# SIM-D: An Agent-Based Simulator for Modeling Contagion in Population
## 3 Related Work
### Agent-Based Stochastic Models  
Stochastic models are probabilistic models and the transmission of the disease depends on the probability of transmission as well as on the other agents (and their demographics). In an agent-based modeling approach, the infection occurs only by contact with infected individuals within the society [12; 29]. These models give time-varying details about the spread of diseases, such as the number of infected people each day and their locations. This is a realistic approach for observing the spread of diseases and understanding its patterns [30].  
Some of the most popular agent-based epidemic simulation models include EpiFast [31], EpiSims [13] and EpiSimdemics [11; 32; 33]. The models were good at modelling the epidemic in shared and distributed environments. AceMod, an agent-based modelling framework for studying influenza epidemics, was able to analyse the spatiotemporal spread of contagion and influenza spatial synchrony across the population [34]. [PERSON] developed a similar simulation engine and its parallel implementation, but the agents in social networks were not real but surrogates [35].  
Several other models have been developed for epidemic modelling. GLEAMviz is a desktop application that provides a simple, intuitive, and visual way to set up simulations, develop disease models and evaluate simulation results using a variety of maps, charts, and data analysis tools [36]. The dengueMe simulation engine can simulate the following cases. Proposed by [PERSON], the first case study describes a simple dengue transmission model based on ordinary differential equations [37]. The second is an Aegypti population dynamics model, which is also based on ordinary differential equations that were proposed by [PERSON] et al. [38]. The second example includes simulating the application of insecticide in some areas of real urban space (geographic database). The third is an agent-based transmission model based on [PERSON] et al. (2011) [39] and simulated in the same real urban space.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & \(\Delta t_{E}\) & \(\Delta t_{I}\) & \(\Delta t_{R}\) \\ \hline SIR & 0\(\rightarrow\) & 0\(\rightarrow\) & \(\infty\) \\ SIS & 0\(\rightarrow\) & 0\(\rightarrow\) & 0 \\ SI & 0\(\rightarrow\) & \(\infty\) & 1 & N/A \\ \hline \hline \end{tabular}  
* Fixed at Simulation Length.  
\end{table}
Table 2: Shows the dwell times in different disease states for SIR, SIS and SI disease models [21].  
The Spatiotemporal Epidemiological Modeler (STEM) is free software available through the Eclipse Foundation [40]. Originally developed for research, the tool is designed to help scientists and public health officials to create and use spatial and temporal models of emerging infectious diseases. These models can aid in understanding and potentially preventing the spread of diseases. EpiFire [41; 42; 43] is an application programming interface and graphical user interface implemented in C++, which includes a fast and efficient library for generating, analyzing, and manipulating networks. Network-based percolation and chain-binomial simulations of susceptible-infected-recovered disease transmission, as well as traditional non-network simulations, can be performed using EpiFire.