Web-based GIS: the vector-borne disease airline importation risk (VBD-AIR) tool

International Journal of Health Geographics (Impact Factor: 2.62). 08/2012; 11(1):33. DOI: 10.1186/1476-072X-11-33
Source: PubMed

ABSTRACT Background
Over the past century, the size and complexity of the air travel network has increased dramatically. Nowadays, there are 29.6 million scheduled flights per year and around 2.7 billion passengers are transported annually. The rapid expansion of the network increasingly connects regions of endemic vector-borne disease with the rest of the world, resulting in challenges to health systems worldwide in terms of vector-borne pathogen importation and disease vector invasion events. Here we describe the development of a user-friendly Web-based GIS tool: the Vector-Borne Disease Airline Importation Risk Tool (VBD-AIR), to help better define the roles of airports and airlines in the transmission and spread of vector-borne diseases.

Spatial datasets on modeled global disease and vector distributions, as well as climatic and air network traffic data were assembled. These were combined to derive relative risk metrics via air travel for imported infections, imported vectors and onward transmission, and incorporated into a three-tier server architecture in a Model-View-Controller framework with distributed GIS components. A user-friendly web-portal was built that enables dynamic querying of the spatial databases to provide relevant information.

The VBD-AIR tool constructed enables the user to explore the interrelationships among modeled global distributions of vector-borne infectious diseases (malaria. dengue, yellow fever and chikungunya) and international air service routes to quantify seasonally changing risks of vector and vector-borne disease importation and spread by air travel, forming an evidence base to help plan mitigation strategies. The VBD-AIR tool is available at

VBD-AIR supports a data flow that generates analytical results from disparate but complementary datasets into an organized cartographical presentation on a web map for the assessment of vector-borne disease movements on the air travel network. The framework built provides a flexible and robust informatics infrastructure by separating the modules of functionality through an ontological model for vector-borne disease. The VBD‒AIR tool is designed as an evidence base for visualizing the risks of vector-borne disease by air travel for a wide range of users, including planners and decisions makers based in state and local government, and in particular, those at international and domestic airports tasked with planning for health risks and allocating limited resources.

