Over the past two decades, complexity science has matured from its origins as a small, largely esoteric subfield of theoretical statistical physics and mathematics into an expansive area of inquiry that bridges disciplines and addresses some of the world’s most acute interconnected challenges. 

Nowhere has this interdisciplinary, applied process become more apparent than with the ongoing convergence of complexity and health. 

This convergence is happening at all scales—individual and collective, private and public—across approaches to prevention, treatment, management, and containment in integrative and public health spaces, as well as through applied research and innovation.

“Our methodological approach goes on and on down to the level of microbiology and finally finding cures for many diseases,” says Italian American physicist and complex networks scientist Alessandro Vespignani. “All that goes through networks and through a vision that has complex systems at the core. Network science is a way to approach complex systems,” Vespignani explained from within Northeastern University’s Network Science Institute, where he serves as director, leads the Laboratory for the Modeling of Biological and Socio-technical Systems (MOBS) lab, and heads several impactful public health initiatives.

“We look at elements and their interactions, and we do not get rid of nonlinear phenomena, feedback loops, et cetera, that exist in real world systems; we need to consider those,” Vespignani continues. “And in that sense, there is even more urgency in the work that we as a community do to respond to those challenges and to be of service to the society.”

Explore the full InterDialogue with Alessandro Vespignani.

The COVID-19 pandemic served as a critical moment of applied research and broad engagement with public health policy for those, like Vespignani, who work at the interface of complexity and health. But theoretical research and its translation into experiments and action began several years prior and has blossomed since.

The Mind-Body-Environment Interconnection as a Complex Adaptive System

Mind, body, community, culture, and ecology are interconnected and interdependent. To understand and maintain the health and resilience of any one of them requires an approach that embraces complexity, dynamic adaptation, and diverse evidence-based perspectives.

Much of modern medicine and medical research still remains anchored to reductionist¹ frameworks that separate, treat, and study systems of the mind and body in isolation from each other and from the environment. 

In recent decades, complexity science has challenged those frameworks and helped society move towards a more integrative understanding² of health. Fundamental principles of complexity pioneered by theoretical physicists and mathematicians working on complex adaptive systems, nonlinear dynamics, and scale-free networks between the late 1980s and early 2000s are shedding new light on the interconnectedness of the human body. Rigorous, data-driven research and increasingly advanced technologies are further uncovering previously unimaginable multitudes and networks³ within and between our nervous, endocrine, digestive, and other body systems. 

“A living person is a complex system. A dying person is losing complexity,” says Greek Brazilian theoretical physicist and complex systems scientist Constantino Tsallis. Decades ago, Tsallis developed a generalized non-additive entropy framework⁴ based on nonlinear dynamics, statistical physics, and complex adaptive systems. That framework has since been applied to various human body systems and diagnostic approaches, ranging from heart rate variability⁵ and imaging for certain cancers⁶ to childhood development disorders. 

Early groundbreaking theoretical work on self-organized criticality⁷ and phase transitions by Per Bak, Dante Chialvo, and others has also been applied to study and treat a range of adaptive, scale-free, and nonlinear complex collective phenomena. These include epilepsy, heart arrhythmias, epidemic spreading, the functional adaptability of proteins,⁸ and much more.⁹

A similar story has played out with trailblazing theoretical work¹⁰ by mathematicians like Steven Strogatz and Duncan Watts. Their early work, focused on nonlinear dynamics, chaos theory, and synchronization, has since been translated into many real-world health applications,¹¹ such as understanding and treating cardiovascular and neurodegenerative¹² diseases.

Explore the full InterDialogue with Javier Buldú.

The now highly interdisciplinary field of complex scale-free networks has also followed this emergent, dynamic path. Early theoretical work by Albert-Laszlo Barabasi,¹³ Vespignani, and others has given way to impactful applied research, modeling, and policy focused on both individual and collective disease emergence¹⁴ and spread. 

Long-term studies based on some of these foundational principles have also revealed that what we put into our bodies and how we metabolize¹⁵ that energy have impacts on both individual and collective health. So do the natural,¹⁶ built, and biocultural environments¹⁷ in which we live and produce our food.

“We obviously didn’t evolve to spend time typing on a computer and doom scrolling,” says Christopher Danforth, an American professor of mathematics and statistics at the University of Vermont, who directs the Vermont Advanced Computing Center and co-directs the Computational Story Lab at the Vermont Complex Systems Institute. “It seems like our brains are able to compress the information our eyes are sending to the brain easier when we’re outside.”

Danforth and his long-time collaborator Peter Dodds developed the hedonometer, a data-driven instrument for measuring daily happiness, and Danforth leads the Lived Experiences Measured Using Rings Study (LEMURS), which studies the wellness behaviors of University of Vermont students using Oura ring technology. “Students who were randomly assigned to have experiences outside have an improved heart rate variability while they’re sleeping compared to people who were assigned to do exercise or talk therapy, so that’s an indication that their body is recovering better from the stresses of their day. Your heart rate overnight contains a lot of information about how your body’s doing.”

