Abstract
The Science of Complex Systems Is Needed to Ameliorate the Impacts of COVID-19 on Mental Health.
Highlights
An Exemplar of Scientific EvolutionTo assist with proactive and effective responses to the global COVID-19 crisis, the scientific community has been rapidly deploying our most advanced analytic tools to model the dynamics of disease transmission based on existing knowledge, data, and available human and material resources
A continued lack of engagement with systems modeling to advance our understanding of the complex interactions between drivers of mental health and suicide, as well as guide mental health policy, system reform, strategic planning, and operational decisionmaking will confine us to simplistic conclusions, delayed actions, wrong turns, trial and error, waste and inefficiency, and a lack of agility to be effective in our responses to a rapidly changing world
Without these tools to help us see forward, on what basis do governments make decisions about how to effectively respond? What combination of initiatives or reforms should be prioritized? What targeting, timing, scale, frequency, and intensity of investments are needed? What impacts should we expect from these investments? How long do we need keep programs and initiatives in place? Will there be rebound effects when we remove them? Will there be unintended consequences? The analytic methods of complex systems science provide us with the tools to answer these important questions; without them decision makers will continue to metaphorically fumble around in the dark as they try to make important strategic decisions that will impact people’s lives in fundamental ways for many years to come
Summary
To assist with proactive and effective responses to the global COVID-19 crisis, the scientific community has been rapidly deploying our most advanced analytic tools to model the dynamics of disease transmission based on existing (albeit imperfect) knowledge, data, and available human and material resources. The multifactorial, multilevel influences on transmission dynamics and the disease’s pervasive impact at the individual, community, and global levels have required the use of the analytic techniques of complex systems science, namely, systems modeling and simulation, to forecast the trajectory of the disease under different conditions, to quantify uncertainty, and to inform effective responses [1,2,3] These methods have been deployed by infectious disease epidemiologists for over a century [4], maturing into a robust interdisciplinary field intersecting mathematics, computational epidemiology, ecology, evolutionary biology, immunology, behavioral science, and public health [5]. The field’s commitment to achieving rapid response capability in the face of changing conditions has led to advances in rapid assessment of the impact of the pandemic, and data assimilation methods that combine theory with empirical observations in a continuous knowledge feedback process facilitating continuous hypothesis development, testing, and refinement in the service of more effective decision making [15,16,17,18,19]
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