Behavioural and transmission dynamics interwoven: mechanistic understanding of multiple waves of emerging infectious diseases in modern era
During SARS-1 outbreaks in 2003-04, the first global public health crisis of the century, most cities experienced two waves within a single season, although classical epidemic models normally lead to a typical outbreak curve with a single wave. It was believed that behavioural changes played a role and a mechanistic model was proposed to explain how multiple waves occur when the population behavioural changes in response to different perceived infection and disease risk from the public information about cases, hospital admissions, ICUs usages and mortality. It was later observed that the behavioural changes could be driven by the media, though both media coverage and the population behaviour develop rapid fatigue so social distancing measures and population adherence "switching space" may locate in the region undesirable for avoidance of subsequent, and potentially larger, waves. This calls for "transparent information and effective scientific communication of the infection and disease risk" and demands public health decision take full consideration of public fatigue when implementing and lifting public health interventions. This remains relevant for managing the on-going COVID-19 pandemic, and we expand the existing models to provide a potential methodology to quantify the population "elasticity" for behavioural changes and resilience.
Jianhong Wu is the co-Principal Investigator of Mathematics for Public Health, and an expert in mathematics and data science. As a Distinguished Research Professor, Jianhong Wu’s innovative research and leadership in the modelling of infectious diseases has contributed to increasing recognition of the role of mathematical modelling and data analytics in supporting a rapid response to and optimal recovery from outbreaks of emerging infectious diseases.