Education, Epidemiology, Research

Exploring Compartmental Models in Epidemiology Effectively

Author Chandana Balasubramanian , 26-Jun-2023

Compartmental models in epidemiology are used to gain insight into the transmission of contagious illnesses and forecast their development. These mathematical models classify individuals within a population into distinct compartments based on their disease status, such as susceptible, infected, or recovered.


Let’s take a look at common compartmental models like the SIR (Susceptible-Infected-Recovered) model and their benefits and limitations. Additionally, we will examine how compartmental models can help personalized vaccine development by analyzing regional variations in transmission patterns. This type of insight can drive effective distribution strategies to control and prevent disease spread.


The role of compartmental models in epidemiology

Compartmental models help epidemiologists and infectious disease experts make sense of the world of disease. Using these mathematical models, epidemiologists analyze the spread of infections, predict how many people may be infected by a particular disease, and assess intervention strategies in the best interest of public health.

Compartmental models offer insights into:

  • Disease transmission: By dividing a population into distinct categories (e.g., susceptible and infected individuals), compartmental models can provide insights into how a disease spreads within that population.
  • The size of an epidemic: Mathematical modeling allows researchers and public health experts to estimate epidemic and outbreak sizes. Often, different scenarios of outbreaks can be simulated by varying parameters like transmission rates or the types of interventions implemented.
  • Informed decision-making: Compartmental models empower healthcare professionals, and public health agencies to understand potential outcomes based on public health measures aimed at controlling an outbreak’s progression before they are actually put into practice.


Variations of compartmental models: from simple to complex frameworks

Compartmental models can vary in levels of complexity. There are many factors that differentiate one from another, including underlying assumptions about disease characteristics or specific needs for a particular scenario. Here are some common compartmental models:

  1. SIR model – Susceptible Infectious Recovered: The SIR model is a simple but very commonly used framework. Here, there are three compartments or groups of people. S = Susceptible individuals (those who can contract an infection but haven’t been exposed yet), I = Infected populations (those who are infected and can transmit the virus or pathogen to others), and R = Recovered populations (individuals who have either recovered or died from the infection).
  2. SEIR model – Susceptible Exposed Infectious Recovered: SEIR is a more complex version that adds an additional “exposed” category to account for individuals who have contracted the virus but have not shown symptoms yet but can transmit the infection to others.
  3. SIRD model – Susceptible Infected Recovered Deceased: The deceased category represents people who have succumbed to the disease and died due to the infection.
  4. SIRV model – Susceptible Infected Recovered Vaccinated: The vaccinated category represents individuals who have been vaccinated against infection.
  5. SIS model – Susceptible Infected Susceptible: In this type of models, individuals who are infected return to the susceptible category.


There are many more models and variations within models but let’s explore the ones listed above in greater detail. In addition to these widely used models, there are also specialized compartmental frameworks like stochastic models which incorporate random elements into their structure as well as those accounting for vital dynamics such as births and deaths within a population over time. The choice of model depends on factors like disease characteristics, available data sources, and specific research objectives being pursued during any given study scenario.

Compartmental models in epidemiology provide a useful tool for understanding the spread of infectious diseases and predicting their evolution.


The SIR model

The Susceptible-Infectious-Recovered (SIR) model is one of the simplest compartmental models consisting of three compartments representing susceptible, infectious, and recovered populations. This mathematical model helps analyze the spread of infections, predict the number of infected individuals, and assess various intervention strategies such as social distancing, lockdowns, and vaccination programs.

  • Susceptible: Individuals who have not yet been exposed to the disease but are at risk due to their interactions with infectious individuals.
  • Infectious: Infected individuals who actively transmit the disease to susceptible people through direct or indirect contact.
  • Recovered: Those who have successfully fought off the infection and developed immunity against future exposures.

The assumption that those who have recovered are not vulnerable to reinfection is a fundamental part of the SIR model.

