The application of chaos theory to crisis management of epidemics.

Chaos theory is mainly known from the example of the Lorenz butterfly effect, where small changes can lead to significant, long-term and catastrophic consequences. What is this theory of chaos, though, and how does it relate to crisis management?

Chaos theory benefits many disciplines, but it still falls within the field of mathematics and is a tool that can define deterministic chaos – that is, seemingly random behavior that does not actually have a random cause. The study of the theory has led to the notion that seeming randomness can also conceal order, patterns, and underlying structure. Chaos theory has found countless applications, such as the study of planetary motion in the solar system, weather forecasts, population dynamics ecology, earthquake modelling, and the definition of the trajectories of space probes.

The aforementioned areas may seem light years away from crisis management, but as the theory focuses mainly on the beginning stages of processes and states that if a significant error is made at the beginning of the process, the rest of the process will be affected in unpredictable consequences. Through the prism of chaos theory, the COVID-19 crisis, which has been able to trigger lasting global change, can also be seen.¹ For example, the hypothetical flawed determination of the COVID-19 infection rate (R) and thus the introduction of mild restrictions in the early stages of the corona crisis could have led to harsh consequences for the medical system, increased infection numbers and mortality, thus ultimately leading to tougher restrictions and serious consequences for business. This, in turn, would also lower economic performance and could also lead to job losses, thereby increasing the number of people in need of social assistance and thus placing a long-term burden on the public purse. This, in turn, does not allow the state to invest in other initiatives in the desired amount, which will also have consequences for a much larger number of people. And all of this would have started with a calculation error in determining the R coefficient.

The global chain of events, which started from the miscalculation of the People’s Republic of China – the failure to recognize, localize, timely and adequately inform the world about the new virus quickly enough – continues to develop in a very unpredictable and chaotic way, and in its aftermath will no longer allow for a return to the usual year-round way of life. Chaos Theory makes it possible to explain the specifics of COVID-19² , and this in turn supports the use of the theory as a valid conceptual framework for studies aimed at examining the psychological, behavioural, social and systemic aspects of other viral epidemics.³ It should be mentioned here that such a global modeling approach, for example using chaos theory, has been a rather rare phenomenon in biological systems, but has been successfully implemented both during the West African Ebola epidemic (2013-2016) and during the first wave of the COVID-19 pandemic, for example, in countries in East Asia.Chaos Theory is particularly suitable for studying the explosive spread of infectious diseases, as it is suitable for modelling and researching unstable dynamic behaviour even when certain variables are unknown, which is often encountered in epidemiology.⁴ Knowledge and implementation of chaos theory is suitable for crisis management, especially because Postavaru et al. (2020) says that crisis responders face challenges in decision-making. One such example would be the importance of applying a mathematical model in assessing the impact of isolation on the population.⁵ If a mathematical model was applied in assessing the effects of isolation, unpredictable consequences could be avoided. In the initial stages of crises, it is also essential to organise access to relevant information to promote decisions and policy objectives based on scientific evidence, giving decision-makers quick, easy and effective access to the best predictions5, which in turn allow even more accurate prediction of the new stages of the pandemic, even across countries.⁶ One’s own point of thought is the availability of basic data, but also the concealment of data in communist societies – such a pattern of behaviour may be the starting point of a crisis with unpredictable consequences, according to chaos theory – COVID has been the case.

On the basis of models, different measures and restrictions have also been implemented in the current crisis in the world, but finding a solution that is unambiguously workable and also implementing it is difficult.⁷ In addition, COVID-19 has made it virtually impossible to make informed decisions without a forecast⁸ , which depend on good level of testing data8, but still does not allow to create a whole picture of the situation, but rather helps to answer specific questions⁹ that policy makers may have in decision-making processes.

Sources used:

¹Boon, I. S., Lim, J. S., Tracy, P. T. A. Y. & Boon, C. S. 2020. Digital healthcare and shifting equipoise in radiation oncology: The butterfly effect of the COVID-19 pandemic. Journal of Medical Imaging and Radiation Sciences, pp. 1–3. https://doi.org/10.1016/j.jmir.2020.10.002

² Resnick, B. 2020. How chaos theory helps explain the weirdness of the Covid-19 pandemic. VOX, May 23, 2020. [Leitav: https://www.vox.com/science-and-health/2020/5/20/21257136/covid-19-future-pandemic-chaos kasutatud 15.06.2022]

³Piotrowski, C. 2020. Covid-19 Pandemic and Chaos Theory: Applications based on a Bibliometric Analysis. Journal of Projective Psychology & Mental Health, 27(2), pp. 1–5.

⁴ Mangiarotti, S., Peyre, M., Zhang, Y., Huc, M., Roger, F. & Kerr, Y. 2020. Chaos theory applied to the outbreak of COVID-19: an ancillary approach to decision making in pandemic context. Epidemiology and Infection, 148(E95), pp. 1–13. https://doi.org/10.1017/s0950268820000990

⁵Postavaru, O., Anton, S. R. & Toma, A. 2020. COVID-19 pandemic and chaos theory. Mathematics and Computers in Simulation, 181(C), pp. 138–149. https://doi.org/10.1016/j.matcom.2020.09.029

⁶ Gibney, E. 2020. Whose coronavirus strategy worked best? Scientists hunt most effective policies. Nature, 581, pp. 15–16. https://doi.org/10.1038/d41586-020-01248-1

⁷ Stuart, E. A., Polsky, D., Grabowski, M. K & Peters, D. 2020. 10 Tips for Making Sense of COVID-19 Models for Decision-Making. Johns Hopkins Bloomberg School of Public Health, April 27, 2020. [Leitav: https://publichealth.jhu.edu/2020/10-tips-for-making-sense-of-covid-19-models-for-decision-making kasutatud 15.06.2022].

⁸ Schneider, E. C. 2020. Failing the Test — The Tragic Data Gap Undermining the U.S. Pandemic Response. The New England Journal of Medicine. 383(4), p. 301. https://doi.org/10.1056/nejmp2014836

⁹ McBryde, E. S. 2020. Role of modelling in COVID-19 policy development. Paediatric Respiratory Reviews, 35, p. 59. https://doi.org/10.1016/j.prrv.2020.06.013