Air Passenger Travel and International Surveillance Data Predict Spatiotemporal Variation in Measles Importations to the United States

Marya L Poterek; Moritz U G Kraemer; Alexander Watts; Kamran Khan; T Alex Perkins

doi:10.3390/pathogens10020155

Air Passenger Travel and International Surveillance Data Predict Spatiotemporal Variation in Measles Importations to the United States

Pathogens. 2021 Feb 3;10(2):155. doi: 10.3390/pathogens10020155.

Authors

Marya L Poterek¹, Moritz U G Kraemer², Alexander Watts^{3

4}, Kamran Khan^{3

4

5}, T Alex Perkins¹

Affiliations

¹ Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA.
² Department of Zoology, University of Oxford, Oxford OX1 3SY, UK.
³ Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON M5B 1T8, Canada.
⁴ BlueDot, Toronto, ON M5J 1A7, Canada.
⁵ Department of Medicine, University of Toronto, Toronto, ON M5S 3H2, Canada.

Abstract

Measles incidence in the United States has grown dramatically, as vaccination rates are declining and transmission internationally is on the rise. Because imported cases are necessary drivers of outbreaks in non-endemic settings, predicting measles outbreaks in the US depends on predicting imported cases. To assess the predictability of imported measles cases, we performed a regression of imported measles cases in the US against an inflow variable that combines air travel data with international measles surveillance data. To understand the contribution of each data type to these predictions, we repeated the regression analysis with alternative versions of the inflow variable that replaced each data type with averaged values and with versions of the inflow variable that used modeled inputs. We assessed the performance of these regression models using correlation, coverage probability, and area under the curve statistics, including with resampling and cross-validation. Our regression model had good predictive ability with respect to the presence or absence of imported cases in a given state in a given year (area under the curve of the receiver operating characteristic curve (AUC) = 0.78) and the magnitude of imported cases (Pearson correlation = 0.84). By comparing alternative versions of the inflow variable averaging over different inputs, we found that both air travel data and international surveillance data contribute to the model's ability to predict numbers of imported cases and individually contribute to its ability to predict the presence or absence of imported cases. Predicted sources of imported measles cases varied considerably across years and US states, depending on which countries had high measles activity in a given year. Our results emphasize the importance of the relationship between global connectedness and the spread of measles. This study provides a framework for predicting and understanding imported case dynamics that could inform future studies and outbreak prevention efforts.

Keywords: air travel; global health; importation; measles.