Tomov L, Miteva D, Sekulovski M, Batselova H, Velikova T. Pandemic control - do's and don'ts from a control theory perspective. World J Methodol 2022; 12(5): 392-401 [PMID: 36186747 DOI: 10.5662/wjm.v12.i5.392]
Corresponding Author of This Article
Tsvetelina Velikova, MD, PhD, Assistant Professor, Chief Doctor, Department of Clinical Immunology, University Hospital Lozenetz, Kozyak 1 Street, Sofia 1407, Bulgaria. tsvelikova@medfac.mu-sofia.bg
Research Domain of This Article
Health Care Sciences & Services
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Latchezar Tomov, Department of Informatics, New Bulgarian University, Sofia 1618, Bulgaria
Dimitrina Miteva, Department of Genetics, Sofia University "St. Kliment Ohridski", Sofia 1164, Bulgaria
Metodija Sekulovski, Department of Anesthesiology and Intensive care, University Hospital Lozenetz, Sofia 1407, Bulgaria
Metodija Sekulovski, Tsvetelina Velikova, Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
Hristiana Batselova, Department of Epidemiology and Disaster Medicine, Medical University, Plovdiv, University Hospital "St George", Plovdiv 6000, Bulgaria
Tsvetelina Velikova, Department of Clinical Immunology, University Hospital Lozenetz, Sofia 1407, Bulgaria
Author contributions: Tomov L and Velikova T designed the research; Sekulovski M, Miteva D, and Batselova T performed the research; Tomov T contributed analytic tools; Tomov T, Velikova T, and Batselova H analyzed the data; Tomov T, Sekulovski M, Miteva D, and Batselova H wrote the paper; Velikova T revised, supervised, and edited the paper. All authors revised and approved the final version of the manuscript.
Conflict-of-interest statement: All authors declare no conflict of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Tsvetelina Velikova, MD, PhD, Assistant Professor, Chief Doctor, Department of Clinical Immunology, University Hospital Lozenetz, Kozyak 1 Street, Sofia 1407, Bulgaria. tsvelikova@medfac.mu-sofia.bg
Received: March 16, 2022 Peer-review started: March 16, 2022 First decision: June 16, 2022 Revised: July 6, 2022 Accepted: August 10, 2022 Article in press: August 10, 2022 Published online: September 20, 2022 Processing time: 184 Days and 0.8 Hours
Abstract
Managing a pandemic is a difficult task. Pandemics are part of the dynamics of nonlinear systems with multiple different interactive features that co-adapt to each other (such as humans, animals, and pathogens). The target of controlling such a nonlinear system is best achieved using the control system theory developed in engineering and applied in systems biology. But is this theory and its principles actually used in controlling the current coronavirus disease-19 pandemic? We review the evidence for applying principles in different aspects of pandemic control related to different goals such as disease eradication, disease containment, and short- or long-term economic loss minimization. Successful policies implement multiple measures in concordance with control theory to achieve a robust response. In contrast, unsuccessful policies have numerous failures in different measures or focus only on a single measure (only testing, vaccines, etc.). Successful approaches rely on predictions instead of reactions to compensate for the costs of time delay, on knowledge-based analysis instead of trial-and-error, to control complex nonlinear systems, and on risk assessment instead of waiting for more evidence. Iran is an example of the effects of delayed response due to waiting for evidence to arrive instead of a proper risk analytical approach. New Zealand, Australia, and China are examples of appropriate application of basic control theoretic principles and focusing on long-term adaptive strategies, updating measures with the evolution of the pandemic.
Core Tip: Controlling an epidemic is a massive challenge due to the nonlinear systems involved and interactions that are hard to model and predict well. Therefore, any pandemic control policy must apply at least the basic principles of control theory, including having multiple measures simultaneously and having models and predictions to combat the time delay between exposure and symptom onset that could lead to loss of life and controllability of the pandemic. In addition, a control-theoretic-based policy needs to factor in a large set of mutual interactions between people, animals, and pathogens that includes media and social networks and their influence on people's behavior, including fake news and the viral spread of disinformation.
