Abstract
Pandemic diseases are fought through efficient intervention procedures including non-pharmaceutical interventions such as social distancing, and closure of events, schools and workplaces. These procedures are ruled by different decisions-makers in local communities, and national authorities. Different procedures are conducted based on the community affected by the pandemic disease to reduce the severity in various social life. During the Coronavirus (COVID-19) pandemic most of universities worldwide closed their campuses and continue the study through online learning. To open back the campuses, universities have to analyze and investigate such decision impact on the outbreaks. This paper develops a simulation model of COVID-19 investigating realistic intervention procedures that can be conducted to control and mitigate outbreaks. The model is constructed to simulate Effat University environment. Our model predicts average COVID-19 attack rates for various intervention scenarios, such as partial closure and social distancing, cleaning and disinfection, COVID-19 symptoms monitoring system are combined with isolate and treat confirmed COVID-19 cases. The findings indicate that combining all the proposed interventions can be substantially more effective than isolate and treat confirmed COVID-19 cases alone from epidemiological standpoint.
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References
S.H. Ebrahim et al., Covid-19 and Community Mitigation Strategies in a Pandemic (2020)
D. Fisher, A. Wilder-Smith, The global community needs to swiftly ramp up the response to contain COVID-19. The Lancet 395(10230), 1109–1110 (2020)
V.J. Lee, C.J. Chiew, W.X. Khong, Interrupting transmission of COVID-19: lessons from containment efforts in Singapore. J. Travel Med. 27.3, taaa039 (2020)
S. Yezli, A. Khan, COVID-19 social distancing in the Kingdom of Saudi Arabia: bold measures in the face of political, economic, social and religious challenges. Travel Med. Infect. Dis., 101692 (2020)
A. Niwaz, Q.W. Ahmed, S. Kamran, An exploration of issues and challenges faced by students in distance learning environment. Glob. Soc. Sci. Rev. (GSSR) IV(IV), 77–83 (2019)
Saudi Ministry of Health (2020), https://covid19.moh.gov.sa/ [21 May 2020]
A. Wilder-Smith, D.O. Freedman, Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. J. Travel Med. 27.2, taaa020 (2020)
J.T. Wu, K. Leung, G.M. Leung, Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet 395(10225), 689–697 (2020)
C. Goldbaum, Subway service is cut by a quarter because of coronavirus. The New York Times 24 (2020), https://www.nytimes.com/2020/03/24/nyregion/coronavirusnyc-mta-cuts-.html?searchResultPosition=6 [10 Mar 2020]
N. Ferguson et al., Report 9: Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID19 Mortality and Healthcare Demand (2020)
L.K. Gee, M.J. Schreck, A. Singh, From lab to field: social distance and charitable giving in teams. Econ. Lett., 109128 (2020)
A. Tomar, N. Gupta, Prediction for the spread of COVID-19 in India and effectiveness of preventive measures.Sci. Total Environ., 138762 (2020)
Z. Hu et al., Evaluation and prediction of the COVID-19 variations at different input population and quarantine strategies, a case study in Guangdong province, China. Int. J. Infect. Dis. (2020)
L. Wang et al., Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm. Sci. Total Environ., 138394 (2020)
S. Gupta, G.S. Raghuwanshi, A. Chanda, Effect of weather on COVID-19 spread in the US: a prediction model for India in 2020. Sci. Total Environ., 138860 (2020)
E. Beretta, Y. Takeuchi, Global stability of an SIR epidemic model with time delays. J. Math. Biol. 33(3), 250–260 (1995)
Saudi Center of Disease prevention and control, Coronavirus disease 19 (COVID-19) Guidelines V1.1, Saudi Ministry of Health, February 2020. Available at https://www.moh.gov.sa/CCC/healthp/regulations/Documents/Coronavirus%20Disease%202019%20Guidelines%20v1.1.pdf [13 May 2020]
World Health Organization, Coronavirus Disease 2019 (COVID-19): Situation Report, 80 (2020). https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports [21 May 2020]
I.M. Longini Jr. et al., Containing pandemic influenza with antiviral agents. Am. J. Epidemiol. 159.7, 623–633 (2004)
Y. Ling et al.,Persistence and clearance of viral RNA in 2019 novel coronavirus disease rehabilitation patients. Chin. Med. J. (2020)
K.K.-W. To et al., Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study. The Lancet Inf. Dis. (2020)
G. Viceconte, N. Petrosillo, COVID-19 R0: Magic number or conundrum? Inf. Dis. Rep. 12.1 (2020)
A. Grifoni et al., Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals. Cell (2020)
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Balfagih, Z., Balfaqih, M. (2021). Simulating and Epidemic Prediction of COVID-19 Transmission in Universities Considering Different Interventions. In: Visvizi, A., Lytras, M.D., Aljohani, N.R. (eds) Research and Innovation Forum 2020. RIIFORUM 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-62066-0_43
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DOI: https://doi.org/10.1007/978-3-030-62066-0_43
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