Pediatric Digital Twins
What We Do
Pediatric digital twins reconstruct the biological timeline behind chronic symptoms, showing what shifted in a child’s immune, autonomic, and endocrine systems and why. By integrating real-world exposures, school environments, reinfections, and developmental stages, these models illuminate the mechanisms that shape long-term health. This is a new layer of pediatric science: dynamic, individualized, and grounded in mechanistic logic rather than guesswork.
A Scientific Framework for Childhood Health
A pediatric digital twin is a computational reconstruction of a child’s biological state, not a snapshot, but a continuously updated model that reflects how infections, environment, autonomic signals, endocrine maturation, sleep physiology, and climate exposure shape long-term health. The goal is not prediction in the abstract. The goal is mechanistic understanding: what changed, when it changed, and why it changed. The twin builds a layered health profile from real-world inputs and evolving physiology, giving pediatric medicine something it has never had: a dynamic map of how a child’s terrain responds to stress, recovery, and developmental phases.
How a Digital Twin Interprets Childhood Disease Onset
When children develop lingering symptoms after influenza, COVID, RSV, EBV, strep, mold exposure, Lyme, or wildfire smoke, the clinical picture becomes confusing. Different triggers can produce nearly identical presentations. Conventional pediatrics can describe the symptoms, but rarely identify the biological turning point. A pediatric digital twin approaches this differently. It analyzes how the child’s system reacted before, during, and after the suspected trigger. Temporal shifts in autonomic regulation, immune volatility, respiratory behavior, cognitive load, endocrine transitions, and environmental data are synthesized into a mechanistic timeline. This allows the model to isolate the driver of chronicity even when multiple events occurred close together.
The Computational Logic Behind the Twin
The twin uses multi-axis signal interpretation. It integrates physiological drift, symptom dynamics, environmental metadata, recovery-window stability, and developmental stage. These layers interact inside the model to reconstruct the biological conditions that pushed the child off baseline. The system does not rely on single biomarkers. It relies on patterns:
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whether the autonomic nervous system regained equilibrium
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whether the immune profile stabilized
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whether sleep architecture normalized
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whether environmental exposures remained tolerable
This pattern logic is what makes pediatric digital twins capable of explaining why two children with the same infection diverge so dramatically in outcome.
A Tool for Public Health
At scale, pediatric digital twins become a high-resolution view of children’s health across regions and school systems. They can reveal geographic clusters of post-infectious illness, identify environmental patterns linked to symptom flares, and model how climate events affect pediatric vulnerability.
This provides public health agencies with an early-warning system for chronic illness trends, long before they show up in healthcare claims or diagnostic statistics.
Why Pediatric Digital Twins Represent a Scientific Turning Point
Children’s health has always been shaped by a mixture of biology, environment, and development. Until now, medicine could only describe these forces after they caused harm. Digital twins allow the field to understand those forces in real time, with the sophistication necessary for modern environmental and infectious challenges. This is the foundation for a new era in pediatric chronic illness research, prevention, and care.
The CYNAERA Intelligence Footprint
50
Core Engines
5B+
Unique AI Modules
1B+
Simulation Profiles
180+
Countries Analyzed
32
Sectors Activated
About the Founder
Cynthia Adinig is a researcher, health policy advisor, author, and patient advocate. She is the founder of CYNAERA and creator of the patent-pending Bioadaptive Systems Therapeutics (BST)™ platform. She serves as a PCORI Merit Reviewer, Board Member at Solve M.E., and collaborator with Selin Lab for t cell research at the University of Massachusetts.
Cynthia has co-authored research with Harlan Krumholz, MD, Dr. Akiko Iwasaki, and Dr. David Putrino, though Yale’s LISTEN Study, advised Amy Proal, PhD’s research group at Mount Sinai through its patient advisory board, and worked with Dr. Peter Rowe of Johns Hopkins on national education and outreach focused on post-viral and autonomic illness. She has also authored a Milken Institute essay on AI and healthcare, testified before Congress, and worked with congressional offices on multiple legislative initiatives. Cynthia has led national advocacy teams on Capitol Hill and continues to advise on chronic-illness policy and data-modernization efforts.
Cynthia’s work with complex chronic conditions is deeply informed by her lived experience surviving the first wave of the pandemic, which strengthened her dedication to reforming how chronic conditions are understood, studied, and treated. She is also an advocate for domestic-violence prevention and patient safety, bringing a trauma-informed perspective to her research and policy initiatives.