Benefits of SIR model

The SIR model has its advantages. The main advantage is its simplicity. It is used for infections that offer some level of immunity after infection, where people “recover” without reinfection in the short term.

Diseases that can be modeled using SIR:


Limitations of the SIR model

The simplicity offered by the basic SIR framework has its limitations. In particular, it can be hard to capture real-world complexities associated with infectious diseases like COVID-19 pandemic scenarios. Here,  factors like varying incubation periods or asymptomatic carriers play crucial roles in overall epidemic sizes observed among affected communities worldwide. The SIR model also does not account for vital dynamics like births and deaths within a population, which can significantly impact disease transmission patterns over time.

The SIR model, though a straightforward way to grasp the spread of contagious illnesses, should not be overlooked in its constraints stemming from its simplicity. Building on this foundation, the SEIR model offers additional advantages which can help healthcare professionals study disease transmission more effectively.

Key Takeaway:  The SIR model is a basic compartmental model used to understand and predict the dynamics of infectious diseases. It consists of three compartments representing susceptible, infectious, and recovered populations, and helps analyze disease transmission rates between these groups. However, its simplicity has limitations in accurately capturing real-world complexities associated with infectious diseases like COVID-19 pandemic scenarios where factors such as varying incubation periods or asymptomatic carriers play crucial roles in overall epidemic sizes observed among affected communities worldwide.


The SEIR model

While the SIR model has been instrumental in understanding infectious diseases, its simplicity can sometimes be a limitation. To address this issue, epidemiologists have developed more complex compartmental models called the Susceptible-Exposed-Infectious-Recovered (SEIR) model. This framework adds an additional ‘exposed’ category to represent individuals who have contracted the virus but are not yet showing symptoms or capable of transmitting it to others.

Benefits of SEIR over simpler SIR-based frameworks

Including an ‘exposed’ compartment allows for a more accurate representation of disease transmission dynamics by accounting for the latent period between infection and contagiousness. As a result, the SEIR model provides better estimations when analyzing infections with significant incubation periods like COVID-19 or Ebola.

The SEIR model offers:

  • Better accuracy: The addition of an exposed category improves predictions on epidemic size and peak timings compared to simpler models.
  • Incorporating interventions: By considering different stages of infection, SEIR models can help assess various intervention strategies such as quarantine measures or vaccination programs.
  • Versatility: The structure can be further modified to account for other factors like population heterogeneity or vital dynamics that influence disease spread within communities.


Diseases for which the SEIR model works well are:

  • COVID-19: The SEIR model factors the incubation period for the virus, to account for how the virus spread in populations.
  • Influenza: Seasonal influenza or new influenza strains can be modeled using the SEIR model.
  • Ebola: SEIR has been used to model the dynamics of Ebola outbreaks. The Ebola virus has an incubation period of anywhere from 2 to 21 days (average 8-10 days). This incubation period can be factored in by the SEIR model.
  • SARS (Severe Acute Respiratory Syndrome)
  • Zika


The SEIR model’s ability to account for additional complexities related to disease progression makes it an important framework in epidemiology.

Limitations of the SEIR model

Although the SEIR model is used to factor in incubation periods of pathogens, it has its limitations. It is still a simplified model for the complex world of infectious diseases. For one, it assumes that populations are homogenous (similar). It does not account for differences in population demographics, contact patterns that spread disease, and more.

The SEIR model also assumes that the incubation period, transmission rates, and period of infection are constants. In reality, disease parameters change over time due to many factors like mutant strains, changing population behavior, or the impact of public health policies. SEIR relies on having already-available data to analyze. If there is limited or inaccurate data used as its foundation, the model’s results may also reflect uncertainties and inaccuracies.

Key Takeaway:  The SEIR model is a more complex compartmental model used in epidemiology that adds an “exposed” category to account for the latent period between infection and contagiousness. This framework provides better estimations when analyzing infections with significant incubation periods like COVID-19 or Ebola, allows for assessing various intervention strategies, and can be modified to account for other factors like population heterogeneity or vital dynamics.


