Research on the Influence of Information Diffusion on the Transmission of the Novel Coronavirus (COVID-19)

Lin Shanlang; Ma Chao; Lin Ruofei; Huang Junpei; Xu Ruohan; Yuan Aini

Since the beginning of the 21st century, due to the outbreak of SARS, avian influenza, novel H1N1 influenza and Ebola cross the world, the public has been increasingly concerned about the emerging infectious diseases, and the problem of disease transmission has been widely studied (Mao & Yang, 2012) . In general, the spread of an epidemic is considered to be a dynamic process in which the disease passes from one individual to another through contact between individuals on the contact network (kleczkowski et al., 2011) . Disease transmission often occurs in a dynamic social environment, and individual health behavior decision-making is guided by cultural norms, peer behavior and media reports (Kim et al., 2019) . Although vaccination is a major strategy to protect individuals from infection, the development, testing and production of new vaccines often take a long time (stohr & esveld, 2004) . Before getting enough vaccines, the best protection for individuals is to take preventive actions, such as wearing masks, washing hands frequently, taking drugs, avoiding contact with patients, etc. (Centers for Disease Control and prevention, 2008) . The historical experience of SARS tells us that effective national control measures, such as early identification and isolation of SARS cases, tracking and isolation of the contacts, screening of travelers, and raising public awareness of risk, can help to contain the spread of the virus (Ahmad et al., 2009) . As the public gradually realized the importance of personal behavior in preventing the spread of infection, researchers began to explore the mathematical model of disease transmission including personal behavior. These models have been used to guide strategies for disease transmission control (vardavas et al., 2007) and quantify the role of individual protective measures in controlling several outbreaks, including the Ebola virus outbreak in West Africa in 2014 (fast et al., 2007) , the SARS outbreak in Hong Kong in 2003 and the H1N1 outbreak in central Mexico in 2009 Mexico in (springborn et al., 2007 . Saunders et al. (2017) also tested the effectiveness of personal protective measures in preventing the spread of pandemic influenza in humans. Recently, the research on the outbreak of COVID-19 from the perspective of transmission dynamics is also increasing. Sun et al. (2020) evaluated the epidemiological trend of COVID-19 based on the public epidemic data, and studied the outbreak progress in all parts of China. Understanding the impact of the media on the spread of the disease can help improve the prediction of epidemics and identify preventive measures to slow the spread of the disease. Many models also link the disease-related media transmission with the protection function, usually assuming that the influence of media will reduce the effective transmission rate and slow down the spread of diseases. These studies indicate that the impact of media increases with the number of people infected (Sun et al., 2011; , or both with the number and the rate of change (tchuenche & bauch, 2012; Xiao et al., 2015) . When the number of cases is high or the prevalence of diseases increases rapidly, the information diffusion slows down the spread of diseases and creates interesting disease transmission dynamics, such as multi-wave outbreaks . However, it is not clear whether the media function formalization proposed by the model fully reflects the actual influence. The choice of media function dire