METHODS: A retrospective cross-sectional study (5 years) was conducted at the respiratory department, Hospital Pulau Pinang. Patients aged ⩾65 years with confirmed diagnosis of RTI were included in the study.

RESULTS: A total of 474 patients were included, and the most prevalent RTIs were community-acquired pneumonia (65.6%) followed by chronic obstructive pulmonary disease (20.7%), bronchitis (8.2%) and hospital-acquired pneumonia (5.5%). Amoxicillin/clavulanate (69.8%), ampicillin/sulbactam (9.1%) and cefuroxime (6.5%) are the most common antibiotics prescribed to treat RTIs among geriatrics. Smoking, alcohol consumption, polypharmacy and presence of other co-morbidities are statistically significant factors associated with treatment outcomes of RTIs among geriatrics.

METHOD: This cross-sectional study was conducted on a sample of 256 conveniently selected elderly Malaysians who were residing in the states of Selangor and Kuala Lumpur. A pre-validated interview-administered questionnaire was used to gather information. Data was entered into PASW version 18 and analyzed.

RESULTS: A total of 256 questionnaires were included in the study. A response rate of 64% was achieved. Out of 256 respondents, 92 (35.9%) were male while 164 (64.1%) were female. More than half of the respondents (n = 141; 55.1%) agreed that CAM is more effective than allopathic medicine. Chinese respondents showed strong belief in the effectiveness of CAM. In terms of safety of CAM, close to three quarters of respondents (n = 178; 69.5%) believed that CAM is safer than allopathic medicine. A large majority of respondents agreed that CAM has less side effects compared to allopathic medicine (n = 201; 78.5%) and also agreed that CAM is good to maintain overall health and wellbeing (n = 212; 82.8%). A majority of the respondents expressed that they use CAM because allopathic medicine is less effective (n = 113; 44.1%).

METHODS: We conduct a side-by-side comparison of the machine learning approach with the traditional time series model. The multilayer perceptron model (MLP), a machine learning technique, and the Box-Jenkins methodology, also known as the ARIMA model, are used for classical modeling. Both methods are applied to the Monkeypox cumulative data set and compared using different model selection criteria such as root mean square error, mean square error, mean absolute error, and mean absolute percentage error.

RESULTS: With a root mean square error of 150.78, the monkeypox series follows the ARIMA (7,1,7) model among the other potential models. Comparatively, we use the multilayer perceptron (MLP) model, which employs the sigmoid activation function and has a different number of hidden neurons in a single hidden layer. The root mean square error of the MLP model, which uses a single input and ten hidden neurons, is 54.40, significantly lower than that of the ARIMA model. The actual confirmed cases versus estimated or fitted plots also demonstrate that the multilayer perceptron model has a better fit for the monkeypox data than the ARIMA model.

CONCLUSIONS AND RECOMMENDATION: When it comes to predicting monkeypox, the machine learning method outperforms the traditional time series. A better match can be achieved in future studies by applying the extreme learning machine model (ELM), support vector machine (SVM), and some other methods with various activation functions. It is thus concluded that the selected data provide a real picture of the virus. If the situations remain the same, governments and other stockholders should ensure the follow-up of Standard Operating Procedures (SOPs) among the masses, as the trends will continue rising in the upcoming 10 days. However, governments should take some serious interventions to cope with the virus.