# SIM-D: An Agent-Based Simulator for Modeling Contagion in Population
## 8 Conclusions and Future Works  
The recent wave of COVID-19 has shown that epidemic diseases could spread very quickly and incur huge human and financial losses. The healthcare systems across the globe were not prepared for its fast spread. The isolation or lockdown measures enforced by different countries were not very effective either, as they were not backed by proper simulation and modeling tools. Therefore, it is very important to have an epidemic modeling tool that will help before the epidemic starts and during epidemics get insights into the spread of infectious diseases.  
Our simulation tool SIM-D tries to fill this gap by simulating the spread of disease in population. SIM-D is an agent-based model that computes interactions between agents and shows infections at locations over simulation time-steps. It effectively models the realistic behavioral phenomena of persons in a society; this means that the transmission of infectious disease could only occur if an infectious person meets with another susceptible person.  
SIM-D is very helpful for the policy-makers and health authorities. It is generic and can simulate the infectious diseases belonging to SIR, SIS and SI disease models. Additionally, it has the capability to show the effect of non-pharmaceutical interventions on the spread of disease.  
As part of future work, we would like to extend the functionality of SIM-D to all contagions, such as the spread of fear, information, habits and rumors. We would also like to develop parallel algorithms of SIM-D to increase its performance capacity to simulate larger data-sets and more complex interventions. An addition that covers mutations of diseases (viruses) will be a valuable addition and we plan to do that as well. A post simulation analysis on the statistics (i.e., age-groups and localities effected) would give us more insights and should be considered as a future work. Furthermore, the current version does not support the pharmaceutical interventions and it would be a good addition in future work. A GUI interface to the software will make it easier to use for the end-users, helping them perform quick analysis without understanding the complexities of the system.  
In future, we would like to use more personal demographics like age, gender, social status, literacy, economic condition etc. to model the person in a better way. Equations that compute the individual and collective effects of these demographic will help in further improving the SIM-D model.  
**Author Contributions:** Conceptualization, M.W., T.K. and A.K.; methodology, M.W., T.K., T.-W.U. and A.K.; software, M.W., T.K. and Z.U.Z.; validation, T.K., Z.U.Z., T.-W.U. and A.K.; formal analysis, T.K., T.-W.U. and A.K.; investigation, M.W., Z.U.Z. and T.K.; resources, T.K. and T.-W.U.; data curation, M.W. and T.K.; writing--original draft preparation, M.W. and T.K.; writing--review and editing, M.W., T.K. and T.-W.U.; visualization, M.W., T.-W.U. and T.K.; project administration, T.-W.U.; and funding acquisition, T.-W.U. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00833).  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.