In Beijing, the capital of China, routine measles mass vaccination has been in place for decades with high coverage; and since the 2000s, catch-up vaccination programmes have been implemented for migrant workers coming to the city. However, measles epidemics in Beijing persisted. Here, we explored the contributing factors of persistent measles transmission in Beijing using an epidemic model in conjunction with a particle filter. Model inputs included data on birth, death, migration, and vaccination. We formulated a series of hypotheses covering the impact of migrant influx, early waning of maternal immunity, and increased mixing among infants; we compared the plausibility of the hypotheses based on model fit to age-grouped, weekly measles incidence data from January 2005 to December 2014, and out-of-fit prediction during 2015-2019. Our best models showed close agreement with the data, and the out-of-fit prediction generally captured the trend of measles incidence from 2015 to 2019. We found that large influx of migrants with considerably higher susceptibility likely contributed to the persistent measles transmission in Beijing. Our findings suggest that stronger catch-up vaccination programmes for migrants may help eliminate measles transmission in Beijing.
Keywords: catch-up vaccination; mathematical modelling; measles (rubeola); migration; particle filtering.