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1. Sociocultural determinants of global mask-wearing behavior

   https://doi.org/10.1073/pnas.2213525119  

   Yang, Luojun; Constantino, Sara M.; Grenfell, Bryan T.; Weber, Elke U.; Levin, Simon A.; Vasconcelos, Vítor V. (October 2022, Proceedings of the National Academy of Sciences)

   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. 

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2. Affect mediates culture’s effects on COVID-19 risk perceptions, behavioral intentions, and policy support among americans

   https://doi.org/10.1080/13698575.2024.2429374  

   Johnson, Branden B; Kay, Cameron S (February 2025, Health, Risk & Society)

   The Affect Heuristic-Cultural Cognition Theory (AH-CCT) model and the Solution Aversion-based (SA) model both suggest affect, meaning feelings or discrete emotions about a target, mediates associations between ‘culture,’ such as political ideology or cultural biases, and risk responses, such as risk perceptions, protective behaviours, and supportive attitudes towards protective policy. However, the models differ respectively by defining negative affect as directed towards the hazard (‘hazard affect’) or a specific behaviour or policy response (‘solution aversion,’ negative affect about a proposed risk reduction method). We compare these models with longitudinal mediation analysis of U.S. COVID-19 survey data (n = 866 in smallest-sample wave). Solution aversion accounted for more associations of culture with risk perceptions, such as personal risk, collective risk, and risk severity; behaviour and behavioural intentions, regarding mask wearing, avoiding large public gatherings, and vaccination; and support for risk mitigation policies, regarding mask mandates, public gathering bans, and vaccination mandates. Statistically significant direct effects were rare and were mainly for egalitarian cultural bias; indirect effects occurred for egalitarians, political conservatives, and individualists. Implications for further research on risk responses are discussed relative to limited previous work on these affect-mediation models. 

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3. An Exploration of Methods Using Social Media to Examine Local Attitudes Towards Mask-Wearing During a Pandemic

   Elrod, N.; Mahajan, P.; Katragadda, M.; Halse, S.; Kropczynski, J. (January 2021, Proceedings of the International ISCRAM Conference)

   null (Ed.)

   During the COVID-19 health crisis, local public officials continue to expend considerable energy encouraging citizens to comply with prevention measures in order to reduce the spread of infection. During the pandemic, mask-wearing has been accepted among health officials as a simple preventative measure; however, some local areas have been more likely to comply than others. This paper explores methods to better understand local attitudes towards mask-wearing as a tool for public health officials’ situational awareness when preparing public messaging campaigns. This exploration compares three methods to explore local attitudes: sentiment analysis, n-grams, and hashtags. We also explore hashtag co-occurrence networks as a possible starting point to begin the filtering process. The results show that while sentiment analysis is quick and easy to employ, the results offer little insight into specific local attitudes towards mask-wearing, while examining hashtags and hashtag co-occurrence networks may be used a tool for a more robust understanding of local areas when attempting to gain situational awareness. 

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4. Modeling COVID-19 scenarios for the United States

   https://doi.org/10.1038/s41591-020-1132-9  

   Reiner, Robert et (January 2021, Nature Medicine)

   null (Ed.)

   Abstract We use COVID-19 case and mortality data from 1 February 2020 to 21 September 2020 and a deterministic SEIR (susceptible, exposed, infectious and recovered) compartmental framework to model possible trajectories of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the effects of non-pharmaceutical interventions in the United States at the state level from 22 September 2020 through 28 February 2021. Using this SEIR model, and projections of critical driving covariates (pneumonia seasonality, mobility, testing rates and mask use per capita), we assessed scenarios of social distancing mandates and levels of mask use. Projections of current non-pharmaceutical intervention strategies by state—with social distancing mandates reinstated when a threshold of 8 deaths per million population is exceeded (reference scenario)—suggest that, cumulatively, 511,373 (469,578–578,347) lives could be lost to COVID-19 across the United States by 28 February 2021. We find that achieving universal mask use (95% mask use in public) could be sufficient to ameliorate the worst effects of epidemic resurgences in many states. Universal mask use could save an additional 129,574 (85,284–170,867) lives from September 22, 2020 through the end of February 2021, or an additional 95,814 (60,731–133,077) lives assuming a lesser adoption of mask wearing (85%), when compared to the reference scenario. 

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5. Association mining based approach to analyze COVID-19 response and case growth in the United States

   https://doi.org/10.1038/s41598-021-96912-5  

   Katragadda, Satya; Gottumukkala, Raju; Bhupatiraju, Ravi Teja; Kamal, Azmyin Md.; Raghavan, Vijay; Chu, Henry; Kolluru, Ramesh; Ashkar, Ziad (September 2021, Scientific Reports)

   Abstract Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces a novel data mining-based approach to understand the effects of different non-pharmaceutical interventions in containing the COVID-19 infection rate. We used the association rule mining approach to perform descriptive data mining on publicly available data for 50 states in the United States to understand the similarity and differences among various policies and underlying conditions that led to transitions between different infection growth curve phases. We used a multi-peak logistic growth model to label the different phases of infection growth curve. The common trends in the data were analyzed with respect to lockdowns, face mask mandates, mobility, and infection growth. We observed that face mask mandates combined with mobility reduction through moderate stay-at-home orders were most effective in reducing the number of COVID-19 cases across various states. 

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