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.
Highlights
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 new coronavirus disease (COVID-19) caused by severe acute respiratory syndrome (SARS)-CoV-2 virus infection was identified by the World Health Organization (WHO) as a global pandemic in mid-March 2020 [1]
Several epidemiological models were implemented to predict the projection of the spread of COVID-19 in Saudi Arabia
Summary
The new coronavirus disease (COVID-19) caused by severe acute respiratory syndrome (SARS)-CoV-2 virus infection was identified by the World Health Organization (WHO) as a global pandemic in mid-March 2020 [1]. As of 21 July 2020, this disease has affected more than 15 million people, including approximately 622,000 deaths reported globally, creating a global threat to public health [2]. Patients with COVID-19 can develop mild to severe symptoms following infection; for example, signs of fever, cough, dyspnea, myalgia, and fatigue can arise in patients mildly affected. The virus can lead to severe pneumonia, acute respiratory distress syndrome (ARDS), or multi-organ failure in some patients [4,5]. SARS-CoV-2 has combined with an inflammatory cytokine storm primarily characterized by elevated interleukin 6
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