# Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models
## 3 Results
### Prediction of the Spread of COVID-19 in Kuwait after the Repatriation of Citizens back to Kuwait  
Kuwait will implement a repatriation plan for bringing back 40,000 citizens who are located in various countries starting from 19 April 2020 until 7 May 2020. Because some of these citizens are located within epicenters of coronavirus, the government of Kuwait is planning to conduct strict measures for preventing the spread from infected travelers. Nevertheless, it will be a challenging phase due to the asymptomatic nature of COVID-19. The government of Kuwait, therefore, has arranged quarantine centers for these citizens at various locations to guarantee the containment of the spread. Each person will be provided with a smart wristband and a smart mobile application that was developed by the ministry of health for tracking patients and monitoring the social distancing measures [21]. The smart application serves as a hotline for offering consultation with the medical staff in the case of visible symptoms among them. In this section, the SIR model is utilized to predict the spread of COVID-19 infections in Kuwait after the start repatriation period as a measure to estimate the epidemic size and end phase. The purpose of these simulation scenarios is to predict the epidemic peak associated with the repatriating plan of Kuwait to assess the risk and act appropriately with required prevention and containment measures. The conducted simulation scenarios were based on varying the reproduction rate \(R_{o}\) between 2.2 to 2.3 as follows:  
* \(R_{o}=2.2\) To reflect the current situation with the partial curfew and social distancing measures. * \(R_{o}=2.3\) To reflect the arrival of citizens from COVID-19 epicenters, higher probability of infected persons, with the partial curfew and social distancing measures. The simulation results of the SIR model with the predefined scenarios are shown in Figures 5 and 6 to illustrate the prediction of the cumulative infections and the rate of infection peaks respectively. The simulation parameters of each scenario are provided in Table 6. In Figure 5, the highest predicted cumulative cases are estimated to be 6023 with \(R_{o}=2.3\) along with an estimated start of the ending phase to occur at 23 July 2020. In addition, the infection and recovery rates are \(\beta=0.397\) and \(\gamma=0.173\) respectively. Figure 6 illustrates the impact of varying the reproduction number on controlling the spread and peak of the infection. It is evident that the increase of the reproduction number would delay the estimated ending phase by approximately seven weeks, with an estimated increase in the epidemic size of 29.57%. This increase could be attributed to the fact that the citizens are repatriating from epicenters with higher reproduction number and could be asymptomatic and thus may spread the infection to the susceptible population. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & \(R_{o}\)**=2.2 & \(R_{o}\)**=2.5 \\ \hline Infection rate \(\beta\) & 0.38 & 0.43 \\ \hline Recovery rate \(\gamma\) & 0.173 & 0.173 \\ \hline Infectious period per infected person & 5.8 days & 5.8 days \\ \hline Estimated epidemic size (cumulative cases) & 4244 & 17684 \\ \hline Estimated peak date & 23-4-2020 & 9-5-2020 \\ \hline Estimated peak day cases & 124 cases & 363 cases \\ \hline Estimated start of ending phase date & 28-5-2020 & 20-8-2020 \\ \hline Root mean square error & 50.2 & 52.76 \\ \hline R\({}^{2}\) & 0.993 & 0.996 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Susceptible–infected–recovered (SIR) simulation results with \(R_{o}=2.2\) and \(R_{o}=2.5\). Figure 5: Predicted cumulative infections with various reproduction numbers of \(R_{o}=2.2\) and \(R_{o}=2.3\). Figure 6: Predicted infection rates with various reproduction number of \(R_{o}=2.2\) and \(R_{o}=2.3\)  
Prediction of the Spread of COVID-19 in Kuwait with Time-Varying Numbers of Susceptible and Infected Individuals  
There is an associated amount of uncertainty in modeling the spread of COVID-19 [22].