Sociocultural determinants of global mask-wearing behavior

Abstract

Behavioral responses influence the trajectories of epidemics. During the COVID-19 pandemic, nonpharmaceutical interventions (NPIs) reduced pathogen transmission and mortality worldwide. However, despite the global pandemic threat, there was substantial cross-country variation in the adoption of protective behaviors that is not explained by disease prevalence alone. In particular, many countries show a pattern of slow initial mask adoption followed by sharp transitions to high acceptance rates. These patterns are characteristic of behaviors that depend on social norms or peer influence. We develop a game-theoretic model of mask wearing where the utility of wearing a mask depends on the perceived risk of infection, social norms, and mandates from formal institutions. In this model, increasing pathogen transmission or policy stringency can trigger social tipping points in collective mask wearing. We show that complex social dynamics can emerge from simple individual interactions and that sociocultural variables and local policies are important for recovering cross-country variation in the speed and breadth of mask adoption. These results have implications for public health policy and data collection.

Keywords: epidemics; institutions; public health; risk perceptions; social norms.

Fig. 1.

Global dynamics of mask-wearing behavior during the COVID-19 pandemic and model diagram. (A) Proportion of people who report wearing masks in public from March 2020 to October 2021 by country and region (12). Larger, darker circles indicate more new deaths attributed to COVID-19 (per million, 7-d smoothed) in Singapore, United Kingdom, and Sweden (thickened trajectories) (13). (B) Phase planes showing mask wearing vs. COVID-19 deaths in Singapore, United Kingdom, and Sweden. Darker color indicates later sample time. (C) Diagram of the three key elements of our mask-adoption model. (D) Possible tipping points in collective mask wearing. The dynamics of mask wearing are characterized by two stable equilibria (solid lines) and one unstable equilibrium (dashed line). When a population begins with low rates of mask wearing and experiences a neutral or negative institutional signal, conformity to the predominant “no mask” norm stabilizes that behavior. If there is an increase in the institutional pro–mask-wearing signal, mask wearing will slowly increase up to a critical level (red line, point 1) at which point a small increase in the institutional signal creates a behavioral cascade that results in full mask adoption (point 2). As the institutional signal wanes (point 3), the new mask-wearing norm self-sustains (blue line). A significant negative signal is required for the population to revert to non–mask-wearing equilibria (point 4).

Fig. 2.

Collective dynamics of mask wearing under different social conformity and policy regimes. (A) Simulated epidemic (gray, dashed line) and mask-wearing dynamics under low and high levels of cultural tightness and policy stringency (solid lines). At the start of the epidemic, the policy stringency is set at neutral. As disease prevalence increases and falls, the policy response is introduced on day 100 and ends on day 200 (gray area) at low or high stringency. (B) Cumulative mask wearing following policies introduced at different levels of risk (onset), lasting different amounts of time (duration), and varying in strength (stringency) under low and high levels of social conformity. Color indicates the area under the mask-wearing dynamic curves (AUC) as illustrated in A, with lighter colors representing more mask wearing. In Upper row, onset of the policy is fixed at day 100 and we vary its duration. In Lower row, the duration of the policy is fixed at 100 days and we vary its onset. See Materials and Methods and SI Appendix for numerical implementation of the model and description of parameters.