The SIRD model

The SIRD model stands for Susceptible (S), Infected (I), Recovered (R), and Diseased (D). ‘Susceptible’ represents people who are not infected but are susceptible to infection if exposed to the pathogen. ‘Infected’ is for people who are infected with the pathogen. ‘Recovered’ represents people who have recovered from the disease. Recovered individuals are assumed to have gained immunity and are not susceptible to infection in the short term. ‘Deceased’ represents individuals who passed away from the infection.

Benefits of the SIRD model

The SIRD model is simple so it is easier to understand and use. There are clear distinct categories of populations that help track the transition from one to another. The SIRD focuses on outcomes; providing a compartment for deceased individuals allows researchers to understand the impact of disease progression on mortality. This is significant in diseases with a higher mortality rate.

The SIRD works well for diseases where reinfection is not common and those that have a higher mortality rate:


Limitations of the SIRD model

The SIRD model works well for higher-level analyses, but this simplistic structure does not work when a more granular and accurate model of a disease is needed. The SIRD model has rigid and simplistic compartments and does not take into account factors like the incubation period, or time-dependent parameters like varying transmission rate and recovery rate. In real life, these parameters change over time. The SIRD relies on already-available data. Because of this, the results are also dependent on the type and amount of data being fed into the model. The SIRD also does not deal with in-depth details of outbreaks like different contact patterns, group dynamics and behaviors, and other such parameters.

Key takeaway: The SIRD model (Susceptible, Infected, Recovered, Deceased) model is a simple, outcome-focused framework for understanding diseases. The model takes into account people who succumbed to the infection and died from it. In particular, it is helpful to estimate disease spread for diseases with a high mortality rate. The limitation is that it is too simple for real-world disease modeling and does not take time-dependent variables into account.


The SIRV model

The SIRV model represents Susceptible (S), Infected (I), Recoverd (R), and Vaccinated (V) individuals. It is an extension of the SIR framework that incorporates the effects of vaccinations on disease outbreaks. Epidemiologists use it to understand how a disease spreads within a community and what the effects of vaccination efforts.

Benefits of the SIRV model

The SIRV model works well for diseases where vaccinations play a significant role in controlling outbreaks. These include:


Limitations of the SIRV model

The SIRV model works well for a fast overview of how vaccination efforts impact population immunity. However, it is a simplistic framework that does not take into account time-dependent and changing variables like mutant strains and time-dependent factors like transmission and recovery rates. It also assumes a fixed efficacy for vaccines over time, which, as we know, is not representative of how vaccines work in the real world. It also does not take into account how different types of people respond to the vaccines and that immunity levels may vary within a compartment.

The SIRV model also does not consider the side effects of vaccines or adverse events associated with vaccines. These factors may affect the overall impact of immunization on the spread of the disease. SIRV also does not consider how public perception and human behavior affect population immunity. These parameters greatly impact how diseases spread, how vaccination drives are received, and overall immunity. There are more complex and nuanced models that can be used for more in-depth analyses.

Key takeaway: The SIRV model (Susceptible, Infected, Recovered, Vaccinated) model is a simple, framework to understand the impact of vaccines on a disease outbreak. It is used to model diseases where vaccines play a significant role in controlling epidemics. However, it is limited because of its simplicity. It does not account for changing vaccine efficacy over time, the impact of vaccines on different demographics, and how public behavior impacts population immunity.


The SIS model

The SIS model is a very basic model in epidemiology. It represents Susceptible (S), Infected (I), and Susceptible (S) groups of people. It is used to understand diseases with a higher rate of reinfections. In the SIS model, there are only two compartments – people are either susceptible to infection or infected. The model assumes that there is direct transmission between individuals who are infected and susceptible. In the SIS model, there is no ‘Recovered’ group of people – once people are no longer infected, they move to the susceptible category, where they may be infected again.

