Global Malaria Connectivity Through Air Travel
Global Malaria Connectivity Through Air Travel
The continuing growth in air travel is playing an important role in the global epidemiology of malaria. Flight routes now connect previously isolated malaria-endemic regions to the rest of the world, and travellers on these routes can carry infections to the opposite side of the world in less than 24 hours. While many endemic areas still remain relatively isolated, the malaria-endemic world is becoming increasingly connected to both malaria-free areas and other endemic regions. The impacts of this can be seen in imported cases, vector invasions and the spread of drug-resistant parasite strains. Here a spatial network analysis approach were presented to demonstrate the connectivity that exists across the malaria endemic world through air travel, and provide quantitative indicators of the risks it results in for malaria movement.
Results highlight 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 high rates of prevalence-scaled flow standing out (Figure 1). The structures of these networks are often not geographically coherent, with historical, economic and cultural ties evident. As new routes continue to be established, these communities will likely change, with new popular travel routes, such as those between China and Africa likely altering global malaria flow routes, and new destinations that might encounter increased risks of imported malaria will emerge (Additional file 4 and Table 1 ). These community maps (Figure 1) and lists of cities by likely import/export of infections (Additional file 4) and hubs for infection flow ( Table 1 ) provide a quantitative picture of how malaria infections are likely moving globally through air travel, and information from which global surveillance strategy design can draw upon. Additional file 4 and Table 1 highlight that certain airports provide significant hubs and gateways for the movement of infections and their entry into countries, and that these are widely distributed across the world. Their role in providing important nodes as both significant through-flow of infections in the network, and entry and exit gateways for cases to/from regions means that they potentially represent valuable sentinel sites for focussed surveillance. Finally, Figure 2 provides a stark reminder of 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. These routes can provide a first-step quantification to support the global plan against artemisinin resistance containment and design of surveillance systems, and should be refined with information on the locations of resistance found. Such data could also inform decisions on where and how to limit the risk of spread, for example by pre-travel or arrival screening and treatment.
A range of limitations and uncertainties exist in the analyses presented here. In terms of the quantification of malaria transmission, the use of static maps of annual average prevalence neglects the seasonality in transmission that is common to many areas, and also the substantial changes in transmission intensity seen in a variety of locations in recent years. In most parts of the world, the densities of Anopheles mosquitoes change seasonally, thus impacting the receptivity of these areas to malaria flows incoming through air travel. While data on changes in Anopheles densities globally are not available, temperature-driven models of malaria transmission suitability could be integrated in future work to better account for this and the seasonally varying topologies of the global malaria connectivity network studied. The demographic and behavioural differences between passengers are not accounted for here. Those taking regular air travel are often richer, and less likely to be infected, while those that are actually infected and showing symptoms may be less likely to travel. Hence, the air travel passenger dataset used here clearly contains some biases when addressing malaria risks. Further, only parasite prevalence was used as a malaria metric, and while this may be an adequate measure of population prevalence at origin locations, it is not so appropriate for assessing the risk of infection acquisition for naïve travellers, and entomological-based indices are likely more appropriate here, as used in more local studies. Finally, the examination of relative artemisinin resistance spread risk focuses simply on all travel from four countries, and thus does not account for any heterogeneity in resistance in the region.
Uncertainties and limitations relating to the travel data used also exist. The modelled passenger flows represent just a 2010 snapshot, and thus routes and changes since then are not captured, while inherent uncertainty due to the modelling process also exists. Moreover, the types of traveller and their activities during travel and their residential location are unknown, each of which contributes to differing malaria infection risks. Finally, overland and shipping travel flows are not considered here, which also contribute to local, regional and global malaria connectivity and flows.
This work forms the basis for future analyses on imported malaria, elimination feasibility and the risks and potential routes of artemisinin resistance spread. Rates and routes of imported malaria have been shown to be significantly related to a combination of numbers of travellers to/from endemic destinations and the prevalence of malaria there. The potential thus exists to construct a model based on global malaria prevalence, the local spatial interaction and accessibility to an airport within a region, transmission models for attack rate estimation, and traveller flow data, that can be used to forecast imported malaria rates, validated with imported malaria data reported by health facilities/organizations.
As nations make progress towards elimination, the importance of human movement and imported cases increases. This work contributes to an on-going initiative, the human mobility mapping project, aimed at better modelling human and disease mobility, and will form one aspect of continued multimodal assessments of malaria movements and assessment of malaria elimination strategies. Finally, the potentially disastrous consequences of the rise and spread of artemisinin resistance requires that detailed and effective planning be implemented in preparation for containing and stemming any spread. A basic assessment here were provided of prevalence-scaled travel from the four Southeast Asian countries where resistance has previously been observed, but significant refinements of these estimates and modelling methods should be undertaken. These may include improved tracking and mapping of observed resistance and human movement patterns in Southeast Asia, as is being undertaken by the TRAC project, as well as scenario modelling of the risks of resistance escape to Africa or Latin America. Further, the incorporation of accessibility and travel data with drug use data (e.g., ), prevalence information and models, all undertaken within a probabilistic modelling framework (e.g., ), could aid in estimation of spread routes should resistance arise elsewhere.
