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COVID-19 has become a major global public health threat. It started from Wuhan a province of China in December 2019 and has spread in every country of the globe with over 100 million people currently infected and over three million deaths as of May 2021. Nearly a year on, with many town and cities are in lockdown and with all public health measures to minimize transmission of the virus SARS-COV-2 and the scientific achievements with many vaccines developed in record time, the transmission in the second wave is still strong with new variants of the virus emerging. The pandemic has caused a serious impact on the economic, social, political, and cultural dynamics of people, globally. The majority of people with mild and moderate symptoms of COVID-19 seek primary care (PC) service as the entry point for getting medical intervention, reducing flow to hospital care. Currently, primary care settings are facing major challenges including lack of funds, poor infrastructure in some countries, and shortage of health care workers and equipment. Most, if not all primary care centers are not designed to separate people with the virus. The pandemic so far shows that strengthening primary care as the first point of contact with the health system is vital. The health systems of the future cannot be resilient without strong primary care for all, capable of meeting population health needs and aspiration for good health and well-being. [ABSTRACT FROM AUTHOR] Copyright of Middle East Journal of Family Medicine is the property of Medi+WORLD International Pty. Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: In recent decades, the Arab population has experienced an increase in the prevalence of type 2 diabetes (T2DM), particularly within the Gulf Cooperation Council. In this context, early intervention programmes rely on an ability to identify individuals at risk of T2DM. We aimed to build prognostic models for the risk of T2DM in the Arab population using machine-learning algorithms vs. conventional logistic regression (LR) and simple non-invasive clinical markers over three different time scales (3, 5, and 7 years from the baseline). Design: This retrospective cohort study used three models based on LR, k-nearest neighbours (k-NN), and support vector machines (SVM) with five-fold cross-validation. The models included the following baseline non-invasive parameters: age, sex, body mass index (BMI), pre-existing hypertension, family history of hypertension, and T2DM. Setting: This study was based on data from the Kuwait Health Network (KHN), which integrated primary health and hospital laboratory data into a single system. Participants: The study included 1,837 native Kuwaiti Arab individuals (equal proportion of men and women) with mean age as 59.5 ± 11.4 years. Among them, 647 developed T2DM within 7 years of the baseline non-invasive measurements. Analytical methods: The discriminatory power of each model for classifying people at risk of T2DM within 3, 5, or 7 years and the area under the receiver operating characteristic curve (AUC) were determined. Outcome measures: Onset of T2DM at 3, 5, and 7 years. Results: The k-NN machine-learning technique, which yielded AUC values of 0.83, 0.82, and 0.79 for 3-, 5-, and 7-year prediction horizons, respectively, outperformed the most commonly used LR method and other previously reported methods. Comparable results were achieved using the SVM and LR models with corresponding AUC values of (SVM: 0.73, LR: 0.74), (SVM: 0.68, LR: 0.72), and (SVM: 0.71, LR: 0.70) for 3-, 5-, and 7-year prediction horizons, respectively. For all models, the discriminatory power decreased as the prediction horizon increased from 3 to 7 years. Conclusions: Machine-learning techniques represent a useful addition to the commonly reported LR technique. Our prognostic models for the future risk of T2DM could be used to plan and implement early prevention programmes for at risk groups in the Arab population.