## 5 Conclusions  
An end-to-end solution utilizing CCTV footage to provide a practical and versatile mechanism for monitoring crowds to identify possible instances of spreading COVID-19 is proposed in this paper. The proposed system detects people, their locations, their individual actions (wearing masks), and their dyadic interactions (handshakes) using deep learning-based video interpretation. This information is stored in temporal graphs to enable further insights such as identifying social groups. Finally, these temporal graphs undergo a more holistic interpretation using rule-based algorithms. This analysis uncovers the individual's contributions to the spread of COVID-19 (not wearing masks) and pairwise contributions (handshakes, staying close to others) on a per frame basis. These results are brought together to calculate the total threat levels in frames. Finally, these outputs are examined against expert human opinion. Consistent accuracies over 75% across all datasets could be considered a strong indication of the robust performance of the proposed system. Furthermore, this unified framework allows for the future incorporation of possible other future measures for curtailing the spread of COVID-19 or any other epidemics impacting the health and safety of society. Therefore, this proposed framework may be strengthened by incorporating additional COVID-19 specific features, and it could be adapted and adopted for similar other scenarios as well that may benefit from video or CCTV-based non-intrusive observations. The proposed system evaluation is limited by the availability of datasets. While it was tested on existing datasets and a newly collected dataset, testing this on a wider range of diverse scenes is required. Moreover, threat level estimation is performed at the frame level. As a result, the contribution of the time of interactions such as approaching close to each other and shaking hands is not taken into account in our system. Future work could improve the system by processing the time series of the threat level (individual contributors such as interactions as well as the total score) by techniques such as moving averages and filtering. Due to the widespread use of CCTV cameras, the proposed system is applicable to a wide variety of real-world scenarios. The decreasing performance-to-cost ratio of computing hardware has enabled even small organizations to acquire the system. The release of the codebase as free and open source software (FOSS) can accelerate both third-party deployment and solution improvement. However, concerns about privacy, bias, and fairness in conducting analytics on people's CCTV footage should be addressed on an individual basis in accordance with the rules and regulations of individual organizations and countries. Conceptualization, G.J., J.H., S.S., R.G., P.E., V.H., J.E. and S.D. ; Data curation, G.J., J.H. and S.S.; Formal analysis, G.J. and S.S.; Funding acquisition, R.G., P.E., V.H., J.E. and S.D. ; Methodology, G.J., J.H. and S.S.; Project administration, R.G., P.E., V.H. and J.E. ; Software, G.J., J.H. and S.S.; Supervision, R.G., P.E., V.H., J.E. and S.D. ; Validation, J.B.S. and H.W. ; Visualization, G.J., J.H., S.S., J.B.S. and H.W. ; Writing--original draft, G.J., J.H. and S.S.; Writing  
Figure 19: Edge case 2 threat level interpretations. System threat evaluation output—Increases, Human expert opinion output—Decreases. review & editing, G.J., J.H., S.S., J.B.S., H.W., R.G., P.E., V.H. and J.E. All authors have read and agreed to the published version of the manuscript. This work is funded by (1) International Development Research Centre (IDRC), Canada through grant number 109586-001 and (2) Lewis Power, Singapore. The APC was funded by IDRC as well. Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study for all stages including dataset creation and system evaluation. The data collected in this study could be requested from J.E. via email ([EMAIL_ADDRESS]). The requests will be handled in a case by case basis.