Critical Review of Compartmental models in epidemiology: bridging the gap with operations research for enhanced epidemic control
Introduction
The critical analysis of the scientific literature concerning epidemiological modelling becomes difficult for scholars due to complex mathematical formulas, different modelling assumptions, and the multidisciplinary nature of the issue. The increasing number of communicable diseases such as SARS, Ebola, H1N1, and, more recently, COVID-19 has made it evident that compartmental models are needed to understand disease spread and assist in public health decisions. The application of the models has become a crucial element in this respect. Besides, developments in operations research have aided in efficient resource allocation and vaccinations in times of health crises.
In the article titled Compartmental Models in Epidemiology: Closing the Gap Using Operations Research Techniques to Improve Epidemic Control, Mirsaeedi et al. (2026) analyse compartmental epidemiological models and discuss the link between such models and optimisation methods in operations research. The article looks at model assumptions, parameter estimation techniques, applications, among others and points out areas for improvement. This is important in the context of Operations Research, which is increasingly becoming a popular field.
In addition, the paper stresses the rising importance of incorporating epidemiological models into decision-making operations to ensure better epidemic preparedness. This work, by critically reviewing the literature and filling in the identified gaps in methods used in current research, provides an important base for developing Operations Research.
Summary of the article
The paper provides a detailed review of Models in Epidemiology, with emphasis on the Susceptible-Infectious-Recovered (SIR) models and their variations such as SEIR, SIRD, SVIR, SIHR, and SIQR models. They undertake a systematic review of articles from the year 2019 to 2024 within the context of PRISMA and analyse 236 papers. They aim to explore the application of epidemiological models in combination with optimisation for better Operations Research in Healthcare.
The paper highlights some of the important assumptions and population dynamics involved in the modelling process of epidemics, along with the interventions employed in dealing with the epidemics and ways of estimating parameters in epidemic models. In this paper, special emphasis has been laid on the pharmaceutical and non-pharmaceutical interventions, including vaccination, quarantine, social distancing, and health resource management.
This study suggests that applying operations research techniques together with Epidemic Control Models would help to make more effective decisions in case of an epidemic outbreak. Still, there are numerous difficulties associated with such an approach, among which are untruthful modelling assumptions, little attention paid to behavioural issues, and weak interaction between epidemiological forecasting and operations planning.
Critique
Significance and contribution of the field
One of the main advantages of the given literature review is the extensive coverage of the development of compartmental modelling in epidemics along with optimisation techniques. In contrast to other literature reviews that concentrate mostly on the mathematical aspects of the issue, Mirsaeedi et al. (2026) consider how epidemiological models could be used for practical purposes in the healthcare sector through resource allocation, vaccination plans, and logistics management.
These results confirm the conclusions by Brandeau (2008), stressing the significance of optimisation in the public health decision-making process, and Blasioli et al. (2023), who proved the contribution of Operations Research in Epidemiology. Furthermore, according to Ivanov and Dolgui (2020), the integration of epidemic models into supply chain management contributes to increasing the resistance to pandemic shocks.
This literature review also adds value to the study by Hethcote (2000), which proved the significance of mathematical models for understanding the transmission of infectious diseases. Integrating these models into optimisation allows widening their application not only to prediction but also to the planning processes in healthcare.
However, the strengths of optimisation in conjunction with epidemiological modelling are identified in the paper, but there is no mention of any opposing arguments. Holmdahl & Buckee (2020) state that epidemic models need to be treated carefully since the validity of the model may be influenced by the wrong assumptions and inaccurate data. Such issues may have been included in the paper.
Methodology and research design
This study employs systematic epidemiological research methods by adopting a systematic literature review approach based on the PRISMA model, which makes the methodology easy to understand and reproduce. The inclusion of 236 studies that have been selected from reputable sources like Scopus, PubMed, ScienceDirect, and Google Scholar helps in capturing all the latest developments in the field. The inclusion of the bibliometric analysis approach using VOSViewer adds value to this systematic approach in carrying out the review.
The chosen methodology follows the guidelines provided by Page et al. (2021), who emphasised the need for transparency in systematic reviews based on the PRISMA 2020 Statement. Similarly, Donthu et al. (2021) showed that bibliometric analysis can be considered an efficient tool for tracking trends in research, impactful articles, and future directions in research.
The strength of the chosen methodology is, first, the possibility to summarise a huge amount of literature to provide qualitative and quantitative information about the development of compartmental epidemiological modelling.
However, there are some limitations to the methodology employed in this review. First, the review has only considered those studies that were conducted after the emergence of the novel coronavirus. Also, the review does not consider any network-based or fractional models of epidemics. This means that more models can be added to the review as suggested by Keeling & Rohani (2011).
Theoretical and Interdisciplinary Analysis
The paper uses an interdisciplinary method that incorporates notions drawn from disciplines such as epidemiology, Operations Research for Epidemic Control, public health, and healthcare management to shed light on the development of the compartment models. With the help of vaccination strategies, non-pharmacological measures, healthcare logistics, and optimisation methods, the paper proves that mathematical modelling could be useful for efficient epidemic planning.
