Application of the metrics on world aviation network

break scenarios and in fact most of the gates are hubs of considerable size. Exceptions arise when the outbreak is initiated at an isolated airport, having access to the WAN via a single connection to a bigger local airport. In this scenario the second airport becomes the major gate for the outbreak without having high flux itself.

In comparison to scope, confluence shows a similar trend, but there are multiple excep-tions starring high scope and low confluence ( e.g. London Heathrow, New York J.F. Kennedy and Los Angeles International) and vice versa (Hartsfield–Jackson Atlanta, Istanbul Atatürk and Chicago O’Hare; see figure 4.5). This is not surprising: on an effective distance tree a node, which has low confluence and a child with high scope, can have similar scope as a node with high confluence and hence high number of leaf node children. Nonetheless, it gives an interesting insight into the structure of the WAN, where a small node can become a gate by linking to a global hub. This case is rarely observed in the global average. An interesting example is Domodedovo Intl. airport (DME), Moscow, which displays second highest average confluence while having low average scope of approximately 1%. This air-port is not base to any major airline but is a destination of several international and especially European airlines. The discrepancy can be observed on a much weaker scale in degree and betweenness as well. To our knowledge the difference can be explained by a high number of low traffic connections. While DME has a lot of children they account for low flux, giving it high confluence and low scope. Istanbul-Atatürk airport shows similar behaviour. Such discrepancies between scope and confluence might hint to specific network properties and are important for further investigation.

4.2 Application of the metrics on world aviation network

Scope of nodenindicates the role of a node in a certain outbreak, its participation in the most probable transmission routes and the population fraction which can be reached through it.

Higher scope means more infectious agents are likely to traverse the node, thus the node takes on a role of a gate into specific region. For this reason, high scope nodes are natural tar-gets for intervention deployment like passenger entry or exit screening. Several papers have evaluated the effectiveness of exit screening with mixed results. While the potential of entry and exit screening to delay disease spread has been acknowledged in multiple publications [36, 27, 71, 13], current effectiveness of its implementations is controversial [89, 49, 59]. It has been emphasised that screening procedures are able to delay, but not prevent the spreading.

It is most effective on early stages of the epidemic when numbers of infected are low [40].

Hence it is advisable to implement this countermeasures as early as possible in parallel with local containment at the outbreak location. This aligns well with the benefits of scope, which can be calculated very early knowing the origin only. Using WAN we investigate exemplary hub nodes and outbreak scenarios to highlight the application of the devised metrics.

In absence of an epidemic we want to characterise the airports to get a general under-standing for the role of a node. As outlined above fully averaged scope is of limited value, but it is hardly possible to interpret the scope of a node for each outbreak location separately, since there are nearly 4000 possible outbreak locations. Thus we define a regional profile of an airport by averaging its scope across each of the 22 geographical regions. The regions are in accordance with the information provided by [58]. These profiles can be used to roughly judge the importance of an airport in case of regional outbreaks when no further information is available. Figure 4.6 shows the profiles of the biggest airport on each continent (A) and subsequent six biggest airports worldwide (B). Multiple depicted airports only play a role in case of an outbreak inside their own region. In this case most of the traffic leaving the region is directed through this node as it takes the role of a local hub. Many airports of this kind

42 Defining the scope: A context specific approach to identifying key airports during a pandemic

are observed in the network in general and in this figure in particular. The most prominent example is Johannesburg O.R. Tambo (JNB). In case of JNB its importance in outbreak sce-narios in the Southern African region is tremendous, with scope reaching about 71 % of the network. Further examples are São Paulo-Guarulhos and Sydney Kingsford Smith, the latter being important for two neighbouring regions. The scope of airports New York J.F.Kennedy (JFK) and Tokyo Narita (NRT) is dominated by only one region, but different than their own:

Caribbean region in case of JFK and Micronesia in case of NRT. Both are small regions which use the respective airport as main transit hub. We can classify airports of this type as spe-cialists, regardless if they are important for their own or other single or few regions. On the other hand there are generalist airports, which play an important role for many regions while never being the dominant transit airport for the whole network. Those airports shouldn’t be neglected in any outbreak and can be safely considered important if no information what-soever is available about the outbreak. Examples of such airports are London-Heathrow (LHR) and Dubai International (DXB). Scope of both airports varies across broader regions, e.g. low scope in case of outbreaks on American continent or higher scope in case of an outbreak in African and multiple Asian regions. Multiple airports lie in between both types:

they behave as a gate for multiple regions, nevertheless the scope for remaining regions is non negligible. Examples of this mixed type are Beijing Capital (PEK) and Los Angeles Intl.

(LAX) airports. PEK shows strong association with its own region being a central hub for China, but also uniformly increased scope for all other regions except Oceania and South- / South-East Asia. The latter combination is not surprising as airports from South-Eastern Asia often act as transit hubs for traffic from Oceania and vice versa. LAX has no single dominant region and multiple locations with scope close to zero. At the same time LAX gains high scope from many locations, mainly in Oceania and America. While clear distinction between those types is only possible in extreme cases, the information provided by the profile gives valuable insight into the role of an airport.

