Prompt understanding of the temporal and spatial patterns of the COVID-19 pandemic on a national level is a critical step for the timely allocation of surveillance resources. Therefore, this study explored the temporal and spatiotemporal dynamics of the COVID-19 pandemic in Kuwait using daily confirmed case data collected between the 23 February and 07 May 2020. Methods: The pandemic progression was quantified using the time-dependent reproductive number (R (t) ). The spatiotemporal scan statistic model was used to identify local clustering events. Variability in transmission dynamics was accounted for within and between two socioeconomic classes: citizensresidents and migrant workers. Results: The pandemic size in Kuwait continues to grow (R (t) s 2), indicating significant ongoing spread. Significant spreading and clustering events were detected among migrant workers, due to their densely populated areas and poor living conditions. However, the government's aggressive intervention measures have substantially lowered pandemic growth in migrant worker areas. However, at a later stage of the study period, active spreading and clustering events among both socioeconomic classes were found. Conclusions: This study provided deeper insights into the epidemiology of COVID-19 in Kuwait and provided an important platform for rapid guidance of decisions related to intervention activities.

Background: Demographic and clinical features of COVID-19 patients are critical components in shaping their symptomatic status. However, the relationship between patients' symptomatic status and their features are typically complicated and nonlinear.Methods: We explored important features that drive the symptomatic status of COVID-19 patients and reveal their interactions with other relevant factors. We used an extensive multi-algorithm machine learning (ML) pipeline and 68 demographic and clinical features to fit a predictive model to 3,995 patients in the State of Kuwait between February and June 2020. Our ML pipeline comprised five algorithms, including logistic regression (LR), random forest (RF), support vector machine (SVM), gradient boosting (GBM), and extreme gradient boosting (XGM).Results: SVM outperformed all algorithms (AUC = 0.77 and accuracy = 70.01%), while logistic regression had the lowest predictive power (AUC = 0.65 and accuracy = 66.14%). Our ML model identified C-reactive, respiratory rate, transmission dynamics, and other demographics as the most important predictors of COVID-19 symptomatic patients. While, only demographic features were important predictors for asymptomatic patients. However, our ML model further revealed that the non-linear relationships between impaired renal function, other clinical biomarkers and demographic features were critical in shaping the risk of being symptomatic patient. Conclusions: We demonstrated remarkable predictive performance of our ML model over traditional statistical methods in identifying important clinical and demographic features of symptomatic vs. asymptomatic. Further application of our ML pipeline in the COVID-19 case definition and guiding pharmaceutical and none-pharmaceutical interventions will help reduce the public health and economic implications of this devastating virus on local and global scales.