Downgrading disease transmission risk estimates using terminal importations

As emerging and re-emerging infectious arboviruses like dengue, chikungunya, and Zika threaten new populations worldwide, officials scramble to assess local severity and transmissibility, with little to no epidemiological history to draw upon. Indirect estimates of risk from vector habitat suitability maps are prone to great uncertainty, while direct estimates from epidemiological data are only possible after cases accumulate and, given environmental constraints on arbovirus transmission, cannot be widely generalized beyond the focal region. Combining these complementary methods, we use disease importation and transmission data to improve the accuracy and precision of a priori ecological risk estimates. We demonstrate this approach by estimating the spatiotemporal risks of Zika virus transmission throughout Texas, a high-risk region in the southern United States. Our estimates are, on average, 80% lower than published ecological estimates-with only six of 254 Texas counties deemed capable of sustaining a Zika epidemic-and they are consistent with the number of autochthonous cases detected in 2017. Importantly our method provides a framework for model comparison, as our mechanistic understanding of arbovirus transmission continues to improve. Real-time updating of prior risk estimates as importations and outbreaks arise can thereby provide critical, early insight into local transmission risks as emerging arboviruses expand their global reach. Author summary When novel infectious diseases like chikungunya or Zika emerge and threaten global spread, public health officials worldwide must assess the risk for local introductions and outbreaks. These assessments are made in anticipation of local case data, and officials must draw upon historic evidence from similar diseases or locations. Thus, accurate local risk assessments have most often been limited to retrospective analyses and have been unavailable in real-time during an emerging epidemic. Here we present a method that can harness both historic and current data to produce early risk assessments and update projections in real-time. We demonstrate our approach by estimating local transmission risk for Zika throughout the state of Texas. Our findings suggest that the majority of Texas counties face little risk for sustaining a Zika epidemic, and successfully predict the number of locally transmitted cases across the state. Real-time updating of local transmission risk estimates during an emerging epidemic can thus provide actionable, early insight for public health response as emerging arboviruses expand their global reach.