Mathematical Modeling and COVID-19 Seasonality

Observing time series data of daily new cases of COVID-19 worldwide, the epidemics birth and death persistence present different outcomes for each country of observation, with many delays and fluctuations for the outbreaks, peak and control phases. These data with distinct outcomes among countries originate false phenomenon observations to predictive analysis based on SIR (Susceptible, Infectious, Recovered) models and its derivations. This is due since these models rely upon defined types of health policies interventions, pre-assumed human behavior and predefined spatial or temporal analysis. To overcome these limitations of SIR model equation and in order to identify an endemic stability of the disease, it is mandatory to elucidate the mixture of variables that sustain an indeterminate pattern of growing or reduction rates among countries.

We invite researches from interdisciplinary fields to contribute with this special issue, covering the topics, but not limited to:

1. Epidemiological models
2. SIR model limitations
3. COVID-19 mathematical modeling
4. Nonlinearity
5. Bifurcation and Stability
6. COVID-19 seasonality patterns
7. Environmental driven forces
8. Floquet Theory
9. Fourier transforms
10. Pattern formation