Mathematical model verifies a correct understanding of epidemic’s severity facilitates disease control
A joint research led by City University of Hong Kong (CityU) has built a mathematical model to explore and analyse the relationship between disease transmission, people’s awareness about the disease and their resulting behaviours, as well as disease information spread by the mass media and opinion leaders. The research may shed some insights on responding to Covid-19 and other similar infectious diseases.
The research team is led by Dr Zhang Qingpeng, Associate Professor of the School of Data Science (SDSC) at CityU. Their findings have been published in the scientific journal Physical Review E, titled “Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission”.
Factors motivating the public to fight against the diseases
Individuals taking adequate measures to fight against the diseases can stop it from spreading. But before the public could develop a self-protection awareness, they have to know about the diseases with correct understanding. However, on the basis of knowing the occurrence of an epidemic, people’s subjective perceptions of the severity of the epidemic would be affected by factors including past experience, information spread by the media and opinion leaders, therefore resulting in different preventive behaviours.
Dr Zhang shared his observation on this, “for example in Hong Kong, people were deeply concerned about Covid-19 in the early stage of its outbreak as they had been stormed by SARS (Severe Acute Respiratory Syndrome) in 2003. Many citizens started wearing masks consciously to protect themselves even before a single case was confirmed in Hong Kong”. He believed that because many citizens have developed self-protection awareness and taken actions like wearing antibacterial masks and using alcohol-based handrubs, Hong Kong has successfully contained the first wave of Covid-19 during the 2020 Chinese New Year.
“On the contrary, people in Europe and the United States did not take it seriously in the early outbreak of Covid-19. They did not wear masks, and even the government did not stock up sufficient resources for fighting the disease,” said Dr Zhang. He pointed out that even later, when the situation has worsened in the United States, many people still did not implement or even oppose the epidemic control measures.
One key factor: perception and judgement of the epidemic’s severity
Some previous studies have used mathematical models to investigate how people’s awareness of diseases affects its outbreak. However, few studies considered the heterogeneity of public responses towards media coverages and opinion leaders’ views. Referencing to other researches, Dr Zhang pointed out that public’s willingness to take self-protection actions (for example wearing masks and keeping social distance) and to share disease-related information with the others would be influenced not only by media coverages, but also by the personal risk perception, which is their subjective judgement about the severity of the disease.
The research team observed that in the ongoing Covid-19 pandemic, there are differences in people’s risk perceptions which resulted in different behaviours in responses to the outbreak. It has driven the research team to conduct this research. Dr Zhang’s team proposed a mathematical model to analyse disease transmission, behaviour change and information diffusion. And it is the first study that considered both the subjective risk perception and the public’s awareness of diseases.
Dr Zhang explained the relationship between disease, behaviour, and information. “Mass media and opinion leaders would spread the information about the disease, including the transmissibility and severity. People who have access to these information would be aware of the disease, and then they would judge their own risk of being infected. Some people would take action to protect themselves, for example, purchasing protective gears like masks. This would subsequently affect disease transmission by changing the actual infection rate.”
Scientific and fair media coverages help in building awareness of fighting the epidemic
Their calculation results showed that if enough number of citizens are informed of the transmissibility and severity of the disease and willing to adopt personal protection actions, the outbreak could be effectively contained. Dr Zhang pointed out that people who are unaware of the disease information usually would not protect themselves as they did not know there is a risk. Individuals who have access to the disease information with a higher subjective risk perception are found to be more actively engaging in self-protection and information sharing.
Dr Zhang believed that opinion leaders’ views and media coverages on the epidemic would raise the public's awareness and influence them in consideration of taking self-protection measures. Therefore, both the opinion leaders and media played important roles in the prevention and control of the epidemic. However, Dr Zhang elaborated that they should be careful while disseminating epidemic information. “Deliberately downplaying the severity of the epidemic may cause the public to let their guard down and lead to more infections. On the other hand, over-exaggeration of the severity of the epidemic may make the public distrust the public health system,” he added.
Moreover, their mathematical model calculations showed that social influence is also an important factor. People’s behaviours would be affected by their friends’ behaviours. In social networks, if opinion leaders have taken appropriate anti-epidemic behaviours, the wider public would also be motivated to take anti-epidemic actions, which could significantly reduce the scale of disease outbreaks.
Dr Zhang is the corresponding author of the paper. The first author is Ye Yang from SDSC at CityU, Dr Zhang’s PhD student. Other collaborating researchers come from Zhejiang University of Technology and Institute of Automation of the Chinese Academy of Sciences.
The study received funding support from the National Natural Science Foundation of China, the Ministry of Science and Technology of China, and the Chinese Academy of Sciences.
DOI number: 10.1103/PhysRevE.102.042314