Rationale

One of the distinguishing characteristics of scientific inquiry is the ability to recognize that the same underlying principles can explain phenomena that appear, at first glance, to be entirely unrelated. Throughout the history of science, ideas developed within one discipline have often found unexpected applications in another because they share common underlying structures. Mathematical modeling embodies this principle by representing complex systems through simplified abstractions that preserve their essential behavior while making them amenable to analysis, experimentation, and prediction.

Mathematical epidemiology provides an excellent example of such abstraction. Although traditionally developed to describe the transmission of infectious diseases, epidemiological models fundamentally describe interactions among members of a population whose states change over time through contact, influence, or intervention. These same principles extend well beyond biological diseases. The spread of information, beliefs, addictions, political affiliations, criminal behavior, misinformation, technological adoption, and many other social phenomena can likewise be interpreted as contagion processes whose dynamics can be investigated using similar mathematical and computational frameworks.

This research was motivated by an unconventional but scientifically meaningful question:

Can fictional epidemiological events serve as computational abstractions for understanding real-world contagion processes?

Rather than viewing fictional narratives merely as entertainment, this project explored their potential as conceptual laboratories in which complex interaction dynamics could be investigated without the ethical, practical, and social constraints associated with real-world experimentation. Fictional worlds provide richly defined populations, recognizable interaction rules, observable behavioral transformations, and clearly distinguishable outcomes—all desirable characteristics for constructing mathematical models and computer simulations.

Many fictional narratives naturally resemble epidemiological systems.

The attack of vampires, for example, has long been recognized as a metaphor for the transmission dynamics of HIV infection. A human bitten by a vampire gradually undergoes transformation before ultimately becoming another vampire capable of infecting others. Avoiding contact with infected individuals represents prevention, while the irreversible nature of the transformation parallels terminal infectious diseases in which recovery is impossible.

Similarly, zombie apocalypses portray catastrophic outbreaks in which infected individuals progressively overwhelm susceptible populations through repeated transmission events. These fictional scenarios have become well-established educational tools in mathematical epidemiology because they simplify complex biological processes into intuitive interaction patterns while preserving the essential mathematics governing epidemic spread.

While previous studies largely focused on irreversible, terminal epidemics, this research investigated a different class of fictional epidemiological events in which infection may be prevented, reversed, or rehabilitated. These reversible transformations provide significantly richer opportunities for modeling intervention strategies and recovery processes that more closely resemble many contemporary societal challenges.

The project expanded the scope of epidemiological modeling by considering fictional narratives that could serve as metaphors for a wide variety of reversible contagion processes.

Among these were the cybernetic assimilation of the Borg from Star Trek, the siege of the Aswangs from Filipino folklore, and other fictional scenarios whose interaction dynamics resemble processes observed in society.

Within these abstractions, infection need not represent biological disease alone. Instead, it may symbolize the gradual adoption or acquisition of characteristics that propagate through social interaction. Examples include:

• substance dependence and behavioral addictions;

• shifts in political allegiance independent of ideological conviction;

• recruitment and ideological radicalization within extremist organizations;

• the diffusion of gossip, misinformation, and deceptive commercial information;

• various forms of abuse and coercive social behavior.

Unlike classical epidemic models, many of these processes admit opportunities for rescue, rehabilitation, education, and long-term resilience. These additional stages fundamentally alter the dynamics of the system and motivate the development of new mathematical frameworks capable of representing recovery as an active component of contagion rather than merely its termination.

Real-world social systems are extraordinarily complex. Ethical considerations, financial constraints, logistical difficulties, and the impossibility of conducting controlled experiments often prevent researchers from directly investigating intervention strategies under realistic conditions.

Mathematical models provide simplified yet rigorous representations through which hypotheses can be formulated, tested, and refined. Computer simulations further extend these models by allowing researchers to observe the emergent consequences of countless interactions among individuals under varying assumptions and environmental conditions. Together, mathematical modeling and simulation enable systematic exploration of questions that would otherwise remain inaccessible.

Within fictional epidemiological settings, these computational experiments become particularly valuable because the governing rules can be explicitly defined while preserving strong conceptual parallels to real-world contagion processes.

The principal contribution of this research lies not in the fictional narratives themselves, but in demonstrating that fictional epidemiological events can serve as scientifically meaningful computational abstractions for investigating generalized contagion processes.

By extending classical epidemiological models to include stages such as capture, assimilation, rescue, rehabilitation, education, and long-term resistance, the project explored interaction dynamics that traditional compartmental models seldom represent explicitly. These extensions broaden the applicability of epidemiological modeling beyond communicable diseases toward a more general framework for studying reversible processes of transmission, intervention, and recovery.

The resulting models provide a foundation for examining diverse phenomena that share common contagion-like behavior while encouraging interdisciplinary dialogue among mathematics, computer science, epidemiology, education, and the social sciences.

The ideas introduced in this rationale establish the conceptual foundation for the remainder of this archive. The succeeding sections trace how these motivations evolved into a structured research program, beginning with the scientific background that inspired the work, followed by its research objectives, the development of a generalized modeling framework, the formulation of mathematical and computational models, and the scholarly outputs that emerged from the project.