Can transmission of infections air borne viruses be controlled?

Several respiratory diseases infectious diseases [such as SARS (2003), H1N1 (2009), and COVID (2019)] follow phenological pathways with potential of recurrence under certain climatic and socio-demographical landscapes. Our ability to evaluate and compare different behavioral interventions that may limit the transmission dynamics will be immensely valuable for mitigation of public health disasters. However, there are limited scenario based models available can assimilate a complex but adaptive system of virus-human interaction and can offer insights to a range of possible future adaptations that will decrease disease burden. We will show how a hybrid agent-based modeling (ABM) strategy was developed to simulate human behavior with socio-demographical and climatic processes using multitude of simple rulesets. Agent-based approaches model individuals with distinct characteristics and provide a realistic result when compared to classical mathematical models which consider a homogeneous populations. ABM developed and presented here is based on distinct individual profile of each agent acting on relevant scenarios to simulate the spread of disease for a population center. The study aims to explore the predictive simulative behavior of ABM's for various social and climatic factors to determine their adequacy for reliable disease outbreak predictions for diseases such as coronavirus (COVID-19). Various behavioral interventions by the policy makers and individuals were taken into account and found to be effective understanding the impacts of spread of coronavirus.