Benefits of the SIS model

The SIS model is used when diseases have a high rate of reinfection and where people who get infected do not get long-term immunity from the disease. It helps understand the high-level patterns of transmission, the impact of different interventions, and the potential for endemicity (how a disease circulates within a population).

The SIS model can be used on diseases like:


Limitations of the SIS model

The SIS model has its limitations. This model is not applicable to diseases that offer some level of longer-term immunity after recovery. It does not take into account how vaccines may affect disease spread and assumes that all individuals within a compartment have equal levels of susceptibility to getting infected. In the real world, this scenario is unlikely. The SIS model does include information about factors that may affect infection rates within a compartment. For example, age, sex, co-morbidities, and other parameters can play a role in how susceptible people are to infection.

Population density, socio-economic determinants, behavioral aspects, and much more can impact disease spread and outbreaks. The SIS model cannot incorporate these factors into its framework.

Key takeaway: The SIS model (Susceptible, Infected, Susceptible) can be used to model diseases with a high infection rate. It is a simple framework that is easy to use and offers a  fast, high-level understanding of disease reinfection rates during an outbreak. Its limitations are that it does not include demographic variations, social determinants of health, population density, immunity from vaccines, and other factors.


How compartmental models help develop COVID-19 vaccines

Compartmental models have been used on larger scales to study COVID-19 transmission across continents to develop personalized vaccines catering to regional variations seen among affected populations worldwide.

Regional variation in disease transmission patterns

The spread of infectious diseases like COVID-19 is influenced by various factors such as population density, climate, and social behavior. These factors contribute to significant regional differences in disease transmission patterns. By utilizing compartmental models, researchers can analyze these variations and gain valuable insights into how a particular virus spreads within different communities or regions.

Applications in vaccine development and distribution strategies

Vaccine development has always been an essential aspect of controlling infectious diseases; however, creating effective vaccines becomes even more critical during pandemics like the ongoing one caused by the SARS-CoV-2 virus. Compartmental models play a crucial role not only in understanding disease dynamics but also in guiding vaccine development efforts. In particular, they help address unique challenges from varying transmission rates between distinct geographical locations.

The compartmental frameworks help vaccine development by offering:

  • A data-driven approach: Compartmental models provide data-driven predictions regarding epidemic size and progression over time which helps guide targeted vaccine research initiatives aimed at developing region-specific solutions capable of effectively combating local infection trends seen within diverse populations.
  • Optimizing distribution strategies: By understanding the regional differences in disease transmission, healthcare professionals can optimize vaccine distribution strategies to ensure that vaccines reach those who need them most. For example, during the COVID-19 pandemic, compartmental models have been used to identify priority groups for vaccination and allocate resources accordingly.
  • Evaluating intervention measures: Compartmental models also allow researchers to evaluate the effectiveness of various public health interventions such as social distancing or lockdowns when implemented alongside vaccination campaigns.



Compartmental models in epidemiology are powerful tools to predict disease spread and develop effective prevention strategies. The SIR, SEIR, SIRV, SIRD, and SIS models are just some of the ways healthcare professionals can understand infection dynamics and make informed decisions about vaccine development and distribution.

By incorporating behavioral strategies like social distancing measures into these models, we can further improve their accuracy and effectiveness. Additionally, applying compartmental models across age groups and species allows us to consider unique vulnerabilities within different populations.


The GIDEON difference

GIDEON is one of the most well-known and comprehensive global databases for infectious diseases. Data is refreshed daily, and the GIDEON API allows medical professionals and researchers access to a continuous stream of data. Whether your research involves quantifying data, learning about specific microbes, or testing out differential diagnosis tools, GIDEON has you covered with a program that has met standards for excellence.


Chandana Balasubramanian

Chandana Balasubramanian is an experienced healthcare executive who writes on the intersection of healthcare and technology. She is the President of Global Insight Advisory Network, and has a Masters degree in Biomedical Engineering from the University of Wisconsin-Madison, USA.

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