Recent experience with SARS and MERS-CoV reveals the hurdles of surveying pandemic risk of emerging infectious diseases. Initially, both diseases showed low transmissibility, insufficient to start pandemics. However, concerns were that, after a short circulation period within human populations, the virus might adapt to efficacious inter-human transmission. R0, the number of secondary cases per index case in a disease-naive population, is a sound indicator of the pathogen transmissibility. When R0 > 1, epidemic potential is reached, or else R0 < 1 and disease transmission goes extinct. We propose a new method to detect small temporal changes in R0 using surveillance data. The method is based on Bayesian statistics, consistent with the concepts of surveillance and learning. Furthermore, Bayesian inference is flexible, easily accommodating data of various type and quality. We will use mathematical modeling to construct the likelihood of observing the dataset and extract the dynamic of R0 using Bayesian analysis.
Currently, there exists no definite method to monitor the pandemic risk of an emerging infectious disease using surveillance data. Given recent experience in global health (e.g., SARS and MERS-CoV), the need for such a method can hardly be underestimated. Notably, changes in R0 are not only due to pathogen changes, but also to social changes and public health interventions. If validated, our method could be used for validation of interventions against emerging infectious diseases. Such interventions include: mass vaccination and state of emergency comprising quarantine and traffic restrictions.