Estimating the potential for global dissemination of pandemic pathogens using the global airline network and healthcare development indices

Abstract

Pandemics have the potential to incur significant health and economic impacts, and can reach a large number of countries from their origin within weeks. Early identification and containment of a newly emerged pandemic within the source country is key for minimising global impact. To identify a country's potential to control and contain a pathogen with pandemic potential, we compared the quality of a country's healthcare system against its global airline connectivity. Healthcare development was determined using three multi-factorial indices, while detailed airline passenger data was used to identify the global connectivity of all countries. Proximities of countries to a putative 'Worst Case Scenario' (extreme high-connectivity and low-healthcare development) were calculated. We found a positive relationship between a country's connectivity and healthcare metrics. We also identified countries that potentially pose the greatest risk for pandemic dissemination, notably Dominican Republic, India and Pakistan. China and Mexico, both sources of recent influenza and coronavirus pandemics were also identified as among the highest risk countries. Collectively, lower-middle and upper-middle income countries represented the greatest risk, while high income countries represented the lowest risk. Our analysis represents an alternative approach to identify countries where increased within-country disease surveillance and pandemic preparedness may benefit global health.

Figure 1

Average global connectivity ($\widehat{\mathrm{\varphi }}$) for the period ranging from January 2010 to May 2015, highlighting the global average (with confidence intervals in grey) as well as specific countries of interest.

Figure 2

(A) Distribution of countries according to their average connectivity ($\widehat{\mathrm{\varphi }}$) and healthcare development index (Rand index). Each country is colour-coded according to their World Bank income group. The dashed line is a fitted spline with 95% confidence intervals denoted by the grey area. The annotated WCS point represents the ‘Worst Case Scenario’ (greatest connectivity but worst healthcare index), and grey dotted arcs indicate equivalent distance from the WCS. (B) Proximity measurement to the Worst Case Scenario (WCS) for all countries, derived using the Rand index. Countries are grouped by World Bank income category and ranked within group. Countries close to the WCS score high, while countries further away from the WCS score low. Group mean averages are denoted by coloured horizontal lines.