Background In this research, the factors that influence the self-precautionary behavior during the pandemic are explored with the combination of social support and a risk perception attitude framework. Methods An online survey was conducted among 429 members to collect information on demographic data, social support, perceptions of outbreak risk, health self-efficacy, and self-precautionary behaviors with the guide of the Social Support Scale, the COVID-19 Risk Perception Scale, the Health Self-Efficacy Scale and the Self-precautionary Behavior Scale. Results The research shows that among the three dimensions of social support, both objective support and support utilization negatively predict risk perception, while subjective support positively predicts health self-efficacy; health self-efficacy and risk perception significantly predict self-precautionary behavior; the relationship between risk perception and self-precautionary behavior is significantly moderated by health self-efficacy. Conclusions The combined influence of social capital and risk perception attitudinal frameworks on self-precautionary behavior is highlighted in this study, with the relationship between the public’s risk perception, health self-efficacy, and self-precautionary behavior intentions examined against the background of coronavirus disease 2019 (COVID-19). These findings contribute to understanding the impact of social capital factors on risk perception and health self-efficacy, which provides insight into the current status and influencing factors of the public’s precautionary behavior and facilitates early intervention during a pandemic.

Background With the accelerated global integration and the impact of climatic, ecological and social environmental changes, China will continue to face the challenge of the outbreak and spread of emerging infectious diseases and traditional ones. This study aims to explore the spatial and temporal evolutionary characteristics of the incidence of Class B notifiable infectious diseases in China from 2007 to 2020, and to forecast the trend of it as well. Hopefully, it will provide a reference for the formulation of infectious disease prevention and control strategies. Methods Data on the incidence rates of Class B notifiable infectious diseases in 31 provinces, municipalities and autonomous regions of China from 2007 to 2020 were collected for the prediction of the spatio-temporal evolution and spatial correlation as well as the incidence of Class B notifiable infectious diseases in China based on global spatial autocorrelation and Autoregressive Integrated Moving Average (ARIMA). Results From 2007 to 2020, the national incidence rate of Class B notifiable infectious diseases (from 272.37 per 100,000 in 2007 to 190.35 per 100,000 in 2020) decreases year by year, and the spatial distribution shows an “east-central-west” stepwise increase. From 2007 to 2020, the spatial clustering of the incidence of Class B notifiable infectious diseases is significant and increasing year by year (Moran’s I index values range from 0.189 to 0.332, p < 0.05). The forecasted incidence rates of Class B notifiable infectious diseases nationwide from 2021 to 2024 (205.26/100,000, 199.95/100,000, 194.74/100,000 and 189.62/100,000) as well as the forecasted values for most regions show a downward trend, with only some regions (Guangdong, Hunan, Hainan, Tibet, Guangxi and Guizhou) showing an increasing trend year by year. Conclusions The current study found that since there were significant regional disparities in the prevention and control of infectious diseases in China between 2007 and 2020, the reduction of the incidence of Class B notifiable infectious diseases requires the joint efforts of the surrounding provinces. Besides, special attention should be paid to provinces with an increasing trend in the incidence of Class B notifiable infectious diseases to prevent the re-emergence of certain traditional infectious diseases in a particular province or even the whole country, as well as the outbreak and spread of emerging infectious diseases.