of pandemic risk for animal-origin coronavirus using a deep learning method

Background:Coronaviruses can be isolated from bats,civets,pangolins,birds and other wild animals.As an animalorigin pathogen,coronavirus can cross species barrier and cause pandemic in humans.In this study,a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes.Methods:A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library.We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution.The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk.The best performances were explored with the use of pre-trained DNA vector and attention mechanism.The area under the receiver operating characteristic curve(AUROC)and the area under precision-recall curve(AUPR)were used to evaluate the predictive models.Results:The six specifc models achieved good performances for the corresponding virus groups(1 for AUROC and 1 for AUPR).The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups(1 for AUROC and 1 for AUPR)while those without pre-training vector or attention mechanism had obvi‑ously reduction of performance(about 5–25%).Re-training experiments showed that the general model has good capabilities of transfer learning(average for six groups:0.968 for AUROC and 0.942 for AUPR)and should give reason‑able prediction for potential pathogen of next pandemic.The artifcial negative data with the replacement of the coding region of the spike protein were also predicted correctly(100%accuracy).With the application of the Python programming language,an easy-to-use tool was created to implements our predictor.Conclusions:Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic.

Risk Perception of Seasonal and Swine Influenza Among University College Students： Does Study Direction Influence Attitudes？

In 2009, Norway faced the global challenge of the influenza pandemic. Risk communication is an important tool within healthy promoting work. In this study the main aim was to explore reflections of students on the risk assessment of season flu and the swine flu in 2009 according to field of study. A cross-sectional questionnaire survey based on 505 students is presented. 42.4% were health subject students, and 57.6% were non-health subject related students. The majority of the students were 20-24 years old. Most of the respondents were not concerned at being infected with the swine flu, and did underestimate the death toll of the common flu. Students were more concerned about the swine flu than the regular season flu. By logistic regression, the odds ratio for taking the swine flu vaccine was greater among students who were concerned （O.R. = 2.5）. During the swine flu pandemic, student trust towards the health authorities was low. Among the students, 74% stated they would consider advice from the health authorities, 37% from their parents and 20% from mass media. Stating risk of getting the common flu was at the medium or great risk level for far less non-health students than for health students, 38.2% versus 55.6%, P = 0.001. The perceived infection risk was likewise higher in the health student group, 52.4% versus 36.2%, P = 0.001. The respondents had little faith in general public vaccination as well as being critical concerning side effects of vaccination. The results from the study indicated that the students would rather follow advice about their personal hygiene than advice to take the swine flu-vaccine.