American Journal of Engineering and Applied Sciences

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

Guest Editors

Charles Roberto TellesFederal University of Paraná, Brazil
Alexandru Marius AvramUniversity of Arkansas, United States
Dhawal JainHarvard Medical School, United States
Mehmet KanikMassachusetts Institute of Technology, United States
Mehmet SarikayaUniversity of Washington, United States
Manuel Hernández RosalesUniversidad Nacional Autónoma de México, Mexico
Isaac BadilloGaleno Sigma, Mexico
Luisa Di PaolaUniversità Campus Bio-Medico di Roma, Italy
Iqra MustafaInstitute of Microbiology, Pakistan

Important Dates

Manuscript Submission DeadlineFebruary 1, 2022
Review Completed byMarch 20, 2022
Possible Publication DateApril 15, 2022