We already mentioned the most obvious types of profiles an airport can exhibit. To explore it further we clustered 100 airports with the highest total passenger flux according to their scope profile. The airports were clustered hierarchically based on mean correlation. Two approaches were employed. The result of the first is shown in 4.7Band is based on profiles as demonstrated in figure 4.6, where the scope values are associated with geographical regions.

To produce the clustering shown in 4.7Athe scope profile of each airportnwar reordered so thatsn(i)<_sn(i+1)whereiis the index of the scope value. The airports were then clustered according to these modified profiles.

While the first approach associated airports by their region of influence, the later is an indication of the specialisation level discussed above. With the latter clustering approach airports are partitioned in two classes, reflecting the extremes mentioned above: generalists and specialists. Generalists are predominant among the 100 biggest airports, accounting for about two thirds of the airports. In a sense this is a disadvantage for airport administration, as generalists need further investigation at every outbreak. Nonetheless it is interesting that the airports are divided into two distinct clusters despite of the boundaries being smooth.

When airports are clustered using full profile information the resulting clusters can be easily tied to regions for which they have the highest importance. To assign a region to the cluster, scope profiles of all airports contributing to the cluster were investigated and the regions were ranked according to scope they grant to the airport. If a region was present in the top 4 of all but one or all airports of the cluster, the cluster was tied to this region.

An exception to this rule is the mixed African cluster (purple): here the top 6 regions were considered, otherwise a classification was not possible. Detailed lists of region association can be found in Appendix 8.2. In several clusters the region of influence and geographical regions of airports overlap, e.g North-American cluster and Central Asian cluster. Here multiple

§4.2 Application of the metrics on world aviation network 43

Figure 4.6: Scope profiles of big airports with respect to 22 regions of the air transportation network. The bars represent average scope of the airport with regard to different outbreak regions.(A) Biggest airports from each of the six continents. Some airports (i.e. Johannesburg O.R. Tambo and Sao Paolo Guarulhos) act as gates in case of an outbreak in the region they belong to, but play minor role when outbreaks happens in other regions. Other airports like Beijing Capital and London Heathrow play an important role for multiple regions. (B) Examples of hub airports displaying different profiles. Los Angeles International plays an important role for outbreaks in all 4 regions of oceania. Frankfurt International is a big hub in terms of passenger flux, but it has a relatively low profile for all regions and is not a gate

for any of the regions.

44 Defining the scope: A context specific approach to identifying key airports during a pandemic

Figure 4.7: Hierarchical clustering of the top 100 airports according to their regional pro-files. Final clusters were established using ’fclust’ algorithm from scipy.cluster package. All branches inside one cluster share the same colour. Branch length shows the distance between the adjacent groups. (A) Clustering of the airports considering full profile information. Node labels are IATA letter codes of the airports and coloured according to the geographical region of the airport. Clusters are coloured according to the regions for which the most airports in-side the cluster act as gates (see main text for more information). Note that the geographical location of the airport is not a determining factor for the assignment to a particular cluster.

Best example is the African cluster (purple), which contains airports located in several geo-graphic regions. Paris Charles de Gaule is inside this cluster and as we showed in previous figures is an important gate in case of an outbreak in Africa. London Heathrow, also shown to be an important gate from Africa, is assigned to a more specific South-African cluster (vi-olet). (B) For this, clustering the regional information of the profiles were stripped and the scope values sorted. Airports were clustered according to the shape of the resulting profile.

All nodes are partitioned into two clusters: generalists (dark grey) and gate (light grey). The gates play very important role for small number of regions. Examples of gate airports are Johannesburg O.R.Tambo, Sao Paolo Guarulhos and New York J.F.Kennedy. Generalist air-ports play a role in case of an outbreak in multiple regions but are never an exclusive gate into a region. Examples of generalist airports are London Heathrow, Paris Charles de Gaule,

Beijing Capital and Dubai International.

§4.2 Application of the metrics on world aviation network 45

smaller airports from the respective regions can be observed. Other clusters have influence in a broader region around their geographical location, e.g. the multi-regional Asian cluster starring airports from western Asia exclusively. In his case all airports combined in this cluster are big hubs and base airports of one or multiple airlines (with the exception of Sabiha Gökçen Intl. Airport, SAW). Many flight connections between Western and Asian regions are routed through airports in this cluster.

The most interesting clusters contain airports from a variety of geographical regions, which share the same region of influence. For example the multi regional African cluster with airports from Europe, USA and Asia. Some historical ties can be seen in this cluster:

two French airports are present in it, accounting for the long history of French colonisation.

Brussels presence in this cluster is also linked to the colonial ties it shares with Congo and Ruanda-Urundi. Other airports may or may not be attributed to history. Without a doubt all airports in this cluster accumulate a high fraction of traffic to and from Africa. This cluster highlights an important feature of infectious disease spreading. Outbreaks in certain geo-graphic regions create very complex spreading patterns involving gates from distinct regions and continents. Such outbreaks inevitably require an international coordinated response to prevent a global outbreak.