A broad spectrum of research is further demonstrating that the early stages of human development¹⁸ are the most critical, nonlinear, and dynamic periods to navigate and identify these many interrelated genetic and environmental forces. Supporting mentally and physically invigorating activities throughout life,¹⁹ from childhood to old age, is being shown to help maintain the complex and adaptive²⁰ interconnection between mind and body.

The Nonlinear Nature of Disease

Diseases come in many forms, are often caused by interrelated genetic and environmental factors,²¹ and impact all scales of interconnected networks within the human (and non-human) ecosystem.  

Three broad categories²² encompass most of what are generally referred to as “health issues”: non-communicable diseases (NCDs); emergent, evolving, or endemic infectious diseases; and mental, developmental, and injury-induced afflictions. Complexity researchers, medical practitioners, and public health officials alike are increasingly understanding²³ that underlying the extremely wide-ranging spectrum of known maladies is their nonlinear nature—in development, progression, and spread. 

This deeper understanding is leading to more effective, nuanced, data-driven approaches to medical science, with applications to disease prevention, treatment, containment, modeling, imaging, and more.

Cancer,²⁴ metabolic disorders²⁵ like heart disease and diabetes, and degenerative neurological²⁶ conditions like Alzheimer’s and Parkinson’s are all examples of NCDs. Interdisciplinary approaches guided by fundamental principles of complexity have helped to uncover the intertwined genetic and environmental origins of these diseases while improving diagnostic and treatment options.

Jordi Soriano is a Spanish neurophysicist at the University of Barcelona who works in collaboration with medical doctors and researchers on complex neurodegenerative diseases,²⁷ including Alzheimer’s, genetic Parkinson’s, Huntington’s, and Sanfilippo. From within his lab full of live neuronal cultures,²⁸ he explained his innovative work and its foundations: “Neurophysics is neuroscience but understood from the context of physical tools, like connectivity, dynamics of systems, information theory, and concepts like chaos and attractors. It’s based on nonlinear dynamics but also complex networks.” 

Soriano hopes that his work with live neuronal cultures and the modeling of complex neural networks will translate to treatments for these notoriously complex disorders. By better informing doctors and through the transplantation and integration of healthy modified neural cultures into the brains of those suffering from these conditions, patient outcomes may improve. “The idea is that if the system is working well in vitro, people can then try to insert the healthy cells [modified in the lab] into the patients to see if they can integrate with the damaged area to recover their lost functions,” says Soriano. “In principle, you could do personalized medicine to help a particular person to make the cell line and then insert the cured cell line back into him or her.”

Explore the full InterDialogue with Jordi Soriano.

Data-driven approaches based on complexity have also become essential for the diagnosis and treatment of established, evolving, and emergent infectious diseases—be they viral, bacterial, fungal or parasitic, and whether transmitted person-to-person (including non-human to human, in the case of zoonotic diseases²⁹), as in the flu, Covid-19, STDs, Ebola, and many more; or as vector-borne diseases such as malaria, dengue, zika, and the plague. This is especially true for modeling and forecasting complex scale-free networks based on mobility data in order to inform public health decisions. 

Public Health and Complex Networks

“Every disease is different,” says Vespignani. “Every pandemic would be different from the previous one. So every disease is a story by itself. At the same time, the methodology and the way that we can approach and describe their trajectories is similar.” It was Vespignani’s pioneering 2001 paper co-authored with Romualdo Pastor-Satorras, “Epidemic Spreading in Scale-Free Networks,”³⁰ that helped to lay the foundations for what is now a fortified bridge between complex networks science and epidemiology. 

“We try to bracket or generate maps that the policy makers can use to orient themselves in the process,” he continues. “This has involved theoretical work, such as how to develop specific models, global models, local models, and what are the right complexity reduction techniques. There are a lot of technical problems, but then also there is the entire operational side.” 

Vespignani helps to lead Epistorm, an initiative of the Centers for Disease Control and Prevention (CDC) for tracking and preventing emerging infectious diseases, and collaborates actively with the World Health Organization (WHO) and public health officials across scales and continents. He has worked with epidemics of the flu, dengue, H1N1, and Ebola,³¹ among several other established and emerging diseases. He also worked in various vital capacities during the Covid-19 pandemic.³²

“When you think of public health as epidemic modeling and as a public service in terms of forecasts and scenarios,” explains Vespignani, “then you have really to think about how you operationalize that for the society, for the agencies working on the field, how you can do that in real time, how you can have best practices, standards in reporting, and so on and so forth. And that has been a large effort.” 

Explore the full InterDialogue with Alessandro Vespignani.