Discussion
The continuing growth in air travel is playing an important role in the global epidemiology of malaria. Flight routes now connect previously isolated malaria-endemic regions to the rest of the world, and travellers on these routes can carry infections to the opposite side of the world in less than 24 hours. While many endemic areas still remain relatively isolated, the malaria-endemic world is becoming increasingly connected to both malaria-free areas and other endemic regions. The impacts of this can be seen in imported cases, vector invasions and the spread of drug-resistant parasite strains. Here a spatial network analysis approach were presented to demonstrate the connectivity that exists across the malaria endemic world through air travel, and provide quantitative indicators of the risks it results in for malaria movement.
Results highlight 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 high rates of prevalence-scaled flow standing out (Figure 1). The structures of these networks are often not geographically coherent, with historical, economic and cultural ties evident. As new routes continue to be established, these communities will likely change, with new popular travel routes, such as those between China and Africa likely altering global malaria flow routes, and new destinations that might encounter increased risks of imported malaria will emerge (Additional file 4 and Table 1 ). These community maps (Figure 1) and lists of cities by likely import/export of infections (Additional file 4) and hubs for infection flow ( Table 1 ) provide a quantitative picture of how malaria infections are likely moving globally through air travel, and information from which global surveillance strategy design can draw upon. Additional file 4 and Table 1 highlight that certain airports provide significant hubs and gateways for the movement of infections and their entry into countries, and that these are widely distributed across the world. Their role in providing important nodes as both significant through-flow of infections in the network, and entry and exit gateways for cases to/from regions means that they potentially represent valuable sentinel sites for focussed surveillance. Finally, Figure 2 provides a stark reminder of 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. These routes can provide a first-step quantification to support the global plan against artemisinin resistance containment and design of surveillance systems, and should be refined with information on the locations of resistance found. Such data could also inform decisions on where and how to limit the risk of spread, for example by pre-travel or arrival screening and treatment.
A range of limitations and uncertainties exist in the analyses presented here. In terms of the quantification of malaria transmission, the use of static maps of annual average prevalence neglects the seasonality in transmission that is common to many areas, and also the substantial changes in transmission intensity seen in a variety of locations in recent years. In most parts of the world, the densities of Anopheles mosquitoes change seasonally, thus impacting the receptivity of these areas to malaria flows incoming through air travel. While data on changes in Anopheles densities globally are not available, temperature-driven models of malaria transmission suitability could be integrated in future work to better account for this and the seasonally varying topologies of the global malaria connectivity network studied. The demographic and behavioural differences between passengers are not accounted for here. Those taking regular air travel are often richer, and less likely to be infected, while those that are actually infected and showing symptoms may be less likely to travel. Hence, the air travel passenger dataset used here clearly contains some biases when addressing malaria risks. Further, only parasite prevalence was used as a malaria metric, and while this may be an adequate measure of population prevalence at origin locations, it is not so appropriate for assessing the risk of infection acquisition for naïve travellers, and entomological-based indices are likely more appropriate here, as used in more local studies. Finally, the examination of relative artemisinin resistance spread risk focuses simply on all travel from four countries, and thus does not account for any heterogeneity in resistance in the region.
Uncertainties and limitations relating to the travel data used also exist. The modelled passenger flows represent just a 2010 snapshot, and thus routes and changes since then are not captured, while inherent uncertainty due to the modelling process also exists. Moreover, the types of traveller and their activities during travel and their residential location are unknown, each of which contributes to differing malaria infection risks. Finally, overland and shipping travel flows are not considered here, which also contribute to local, regional and global malaria connectivity and flows.
This work forms the basis for future analyses on imported malaria, elimination feasibility and the risks and potential routes of artemisinin resistance spread. Rates and routes of imported malaria have been shown to be significantly related to a combination of numbers of travellers to/from endemic destinations and the prevalence of malaria there. The potential thus exists to construct a model based on global malaria prevalence, the local spatial interaction and accessibility to an airport within a region, transmission models for attack rate estimation, and traveller flow data, that can be used to forecast imported malaria rates, validated with imported malaria data reported by health facilities/organizations.
As nations make progress towards elimination, the importance of human movement and imported cases increases. This work contributes to an on-going initiative, the human mobility mapping project, aimed at better modelling human and disease mobility, and will form one aspect of continued multimodal assessments of malaria movements and assessment of malaria elimination strategies. Finally, the potentially disastrous consequences of the rise and spread of artemisinin resistance requires that detailed and effective planning be implemented in preparation for containing and stemming any spread. A basic assessment here were provided of prevalence-scaled travel from the four Southeast Asian countries where resistance has previously been observed, but significant refinements of these estimates and modelling methods should be undertaken. These may include improved tracking and mapping of observed resistance and human movement patterns in Southeast Asia, as is being undertaken by the TRAC project, as well as scenario modelling of the risks of resistance escape to Africa or Latin America. Further, the incorporation of accessibility and travel data with drug use data (e.g., ), prevalence information and models, all undertaken within a probabilistic modelling framework (e.g., ), could aid in estimation of spread routes should resistance arise elsewhere.
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