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Disease Modeling
Researchers often employ concepts of ecology in the modeling of infectious diseases within both wildlife and human populations. This aspect of disease ecology involves quantifying components of host-parasite relationships, or parameters, to project the spread of these pathogens within populations.

These models provide an opportunity for scientists to explore the regulatory effect of infectious agents on populations. As a result, these ecological models are becoming increasingly popular in informing response strategies and public policy.

Simple Epidemic Models
See also: Compartmental models in epidemiology

Although many disease models derive their general structure from the same basic model, variation arises depending on the type of pathogen, i.e. microparasite versus microparasite, and its transmission mechanisms. Generally, researchers simplify these differences by categorizing pathogens as either being directly or indirectly transmission. Directly transmitted agents are those which are spread as a result of close contact with an infectious individual. Indirectly transmitted parasites involve environmental stages, such as developmental stages or vector transmission.

Simple epidemic models are often referred to as compartmental models because they rely on classifying members of a population according to their ability to transmit the disease. These categories include :

Susceptible-an individual capable of being infected with a pathogen with little to no immunity

Exposed-an individual with confirmed contact with an infectious individual who does present signs of infection and is incapable of transmitting the pathogen

Infectious-an individual with sufficiently high levels of the pathogen which is therefore capable of transmitting the pathogen

Recovered-a person who was previously infected but is no longer capable of transmitting the pathogen

Basic Compartmental SIR Model
For simplicity, the exposed group is often excluded from these models because it is difficult to calculate the exact incubation period, the time between exposure and disease onset, for a disease. This model also assumes a period of immunity follows an infection, during which an individual is incapable of being re-infected.

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Each of the equations above represents the number of individuals in a population transitioning between infectious classifications, with  representing the rate at which susceptible individuals become infected,  representing the number of infectious individuals, and  represents the change in the number of recovered individuals overtime.

Frequency-Dependent Models
Similar to the mode of transmission, a distinction exists between density and frequency-dependent models. Researchers assume that the contact rate between susceptible and infectious individuals increases with population size under a density-dependent model. Frequency-dependent models represent a situation in which population size does not affect the contact rate. As a result, the method for calculating parameters under a frequency-dependent model differs slightly from a density-dependent, as the population size is no longer a factor in the contact rate. The main distinction arises in the formula for calculating the transmission rate.

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These models are constructed under the assumption that the total number of individuals in a population remains the same throughout epidemic progression. This assumption fails to account for natural host death, pathogen-induced mortality, and host birth-rates. To construct models more reflective of biological host-pathogen systems, researchers incorporate demographics into the SIR model.

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Multiple variations of this basic SIR model exist, including an SIS model, in which a host returns to the susceptible group following recovery or treatment. The SI model is often used to describe disease transmission in plants.

Basic Reproductive Number
See also:Basic reproduction number

Although it is not involved in basic deterministic models, the basic reproductive number or the number of secondary infections produced by an infected individual is an essential component of disease modeling and response. For a disease outbreak to occur, a pathogen's R0 must exceed one. Many modeling efforts attempt to predict this value and the effects of various mitigation strategies, including vaccination, on this parameter. The ultimate goal of interventions being to drive this value below one. Calculations of R0 incorporate demography and parasite-induced death, resulting in the following equation :

Challenges in Disease Modeling
The simplicity of the basic SIR model and certain modified versions pose a challenge to forecasting pathogen outbreaks. The differences between classifications are somewhat of a gray area as the transition between groups is gradual rather than an immediate change. Similarly, different pathogens can exhibit both frequency-dependent and density-dependent. Additionally, during the early stages of an outbreak, many of the parameters necessary for predicting a disease's progression are unknown. Initial estimates of these quantities rely on preliminary data and incident reports, which introduce a level of uncertainty in model estimates.

Additionally, these models rely on parameters, including the transmission rate and the basic reproductive number is constant across the entire population. Research shows that these values vary between individuals, with a small subset of the population, super spreaders contributing to the majority of new cases. Multiple factors contribute to these parameters' heterogeneity, including duration of infection, an individual's infectiousness, and their contact rate with susceptible individuals. To account for this variation, researchers attempt to quantify these components through simulating patterns of social behavior, or contact networks, and exploration of differences in immune function.

The construction of contact networks provides researchers with a method for understanding individual variation in infection risk and transmission based on a population's social structure. An individual's degree of connections, or their number of contacts, and degree of centrality, a measure of an individual's connectivity, determines the likelihood of an individual becoming infected with and transmitting a disease. Researchers incorporate calculated network metrics into their parameters as a method for constructing more realistic models that more accurately reflect epidemic progression. Additionally, identifying and targeting individuals or groups with greater super-spreading capability provides a means for more implementing more effective disease control interventions.

Finally, most compartmental modeling focuses on a single host-pathogen system, but another source of significant heterogeneity is coinfection by multiple pathogens. Pathogens are often capable of manipulating host immune function, resulting in the infected host being more susceptible to additional infections (heterogeneity). Within the host, pathogens can interact competitively or cooperatively, although it is difficult to discern the exact effects of coinfection. For this reason, modeling multi-pathogen systems present a challenging and ongoing field of research.