Download full-text


Available from: Zhuojie Huang, Feb 18, 2014
1 Follower
  • Source
    • "In current years, the number of travel-related CHIKV infections increased in many European countries [73]. Combined assessment of potential virus introduction by using e.g. the VBD-Air tool [74] with climatic zones may form an evidence base for concepts of efficient mitigation strategies. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Chikungunya was, from the European perspective, considered to be a travel-related tropical mosquito-borne disease prior to the first European outbreak in Northern Italy in 2007. This was followed by cases of autochthonous transmission reported in South-eastern France in 2010. Both events occurred after the introduction, establishment and expansion of the Chikungunya-competent and highly invasive disease vector Aedes albopictus (Asian tiger mosquito) in Europe. In order to assess whether these outbreaks are indicative of the beginning of a trend or one-off events, there is a need to further examine the factors driving the potential transmission of Chikungunya in Europe. The climatic suitability, both now and in the future, is an essential starting point for such an analysis. The climatic suitability for Chikungunya outbreaks was determined by using bioclimatic factors that influence both vector and pathogen. Climatic suitability for the European distribution of the vector Aedes albopictus was based upon previous correlative environmental niche models. Climatic risk classes were derived by combining climatic suitability for the vector with known temperature requirements for pathogen transmission, obtained from outbreak regions. In addition, the longest potential intra-annual season for Chikungunya transmission was estimated for regions with expected vector occurrences.In order to analyse spatio-temporal trends for risk exposure and season of transmission in Europe, climate change impacts are projected for three time-frames (2011--2040, 2041--2070 and 2071--2100) and two climate scenarios (A1B and B1) from the Intergovernmental Panel on Climate Change (IPCC). These climatic projections are based on regional climate model COSMO-CLM which builds on the global model ECHAM5. European areas with current and future climatic suitability of Chikungunya transmission are identified. An increase in risk is projected for Western Europe (e.g. France and Benelux-States) in the first half of the 21st century and from mid-century onwards for central parts of Europe (e.g. Germany). Interestingly, the southernmost parts of Europe do not generally provide suitable conditions in these projections. Nevertheless, many Mediterranean regions will persist to be climatically suitable for transmission. Overall, the highest risk of transmission by the end of the 21st century was projected for France, Northern Italy and the Pannonian Basin (East-Central Europe). This general tendency is depicted in both the A1B and B1 climate change scenarios. In order to guide preparedness for further outbreaks, it is crucial to anticipate risk as to identify areas where specific public health measures, such as surveillance and vector control, can be implemented. However, public health practitioners need to be aware that climate is only one factor driving the transmission of vector-borne disease.
    International Journal of Health Geographics 11/2013; 12(1):51. DOI:10.1186/1476-072X-12-51 · 2.62 Impact Factor
  • Source
    • "Recently however, the availability of various HPM data types, high resolution spatially-referenced Plasmodium falciparum and Plasmodium vivax malaria metric data [25,26], mathematical models [27-30] and computational tools have provided an alternative approach to indirectly measure malaria movements [18]. Airline passenger networks and P. falciparum malaria transmission maps been used to model large-scale malaria movements [31,32]. Novel study methods based on mobile phone usage data combined with P. falciparum malaria transmission maps, for example, have begun to tackle HPM and malaria movement dynamics at other scales [19], such as in Zanzibar island and at a national level in Kenya [9,33]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The quantification of parasite movements can provide valuable information for control strategy planning across all transmission intensities. Mobile parasite carrying individuals can instigate transmission in receptive areas, spread drug resistant strains and reduce the effectiveness of control strategies. The identification of mobile demographic groups, their routes of travel and how these movements connect differing transmission zones, potentially enables limited resources for interventions to be efficiently targeted over space, time and populations. National population censuses and household surveys provide individual-level migration, travel, and other data relevant for understanding malaria movement patterns. Together with existing spatially referenced malaria data and mathematical models, network analysis techniques were used to quantify the demographics of human and malaria movement patterns in Kenya, Uganda and Tanzania. Movement networks were developed based on connectivity and magnitudes of flow within each country and compared to assess relative differences between regions and demographic groups. Additional malaria-relevant characteristics, such as short-term travel and bed net use, were also examined. Patterns of human and malaria movements varied between demographic groups, within country regions and between countries. Migration rates were highest in 20--30 year olds in all three countries, but when accounting for malaria prevalence, movements in the 10--20 year age group became more important. Different age and sex groups also exhibited substantial variations in terms of the most likely sources, sinks and routes of migration and malaria movement, as well as risk factors for infection, such as short-term travel and bed net use. Census and survey data, together with spatially referenced malaria data, GIS and network analysis tools, can be valuable for identifying, mapping and quantifying regional connectivities and the mobility of different demographic groups. Demographically-stratified HPM and malaria movement estimates can provide quantitative evidence to inform the design of more efficient intervention and surveillance strategies that are targeted to specific regions and population groups.
    Malaria Journal 11/2013; 12(1):397. DOI:10.1186/1475-2875-12-397 · 3.49 Impact Factor
  • Source
    • "Information on the longitude, latitude, city name and airport code for a total of 1,449 airports which serve cities with more than 100,000 people, and a modelled ‘actual’ traffic flow (i.e. number of passengers travelling between each location and every other, irrespective of stopovers) connectivity list with 644,406 routes amongst these airports were obtained [6,36,37]. This list documented, for each origin and final travel destination, the estimated number of passengers taking this route [36] regarding the hub-and-spoke structure of the air travel network [38]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Air travel has expanded at an unprecedented rate and continues to do so. Its effects have been seen on malaria in rates of imported cases, local outbreaks in non-endemic areas and the global spread of drug resistance. With elimination and global eradication back on the agenda, changing levels and compositions of imported malaria in malaria-free countries, and the threat of artemisinin resistance spreading from Southeast Asia, there is a need to better understand how the modern flow of air passengers connects each Plasmodium falciparum- and Plasmodium vivax-endemic region to the rest of the world. Recently constructed global P. falciparum and P.vivax malaria risk maps, along with data on flight schedules and modelled passenger flows across the air network, were combined to describe and quantify global malaria connectivity through air travel. Network analysis approaches were then utilized to describe and quantify the patterns that exist in passenger flows weighted by malaria prevalence. Finally, the connectivity within and to the Southeast Asia region where the threat of imported artemisinin resistance arising is highest, was examined to highlight risk routes for its spread. The analyses demonstrate the substantial connectivity that now exists between and from malaria-endemic regions through air travel. While the air network provides connections to previously isolated malarious regions, it is clear that great variations exist, with significant regional communities of airports connected by higher rates of flow standing out. The structures of these communities are often not geographically coherent, with historical, economic and cultural ties evident, and variations between P. falciparum and P. vivax clear. Moreover, results highlight how well connected the malaria-endemic areas of Africa are now to Southeast Asia, illustrating the many possible routes that artemisinin-resistant strains could take. The continuing growth in air travel is playing an important role in the global epidemiology of malaria, with the endemic world becoming increasingly connected to both malaria-free areas and other endemic regions. The research presented here provides an initial effort to quantify and analyse the connectivity that exists across the malaria-endemic world through air travel, and provide a basic assessment of the risks it results in for movement of infections.
    Malaria Journal 08/2013; 12(1):269. DOI:10.1186/1475-2875-12-269 · 3.49 Impact Factor
Show more