LIBRARY
Malaria Research
Infectious Disease
Models | Real Time Analysis
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The Limitations of Scientific Models in Capturing Life’s Complexity
Science involves a collection of well-supported concepts that help us understand how dynamic systems work. However, these ideas may not always perfectly represent the real-world processes happening at any given moment. Our ability to experience the world remains a scientific enigma, similar to an asymptote that continually approaches but never quite reaches a definitive answer. No matter how far we progress into the future, no model can fully capture the complexity of your life and its countless interactions.
TLDR; Science offers well-supported concepts to explain dynamic systems but can’t fully capture the living processes and interactions in real time. The experience of the world is a scientific enigma, and no model can ever completely represent the complexity of our lives
Infectious Disease Modeling
Infectious disease is a discipline of mathematics that aims towards modeling transmittable pathogens between hosts. It considers the biological basis of a pathogen, but ultimately aims to formulate some function that includes as few meaningful variable possible. For example, imagine making a function that describes the biochemical processes for your circadian rhythm. Then, consider that model in light of your current sleep deficit, physical and mental fatigue, nutrition, comfort, et cetera. Each with drastically compounded input factors like time-dependent blue-light input some time prior to rest. Additionally, this only considers you alone, a single organism.
Consider modeling the circadian rhythm for your family, your friends, your community, your localized area, your city, your region, your state, your side of the country, your continental location, your angle of the globe, your singular existence as one complex human being among trillions, one human in a set of human beings alive on the planet currently as compared to all those who came before and who will come after you.
There is no way to accurately model the entire interacting and dynamic biosphere we all belong too. This is because, we ourselves are the interacting dynamic model that gives rise to your unique and individual experiences that will never again happen. Being alive is being part of this mystery, an ever-approaching asymptote towards a sense of understanding.
TLDR; Infectious disease mathematics models pathogen transmission between hosts, focusing on simplified yet meaningful variables. Modeling complex factors like circadian rhythms for individuals and larger populations is challenging due to the dynamic and unique nature of each person’s experiences. Our existence contributes to the ever-evolving, intricate biosphere, making complete understanding an elusive goal.
The Challenge of Modeling Complex Systems
Infectious disease researchers know that all models are wrong, but recognizes that some are useful. It also depends on the interdisciplinary cooperation between scientists who know the information for biological concepts, but rely on the formulation of accurate mathematical models to demonstrate them in real-time events. In a way, these models serve as a sort of snapshot for a very unique point in time, a lot like your unique life experiences. Models for the COVID-19 pandemic demonstrated a real-time analysis for some time before trends shifted from frame and rendered them obsolete. Infectious disease hopes to capture the dynamics of pathogens as accurately possible, for the time of relevant study.
Other times, it hopes to capture the future of transmittable diseases for preemptive actions to take form. For example, the flu vaccine requires highly coordinated interdisciplinary work to model possible mutations for the virus’s hemagglutinin and neuraminidases proteins. When modeled successfully, vaccines are distributed to populations and provide immunological memory against a variety of strains. When models fail to capture the possible mutations, people are infected because the vaccine fails to provide immunological memory against missed epitopes.
TLDR; Infectious disease models, while imperfect, are useful tools that rely on interdisciplinary collaboration to capture real-time events or anticipate future trends. These models are snapshots of unique moments, like individual life experiences. They can guide actions like flu vaccine development, but their accuracy may vary, sometimes failing to cover all possible virus mutations, leading to infections.
The Utility of Imperfect Models
Whenever infectious disease models consider host dynamics like in a human, this will involve partial differential equations that include pertinent factors like new and old erythrocytes for malaria patients. Oftentimes, an immunologist will think of all erythrocytes as circulating with splenic turnover, but infectious disease specialists will account for infection that encompasses the whole range of erythrocyte ages. The difference that information provides for the model could give rise to two entirely different understandings for malaria—and the most accurate one is the correct one.
However, when infectious disease models consider populations and large sets of individual people, the model leans towards time-dependent quantities. These quantities are used to demonstrate broad-stroked trends occurring as rifts throughout the population. SARS-CoV-2 swept the world in a year and gave rise to the COVID-19 pandemic. Infectious disease models attempted to track, define, and predict the future of the virus but when it mutated with enhanced virulence; those earlier models failed immediately. They no longer represented an accurate picture of reality. Those models remain photos in an album book. A memory of the past.
TLDR; Infectious disease models adapt to different scales, considering host-specific factors like erythrocyte ages in individual cases, and time-dependent trends in populations. While these models attempt to predict outbreaks like COVID-19, they can quickly become outdated due to factors like viral mutations, ultimately serving as snapshots of a past reality.
Modeling Host Dynamics and Population-Level Effects
An interestingly unreachable goal of infectious disease doctors is to accurately provide a model that simultaneously embodies both the host dynamics of its subjects and the larger population to which they belong. This is termed multi-scope modeling and such a feat has never appropriately or definitively been produced. The implications in producing a model of this accuracy is readily apparent if you tried to model all your daily interactions with other people, and then all their daily interactions. You would have an exhaustive and nearly infinite set of variables and values. Modeling as in, formulating a function with variables that can have perimeters like number of people interacted with, for how long, degree of aerosol production, et cetera. It’s an unlikely human endeavor, and likely an AI one.
Modeling malaria considers similar factors as COVID-19 or ebola by determining the prevalence, incidence, and mortality. Simply, the number of cases at a given time, the number of new cases, and the rate of death due to a particular disease. Viruses require one set of data because humans are the only set of organisms to consider. When malaria is considered in differential equations, two sets of data are included, the Anopheles female mosquitos and the people infected. Thinking back to modeling human behaviors, imagine the dismay of accurately modeling mosquito interactions. This isn’t to say it can’t be done; only to recognize the endeavor of formulating an appropriate model.
Modeling malaria is a feat, and modeling it accurately for a global understanding of its current snapshot is probably some time out. Instead, modeling localized areas or specific individuals is a more realistic goal. Malaria is symptomatically difficult to diagnose because it presents with fever and chills like other diseases. A true blood analysis would reveal Plasmodial parasite infection, but a working model to find trends in people for symptoms may facilitate easier diagnosis or treatment. Modeling community interactions on a broad sense to reduce mosquito transmission and reduce fatality is another worthy goal that modeling can support.
TLDR; Multi-scope modeling, which captures both host dynamics and larger populations, is a challenging and complex goal in infectious disease research. Current models focus on localized areas or specific individuals. Modeling malaria, which involves human and mosquito interactions, supports goals like easier diagnosis, treatment, and community intervention to reduce transmission and fatality.
Faculty contributor(s)
- Dr. Aguilar, who also provided the depth of understanding explored in this page's content based on his work with non-human primates and malaria in the dry-lab.