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.

METHODS AND ANALYSIS: The methodology draws on Arksey and O'Malley's seminal framework for the scoping review. The literature search will be conducted by using keywords to find suitable published literature. The existing literature will be searched using selected electronic databases such as PubMed/MEDLINE, CINAHL, Scopus, ProQuest and Web of Science from the years 2011 and 2021. The selected publications will focus on 10 Southeast Asian countries: Malaysia, Indonesia, Singapore, Thailand, Brunei, Philippines, Laos, Vietnam, Cambodia and Myanmar. Two reviewers will be performing title and abstract screening for the criteria of each publication, in which they will be working independently of each other. The included publication will undergo a full-text review and references cited will be examined for relevance using the same inclusion criteria. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram will guide throughout the process. Data will be extracted, analysed and charted within each category from the selected publications for each Southeast Asian country.

METHODS: This was a prospective cohort study in a single diabetes centre in Malaysia. Empagliflozin group were on study drug for at least three months. For control group, subjects not receiving SGLT2 inhibitors were recruited. Follow-up were performed before and during Ramadan fasting. Anthropometric measurements, blood pressure, renal profile, and blood ketone were recorded during visits. Hypoglycaemia symptoms were assessed via hypoglycaemia symptom rating questionnaire (HypoSRQ).

RESULTS: We recruited a total of 98 subjects. Baseline anthropometry, blood pressure, and renal parameters were similar in two groups. No significant changes in blood pressure, weight, urea, creatinine, eGFR, or haemoglobin levels during Ramadan was found in either group. Likewise, no difference was detected in blood ketone levels (empagliflozin vs control, 0.17 ± 0.247 mmol/L vs 0.13 ± 0.082 mmol/L, p = 0.304) or hypoglycaemia indices (empagliflozin vs control, 19.1% vs 16%, p = 0.684).