Scientific progress has often been driven by the development of abstractions that make complex phenomena easier to understand, analyze, and predict. Rather than attempting to investigate every detail of reality simultaneously, scientists construct simplified representations that preserve the essential mechanisms governing a system's behavior. Mathematical models are among the most powerful of these abstractions. By expressing relationships through equations, logical rules, and computational processes, they transform complex real-world systems into forms that can be systematically explored, tested, and refined.
When mathematical models are coupled with computer simulation, they become virtual laboratories. Researchers can manipulate parameters, examine alternative scenarios, and evaluate intervention strategies that would otherwise be impractical, prohibitively expensive, ethically challenging, or impossible to investigate directly. In epidemiology, such models have become indispensable tools for understanding disease transmission, evaluating public health strategies, estimating outbreak trajectories, and exploring the consequences of interventions before they are implemented in real populations.
The same computational principles, however, are not limited to biological diseases. Any phenomenon characterized by transmission through interaction—whether biological, social, cultural, or technological—may be investigated using similar mathematical frameworks. This recognition has gradually expanded the scope of epidemiological modeling beyond infectious diseases toward the broader study of contagion processes in complex systems.
Classical epidemiological models describe how individuals move between health-related states such as susceptibility, infection, and recovery through interactions within a population. Although these compartmental models were originally developed for studying communicable diseases, their mathematical structure is remarkably general. The concepts of transmission, exposure, recovery, immunity, and intervention describe patterns of change that also arise in many other domains.
Computer simulation further enhances these models by allowing researchers to investigate how local interactions among individuals give rise to population-level behavior. Parameters such as transmission rates, recovery probabilities, mortality, relapse, mobility, spatial distribution, and population density can be systematically modified to observe their effects on the dynamics of the entire system. Through repeated computational experiments, researchers gain insights that would be difficult—or impossible—to obtain through observation alone.
Consequently, mathematical epidemiology has evolved from being solely a tool for public health into a broader computational methodology for studying dynamic systems whose behavior emerges through repeated interactions among individuals.
One of the more creative developments in mathematical epidemiology has been the use of fictional narratives as computational abstractions.
Stories involving zombies, vampires, aliens, and mythical creatures naturally exhibit many of the same interaction dynamics observed in infectious diseases. Individuals become "infected" through contact, undergo recognizable stages of transformation, and eventually influence others in similar ways. Although fictional in setting, these narratives possess clearly defined interaction rules that lend themselves remarkably well to mathematical formulation.
This observation led researchers to explore whether fictional epidemics could serve as educational and scientific laboratories for understanding epidemiological principles. Rather than trivializing science, fictional scenarios offered an accessible means of illustrating complex mathematical concepts while encouraging creativity, interdisciplinary thinking, and public engagement.
One of the earliest and most influential contributions to fictional epidemiology was the work of Munz et al. (2009), who demonstrated that a hypothetical zombie outbreak could be analyzed using the same mathematical techniques employed in classical infectious disease epidemiology. Although presented with humor and inspired by popular culture, the work illustrated a serious scientific principle: fictional scenarios can serve as effective computational abstractions for investigating transmission dynamics. Its popularity extended far beyond the mathematical modeling community, inspiring numerous subsequent studies that expanded the use of fictional epidemics for research, education, and public engagement with science.
While many fictional epidemiological studies focused on catastrophic "end-of-the-world" scenarios, they also revealed a deeper scientific insight.
The mathematics describing fictional outbreaks was fundamentally independent of the fictional characters themselves.
Whether the interacting agents were zombies, vampires, extraterrestrials, or mythical creatures, the governing principles remained the same: susceptible individuals interacted with infected individuals, transitions occurred between well-defined states, and population-level behavior emerged from numerous local interactions.
This realization suggested that fictional epidemiological events could serve as computational abstractions for a much broader class of contagion processes.
Instead of representing biological infection alone, the state transitions could symbolize many forms of social influence, behavioral change, ideological adoption, or information diffusion. In this broader interpretation, "infection" becomes a mathematical abstraction describing any process through which characteristics propagate through a population via repeated interactions.
Such an interpretation considerably expands the applicability of epidemiological modeling beyond medicine while preserving its rigorous mathematical foundation.
Most fictional epidemiological models reported in the literature describe irreversible transformations leading to terminal outcomes. Zombie outbreaks, for example, typically culminate in the extinction of humanity, while classical vampire narratives often portray permanent transformation following infection. These models naturally emphasize transmission, survival, and population collapse.
Many real-world contagion processes, however, follow a different trajectory.
Individuals affected by addiction may recover through rehabilitation.
Victims of misinformation may later become informed.
Persons influenced by extremist ideologies may undergo successful deradicalization.
Victims of exploitation may be rescued, educated, and empowered to prevent others from experiencing similar harm.
These examples share an important characteristic: the effects of contagion are potentially reversible.
Recognizing this distinction opens opportunities for developing richer epidemiological models in which intervention, rescue, rehabilitation, education, and long-term resilience become explicit components of the system dynamics rather than exceptional cases.
This perspective forms the central motivation of the present research.
Building upon previous work in mathematical epidemiology, computer simulation, and fictional epidemiological modeling, this research extends the field in a new direction.
Rather than treating fictional narratives simply as metaphors for terminal infectious diseases, the project investigates fictional epidemiological events whose dynamics naturally incorporate recovery, rehabilitation, education, and resistance. Through these richer interaction models, fictional worlds become computational laboratories for exploring generalized contagion processes that more closely resemble many contemporary societal challenges.
The resulting mathematical and computational framework seeks not merely to model fictional outbreaks, but to demonstrate how carefully constructed abstractions can illuminate the dynamics of complex systems far beyond their original fictional settings.
The emergence of fictional epidemiology illustrates an enduring characteristic of scientific discovery: meaningful advances often arise when familiar mathematical tools are applied to unfamiliar questions. What initially appeared to be imaginative explorations of fictional outbreaks gradually evolved into legitimate computational frameworks for studying complex systems, enriching both scientific research and science education. The present work continues this progression by extending fictional epidemiology from models of irreversible catastrophe toward models that explicitly incorporate intervention, rehabilitation, and resilience, thereby broadening the scope of epidemiological modeling as a general methodology for understanding contagion in biological and social systems alike.
Every scientific discovery begins by standing on ideas that others have already uncovered, and then asking a question they did not yet ask.