Background This study examined why some individuals have not properly performed health prevention behavior during the coronavirus disease 2019 (COVID-19) pandemic. We used data from a community health survey conducted by public health centers in South Korea to identify factors affecting COVID-19 prevention behavior in urban and rural areas. Also, we examined whether individual-level demographic, socio-psychological, and structural variables affected COVID-19 prevention behavior by referring to a model explaining individuals’ health prevention behavior. In particular, the study is significant as not many other measures were suggested besides compliance with personal quarantine rules during the early phase of the pandemic in 2020. We hope that the results of this study will be considered in further analysis of infection preventive behavior and in future health crises. Methods Probability proportional and systematic sampling were used to collect data in 2020 from 229,269 individuals. After exclusion, the valid data from 141,902 adults (86,163 urban and 44,739 rural) were analyzed. We performed t-tests and analyses of variance to ascertain the differences in COVID-19 preventive behaviors according to demographic characteristics, and a post-hoc analysis was conducted using Scheffé’s test. Factors that affected participants’ COVID-19 preventive behaviors were analyzed using multiple regression analyses. Results The variables significantly influencing COVID-19 preventive behaviors in urban areas were age, gender, living with two or more people, educational level, monthly household income, working status, influenza vaccination, daily life stress, and perceived threat. In rural areas, age, gender, living with two or more people, education level, influenza vaccination, daily life stress, perceived threat, and perceived social factors were significantly associated with increased COVID-19 preventive behaviors. Conclusions Several demographic characteristics were associated with urban and rural residents’ COVID-19-related preventive behaviors. A different approach is needed for the two regions in future policy. Future studies should aim to improve the power of the model and include other factors that may be related to COVID-19 preventive behavior.