正如Covid-19的大流行所证明的那样，系统建模可以提供强大的决策支持能力，以帮助政府成功驾驶复杂和不断发展的公共卫生危机。
In countries such as Australia and New Zealand, governments atnational和state水平与科学家紧密合作，使用系统模型作为虚拟测试场地，用于不同的公共卫生措施组合，在不同时间以及在不同的时间，地理区域和疾病传播阶段实施。
这有助于减少现实世界中的反复试验，并及时，果断和有效的回应，对大流行，有效的反应，saving tens of thousands of livesin Australia.
Many countries and international agencies now recognize the significant impact COVID-19 and the attendant global recession will have on mental health, withdisruption of critical mental health services和increases in psychological distress, alcohol and other drug misuse, domestic violence, and自杀行为.
迫在眉睫的危机被称为“阴影大流行‘，与年轻人，妇女以及受到最大影响最大的社会经济处境不利。
我们已经看到，当通过建模和模拟来激发和告知时，政府可以对身体健康威胁进行积极和有效的反应。是否会以可比的严谨和活力来解决心理健康威胁是否会被待观察。
即使在澳大利亚，系统建模在众所周知，在告知对Covid-19的有效反应以及心理健康挑战的情况下发挥了关键作用，政府对心理健康的反应威胁到对传统方法的失望。akin to flying blind.
The investments made have been reactive rather than strategic, formulated “on the run”, based primarily on what seemed to represent sound evidence-based decision making, but devoid of estimates of the impact the actions are likely to have.
The science of complex systemsreveals the deficiencies that emerge from relying simply on estimates of impact based on highly controlled trials or evaluations of individual programs. Recent applications of systems modelling in mental health services planning and suicide prevention reveal insights into why evidence-based programs and initiatives can be ineffective, or even counterproductive when introduced into complex systems.
For example, a validated and peer-reviewed系统模型developed for the rural population catchment of Western New South Wales, Australia, demonstrated that introducing general practitioner training (有效的at improving GP recognition of suicide ideation and referral to appropriate services) together with mental health education programs (有效的在提高心理健康素养和寻求帮助的方面），自相矛盾的是，该地区自杀死亡的增加。
This unexpected finding was explained by a lack of service capacity to meet the increase in service demand that would be generated by this combination of evidenced-based initiatives, leading to service disengagement, increased duration of psychological distress, and increased suicide risk. While unexpected, the findings reflect the lived experience of many who seek help for mental health issues.
系统建模applications tackling alcohol-related harmsfound that some combinations of evidence-based interventions were projected to deliver synergistic effects (where the impacts of combining early closing of licenced venues with ‘lockouts’ and an expansion of treatment services for heavy drinkers were projected to be greater than the sum of their individual effects).
最后，一个suicide prevention modeldeveloped for a metropolitan catchment highlighted important threshold effects (a dynamic ‘tipping point’) in the relationship between the availability of psychiatric beds and rates of suicide that depends on the capacity of community-based specialist mental health services.
这些例子强调，计划有效缓解大流行和衰退对心理健康和自杀成果的影响将取决于对实施新计划和服务组合的关键平衡和时机的理解，以及增强现有服务能力，以增强现有服务能力给定上下文。
为了有效地应对迫在眉睫的危机，研究人员和决策者将不仅需要简单地依靠个人计划和计划的有效性研究，并了解不一定按预期的对可能可能发生的预期做出反应的复杂现实世界系统的行为似乎是合理的，基于证据的解决方案。
系统建模的系统利用了广泛的现有数据和研究证据，但重要的是捕获系统的复杂性。在人口统计学，行为因素，卫生服务能力和需求供应动态，惯性和延迟的现实来源，反馈循环以及干预互动和约束方面考虑了随着时间的流逝的变化。
BMC医学’s current focus on心理健康研究的复杂性是及时的欢迎。系列功能系统模型lingof the social determinants of youth mental health and suicide, work that is contributing to the Rockefeller Foundation-Boston University3-D Commissionwhich is showcasing best ways to leverage research evidence and data to support decision making.
在捕获社会决定因素时，系统模型能够迅速适应模拟与共同相关的失业和社会失业，预测澳大利亚在未来5年内的心理健康轨迹和服务需求，并测试拟议的经济，健康和社会措施，以避免这种轨迹。
Being able to draw on this existing modelling infrastructure to respond to emerging health crises is an advantage that governments should seek to develop for future service planning.
TheBMC医学complexity series and 3-D Commission are providing an important springboard for the promotion of more routine applications of systems modelling and helping to realise a paradigm shift in mental health research, policy, and planning.
As nations are considering how best to respond to the ‘shadow pandemic,’ there has never been a more important time to engage with the advanced analytic tools of complex systems science.
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