Epidemiological Modeling of COVID-19 in Saudi Arabia: Spread Projection, Awareness, and Impact of Treatment

Abstract

The first case of COVID-19 originated in Wuhan, China, after which it spread across more than 200 countries. By 21 July 2020, the rapid global spread of this disease had led to more than 15 million cases of infection, with a mortality rate of more than 4.0% of the total number of confirmed cases. This study aimed to predict the prevalence of COVID-19 and to investigate the effect of awareness and the impact of treatment in Saudi Arabia. In this paper, COVID-19 data were sourced from the Saudi Ministry of Health, covering the period from 31 March 2020 to 21 July 2020. The spread of COVID-19 was predicted using four different epidemiological models, namely the susceptible–infectious–recovered (SIR), generalized logistic, Richards, and Gompertz models. The assessment of models’ fit was performed and compared using four statistical indices (root-mean-square error (RMSE), R squared (R2), adjusted R2 ( Radj2), and Akaike’s information criterion (AIC)) in order to select the most appropriate model. Modified versions of the SIR model were utilized to assess the influence of awareness and treatment on the prevalence of COVID-19. Based on the statistical indices, the SIR model showed a good fit to reported data compared with the other models (RMSE = 2790.69, R2 = 99.88%, Radj2 = 99.98%, and AIC = 1796.05). The SIR model predicted that the cumulative number of infected cases would reach 359,794 and that the pandemic would end by early September 2020. Additionally, the modified version of the SIR model with social distancing revealed that there would be a reduction in the final cumulative epidemic size by 9.1% and 168.2% if social distancing were applied over the short and long term, respectively. Furthermore, different treatment scenarios were simulated, starting on 8 July 2020, using another modified version of the SIR model. Epidemiological modeling can help to predict the cumulative number of cases of infection and to understand the impact of social distancing and pharmaceutical intervention on the prevalence of COVID-19. The findings from this study can provide valuable information for governmental policymakers trying to control the spread of this pandemic.

COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis

“…Despite the success of network-based models, several published studies on COVID-19 modeling, including those supporting policy decision making, have focused on compartmental models [2][3][4][5][6][7][8][9][10][20][21][22][23]43]. Manzo [16] urged researchers to direct their efforts toward network-based SIR models and to start discussing a large-scale collection of empirical network data to foster such models.…”

“…Several existing contributions modeled the COVID-19 outbreak in Saudi Arabia using different models [20][21][22][23]. For instance, Alboaneen et al [20] predicted that Saudi Arabia would have a maximum total cases of 79,000 using logistic growth and SIR models.…”

“…We aimed to match the model simulations with empirical data and then used the model to evaluate the effectiveness of the control measures employed by the Saudi government, to predict the future dynamics of the disease in Saudi Arabia according to different scenarios, and to predict the percentage of individuals that must be vaccinated to stop the outbreak (when a vaccine becomes available). Modeling the spread of COVID-19 in Saudi Arabia has been discussed in the literature [20][21][22][23]; however, no studies used a network-based model that captured the social and dynamic properties that are intrinsic to Saudi society. Further, control measures, such as school closures, mosque closures, domestic flight shutdowns, and curfews, were not considered.…”

Dynamics of an SIR-Based COVID-19 Model With Linear Incidence Rate, Nonlinear Removal Rate, and Public Awareness

“…Both the infected cases (Figure 2A) and removed cases (Figure 2B) show a reasonable quality of fit. Since the optimization task led to local optima, we were guided by the results reported in [22,30,31] in selecting initial conditions and bounds on the fitted model parameters. The optimum values of model parameters are shown in Table 2.…”

“…The challenge for data collection and fitting becomes more severe when the proposed model is highly structured. The balance between model structure and quality of fit was made clear in a number of studies concerning Saudi Arabia, where simple SIR models were shown to provide better fits to COVID-19 data than more complex network-based models [22,23].…”