# Investigating Dynamics of COVID-19 Spread and Containment with Agent-Based Modeling
## 1 Introduction  
COVID-19 has posed a distinct challenge to the world, the likes of which have been rarely witnessed before. It is yet another historical example for the concern that a more connected and intertwined world, has brought us more stability and fragility at the same time [1]. The recent catastrophe of COVID-19, has swept through all continents and most countries [2]. As of the time of writing this manuscript, 171 million cases have been reported, with a death toll of more than three million. In the absence of a vaccine or a definite treatment for the disease, governments and officials have enforced isolation rules to control the increasing number of infected people and take off the burden put on healthcare systems and workers. Understanding the dynamics by which the disease spreads and making the right decisions is vital in combating and containment of this disease. For more than a hundred years, mathematicians and later epidemiologists have put effort into making models that can predict statistical properties of epidemics with focus on key parameters like the \(R_{0}\)[3]. Besides predicting the future state of a pandemic and the number of infections involved, a model to assist decision and policy makers to take the necessary and optimal decisions is necessary. In the case of COVID-19, some governments have adopted epidemiological models and enacted urgent policies based on those models (see for example [4]). Epidemilogical models can be divided into two groups, that of the _deterministic_ and the _stochastic_. There is the classical Kermack Mckendrick model(SIR) [5], SI [6], SEIR [7] which are all classified as deterministic models and their features are discussed by [8]. The other group of epidemic models, the stochastic, mostly draw on the theory of Markov processes [9] which are generally more reliable when dealing with populations of smaller sizes. While providing insight into the threshold nature of epidemics [10], the majority of these traditional models do not incorporate direct contact between individuals as they treat individuals as an aggregate collection. They make demanding unrealistic assumptions such as perfect mixing, usually missing the heterogeneity of agents' behaviors, which could have nonlinear effects (e.g., super spreaders from occupations that frequently travel). They also often fail to address complex spatial and temporal factors such as variable population densities and dynamics. Agent-based modeling approaches allow for a detailed focus on the dynamics of interactions between individuals [11]. They can be used to capture complex social networks and the direct contacts between agents and can overcome the limitations of compartmental and meta-population models [12]. These models have been successfully applied to simulate the spread of 1918 \"Spanish Flu\" epidemic [13; 14; 15], spread of cholera [16], seasonal influenza outbreaks [12], spread of H1N1 [17], and Ebola epidemic [18]. Besides being able to incorporate links contained in individual mobility data, agent-based modeling approaches can explore and investigate the complexities of transmissions of diseases over space and time. They can be integrated with geographic information systems (GIS) and account for the geospatial context of the spread of communicable diseases [19]. The work of [20] is an example of utilizing crowd-sourced geographic information to develop an agent-based model that simulates the aftermath of a catastrophic event. Other studies have also attempted to employ geographic information systems to build spatially explicit agent-based models and simulate and investigate the spread of diseases [21; 22]. Multiple computational studies are being proposed to tackle COVID-19 pandemic. The work of [23] has proposed a coupled SIR with an opinion dynamics epidemic model which evaluates the effect of opinions on severity of an epidemic. Ref. [24] developed an agent-based model that simulates the spread of COVID-19 in Australia and applies several intervention strategies and compares the results.