Global Mapping of Infectious Disease

Philosophical Transactions of the Royal Society B, 04 February 2013

Simon I. Hay, Katherine E. Battle, David M. Pigott, David L. Smith, Catherine L. Moyes, Samir Bhatt, John S. Brownstein, Nigel Collier, Monica F. Myers, Dylan B. George, and Peter W. Gething

“The primary aim of this review was to evaluate the state of knowledge of the geographical distribution of all infectious diseases of clinical significance to humans. A systematic review was conducted to enumerate cartographic progress, with respect to the data available for mapping and the methods currently applied. The results helped define the minimum information requirements for mapping infectious disease occurrence, and a quantitative framework for assessing the mapping opportunities for all infectious diseases. This revealed that of 355 infectious diseases identified, 174 (49%) have a strong rationale for mapping and of these only 7 (4%) had been comprehensively mapped.

A schematic overview of a niche/occurrence mapping process (for example boosted regression trees (BRT)) that uses pseudo-absence data guided by expert opinion. Consensus based definitive extent layers of infectious disease occurrence at the national level (a) are combined with accurately geo-positioned occurrence (presence) locations (b) to generate pseudo-absence data (c). The presence (b) and pseudo-absence data (c) are then used in the BRT analyses, alongside a suite of environmental covariates (d ) to predict the probability of occurrence of the target disease (e).

A schematic overview of a niche/occurrence mapping process (for example boosted regression trees (BRT)) that uses pseudo-absence data guided by expert opinion. Consensus based definitive extent layers of infectious disease occurrence at the national level (a) are combined with accurately geo-positioned occurrence (presence) locations (b) to generate pseudo-absence data (c). The presence (b) and pseudo-absence data (c) are then used in the BRT analyses, alongside a suite of environmental covariates (d ) to predict the probability of occurrence of the target disease (e).

“A variety of ambitions, such as the quantification of the global burden of infectious disease, international biosurveillance, assessing the likelihood of infectious disease outbreaks and exploring the propensity for infectious disease evolution and emergence, are limited by these omissions. An overview of the factors hindering progress in disease cartography is provided. It is argued that rapid improvement in the landscape of infectious diseases mapping can be made by embracing non-conventional data sources, automation of geo-positioning and mapping procedures enabled by machine learning and information technology, respectively, in addition to harnessing labour of the volunteer ‘cognitive surplus’ through crowdsourcing.”