Based on human behavior, immune system functions, and statistical analysis, this paper presents a novel hypothesis for a model for airborne virus transmission. The envisioned model aims to explain the statistical behavior of COVID-19, but also fits to other respiratory viruses. Since it is not possible to do direct experiments with human population and viruses, statistical evidence is used. The 2020 pandemic offered the opportunity to gather plenty of data in an infinity of test conditions that allow to eliminate variables and takes to useful and unexpected conclusions. Unprecedent social experiments, such as lockdown and face masks, never used massively before are some of those conditions. It was found strong correlations between the evolution of 2020 epidemic and other years outbreaks in the countries with the bigger number of cases in the world, performing 45% of COVID-19 in the world in the time of the study. It was also found a strong correlation, in those countries, with human behavior driven by climate conditions and the spread of COVID-19. It is proposed that the value of Rt is an indicator of the predominant contamination mechanism: direct contact or airborne. Finally, it is presented that the proposed transmission model is fully compatible with SIR model, and it is expected that future works could obtain more precise forecasts of viruses’ outbreaks.