Abstract

A study was carried out in Santiago de Chile, located in a geographic basin, on the sustainability and diffusion of the recent SARS-CoV-2 pandemic. Hourly measurements were used (carried out for 3.25 years in seven communes of the city) to quantify the accumulated sick (AS) population, urban meteorology variables (MVs) (temperature (T), relative humidity (RH), and magnitude of wind speed (WS)), and air pollution (P) (PM10, PM2.5, 0(3)). Time series (TS) were constructed for each commune, which related AS to MVs, called AS/VM, and to P, noted AS/P. Chaos theory was applied to each TS, requiring the following variables: the Lyapunov exponent (lambda > 0), the correlation dimension (D-C < 5), Kolmogorov entropy (S-K > 0), the Hurst exponent (H, such that 0 < H < 1), Lempel-Ziv complexity (LZ > 0), and information loss ( < 0). Every TS complied with chaos theory. For each commune, C-K was calculated as a quotient between the sum of AS/T, AS/WS, and AS/RH entropies and the sum of AS/PM10, AS/PM2.5, and AS/O-3 entropies. The results show that the entropy for the AS/P ratio is lower than that of the AS/VM ratio in three of the seven communes, since between 2020 and early 2022, the population was confined, reducing pollution. The TS of the AS/P ratio is more persistent and complex. The predictability times of the ratios are comparable in four of the seven communes. The TS of the AS/MV ratios shows greater information loss and chaos. According to the calculated C-K values, it is possible to relate it to anomalous diffusion (sub/super-diffusion) and the context that favored the expansion of the pandemic: urban densification, pollution, urban meteorology, population density, etc. Using Fr & eacute;chet heavy-tailed probability, the compatibility of the results with C-K is verified.