The results support those by Anderson and May (1991), who developed the theory behind the modelling of infectious diseases, and Hethcote (2000), who illustrated the potential of using compartmental models for disease transmission analysis. Moreover, Enayati and Özaltın (2020) have proved that integration of optimisation tools and epidemic models helps to create an efficient strategy for vaccine distribution and management of healthcare resources, confirming the assumptions mentioned in the review.
This paper also corresponds to Ivanov and Dolgui (2021), who stressed the importance of integration of epidemic modelling and supply chain optimisation to enhance the resistance of the healthcare system to pandemic disruptions. Thus, by merging epidemic modelling with the decision-making process, the review increases the practical value of models under consideration.
However, the theoretical framework is descriptive in nature. Even though there are several extensions of compartmental modelling provided in the review, the comparative discussion of other methods used in epidemiological modelling, such as agent-based modelling and machine learning algorithms, is somewhat limited. The latter could contribute to the interdisciplinary approach.
Ethical Considerations
Several ethical problems relating to epidemic modelling have been identified in the review, such as equal allocation of vaccines, healthcare allocation, and the use of pharmaceutical and non-pharmaceutical interventions. The review also recognises the need for transparency in decision-making and evidence-based policymaking in public health during infectious disease epidemics.
The above ethical considerations align well with those proposed by Emanuel et al. (2020) regarding how to think ethically about allocating limited health care resources in the era of the COVID-19 pandemic. In the same way, Persad et al. (2020) propose that issues related to public health and limited medical supplies should be based on considerations of fairness, transparency, and equity.
However, while the above ethical problems were addressed in the article, the authors did not make an extensive discussion of other possible ethical issues such as algorithmic bias, data privacy issues, and health care access inequities.
Writing Style and Structure
The article is well structured in that it is logically organised from the beginning to the end. The logical presentation of the compartment models through tables, figures, and categorisation makes the review quite easy to comprehend. The use of clear and simple language makes the review quite understandable even for researchers.
Despite the clarity of the paper, some sections of the article are largely descriptive in nature without providing any meaningful critical comparisons between different studies. In other words, though the review provides a proper summary of the reviewed literature, the inclusion of critical analysis of conflicting theories would have made the article more academically valuable.
Conclusion
A significant contribution has been made by Mirsaeedi et al. (2026) through a thorough review of the compartmental models of epidemiology along with their use in combination with optimisation methods to manage epidemics. It is a great example of how mathematical modelling can help with healthcare planning and logistics as well as provide policy guidance and identify current and future directions for research in the field.
Even though the paper can consolidate a vast amount of literature, along with offering some valuable lessons, the mostly descriptive approach of the paper and lack of an assessment of other models used for epidemic studies weaken the level of critical analysis of the paper. The use of new methods such as agent-based modelling, machine learning, and real-time analysis could offer a more comprehensive view of epidemic models used today.
Overall, the review can be considered a useful source of information for researchers, practitioners, and policymakers interested in the current development of optimisation models used for epidemics.
“Need support with your critical review? PhD Assistance Research Lab provides expert guidance for doctoral scholars and early-career researchers to enhance their review in epidemiological research.”
Reference
- Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
- Emanuel, E. J., Persad, G., Upshur, R., Thome, B., Parker, M., Glickman, A., Zhang, C., Boyle, C., Smith, M., & Phillips, J. P. (2020). Fair allocation of scarce medical resources in the time of Covid-19. The New England Journal of Medicine, 382(21), 2049–2055. https://doi.org/10.1056/NEJMsb2005114
- Enayati, S., & Özaltın, O. Y. (2020). Optimal influenza vaccine distribution with equity. European Journal of Operational Research, 283(2), 714–725. https://doi.org/10.1016/j.ejor.2019.11.037
- Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), 599–653. https://doi.org/10.1137/S0036144500371907
- Holmdahl, I., & Buckee, C. (2020). Wrong but useful—What Covid-19 epidemiologic models can and cannot tell us. The New England Journal of Medicine, 383(4), 303–305. https://doi.org/10.1056/NEJMp2016822
- Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727
- Keeling, M. J., & Rohani, P. (2011). Modeling infectious diseases in humans and animals. Princeton University Press. http://math.uchicago.edu/~shmuel/Modeling/Keeling%20and%20Rohani/chap%201.pdf
- Mirsaeedi, F., Sheikhalishahi, M., Mohammadi, M., Pirayesh, A., & Ivanov, D. (2026). Compartmental models in epidemiology: Bridging the gap with operations research for enhanced epidemic control. Annals of Operations Research, 357, 1021–1078. https://doi.org/10.1007/s10479-025-06893-1
- Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021).The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
- Persad, G., Peek, M. E., & Emanuel, E. J. (2020). Fairly prioritizing groups for access to COVID-19 vaccines. JAMA, 324(16), 1601–1602. https://doi.org/10.1001/jama.2020.18513