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Understanding the factors that influence individuals’ adherence to social distancing is critical to effective policymaking in respiratory pandemics such as COVID-19. This study aims to explore the role of social factors in relation to social distancing measures.

Stratified convenience sampling was used in this survey research, involving 450 adults residing in both rural and urban areas of Aligodarz County, Lorestan, Iran.

The findings showed that approximately 14% of participants did not adhere to any of the assessed social distancing behaviors, while only around 30% adhered to all four assessed behaviors. On average, participants reported having 5.13 (SD = 3.60) close physical contact within the 24 h prior to completing the questionnaire. Bridging social capital and gender emerged as the most frequently observed predictors across the assessed social distancing measures. Additionally, age, employment status and residential setting were identified as influential factors for some of the evaluated measures.

This study contributes to the field by highlighting that the likelihood of non-adherence to social distancing measures tends to increase among male participants, those aged above 60, employed individuals, urban residents and those with a higher level of bridging social capital.

The number of natural and man-made disasters is remarkable and threatened human lives at the time of occurrence and also after that. Therefore, an efficient response following a disaster can eliminate or mitigate the adverse effects. This paper aims to help address those challenges related to humanitarian logistics by considering disaster network design under uncertainty and the management of emergency relief volunteers simultaneously.

In this paper, a robust fuzzy stochastic programming model is proposed for designing a relief commodity supply chain network in a disaster by considering emergency relief volunteers. To demonstrate the practicality of the proposed model, a case study is presented for the 22 districts of Tehran and solved by an exact method.

The results indicate that there are many parameters affecting the design of a relief commodity supply chain network in a disaster, and also many parameters should be controlled so that, the catastrophe is largely prevented and the lives of many people can be saved by sending the relief commodity on time.

This model helps decision-makers and authorities to explore optimal location and allocation decisions without using complex optimization algorithms.

To the best of the authors’ knowledge, employee workforce management models have not received adequate attention despite their role in relief and recovery efforts. Hence, the proposed model focuses on the problem of managing employees and designing a disaster logistics network simultaneously. The robust fuzzy stochastic programming method is applied for the first time for controlling the uncertainties in the design of humanitarian relief supply chains.