Campus reopening during the COVID-19: ODE-SIRD model

• Main Authors: Shahirah Atikah, Mohamad Husnin, Norazaliza, Mohd Jamil, Khairul Salleh, Abdul Basit, Noryanti, Muhammad
• Format: Article
• Language: English
• Published: Taylor's University 2023
• Subjects: QA Mathematics; RA Public aspects of medicine
• Online Access: http://umpir.ump.edu.my/id/eprint/38652/1/Campus%20reopening%20during%20the%20COVID-19-%20ODE-SIRD%20model.pdf; http://umpir.ump.edu.my/id/eprint/38652/; https://jestec.taylors.edu.my/Vol%2018%20Issue%203%20June%202023/18_3_21.pdf; https://jestec.taylors.edu.my/Vol%2018%20Issue%203%20June%202023/18_3_21.pdf

The education system went through major transformations and was adversely impacted when all schools and Higher Education Institutions (HEIs) were forced entirely to close due to the country dealing with the coronavirus disease 2019 (Covid-19) pandemic. This is now a phenomenon that significantly concerns people all over the world. Upon campus reopening, the outbreak will occur within the campus community, and the students might get infected. This paper proposed two types of mathematical models based on the Ordinary Differential EquationSusceptible-Infected-Recovered-Dead (ODE-SIRD) framework to study the impact of campus reopening on the dynamic of the outbreak, which are: i) constant epidemiological parameters and ii) time-dependent epidemiological parameters. Other than that, a sensitivity analysis of parameters is carried out to determine the relative influence of the model parameters on disease transmission. We applied this model to observe Covid-19 cases in the selected higher institute in Malaysia. In comparison, the results indicate that the models with timedependent rates better predict the progression of the Covid-19 outbreak. Hence, from this finding, the time-dependent function of epidemiological parameters should be included in a model for the Covid-19 outbreak related to campus reopening. The effect of lockdown time on the number of active cases is also investigated. In conclusion, the results help and improve in making a reasonable prediction about the infection's evolution of the outbreak.