Phased repatriation of Lebanese expatriates stranded abroad during coronavirus disease 2019 (COVID-19) pandemic

Practice of non-pharmaceutical interventions against COVID-19 and reduction of the risk of influenza-like illness: a cross-sectional population-based study

Introduction

While the widespread implementation of the non-pharmaceutical interventions was intended to contain the COVID-19 pandemic, such measures could be also effective in limiting the spread of other respiratory infections. This study aimed to examine the association between the implementation of personal protective measures and the occurrence of influenza-like illnesses (ILI) in the general population.

Methods

An online retrospective cross-sectional observational study was conducted in April 2021 to assess cases of ILI among Lebanese adults aged 18 years and above, from all Lebanese governorates during the 2020–2021 flu season. Data were collected using a convenience sampling method. In addition to their socio-demographic information, participants were asked about their frequency of implementing personal protective measures and if they have experienced symptoms of ILI in the previous 6 months. The overall score of the personal protective measures was computed. Multivariable logistic regression was performed to examine the association between participants’ level of adoption of personal protective measures against COVID-19 and the occurrence of ILI.

Results

Among the 1019 Lebanese adults participating in this study, 352 (34.54%) of them reported symptoms of ILI between October 2020 and March 2021. Lebanese adults who wore their facemasks frequently or always were less likely to suffer from symptoms of ILI than others who did not wear the mask (aOR = 0.452, 95% CI = 0.349–0.693, p < 0.001). Similarly, adults who adopt the following protective measures washing hands (aOR = 0.608, 95% CI = 0.524–0.922, p < 0.001), respecting cough etiquette (aOR = 0.763, 95% CI = 0.598–0.918, p < 0.001), disinfecting surface (aOR = 0.892, 95% CI = 0.632–0.911, p = 0.012), avoiding crowded places (aOR = 0.739, 95% CI = 0.688–0.903, p = 0.049), respecting physical distancing (aOR = 0.646, 95% CI = 0.482–0.833, p = 0.031) on a regular basis (frequently/always) were less likely to report symptoms of influenza-like illnesses when compared with those who did not adhere at all to these measures.

Conclusion

Our study highlighted the potential of personal protective measures against COVID-19 in reducing the transmission of respiratory infections such as ILI. Such findings might be invested during influenza season, particularly among groups at high risk of developing severe complications. Exploring trends detected by the national severe acute respiratory infection surveillance system is recommended to confirm the utility of these measures.

A mixed integer linear programming model and a basic variable neighbourhood search algorithm for the repatriation scheduling problem

Commercial flights nearly halted due to the COVID-19 pandemic in the second quarter of 2020. Consequently, several countries have had to schedule repatriation flights to return their citizens stranded in other countries. Flight routes and schedules are known in normal circumstances, and passengers buy seats on these flights; however, the reverse steps happen in repatriation. Passengers express their need to travel, and flights are scheduled to satisfy their requests. The problem behind this flight schedule can be called the repatriation scheduling problem (RSP), in which we need to repatriate citizens from different countries. The objective of the RSP is to return the most vulnerable citizens first. The capacity of available airplanes and quarantine locations limit the number of repatriated citizens. To address this problem, we have developed a mixed-integer linear program (MILP) to model the RSP. Moreover, we suggest a basic variable neighbourhood search (BVNS) algorithm to solve the problem. We test the BVNS algorithm by creating and solving a set of 108 RSP instances and then comparing the BVNS solutions with the exact ones. Despite allocating only 20 s to run the BVNS algorithm compared to eight hours for a commercial exact solver's branch and bound algorithm, the BVNS algorithm could find better results than the lower bounds for 62 instances and similar values for 17 instances.

COVID-19 repatriation programs - Classification and optimization models

Due to the COVID-19 pandemic and the subsequent measures to control the spread of the virus by border closure and suspension of commercial flights, decision-makers in several countries had to deal with one or more forms of repatriation problems, international and domestic. In addressing an international repatriation problem, a country must schedule flights to return its citizens. Typically, the flight schedules of commercial airlines are announced, and passengers buy their seats accordingly. However, in repatriation, the opposite happens: citizens express their wishes to return to their countries, and flights need to be scheduled. Due to several constraints, decision-makers should repatriate the most vulnerable citizens first. In this work, we offer an overview of repatriation problems from an optimization perspective by visiting real-life cases. We compare the Indian and Jordanian repatriation programs to find similarities and differences between the two. We also develop several mixed-integer linear programs (MILPs) to model different repatriation phases and solve respective illustrative examples to demonstrate the use of the MILPs. Last, we solve two stages of a problem that resembles Jordan's actual repatriation program. Decision-makers and researchers may use and extend this summary and the optimization models for any future pandemic that might lead to border closures and new repatriation problems.