Because of the difficulties in timely vaccine production and distribution, control measures to reduce public contact, such as social “physical” distance, self-isolation (SI), self-quarantine, home isolation, and staying at home, were considered, in addition to hygienic measures, to hamper the pandemic influenza transmission. The effectiveness of this strategy largely depends on public compliance with public health containment and intervention measures, and compliance was shown to increase significantly as the understanding of pandemic influenza increased [8].
Sociodemographics and responses to stay-at-home orders
Analysis of the characteristics of individuals regarding their response to preventive measures is important and to directing social assistance to those most in need. Personal behavior during an epidemic depends, in part, on sociodemographics [13]. Our results found an effect of sociodemographic variables on participants’ response to stay-at-home orders, which was significantly higher among elderly individuals and those with a history of chronic illness, and nonresponders were more likely to be married, working, and have low family income. Zhang et al. identified age, gender, marriage, and education as significant variables that affect Chinese willingness to practice SI when experiencing a pandemic risk [13].
The significant difference in working status between responders and nonresponders implies that employment-related constraints may have a major impact on implementing social distancing.
Many researchers have discussed the influence of socioeconomic factors on control measures during epidemics, including gender differences [14], higher age [15], education [16], perception of the epidemic in the community [13], marital status [17], and living with individuals in need of special care who have a high risk of infection [18]. Our findings did not demonstrate gender or education differences between responders and nonresponders. Contrary to our results, women may suffer higher stress and depression levels than men during pandemics, and those phenomena may affect women’s response to control measures. This difference may be related to their different exposures to health risks and greater vulnerability to health risks than men, in addition to women’s disproportionate burden to perform domestic responsibilities [19]. The lower the education level of a group, the less information they may receive about the pandemic, and the lower their likeliness to understand the importance of these measures [16]. We also found that nonresponders were more likely to be married than responders; however, married individuals may enjoy better physical and mental health than non-married individuals, which affects the former’s behavior and favors their commitment to control measures [17].
Commitment to stay-at-home orders
The participants (98%) left home for different reasons, mainly buying essentials and food (86.2%) and going to work (74.8%). Similarly, in the UK, shopping for groceries, including food, was one of the main reasons individuals reported for breaking SI [20]. By contrast, in Saudi Arabia, 46.9% of the population left home to buy necessities, and 12.8% left to go to work or hospital [21]. Health beliefs conceptualized by the health belief model, including susceptibility to COVID-19, perceived severity of the disease, perceived barriers to staying at home, and having the intention to perform the recommended health behavior (stay-at-home), are possible explanations for compliance with stay-at-home orders when experiencing pandemic risk [22].
Understanding how to devise, present, and implement social distancing measures, including staying at home, such that they are acceptable to the public, is important in planning and responding to infectious disease epidemics. In applying the widespread restrictions during the COVID-19 pandemic, challenges have been identified, including the population’s compliance with these measures, how to monitor the health status of individuals who stayed at home or self-isolated/quarantined, how to provide their essential needs, secondary health effects on vulnerable groups, and the consequences of economic losses, social discrimination, boredom and monotony, and uncertainty about the future [23,24,25].
Many participants (81.6%) opposed stay-at-home orders, which intensifies the need to understand how social distancing is perceived in Egypt. In contexts of infectious disease epidemics, strong public support for the control measures has been proven in high-income countries [26,27,28]; however, our results and those in low-income countries show attitudes of noncompliance [29, 30].
Various issues may affect compliance with and engagement in control measures during epidemics, including interpersonal, environmental, academic, and social factors and risk perception [31,32,33]. The diversity of individuals’ concerns and needs should be considered in interventions aiming to increase voluntary compliance with control measures and widespread restrictions. Kpanake et al. discussed the acceptability of community quarantine during the Ebola epidemic in West Africa. They found that for 18% of the participants, quarantine was never acceptable; for 14%, it depended on the level of contagiousness and lethality; and for 36%, their judgment depended on the availability of support services for quarantined individuals [29]. Hong et al. discussed differences in stay-at-home behaviors in China and the USA during peaks of the COVID-19 pandemic: participants in the USA perceived high levels of susceptibility, participants in China perceived high levels of severity, and both perceptions increased the frequency of stay-at-home behaviors [22].
Trust in governmental measures, community resources, and emergency services
Community preparedness and resources, public engagement, and trust in authorities, governmental action, and emergency services are among the other variables affecting response to stay-at-home orders and are vital for successful interventions during a pandemic [34, 35]. The majority of our participants (84.6%) experienced poor trust in governmental measures. This distrust might be explained by their concern about vaccine availability and effectiveness, appropriateness of care provided by health authorities, and governmental support during the early periods of the pandemic. Distrust may impede health initiatives and programs for infectious disease prevention. In the same context, where distrust prevails, trust-building actions, such as defining rights and obligations, prioritizing “the greater good,” and increasing transparency, are prone to failure. Thus, authorities should exhibit managerial preparedness and competence, engage in intense efforts to inform the public of the seriousness of the pandemic threat, market the need for compulsory policies, and seek approval for these policies from independent scientists, professional groups, and opinion leaders [36, 37].
Zhang et al. suggested that trusted leadership and government and the availability of effective emergency services have significant positive impacts on accepting control measures and promotes the willingness to SI [13].
To rebuild the public’s trust, local governments should improve their communication with their constituents, promote relationships, work with formal and informal leaders, and call for commitment from community organizations and leaders. Several factors should be considered to encourage the public to accept widespread restrictions and control measures related to a pandemic and to increase compliance with public health containment measures. First, to increase public awareness regarding pandemic risks, authorities should conduct extensive publicity in various forms (e.g., trusted spokespeople) to educate the public on the pandemic, stress the benefits of compliance, and reiterate the importance of everyday protective measures to raise their awareness when facing a pandemic risk [13, 38]. However, some studies have questioned the role of publicity in solving the problem or reducing the number of infections [39]. Therefore, an important measure is for the government to establish an early warning system to provide correct information on pandemics and to maintain communication with the public. Second, when there is a pandemic risk, to improve public health social norms, the government, civil society, and the media should cooperate in communicating to the public, and the government should formulate relevant regulations to regulate behavior. Third, public supervision, participation, and empowerment should be encouraged. Fourth, endeavors to increase perceived self-efficacy and provide sufficient public training to decrease the difficulty of applying health behaviors should be attempted. Fifth, the government should build trust with its constituents so that the public will make the sacrifices necessary to implement the measures to diminish the transmission of the diseases [40, 41].
Study limitations
This study has some limitations: (1) Making causal inferences was difficult because a cross-sectional design was used. (2) The snowball method is a nonrandom sampling method; thus, the sample in this study is not representative of a district-level population and is not a random sample of the general population. (3) We used a web-based survey, which can lead to selection bias, an underestimation of the current situation, and limited participation of individuals, such as those who are elderly and poor and who are less likely to use technology (e.g., social media) than their younger and wealthier counterparts. (4) In the sample, health-oriented individuals and those highly concerned about the outbreak are possibly overrepresented. (5) The self-reported information may not be entirely accurate (due to recall bias); thus, it should be viewed with caution (social desirability bias). (6) We did not gain insights from investigating public perception, perceived barriers, and possible incentives regarding staying at home. (7) If the pandemic continues, compliance with protective measures may diminish; thus, caution should be used when generalizing the results. (8) Residual confounding caused by unmeasured covariates possibly occurred, and the findings may vary in other populations with different cultural, ethnic, and geographical backgrounds.
