The study is based on the Elliott Wave Principle, developed by Ralph Nelson Elliott in 1938. This principle suggests that crowd psychology governs stock market fluctuations, with patterns reflecting rhythmic shifts in optimism and pessimism. These patterns are quantifiable and observable across various time scales. The authors posit that, since human emotions are similarly pivotal in shaping pandemic responses, the Elliott Wave Principle could also predict the pandemic's trajectory. They illustrate this with a thought experiment comparing two individuals with differing levels of adherence to public health guidelines. Individual A consistently follows guidelines, while individual B's adherence varies based on subjective factors. While biologically equally susceptible, individual B's inconsistent behavior increases vulnerability and pandemic spread. This premise forms the basis for investigating the principle's applicability to COVID-19 epidemiology. This is, to the authors' knowledge, the first attempt to apply the Elliott Wave Principle to global or regional viral infection studies.

The paper does not contain a dedicated literature review section in the traditional sense, but the introduction section establishes the foundation for the study by referencing Ralph Nelson Elliott's work and the application of the Elliott Wave Principle in financial markets. The authors emphasize the novelty of this application to pandemic modeling.

The core methodology rests on the Elliott Wave Principle, which posits that asset price movements occur in wave patterns. A completed movement consists of five sub-waves: three impulsive waves (i, iii, v) in the direction of movement, and two corrective waves (ii, iv) against the movement. These waves exhibit a fractal nature, with the five sub-waves forming a larger wave of a higher degree. Impulsive waves are divisible into five sub-waves, while corrective waves are divisible into three sub-waves. The waves follow Fibonacci ratios, a mathematical relationship between numbers in the Fibonacci sequence (0, 1, 1, 2, 3, 5, 8, etc.). The authors use the Fibonacci Pinball method to predict future price movements (daily infections in this case). This involves calculating Fibonacci retracements and extensions from past and current data to project future prices and identify support and resistance levels. The data used is from a publicly available database containing daily new COVID-19 cases. The analysis is performed using MATLAB R2018a.

Applying the Elliott Wave Principle to the seven-day moving average of daily new COVID-19 cases, the authors observe patterns consistent with the principle in various countries, including the USA, the European Economic Area (EEA), India, and Brazil. They demonstrate that the pandemic's progression in these regions aligns with the characteristic impulses and correctives of Elliott waves. They attribute these patterns to the crowd psychology reflected in fluctuating adherence to public health policies. For example, in the USA, the initial impulse (wave 1) was followed by a corrective wave (wave 2) after the imposition of restrictions, and then a large impulsive wave (wave 3). Similar patterns were identified in other countries, although the completion of the higher-dimensional waves varied. The authors note that the third wave is typically the largest and most powerful, which is observed in their analysis. Factors like the imposition of restrictions, relaxation of policies, the emergence of variants, and vaccination rates influenced the waves. The authors also considered factors like India's potential better innate immunity to SARS-CoV-2. Based on the Fibonacci Pinball method, projections for daily new infection cases are made, with varying scenarios based on vaccine availability and policy adherence. The projections suggest the pandemic could continue through 2021, with the potential for significantly higher infection rates depending on various factors.

The findings suggest that the Elliott Wave Principle, a tool traditionally used in financial markets, may provide a useful framework for modeling the dynamics of the COVID-19 pandemic. The observed correlation between the pandemic's trajectory and the wave patterns highlights the role of crowd psychology in shaping its progression. The ability to generate predictive scenarios, ranging from best-case to worst-case scenarios based on vaccination rates and policy adherence, demonstrates the potential of this approach for informing public health strategies and resource allocation. However, the authors acknowledge the limitations of this model, particularly the simplification of complex epidemiological factors. Further research is needed to refine this approach and incorporate other crucial epidemiological parameters for greater accuracy.

This study demonstrates the surprising applicability of the Elliott Wave Principle, a tool from financial mathematics, to model the COVID-19 pandemic. The analysis of daily new case data reveals wave patterns consistent with the principle, suggesting that crowd psychology plays a significant role in the pandemic's trajectory. Future research should focus on validating this approach using more comprehensive data, incorporating additional epidemiological factors, and refining the predictive models. The findings highlight the potential of interdisciplinary approaches for understanding and managing complex public health crises.

The study's primary limitation is the inherent simplification of complex epidemiological phenomena. The model relies on crowd psychology and does not fully account for other crucial factors influencing pandemic dynamics, such as viral mutations, healthcare system capacity, and demographic variations. The accuracy of future projections depends heavily on the accuracy of assumptions made regarding vaccine availability and adherence to public health policies. While providing valuable insights, this methodology should be considered a supplementary tool and not a standalone predictor of pandemic trajectory.