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Mathematical modeling and computer simulation to improve mental health policies in Australia

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In response to the socio-economic consequences of the COVID-19 pandemic, And the psychological effects that accompanied all this, Australia has developed a system based on mathematical modeling and computer simulation to identify several scenarios aimed at studying and predicting the future cases of mental illness in the population.
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In response to the socio-economic consequences of the COVID-19 pandemic, And the psychological effects that accompanied all this, Australia has developed a system based on mathematical modeling and computer simulation to identify several scenarios aimed at studying and predicting the future cases of mental illness in the population. The project has been successful in providing decision-makers with tools to predict the impact of policies on the mental health of citizens and to plan strategies based on them.

Mental health depends on the exchange of dynamic relationships across different physical, social, economic and health systems. It produces a complex web of variables that the use of traditional analytical methods to deal with has become difficult and useless. Psychiatric and mental pathology today stands at a sensitive stage, Australia's health authorities have been innovating to mitigate COVID-19-related threats. and the economic and psychological crises that followed. 1 in 5 Australian citizens experience very high levels of pandemic-related stress. Youth and women are also the two groups most vulnerable to psychological crises.

Until recently, The greatest effort of mental health workers has been focused on assessing and studying past events. Consequently, there were insufficient opportunities to predict the future of mental health and estimate the implications of proposed actions and policies to address its challenges. Australia has recently turned to mathematical modeling techniques that allow researchers to simulate alternative scenarios for care programs such as safety planning, or enable them to test the impact of one or several essential components of preventive programs, such as their duration and scope, to choose the most appropriate ones. These programmes deserve to have an optimal impact on the population. All of this provides vital insights for carefully designing mental health programs before they are widely implemented.

To achieve this goal, Over the past year, Australian scientists have worked through mathematical modeling techniques to develop a series of models at the provincial, state and country levels. These models examined the socio-economic impacts of the COVID-19 pandemic on mental health by simulating and presenting cases of mental illness over the next 5 to 10 years. The models also provided an analysis of levels of use of mental health services, expected waiting times for access to services, and suicidal behavior. Scientists have applied the simulation system at both the individual and societal levels, They tended to study the impact of social preventive measures such as employment, education, income support programmes, mental and psychological health and suicide prevention programmes. Awareness campaigns were then launched, services capacity was expanded and psychological care for suicide survivors was strengthened.

The Australian government's interest in mental health is most recently due to what a survey by the Australian Bureau of Statistics revealed. It indicates that the outbreak of the Corona virus and the government's measures to limit it, such as closing cities and imposing social distancing, led to an increase in psychological stress to reach high rates among the population, In particular, many of them have lost their jobs, resulting in financial hardship that led to stress and psychological crises. All of this necessitated the government's response to improve the mental health of its citizens. However, planning to respond to these challenges in the absence of input from the groups concerned may be subject to the introduction of prevention or remedial policies and programmes that are far from reality and may not achieve the desired effect. It can also lead to political and planning decisions that lack societal support.

Through mathematical modelling techniques and estimating the impact of various factors on mental health, the authorities were able to identify the sites most in need of support to act on in time before launching and implementing strategies. Thus, These efforts have contributed to raising public awareness, He urged the Australian government to increase investment in the mental health sector, To reach approximately 1.72 billion US dollars over 5 years. In addition, This approach established a transparent and inclusive approach that helped mobilize community support and rebuild trust between communities and decision-makers to promote mental health.

This development will promote computer-enabled mental health as an emerging field, Via data analysis, which includes the rates and outcomes of mental health service utilization, Monitors changes over time, By improving data quality, developing programs and enabling stakeholders who do not have technical expertise to access them, It will have a positive impact on the participation of all groups in the design and testing even in the absence of technical knowledge.

Digital platforms, if deployed to the most vulnerable sites, can provide real and live monitoring of attempts at self-harm or suicide. Through the information you provide, supported by machine learning techniques, You will develop ever-updating systems to support decision-making and facilitate rapid response to mental health crises at the community level.

As part of the Victorian-led Hope trial, a new programme is being designed to support young people who seek emergency care after attempted self-harm. As I set off, In conjunction with the Australian experience, Similar efforts in other countries such as Colombia, the United States of America and the United Kingdom, To adopt systems modeling for mental health policy planning.





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