Historically, a great many GIS-based analyses of public health data have been based on either points, representing the place of residence of individuals or facility where individuals were treated, or areas, with the latter often derived by aggregating up data on the population at risk by place of residence.\u00a0 With many types of disease, however, particularly infectious diseases and diseases of exposure, patterns of population movement are central to how disease spreads.\u00a0 Not only this, but in the last hundred years, as transport networks have grown, the movement of both populations and pathogens has become increasingly widespread (Tatem, 2014).\u00a0 This raises the question as to whether geospatial analyses of health need to adapt to ever-increasing population mobility.<\/p>\n
Several new forms of data present opportunities to track population movements, particularly either as people use GPS devices, use cell phones, or else interact with social media and make other forms of online transaction.\u00a0 Cell phone data provide a means of tracking population movement because at any given time, each phone handset is registered via a database with the nearest ‘cell’ or mast in a network that provides a mobile phone signal.\u00a0 Such databases can then be used for example to generate counts of the number of handsets registered to a given mast at a given point in time, or potentially even to track handsets as they move in and out of areas endemic for infectious disease.\u00a0 However, such data are challenging to process, both in terms of their sheer size and because such records typically need to be processed in a secure environment for data protection reasons.\u00a0 The FlowMinder Foundation are an example of one group who have pioneered the use of such data.<\/p>\n
Visit the vector-borne disease Airport Importation tool at http:\/\/www.vbd-air.com\/<\/a>. Pick an airport near to where you live using the left-hand ‘find a destination airport’ box.\u00a0 Next, select a disease (e.g. malaria, yellow fever or dengue) and then choose a disease vector.\u00a0 Bear in mind that as this site focuses on vector-borne diseases, for each disease there will be an associated non-human host or vector.\u00a0 In the case of malaria, for example, the vector is the anopheles<\/em> mosquito, the bite of which transfers two parasites, Plasmodium vivax<\/em> and Plasmodium falciparum<\/em>, to humans. Finally, choose a month and decide whether you want to look at direct flights only or one-stop flights where passengers transfer from one flight to another.\u00a0 Press ‘show flights from endemic areas’ to see flights that depart from areas where vectors such as anopheles<\/em> mosquitos are found and arrive in your chosen airport.\u00a0 You can further query the data, for example by identifying the 10 routes that have high capacity (numbers of seats on planes) and where the origin airport shares a similar climate to the destination airport.\u00a0 Try exploring the data further – does the risk of an infected disease vector being imported vary seasonally, for example, and are there\u00a0 different airports that become higher risk at different times of year?<\/p>\n<\/div>\n See the FlowMinder Foundation web site for numerous examples of studies of population mobility and infectious disease transmission, including for example of the West African Ebola outbreak: http:\/\/www.flowminder.org\/practice-areas\/precision-epidemiology<\/a><\/p>\n
\nReferences (Essential reading for this learning object indicated by *)<\/h4>\n