To systematically review data-driven modelling studies that evaluated the effectiveness of interventions implemented during the COVID-19 pandemic and to identify which measures were most frequently reported as effective in controlling disease spread.

Systematic review of modelling studies focused on data-driven, model-informed decision-making for COVID-19 interventions.

A comprehensive literature search was conducted in PubMed, Web of Science and Embase, covering publications from 1 January 2020 to 16 October 2024.

Studies were included if they: (1) used real-world data; (2) had sufficient sample sizes and (3) assessed at least one intervention with measurable outcomes.

Meta-analyses and purely theoretical modelling studies were excluded. Papers were further filtered using a structured screening process to ensure empirical and intervention-based modelling.

Data were extracted from eligible studies and categorised according to modelling approaches, data sources, intervention types and reported effectiveness. Descriptive synthesis was performed to summarise modelling trends and intervention performance. Studies were classified into major intervention categories, including tracing, testing and isolation (TTI); physical and social distancing (PSD); vaccination; lockdowns; mask-wearing; home office or stay-at-home (HOSH) and health infrastructure enhancement (HIE).

Out of 2297 studies identified, 126 met inclusion criteria. Compartmental models were the most frequently used approach, primarily relying on case and death counts to assess intervention impact. The most commonly reported effective interventions were TTI, PSD, vaccination, lockdowns, mask-wearing and HOSH. When considering effectiveness relative to study frequency, the top six interventions were TTI, HOSH, mask-wearing, HIE, PSD and lockdowns. The relatively lower representation of vaccination reflects that most included studies were conducted during the early stages of the pandemic, before widespread vaccine rollout and availability of empirical vaccination data.

This review highlights the critical role of data-driven models in guiding COVID-19 response strategies. Evidence supports the combined effectiveness of non-pharmaceutical interventions, robust testing and tracing systems and health infrastructure strengthening. Real-world impact, however, remains dependent on local healthcare capacity, socioeconomic conditions and cultural contexts. Continued research is essential to refine adaptive modelling approaches and strengthen preparedness for future public health emergencies.

There are many barriers to mental health services, including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited in part by the cyclical nature of psychiatric symptoms. The human voice might have the potential to serve as a valuable biomarker in the identification, early diagnosis or monitoring of psychiatric conditions. Therefore, this protocol presents a proposed scoping review with the aim of synthesising existing knowledge on the application of artificial intelligence (AI) or machine learning (ML) in the management of individuals at risk of/suffering from schizophrenia through audio samples as a biomarker.

Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines and Arksey & O’Malley’s scoping review framework (with recent advancements), we systematically mapped the literature on the application of voice-based biomarkers in schizophrenia. Several databases (PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, Embase, Compendex, CINAHL, Scientific Information Database, Magiran, IranMedex and Barakat knowledge network system) will be systematically searched for relevant studies through 2025. All searches will be conducted for peer-reviewed articles/studies published in Persian and English between 1 January 2012 and 1 September 2025. Two researchers will independently carry out screening of the included studies and extraction of data. Any discrepancies will be resolved by consensus. In case no initial consensus is reached, a third researcher will be consulted to make a decision. Findings will be presented narratively in the form of text, summary tables, charts and figures for each research question.

This proposed scoping review is based on publicly available information and is also a review of primary studies, so ethics and publication ethics approval are not required because all data from this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at national or international congresses and conferences. Importantly, the initial results from this review will serve as a basis for the design and validation of an intelligent clinical decision support system based on acoustic biomarkers for patients with schizophrenia, using AI or ML techniques.

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