The Science of Complex Systems Is Needed to Ameliorate the Impacts of COVID-19 on Mental Health

Abstract

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 (1–3). 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). As a result, there have been numerous advances that have informed policies to control infectious diseases, facilitate epidemic and bioterrorism preparedness, and provide governments with critical tools for managing complexity and weighing alternative responses in the midst of the confusion of an evolving crisis (6–14). 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 (15–19). The individual and societal effects of COVID-19 and resulting economic recession are particularly threatening to mental health and well-being, disrupting nearly every aspect of life; familial, educational, vocational, health, and social structures. Most countries, and international agencies, have begun to recognize the unprecedented magnitude of the adverse impact COVID-19 will have on population-based mental health outcomes, mental health services, and suicide risk (20, 21). The global health, economic, and social recovery from this crisis is likely to be prolonged and incomplete, with more disadvantaged communities experiencing particularly severe effects. The mental health impacts will not be limited to the period of the pandemic but will extend with the depth and duration of economic recession. Some governments, particularly in high income countries, are already instituting measures to reduce economic and social hardship, and linked psychological distress, by making large investments in employment and welfare funding, continuity of education and training, housing stability, family support, and improved access to virtual mental health services, resources, and other psychological supports through various new technologies. However, it is unclear whether these responses will be sufficient or sustained for a long enough period to ameliorate the expected tsunami of mental health issues in the post-COVID-19 era. Unlike in infectious diseases research, systems modeling and simulation has not pervaded mental health research, nor does it underpin advice given to governments. Instead, an epistemological entrenchment primes us to identify and address individual risk factors across the behavioral, social, cultural, economic, environmental, and services spectrum, with little formal (or statistical) recognition of the complex interrelationships between them (22–24). This entrenchment has bound us to a path of linear thinking, and delayed actions, that lacks the agility required to be responsive to a rapidly changing world. Traditional statistical techniques are inadequate for studying complex systems, with feedback loops, threshold effects, and other types of non-linearity bedeviling even more sophisticated analytic techniques such as structural equation modeling and latent class analysis (25). Moreover, traditional approaches focus on looking backwards to generate understanding of the factors that have driven past events, rather than using the current best knowledge and a range of evidence and data sources to look forward and to anticipate and strategically act to mitigate future trajectories. Without adoption of a more advanced approach, the mental health needs of populations—even in times of genuine crisis—may be met with well-meaning acts of government that are reactive rather than strategic, formulated “on the run,” and based on the prioritization of initiatives that have been heavily lobbied or represent “seemingly” good evidence-based investments, but can be ineffective, or even counterproductive (26). The complex systems modeling techniques long used in infectious disease epidemiology focus on pathways; pathways from states of being susceptible to exposed to infected to recovered (or death), upon which risk factors interact to drive rates of flow between the states based on given probabilities. Accordingly, the mental health research community can similarly articulate complex causal pathways to and from psychological distress, mental ill health, suicidal behavior, and associated functional outcomes. These paths can include complex interactions with service pathways, alongside the dynamics of the broader social context (determinants) of mental health. Such systems models can leverage decades of existing research evidence, local data, and expert knowledge, and could be used to undertake continuous experimentation, assumption testing, and rapid hypotheses refinement through integration with continuous streams of data. This would allow mental health researchers to develop more sophisticated tools for rapid deployment to respond to national and regional threats to mental health (and national mental wealth), by being able to work...