International Health
Volume 7 Issue 2 March 2015
http://inthealth.oxfordjournals.org/content/current

Special issue: Digital methods in epidemiology
Digital methods in epidemiology can transform disease control
Extract
Modern society has been transformed by the digital revolution through cellular phones for communication, remote sensing of weather and other terrestrial data, cheap and plentiful digital computation and data storage, genomic sequencing and analysis, GPS for geolocation and navigation, and many other marvels. These advances have been concurrent with major changes in the burden, dynamics and distributions of diseases. The burden of disease remains intolerably high in much of the world,1 and current challenges facing epidemiology include reducing the prevalence of both communicable and non-communicable diseases,1 completing the Global Polio Eradication Initiative,2 developing strategies to control and eliminate malaria,2,3 and responding to outbreaks of emerging infectious diseases such as the recent Ebola epidemic.4 In this special issue of International Health, the authors illustrate both the ways in which modern digital methods are already being applied to these current challenges in epidemiology and also the opportunities for even greater impact.

Advancing digital methods in the fight against communicable diseases
Guillaume Chabot-Couturea,*, Vincent Y. Seamanb, Jay Wengerb, Bruno Moonenb and Alan Magillb
Author Affiliations
aInstitute for Disease Modeling, Intellectual Ventures, Bellevue, 98005, USA
bBill & Melinda Gates Foundation, Seattle, 98109, USA
Received December 23, 2014.
Revision received January 23, 2015.
Accepted January 26, 2015.
Abstract
Important advances are being made in the fight against communicable diseases by using new digital tools. While they can be a challenge to deploy at-scale, GPS-enabled smartphones, electronic dashboards and computer models have multiple benefits. They can facilitate program operations, lead to new insights about the disease transmission and support strategic planning. Today, tools such as these are used to vaccinate more children against polio in Nigeria, reduce the malaria burden in Zambia and help predict the spread of the Ebola epidemic in West Africa.

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The promise of reverse vaccinology
Ashley I. Heinson, Christopher H. Woelk* and Marie-Louise Newell
Author Affiliations
Faculty of Medicine, University of Southampton, Southampton, UK
Received October 22, 2014.
Revision received January 6, 2015.
Accepted January 7, 2015.
Abstract
Reverse vaccinology (RV) is a computational approach that aims to identify putative vaccine candidates in the protein coding genome (proteome) of pathogens. RV has primarily been applied to bacterial pathogens to identify proteins that can be formulated into subunit vaccines, which consist of one or more protein antigens. An RV approach based on a filtering method has already been used to construct a subunit vaccine against Neisseria meningitidis serogroup B that is now registered in several countries (Bexsero). Recently, machine learning methods have been used to improve the ability of RV approaches to identify vaccine candidates. Further improvements related to the incorporation of epitope-binding annotation and gene expression data are discussed. In the future, it is envisaged that RV approaches will facilitate rapid vaccine design with less reliance on conventional animal testing and clinical trials in order to curb the threat of antibiotic resistance or newly emerged outbreaks of bacterial origin.

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Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa
Luigi Sedda, Andrew J. Tatem, David W. Morley, Peter M. Atkinson, Nicola A. Wardrop, Carla Pezzulo, Alessandro Sorichetta, Joanna Kuleszo, and David J. Rogers
Int. Health (2015) 7 (2): 99-106 doi:10.1093/inthealth/ihv005
Abstract
Background
Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI.
Methods
In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa.
Results
This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found.
Conclusions
These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality.