METHODS: The proposed approach outperformed the Autoregressive Integrated Moving Average (ARIMA) and Holt Winters models in all experiments for forecasting future values using COVID-19 and tourism datasets, with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE). The SARIMA model predicts COVID-19 and tourist arrivals with and without the COVID-19 pandemic with less than 5% MAPE error.

RESULTS: The suggested method provides a dashboard that shows COVID-19 and tourism-related information to end users. The suggested tool can be deployed in the healthcare, tourism, and government sectors to monitor the number of COVID-19 cases and determine the correlation between COVID-19 cases and tourism.

METHODS: According to a review of the literature, general practitioners (GPs) rarely investigate any anomalies in liver function tests to the level indicated by national standards. The authors have used data pre-processing in this work. The collection has 30691 records with 11 attributes. The classification model is utilized to construct an effective prediction system to aid general practitioners in identifying a liver patient using data mining.

RESULTS: The collected results indicate that both the Naïve Bayes and C4.5 Decision Tree models give accurate predictions. However, given the C4.5 model offers more accurate predictions than the Naïve Bayes model, it can be assumed that the C4.5 model is superior for this research. Consequently, the liver patient prediction system will be developed using the rules given by the C4.5 Decision Tree model in order to predict the patient class. The training set, suggested data mining with a classification model achieved 99.36% accuracy and on the testing set, 98.40% accuracy. On the training set, the enhanced accuracy relative to the current system was 29.5, while on the test set, it was 28.73. In compared to state-of-the-art models, the proposed approach yields satisfactory outcomes.

METHODS: Samples were obtained from 172/192 children presenting to a site in rural India with acute encephalitis syndrome.

RESULTS: Using the reference VT ELISA, infection with Japanese encephalitis virus (JEV) was confirmed in 44 (26%) patients, with central nervous system infection confirmed in 27 of these; seven patients were dengue seropositive. Of the 121 remaining patients, 37 (31%) were JEV negative and 84 (69%) were JEV unknown because timing of the last sample tested was <10 day of illness or unknown. For patient classification with XCyton, using cerebrospinal fluid alone (the recommended sample), sensitivity was 77.8% (59.2-89.4) with specificity of 97.3% (90.6-99.2). For Panbio ELISA, using serum alone (the recommended sample), sensitivity was 72.5% (57.2-83.9) with specificity of 97.5% (92.8-99.1). Using all available samples for patient classification, sensitivity and specificity were 63.6% (95% CI: 48.9-76.2) and 98.4% (94.5-99.6), respectively, for XCyton ELISA and 75.0% (59.3-85.4) and 97.7% (93.3-99.2) for Panbio ELISA.

METHODS: A new outcome score based on a 15-item questionnaire was developed after a literature review, examination of current assessment tools, discussion with experts and a pilot study. The score was used to evaluate 100 children in Malaysia (56 Japanese encephalitis patients, 2 patients with encephalitis of unknown etiology and 42 controls) and 95 in India (36 Japanese encephalitis patients, 41 patients with encephalitis of unknown etiology and 18 controls). Inter- and intra-observer variability in the outcome score was determined and the score was compared with full clinical assessment.

FINDINGS: There was good inter-observer agreement on using the new score to identify likely dependency (Kappa = 0.942 for Malaysian children; Kappa = 0.786 for Indian children) and good intra-observer agreement (Kappa = 1.000 and 0.902, respectively). In addition, agreement between the new score and clinical assessment was also good (Kappa = 0.906 and 0.762, respectively). The sensitivity and specificity of the new score for identifying children likely to be dependent were 100% and 98.4% in Malaysia and 100% and 93.8% in India. Positive and negative predictive values were 84.2% and 100% in Malaysia and 65.6% and 100% in India.