Greater access to vast amounts of data over the past 15–20 years has helped to fuel the interdisciplinary convergence of complex networks and epidemiology into a new field: computational epidemiology. This data deluge³³ has allowed this work to better inform public policy. 

“Nowadays, we focus largely on a modern vision of data science that includes network science, data mining, machine learning, increasingly AI, applied to broadly social impact and sustainable development with a strong vertical on computational and digital epidemiology,” Italian physicist and computational social scientist Ciro Cattuto said from within the offices of ISI Foundation in Turin, Italy, where he serves as Science Director.³⁴ “So we use non-traditional data sources and mathematical models from complex systems and computer science to reason about the spread of communicable diseases, and on interventions to mitigate that spread.”

Explore the full InterDialogue with Ciro Cattuto.

Central to this complexity-health-data revolution has been mobility data,³⁵ which took center stage during the COVID-19 pandemic. “COVID-19 was an important transition point,” explained Venezuelan American statistical physicist and urban scientist Marta Gónzalez, who leads the HuMNet Lab at UC Berkeley. “The epidemic would be in one location, it started spreading, and we would see how as people move, the epidemic would spread. It became of paramount importance to measure that weighted network of mobility. Then the data became much more free and available at that moment.” 

Explore the full InterDialogue with Marta Gónzalez.

That greater volume and access to mobility data, despite its array of controversies,³⁶ has become a boon to complex networks scientists and to public officials wanting to make evidence-based decisions on public health, sustainable cities,³⁷ and much more.

But González and many of her colleagues acknowledge that it’s not only mobility data that is important and necessary in order to understand, model, and quantify epidemics and other scale-free networks. “I think that after COVID-19 we know perfectly how important social behavior is in shaping the trajectory of an epidemic: beliefs, misinformation, risk perception, changing behaviors,” says Spanish physicist and complex networks scientist Jesús Gómez-Gardeñes, who leads GOTHAM Lab at the University of Zaragoza. 

“A lot of social science ingredients have to be incorporated; it’s not only mobility, demography, containers, and vital information like mutation rates and intrahost dynamics,” he continues. “So it’s really a complex system.” Gómez-Gardeñes has worked actively with public health officials to model, forecast, and prevent the spread of infectious diseases, including vector-borne diseases like dengue.³⁸

Explore the full InterDialogue with Jesús Gómez-Gardeñes.

“As a folklorist who isn’t beholden to large quantitative data sets, I’m interested in what people are actually saying and doing out in the world,” says K. Brandon Barker, an American cognitive folklorist at Indiana University Bloomington who is a co-founder and longtime convener of the Folklore and Science section of the American Folklore Society. Folklorists like Barker, including computational folklorists³⁹ who do work with large quantitative datasets, offer important perspectives from the social sciences into folk beliefs and behaviors around public health. 

In reference to complex challenges encountered⁴⁰ during the COVID-19 pandemic around misinformation and vaccine hesitancy,⁴¹ Barker continues: “I might get insights on the anecdotal reasons that these individuals have for taking or not taking the vaccine, and I think those insights and solving the puzzles of those bits of information that we get through our scaled down quantitative versions of what we do ethnographically, have just as much of a chance of solving the larger problem, which is not only creating the vaccine but convincing a general public to take the vaccine, as the more powerful quantitative sciences do.” 

Explore the full InterDialogue with K. Brandon Barker.

An Interdisciplinary Approach to Individual and Collective Well-being

As complex systems science has matured over the past decades from its origins in theoretical statistical physics and mathematics, research has revealed how fundamental principles of this now broad, interdisciplinary field can be applied to understand countless social, technical, environmental, economic, and biological systems. 

Nonlinear dynamics, scale-free networks, and adaptive systems are seemingly universal phenomena found throughout nature⁴²; The many lessons, challenges, and advances revealed through the impactful convergence of complexity and health can therefore potentially inform and be applied to the evidence-based preservation and recovery of the “health” of other complex systems, at all scales.

With robust data sets aided by the increasingly more powerful⁴³ processing and pattern-detecting power of AI,⁴⁴ along with a growing interdisciplinary and intercultural understanding⁴⁵ of complexity, researchers are beginning to better uncover the collective dynamics and thresholds of cities,⁴⁶ social and information networks,⁴⁷ ecological systems,⁴⁸ hydrological networks, climatic patterns,⁴⁹ and even whole earth systems described by Gaia theory.⁵⁰ 

Approaching each of these complex adaptive systems—metaphorically and empirically—as kinds of superorganisms⁵¹ whose health depends on nonlinear dynamics and vast, diverse network interconnections holds the potential to develop data-driven, evidence-based projects and policy that bring together a broad range of researchers, institutions, local communities, and public officials.

The health of our interconnected minds and bodies, our communities, our ecosystems, and our living planet may very well depend upon such an interdisciplinary and intercultural paradigm shift.

Banner image: “Heart of the Himalayas” – Vannessa Circe – Oil on Canvas – 2014

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