Integrated dataset for air travel and reported Zika virus cases in Colombia (Data and Resources Paper)

TL;DR

This paper presents a comprehensive open-access dataset combining air travel data and Zika virus cases in Colombia, enabling better analysis of disease spread through human mobility during the 2015-2016 outbreak.

Contribution

It provides a novel, geocoded dataset linking air travel volumes with Zika cases and population risk, validated against gravity model predictions, for research on disease transmission.

Findings

Dataset includes over 35 million passengers' travel data.

Validated air travel data with gravity model predictions.

Facilitates research on human mobility and mosquito-borne disease spread.

Abstract

This open-access dataset provides consistent records of air travel volumes between 205 airport catchments in Colombia and the associated number of reported human cases of Zika virus within these catchments during the arbovirus outbreak between October 2015 and September 2016. We associated in this dataset the monthly air travel volumes provided by the Colombian Civil Aviation Authority (AEROCIVIL) with the reported human cases of Zika Virus published by The Pan American Health Organization (PAHO). Our methodology consists of geocoding all the reported airports and identifying the catchment of each airport using the municipalities' boundaries since reported human cases of Zika Virus are available at the municipal level. In addition, we calculated the total population at risk in each airport catchment by combining the total population count in a catchment with the environmental suitability of the Aedes aegypti mosquito, the vector for the Zika virus. We separated the monthly air travel volumes into domestic and international based on the location of the origin airport. The current dataset includes the total air travel volumes of 23,539,364 passengers on domestic flights and 11,592,197 on international ones. We validated our dataset by comparing the monthly aggregated air travel volumes between airport catchments to those predicted by the gravity model. We hope the novel dataset will provide a resource to researchers studying the role of human mobility in the spread of mosquito-borne diseases and modeling disease spread in realistic networks.