COVID-19 pandemic is not slowing down, while the government from various countries is currently designing the ‘new normal’ to restart back their economy. Up to now, there is a significant mortality rate in the health care professionals that act as the frontliners in the hospitals and clinics in battling the COVID-19 menace. Based on computational sciences studied since the 1960s, artificial intelligence (AI) could assist healthcare professionals to protect themselves from the SARS-CoV-2 virus and treat the patients in an efficient and secure manner. AI, starting from the diagnosis, treatment, medication, until the prediction of the next pandemic, could assist in various areas. The objective of this review is to highlight AI in the health sciences in order to assist healthcare professionals in facing the COVID-19 pandemic, and the threat of the future pandemics.International Journal of Human and Health Sciences Vol. 05 No. 02 April’21 Page: 177-184

Classifying epitopes is essential since they can be applied in various fields, including therapeutics, diagnostics and peptide-based vaccines. To determine the epitope or peptide against an antibody, epitope mapping with peptides is the most extensively used method. However, this method is more time-consuming and inefficient than using present methods. The ability to retrieve data on protein sequences through laboratory procedures has led to the development of computational models that predict epitope binding based on machine learning and deep learning (DL). It has also evolved to become a crucial part of developing effective cancer immunotherapies. This paper proposes an architecture to generalize this case since various research strives to solve a low-performance classification problem. A proposed DL model is the fusion architecture, which combines two architectures: Transformer architecture and convolutional neural network (CNN), called MITNet and MITNet-Fusion. Combining these two architectures enriches feature space to correlate epitope labels with the binary classification method. The selected epitope–T-cell receptor (TCR) interactions are GILG, GLCT and NLVP, acquired from three databases: IEDB, VDJdb and McPAS-TCR. The previous input data was extracted using amino acid composition, dipeptide composition, spectrum descriptor and the combination of all those features called AADIP composition to encode the input data to DL architecture. For ensuring consistency, fivefold cross-validations were performed using the area under curve metric. Results showed that GILG, GLCT and NLVP received scores of 0.85, 0.87 and 0.86, respectively. Those results were compared to prior architecture and outperformed other similar deep learning models.

Objective: Spatio-temporal modelling is a method used for data which has spatial (area) and temporal (time) property. Confirmed cases of Covid19 in each province Indonesia were recorded from March 2nd to September 15th, 2020. The spatio-temporal model in this study are split into two parts which are ARIMA(p,d,q) for the temporal pattern and Bayesian Poisson regression to explain the spatial pattern.Method: Data for the study was obtained from Data Repository of Indonesian National Board for Disaster Management - Indonesia Task Force for Covid-19 Rapid Response (Gugus tugas Percepatan penangana Covid19) official website which are an opened source data. The Rstudio, Arcgis and excel was used to carry out the statistical analysis involved in the investigation. In the temporal analysis, data was assumed to have an increasing trend and to create a stationary series, an integrated method was conducted. Box-Jenskin and Ljung-Box method was taken in parameter estimation and model identification process. For the spatial analysis, a Bayesian Poisson Regression is fitted to the dataset with Metropolis algorithm.Result: Model IMA(1,1), in general, can explain he increasing trend in the Covid19 confirmed cases in Indonesia. This model can define that the case number at the particular time is affected by the moving average at lag-1. Meaanwhile, a Bayesian Poisson Regression can elaborate spatial pattern in the data. The fitted model shows that the confirmed cases at particular province is also affected by the population density at those provinces. As there are some limitation in the data and method applied in the study, further analysis and research are needed.

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Objective: To compare the efficacy of SSRI medication alone and SSRI+CBT combined. Methods: NCBI Pubmed, DARE, CSDR and NGC were searched October-November 2019. The population size, as well as the base and endpoint CGAS mean and standard deviation from the three studies included, are recorded. Statistical analysis was done in RStudio with the "meta" package. Results: For the SSRI only, the effect size was -1.82 with a 95% confidence interval between -2.28 and -1.37.  For the SSRI and CBT combined, the effect size was -1.68 with a 95% confidence interval between -2.39 and -0.98. The effect size for both SSRI and SSRI + CBT didn't cross the null effect line, but the heterogeneity exceeds 50%. The result for the comparison of post SSRI vs. SSRI + CBT showed the effect size of -0.05 with a 95% confidence interval between -0.23 and 0.12. The size effect did cross the null effect line, but the heterogeneity was less than 50%. Conclusion: Both methods were shown to be effective. However, due to statistical inconsistencies, it couldn’t be concluded whether the combination of SSRI and CBT is better than treatment with SSRI alone.