Measuring the impact of suspending Umrah, a global mass gathering in Saudi Arabia on the COVID-19 pandemic.

Since the early days of the coronavirus (COVID-19) outbreak in Wuhan, China, Saudi Arabia started to implement several preventative measures starting with the imposition of travel restrictions to and from China. Due to the rapid spread of COVID-19, and with the first confirmed case in Saudi Arabia in March 2019, more strict measures, such as international travel restriction, and suspension or cancellation of major events, social gatherings, prayers at mosques, and sports competitions, were employed. These non-pharmaceutical interventions aim to reduce the extent of the epidemic due to the implications of international travel and mass gatherings on the increase in the number of new cases locally and globally. Since this ongoing outbreak is the first of its kind in the modern world, the impact of suspending mass gatherings on the outbreak is unknown and difficult to measure. We use a stratified SEIR epidemic model to evaluate the impact of Umrah, a global Muslim pilgrimage to Mecca, on the spread of the COVID-19 pandemic during the month of Ramadan, the peak of the Umrah season. The analyses shown in the paper provide insights into the effects of global mass gatherings such as Hajj and Umrah on the progression of the COVID-19 pandemic locally and globally.

Evaluation of Heuristics for the P-Median Problem: Scale and Spatial Demand Distribution.

The objective of the p-median problem is to identify p source locations and map them to n destinations while minimizing the average distance between destinations and corresponding sources. Several heuristic algorithms have been developed to solve this general class of facility location problems. In this study, we add to the current literature in two ways: (1) we present a thorough evaluation of existing classic heuristics and (2) we investigate the effect of spatial distribution of destination locations, and the number of sources and destinations on the performance of these algorithms for varying problem sizes using synthetic and real datasets. The performance of these algorithms is evaluated using the objective function value, time taken to achieve the solution, and the stability of the solution. The sensitivity of existing algorithms to the spatial distribution of destinations and scale of the problem with respect to the three metrics is analyzed in the paper. The utility of the study is demonstrated by evaluating these algorithms to select the locations of ad-hoc clinics that need to be set up for resource distribution during a bio-emergency. We demonstrate that interchange algorithms achieve good quality solutions with respect to both the execution time and cost function values, and they are more stable for clustered distributions.