Predictors of adherence to public health behaviors for fighting COVID-19 derived from longitudinal data

Journal article

The present paper examines longitudinally how subjective perceptions about COVID-19, one's community, and the government predict adherence to public health measures to reduce the spread of the virus. Using an international survey (N = 3040), we test how infection risk perception, trust in the governmental response and communications about COVID-19, conspiracy beliefs, social norms on distancing, tightness of culture, and community punishment predict various containment-related attitudes and behavior. Autoregressive analyses indicate that, at the personal level, personal hygiene behavior was predicted by personal infection risk perception. At social level, social distancing behaviors such as abstaining from face-to-face contact were predicted by perceived social norms. Support for behavioral mandates was predicted by confidence in the government and cultural tightness, whereas support for anti-lockdown protests was predicted by (lower) perceived clarity of communication about the virus. Results are discussed in light of policy implications and creating effective interventions

Authors: Schumpe, BM; Van Lissa, CJ; Bélanger, JJ; Ruggeri, K; Mierau, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Scientific Reports
• Volume: 12
• Issue: 1
• Pages: 3824-3824
• Article number: 3824
• Publication date: 2022-03-09
• DOI: 10.1038/s41598-021-04703-9
• EISSN: 2045-2322
• ISSN: 2045-2322
• Language: English
• Keywords: Betacoronavirus; Pathology; Data science; Nursing; Data mining; Pandemic; Disease; Biology; Coronavirus disease 2019 (COVID-19); MEDLINE; Medicine; Longitudinal study; Internal medicine; 2019-20 coronavirus outbreak; Virology; Outbreak; Infectious disease (medical specialty); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Public health; Longitudinal data; Computer science