Understanding public mental health issues and finding solutions can be complex and requires advanced techniques, compared to conventional data analysis projects. It is important to have a comprehensive project management process to ensure that project associates are competent and have enough knowledge to implement the process. Therefore, this paper presents a new framework that mental health professionals can use to solve challenges they face. Although a large number of research papers have been published on public mental health, few have addressed the use of data science in public mental health. Recently, Data Science has changed the way we manage, analyze and leverage data in healthcare industry. Data science projects differ from conventional data analysis, primarily because of the scientific approach used during data science projects. One of the motives for introducing a new framework is to motivate healthcare professionals to use "Data Science" to address the challenges of mental health. Having a good data analysis framework and clear guidelines for a comprehensive analysis is always a plus point. It also helps to predict the time and resources needed in the early in the process to get a clear idea of the problem to be solved.

Background: Cognitive Muscular TherapyTM (CMT) is an integrated behavioural intervention developed for knee osteoarthritis. CMT teaches patients to reconceptualise the condition, integrates muscle biofeedback and aims to reduce muscle overactivity, both in response to pain and during daily activities. This nested qualitative study explored patient and physiotherapist perspectives and experiences of CMT.Methods: Five physiotherapists were trained to follow a well-defined protocol and then delivered CMT to at least two patients with knee osteoarthritis. Each patient received seven individual clinical sessions and was provided with access to online learning materials incorporating animated videos. Semi-structured interviews took place after delivery/completion of the intervention and data were analysed at the patient and physiotherapist level.Results: Five physiotherapists and five patients were interviewed. All described a process of changing beliefs throughout their engagement with CMT. A framework with three phases was developed to organise the data according to how osteoarthritis was conceptualised and how this changed throughout their interactions with CMT. Firstly, was an identification of pain beliefs to be challenged and recognition of how current beliefs can misalign with daily experiences. Secondly was a process of challenging and changing beliefs, validated through new experiences. Finally, there was an embedding of changed beliefs into self-management to continue with activities. Conclusion:This study identified a range of psychological changes which occur during exposure to CMT. These changes enabled patients to reconceptualise their condition, develop a new understanding of their body, understand psychological processes, and make sense of their knee pain.

Introduction: Following the outbreak of Coronavirus (COVID-19) in Wuhan, China in December 2019, the World Health Organisation (WHO) has declared this infectious disease as a pandemic. Unlike previous infectious outbreaks such as Severe Acute Respiratory Syndrome (SARS) and Middle Eastern Respiratory syndrome (MERS), the high transmission rate of COVID-19 has resulted in worldwide spread. The countries with the highest recorded incidence and mortality rates are the US and UK. Rationale/Objective: This review will compare studies that have used epidemiological models for disease forecasting and other models that have identified sociodemographic factors associated with COVID-19. We will evaluate several models, from basic equation-based mathematical models to more advanced machine-learning ones. Our expectation is that by identifying high impact models used by policy makers and discussing their limitations, we can identify possible areas for future research. Evidence Review: The bibliographic database google scholar was used to search keywords such as ‘COVID-19’, ‘epidemiological modelling’ and ‘machine learning’. We examined data review articles, research studies and government-released articles. Results: We identified that the current SEIR model used by the UK government lacked the spatial modelling to enable an accurate prediction of disease spread. We discussed that machine-learning systems which can identify high-risk groups can be used to establish the disparities in COVID-19 death in BAME groups. We found that most of the data hungry AI models used were limited by the lack of datasets available. Conclusion: In conclusion, advances in AI methods for infectious disease have overcome challenges presented in mathematical models. Whilst limitations do exist, when optimised, these highly advanced models have a great potential in public health surveillance, particularly infectious disease transmission.

In national and regional level, understanding of factors associated with public health issues like mental health is paramount important to improve the awareness. This study aims to use the data mining techniques such as association rule mining to improve the degree of understanding the mental health among various geographical areas by identifying various vital behavioural factors associated with mental health issues. The study will produce interesting relationships among the behavioural factors in form of Association rules. The outcomes of this research will be beneficial to organisations that work in public health sector to improve mental health among the citizens. Also, this proposed new data science approach will be beneficial to improve the degree of understanding by identifying factors associated with mental health within the different geographical areas such as city or state level. The study found that states in US which have low excessive drinking percentage and high obesity and high smoking percentage has the highest frequent of mental distress. Also, these rules have shown high confidence threshold among females rather than males. Mental health related authorities who works in local and national governments may be used these findings to improve awareness of mental health.

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