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Mapping infectious disease in real time

By Amy VanderZanden, special to Humanosphere

Imagine how useful it would be if you could look at a world map and know the exact risk of catching an infectious disease in a country you were planning to visit – and see it update in real time. Consider the potential value of a population’s mobile phone use patterns to forecast how communities will behave following a large-scale disaster.

These are the sorts of opportunities that Simon Hay thinks about as he works to expand the possibilities of infectious disease mapping with his research team at SEEG – the Spatial Ecology and Epidemiology Group.

Hay is a Professor of Epidemiology at Oxford University, where much of his recent work focuses on accurately defining human populations at risk for infectious diseases such as malaria and dengue fever. He investigates the spatial and temporal patterns of these diseases in order to improve the evidence base of disease control and intervention strategies – and then he works to convince global bodies such as the World Health Organization to adopt his findings.

Hay was in Seattle this week and stopped by the Institute for Health Metrics and Evaluation to speak to a standing-room-only crowd about mapping the global distribution of infectious disease. He argued that in the age of Google, Facebook, and Twitter – and their potential to provide “crowd-sourced” data – there is enormous opportunity to develop real-time spatial disease maps.

Global evidence, consensus, risk and burden for dengue, Nature, Apr 2103, Hay et al.

Global evidence, consensus, risk and burden for dengue, Nature, Apr 2103, Hay et al.

Notes: Source: “The global distribution and burden of dengue.”

There are systems in place already that map infectious disease risk, including Hay’s probabilistic map for dengue, above. Hay’s mapping process involves iterating through data on disease occurrence, environmental trends, and “consensus” (where there is agreement, or absence of agreement, that occurrences of dengue fever have taken place). Combining all of these data to produce risk estimates means that Hay can estimate disease burden as well – at a much finer spatial scale than previously.

Other existing maps continually update based on reports of infectious disease occurrence, such as HealthMap, a real-time informal disease outbreak monitoring source. However, what Hay is developing is an automated, dynamic mapping system that combines data on disease risk and disease occurrence that would be freely available to policymakers, health professionals, and interested citizens alike.

This resource, known as ABRAID (the Atlas of Baseline Risk Assessment for Infectious Disease), is currently under development. The pilot “case studies” involve mapping malaria, dengue, and polio. The atlas will be continuously updated as new data are collected, using a broad range of data sources including PubMed and other academic information centers, in combination with input from disease experts and crowd-sourcing resources – including social media networks.

Perhaps sometime soon we will be able to check out an interactive disease risk map just as easily as we can verify an on-time flight departure to some exotic locale.

Simon Hay’s talk was recorded live and can be watched here.

 Amy VanderZandenAmy VanderZanden is a communications data specialist at the University of Washington’s Institute for Health Metrics and Evaluation (IHME). 


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