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Using projected values estimated from nonlinear regression with the Gompertz distribution assumption, we can compare the effects of policy changes by examining differences in predicate values and how they compare to the data. Figure 6 shows the infection curves of the total number of actual cases, the total number of cases predicted from the regression run with the data to the lock and beyond. While the forecast with data from March 6, 2020 to May 30, 2020 totaled more than 120,000 cases at the end of September, the same model, when run with more recent data, as shown in Figure 7 from March 6, 2020 to January 16, 2021, predicts nearly 400,000 cases in Saudi Arabia. Therefore, we find that the easing of the lockdown resulted in higher infection – which was demonstrated by the SIR and Gompertz models. In addition, the Gompertz model also predicts that the peak will be shifted (see graphs in appendix). Containment and other strict measures are taken to suppress infection, but they cannot be considered long-term measures. The closure of economic activities has a significant impact on the economy, and this figure can reach 40% of GDP. Containment and its effects on infections before and after containment measures have been studied in many recent publications [12-15] using various modelling frameworks with some modifications to the Susceptible-Infected-Recovered (SIR) model. In addition, some literature has also used econometric techniques to estimate the impact of easing or enforcing containment, such as Ibrahim et al and Ajide [16,17]. On the other hand, a handful of literature has observed the economic costs of confinement [18]. For example, Harvant et al, 2020 show that although there are negative shocks to GDP due to the lockdown, the impact will be different across sectors. In addition, the literature also has an impact on containment in other sectors such as air quality [19,20].

While lockdowns can help suppress infections and save lives, it also wreaks havoc on the economy, both of which should be factored into decision-making. We used open access data from the Our World in Data online database, a project of the Global Change Data Lab [25]. For analysis, we used data from 6 March 2020 to 16 January 2021. While data was used to collect pre-lockdown forecasts from March 6, 2020 to May 30, 2020, post-lockdown forecast data was used until January 16, 2021 with shutdowns on July 1, 2020, August 5, 2020 and August 16, 2020. January 2021. To understand the spread of the virus and the impact of the different guidelines adopted by Saudi Arabia, we used two types of modelling: the SIR model and the time series model. The SIR model is a compartmental model of infectious diseases commonly used to predict and understand COVID-19 and was developed by Kermack and McKendrick [26]. It basically solves a number of differential equations: (1) (2) (3), where β represents the rate of transmission and γ represents the rate of cure or death in infected people, and R + S + I = N, where N is the population that is thought to be constant. In this study, we compared two predictions of COVID-19 cases in the Kingdom of Saudi Arabia (Saudi Arabia) using data before and after the easing of the lockdown period to provide insight into rational exit strategies. We also applied these projections to understand the economic costs versus health benefits of containment measures. We used two models: CRS and Gompertz for two datasets. This study is the first of its kind in Saudi Arabia to compare forecasts of the COVID-19 outbreak between the period before and after the easing of lockdown measures.

This study is important because it shows how policy changes such as easing lockdowns can affect the spread of the disease. Several studies have used the CRS model to make predictions about the evolution of COVID-19 in different countries and to assess the impact of containment and prevention measures [21,22]. Using the SIR model in Saudi Arabia, one study predicted the peak of the epidemic in Saudi Arabia on May 1, with the stable phase beginning on June 2 and the final phase on June 24, 2020 [23]. The use of the SEIR model is also evident to understand the impact of containment [24]. However, their prediction was made before the easing of restrictions measures. In this article, we compared several predictions with the SIR model – one before the easing of lockdown measures and the others after the easing of lockdowns. This research could provide useful information on the best time and strategy to withdraw from similar future measures to curb infectious disease outbreaks in order to return to safe normal life with minimal loss of life and savings. Although this study mainly compared the number of infections before and after the easing of containment measures in Saudi Arabia, we also examined the economic costs and benefits of such an intervention based on the available evidence. Second, on the basis of the cost-benefit analysis, few recommendations were made. Lessons from Saudi Arabia could also be transferred to other countries to pursue an effective exit strategy from lockdowns and other strict restriction measures. In addition, Figure 5 also shows how preaching and actual cases changed dramatically after 14 days of easing lockdown and restriction measures.

The figure shows that actual and predicted cases (with data from March 6, 2020 to August 5, 2020) changed dramatically, while predicted and actual values remained paired until May 30. Quote: Shimul SN, Alradie-Mohamed A, Kabir R, Al-Mohaimeed A, Mahmud I (2021) Effect of easing lockdowns and restrictions on the projection of the COVID-19 outbreak: a case study from Saudi Arabia. PLoS ONE 16(9): e0256958. doi.org/10.1371/journal.pone.0256958 The Ministry of Health has ruled out the possibility of imposing stricter containment measures if the mutated variant of the Omicron coronavirus spreads, according to ministry spokesman Dr. Muhammad Al-Abdel Ali. We estimated R before and after the lifting of containment and restriction measures. Since COVID-19 has a maximum incubation period of 14 days, we considered these periods to be transition periods. When Saudi Arabia lifted the lockdown, the number of infections dropped, so R was expected to be even lower. If these are the cases that can be seen from the differences between the expected and actual value of R and the infected cases.