Modeling Framework:
A Layered Narrative of Generalized Contagion

How do researchers transform observations about complex human interactions into a mathematical framework that can be analyzed, simulated, and ultimately understood?

Epidemiological models have long been used to understand how infectious diseases spread through populations. Individuals become infected through interactions with others, creating transmission dynamics that can be studied mathematically. Over many decades, these models have helped scientists understand epidemics, evaluate interventions, and design public health strategies.

This research began with a simple but profound question:

Can the mathematical language of epidemiology describe phenomena beyond biological diseases?

Many human experiences spread through social interaction. Ideas spread. Behaviors spread. Addictions spread. Misinformation spreads. Fear spreads. Radicalization spreads. Even hope and resilience can spread through communities.

Although these processes are not caused by viruses, they often exhibit remarkably similar interaction dynamics.

This project therefore explored whether the mathematical principles of epidemiology could be generalized to describe a broader family of human interactions.

Fictional epidemiological events became the experimental laboratory for investigating this question.

Why study vampires, zombies, Borg, or aswangs?

Not because fiction replaces reality.

Rather, fiction simplifies reality.

Fictional worlds establish clear rules governing how individuals interact and change. A vampire bite, Borg assimilation, or an aswang's enchantment each represents a well-defined transition between human states. These simplified interaction rules allow researchers to focus on the mechanisms of transmission, intervention, recovery, and resistance without many of the ethical, social, and practical complexities associated with real-world human populations.

Following the pioneering demonstration by Munz and colleagues (2009) that fictional zombie outbreaks could serve as legitimate case studies in epidemiological modeling, this project broadened the concept beyond irreversible infections. Instead of asking only how contagion spreads, it asked:

What happens when contagion can be reversed?

This seemingly small change opened an entirely new class of mathematical models.

The framework shifts attention away from individual stories toward population-level behavior.

Instead of following each person's unique experience, individuals are grouped according to their current status. These groups—called compartments—represent different stages in the interaction process.

Each compartment answers a simple question:

1. (S)usceptible - Who remains vulnerable to influence?

2. (C)aptured - Who has come under the influence of the contagion but has not yet fully transformed?

3. (A)ssimilated - Who has undergone complete transformation and can now influence others?

4. (R)escued - Who has been removed from the contagion before permanent transformation?

5. (E)ducated - Who has undergone rehabilitation, treatment, or learning following rescue?

6. (D)efiant - Who has developed lasting resilience and is unlikely to succumb again?

Together, these compartments form a conceptual language for describing generalized contagion in recoverable human systems.

Scientific frameworks rarely emerge fully formed.

Rather than beginning with the most comprehensive model, this research developed progressively richer models, each preserving the insights of its predecessor while introducing one additional aspect of human behavior.

The progression can be viewed as a sequence of scientific questions.

• SCA: Can transmission alone explain the phenomenon? The initial framework models the transition from susceptibility to capture and eventual assimilation.

• SCAR: Can individuals be rescued before permanent transformation? Introducing rescue acknowledges that intervention is possible.

• SCARE: Does rescue automatically restore resilience? Recovery is recognized as a distinct process requiring rehabilitation or education.

• SCARED: Can recovery permanently change future behavior? The final framework introduces long-term resilience, recognizing that successful intervention may fundamentally alter future susceptibility.

Viewed retrospectively, the progression from SCA to SCARED represents more than the addition of compartments.

It represents the gradual refinement of a scientific understanding of recoverable contagion.

Although inspired by fictional epidemics, the framework was intentionally designed to extend beyond them.

The same interaction structure can describe many recoverable social phenomena, including:

• addiction and rehabilitation,

• misinformation and correction,

• radicalization and deradicalization,

• behavioral intervention,

• political persuasion,

• social influence,

• education,

• community resilience.

Fiction therefore serves not as the destination of the research, but as the bridge toward understanding real-world systems.

Years after the completion of the original research project, the framework can now be appreciated from a broader perspective.

Originally developed to support mathematical and computational investigations of fictional epidemiological events, the progression from SCA to SCARED also illustrates a more general principle of scientific inquiry.

Scientific frameworks evolve by asking increasingly better questions.

Each successive model preserved what had already been learned while incorporating one additional aspect of reality. Rather than replacing earlier models, the newer frameworks expanded the conceptual language available to describe transmission, intervention, recovery, learning, and resilience.

Looking back, the framework documents not only the development of mathematical models but also the evolution of scientific thought itself.