What we can learn from the exported cases in detecting disease outbreaks - a case study of the COVID-19 epidemic.

ABSTRACT

PURPOSE: Early warning in the travel origins is crucial to prevent disease spreading. When travel origins have delays in reporting disease outbreaks, the exported cases could be used to estimate the epidemic. METHODS: We developed a Bayesian model to jointly estimate the epidemic prevalence and detection delay using the exported cases and their arrival and detection dates. We used simulation studies to discuss potential biases generated by the exported cases. We proposed a hypothesis testing framework to determine the epidemic severity. RESULTS: We applied the method to the early phase of the COVID-19 epidemic of Wuhan, United States, Italy, and Iran and found that the indicators estimated from the exported cases were consistent with the domestic data under certain scenarios. The exported cases could generate various biases if not modeled properly. We presented the required number of exported cases for determining different severity levels of the outbreak. CONCLUSIONS: The exported case data is a good addition to the domestic data but also has its drawbacks. Utilizing the diagnosis resources from all countries, we advocate that countries work collaboratively to strengthen the global infectious disease surveillance system.