Abstract

Although the global burden of measles has been substantially reduced after the introduction of the measles-mumps-rubella (MMR) vaccine, significant outbreaks continue to affect populations around the world. Social and spatial heterogeneity in incidence or vaccine coverage lead to under-immunised areas, whereby importation of cases can cause large transmission clusters and long-lasting outbreaks. This heterogeneity causes two major challenges: i) Identifying what regions are most vulnerable to outbreaks, and ii) Evaluating the overall risks of measles transmission in a country.

Inference tools can help spot regions at risk from routinely collected surveillance data. We developed the R package o2geosocial, which reconstructs transmission clusters from the onset date, location, age and genotype of cases. The cluster size distribution then highlights areas where importations were most likely to lead to further transmissions. We applied o2geosocial to reconstruct transmission history using local simulated data, and the national database of measles cases reported in the United States between 2001 and 2016.

A country becomes eligible for measles elimination status after transmission is interrupted for three years, along with high national vaccine coverage. Nevertheless, recent major outbreaks in countries that had recently reached the elimination status (eg United Kingdom, Brazil, Greece) illustrate that the indicators of elimination may need reframing. We implemented a time-series frequentist model, using the R package surveillance, to study the impact of recent local levels of incidence and vaccine uptake on the risks of importation, cross-regional transmission and local transmissions. We applied this model using the daily number of cases reported in France between 2009 and 2017.