Predictive Model for Border Control during COVID-19: Experiences from Taiwan and New Zealand

Using data on imported and domestic COVID-19 cases from Taiwan and New Zealand between January and June 2020, we develop a Bayesian random-effects Poisson model to detect cluster infections from imported cases. We find remarkable consistency in the predictive power of the model. An increase in one imported case increased the risk of domestic cases by 9.54% in Taiwan and 10.97% in New Zealand. The Taiwan epidemic curve revealed that imported cases did not lead to a large-scale community-acquired outbreak. In New Zealand, a community-acquired outbreak during 29th March-4th April could have been averted if control actions had been taken one-week earlier prior to the predicted cluster infection between 22nd and 28th March. Our model can be used as an early warning of outbreaks during the initial stage of pandemic or the resurgence of outbreaks after lifting containment measures, such as lockdown orders and border control, during a pandemic.