The field of infectious disease prediction and public health response is changing due to the integration of real-time data with machine learning (ML). This paper examines how diverse real-time data types — including mobility patterns, social media activity, wearable sensor data, environmental signals, and electronic health records — can be successfully combined with machine learning approaches to enhance early diagnosis, forecast illness trajectories, and optimize intervention options. The potential of key machine learning models, such as reinforcement learning, deep learning, and supervised learning, to improve forecasting accuracy and facilitate dynamic decision-making is investigated. There is a critical discussion of issues such as algorithmic opacity, privacy problems, data inconsistencies, and a lack of standards. The COVID-19 pandemic case study demonstrates how these tools have already aided in resource allocation and policy planning. A forward-looking outlook on developments in data collecting, explainable Artificial Intelligence, and the necessity of global cooperation is presented in the manuscript's conclusion. When taken as a whole, these elements emphasize how crucial it is to combine technology and international collaboration to fortify public health systems and better prepare for future epidemics. This paper examines how diverse real-time data types — including mobility patterns, social media activity, wearable sensor data, environmental signals, and electronic health records — can be successfully combined with machine learning approaches to enhance early diagnosis, forecast illness trajectories, and optimize intervention options.

Two of the most disruptive technologies of the 21st century are quantum computing and artificial intelligence. Their intersection has led to the emergence of a new discipline referred to as Quantum Machine Learning (QML), which aims to enhance the capabilities of classical machine learning by leveraging the computational advantages of quantum devices. This paper provides a survey of the most advanced Quantum Machine Learning (QML) algorithms, including Quantum Support Vector Machines (QSVMs), Quantum k-nearest Neighbours (QkNN), Quantum Principal Component Analysis (QPCA), Quantum Neural Networks (QNNs), and Quantum Reinforcement Learning (QRL). The theoretical and practical status, as well as the empirical performance, of these algorithms, were summarised using a structured review method. The findings reveal a potential for speed-ups in classification, clustering, and optimisation among a range of applications, particularly for perfect quantum systems. However, hardware constraints, software irregularities, and training issues, such as barren plateaus, have limited the practical utility of this approach. Applications of QML in areas such as disaster preparedness and management, drug discovery, environmental sustainability, urban planning methodology, NLP, and finance demonstrate both the potential and current limitations of QML, with most applications still at the proof-of-concept level. In this review, we conclude that QML could be revolutionary, but its feasibility ultimately relies on improvements in physical hardware, the robustness of algorithms, and the standardisation of benchmarks.

The SARS-CoV-2 main protease (Mpro), essential for viral replication, remains a prime target for antiviral drug design against COVID-19 and related coronaviruses. In this study, we present a systematic investigation into the molecular determinants of Mpro inhibition using an integrated approach combining large-scale data mining, cheminformatics, and quantum chemical calculations. A curated dataset comprising 963 high-resolution structures of Mpro–ligand complexes—348 covalent and 615 non-covalent inhibitors—was mined from the Protein Data Bank. Cheminformatics analysis revealed distinct physicochemical profiles for each inhibitor class: covalent inhibitors tend to exhibit higher hydrogen bonding capacity and sp³ character, while non-covalent inhibitors are enriched in aromatic rings and exhibit greater aromaticity and lipophilicity. A novel descriptor, Weighted Hydrogen Bond Count (WHBC), normalized for molecular size, revealed a notable inverse correlation with aromatic ring count, suggesting a compensatory relationship between hydrogen bonding and π-mediated interactions. To elucidate the energetic underpinnings of molecular recognition, 40 representative inhibitors (20 covalent, 20 non-covalent) were selected based on principal component analysis and aromatic ring content. Quantum mechanical calculations at the double-hybrid B2PLYP/def2-QZVP level quantified non-bonded interaction energies, revealing that covalent inhibitors derive binding strength primarily through hydrogen bonding (~63.8%), whereas non-covalent inhibitors depend predominantly on π–π stacking and CH–π interactions (~62.8%). Representative binding pocket analyses further substantiate these findings: the covalent inhibitor F2F-2020198-00X exhibited strong hydrogen bonds with residues such as Glu166 and His163, while the non-covalent inhibitor EDG-MED-10fcb19e-1 engaged in extensive π-mediated interactions with residues like His41, Met49, and Met165. The distinct interaction patterns led to the establishment of pharmacophore models, highlighting key recognition motifs for both covalent and non-covalent inhibitors. Our findings underscore the critical role of aromaticity and non-bonded π interactions in driving binding affinity, complementing or, in some cases, substituting for hydrogen bonding, and offer a robust framework for the rational design of next-generation Mpro inhibitors with improved selectivity and resistance profiles.

Sexual health behaviours among young adults are influenced by multiple factors, including digital media exposure, parental communication, and peer influence. The increasing reliance on digital platforms for sexual health information, the role of parental guidance, and the impact of social circles raise important questions about how these factors may shape sexual responsibility, condom use, and risk perception. Despite increased access to sexual health information, many young adults continue to engage in risky sexual behaviours. This study aims to examine the effects of digital media exposure, parental communication, and peer influence on the sexual health behavioural patterns of young adults. A survey-based cross-sectional study was conducted among young adults, utilizing structured questionnaires. A total of 542 participants submitted their responses. Data was analysed using descriptive statistics and regression analysis to determine the impact of these variables on sexual health knowledge and condom use frequency. When it comes to the source of exposure to sexual health information, Digital Media was the most frequent source, with over 90% of respondents reporting at least an occasional exposure. Parental/Guardian Communication showed lower exposure levels, with 31.4% indicating they were never had sexual health discussion with a parent/guardian. Peer Influence was a significant factor, with 54.9% of respondents reporting that they were often exposed to sexual health discussions from peers. Regression analysis revealed that peer influence had the most substantial effect on sexual health behaviours (β = 0.40, p < 0.001), followed by digital media exposure (β = 0.30, p = 0.001) and parental communication (β = 0.25, p = 0.028). Notably, 40.2% of respondents considered themselves quite aware of sexual health risks, yet a significant 17% reported never using condoms. Further analysis of exposure levels showed that digital media and peer discussions were more influential than parental communication in shaping sexual health decisions. Parental communication strategies should be strengthened to create an open dialogue about sexual health. Public health efforts should focus on leveraging peer influence, promoting digital health literacy, and encouraging family-based discussions to improve responsible sexual health behaviours among young adults.