Another example is the cluster associated with Oceania, in which two American and one Canadian airports are present. Two of these airports are located on the West Coast and are hubs used for transit flights from Australia and Oceania to multiple American and European destinations. The Oceania cluster also highlights strong association between Oceania and South-East Asia, represented by Changi Airport in Singapore. Another clusters highlighting historical relations are South-African cluster, which includes London-Heathrow, and Central Asia cluster, where the importance of Domodedovo airport, Moscow, may be attributed to the legacy of Soviet Union.

Scope was designed to be context sensitive and applied to different outbreak scenarios where detailed or limited information is available. Figure 4.8 A demonstrates this on real world outbreaks while figure 4.8Boutlines some hypothetical outbreak scenarios. The figure shows worldwide, top European and US American airports according to scope. We chose a subset of outbreak settings spanning different continents and sizes. In the worldwide ranking diverse airports are represented. In regional ranking, on the other hand, multiple airports occur in every scenario. For case import into European region those are Paris Charles de Gaule (CDG), London-Heathrow (LHR) and Amsterdam Schiphol (AMS). In every case either of the first two is being the top airport inside of the European region. The role of LHR and CDG is especially striking in case of the Ebola outbreak, where 50% of the network will most probably be reached through either of those airports. In this scenario the scope of LHR or CDG lies almost one order of magnitude above the scope of the airport ranking third (s_LGW = 0.03). Again we can observe how historical ties influenced the structure of the world air traffic. We can conclude that these three airports, LHR, GDG and AMS, are European top targets for countermeasure deployment if little information is available on the outbreak. They appear among the worldwide top 10 and are predominant gates in the European region. All of these airports are generalists with high capacity, thus their presence it the worldwide ranking of many scenario is not surprising.

A similar situation arises in the US ranking where New York J. F. Kennedy (JFK), Los An-geles Intl. (LAX) and Atlanta Heartsfield-Jackson (ATL) are present in every scenario with JFK ranking highest in many settings. JFK is present in the worldwide top 10 and is the biggest gate in case of a hypothetical outbreak in the Caribbean region. Similar to the situ-ation with European gate airports, JFK, LAX and ATL can be valid targets for interventions when little information is available. Further, it is noteworthy that in two scenarios where either a European or American airport is taking on the role of the global gate the respective

46 Defining the scope: A context specific approach to identifying key airports during a pandemic

Figure 4.8: Application of scope on exemplary outbreak scenarios. Bars show the top airports with respect to scope in case of a real outbreak (A) and an outbreak in a geographic region (B). The top row shows top ten airports on the global scale, middle row - top 7 airports inside Europe, bottom row - top 7 airports inside the US. The outbreak region is shown on the map (red color). Bars are coloured according to the geographical location of the airport on the x-axis, labels are IATA letter codes of the airports. Different outbreak regions lead to distinct scope distributions with different airports being the main gate for the outbreak. At the same time the figure highlights that inside of a region there are important hubs which act as gates in a variety of scenarios, e.g. Paris Charles de Gaule and London Heathrow in Europe. In case of Ebola their role is especially prominent while there are no obvious gates among US airports. Reverse is true for an outbreak in the Caribbean region. Using proposed metrics countermeasures like passenger screening can be deployed in the gate airports, which lie on

the most probable route from the outbreak location into entire regions.

§4.2 Application of the metrics on world aviation network 47

other region exhibits rather low scope overall.

Worldwide ranking is showing a diverse picture for every scenario. Airports from mul-tiple geographical regions, which are often different from the outbreak region, are starring in the ranking. Those airports act as gates into their own and other regions, distributing the disease worldwide. In case of ZIKA outbreak, the top gate is located inside the affected region, while the second largest gate is in the unaffected part of the USA. Multiple European airports are also represented in the worldwide top 10. In case of the MERS outbreak the top 5 gates lie inside the region affected by the disease. Those airports facilitate a big amount of traffic and act as worldwide traffic hubs. Hence it is not surprising that they take on the role of gates in this scenario. The dominant gate in this case is Dubai Intl. giving access to 49%

of the networks population. This airport also enters the picture in context of Yellow Fever endemic region, side by side with multiple African airports. In the hypothetical scenario of an outbreak in Eastern Asia, PEK is the dominant gate, followed by multiple airports from the same region. While PEK accounts for only 20% of the scope, there is still a 10% gap in scope between it and the second ranking airport. In this scenario LAX and CDG are also present among the worldwide top 5.

Figure 4.8 demonstrates that there are some recurring patterns in the outbreaks and re-spective rankings. Some hubs always rank high when compared to other regional airports, often being distinct dominant gates. On the other hand when viewed globally, a dominant gate is difficult to predict using geographical information only. It often lies inside the affected region, but it is not guaranteed to, as can be seen in case of an outbreak in the Caribbean.

Until now we have concentrated on outbreak scenarios and characterised the role of nodes in context of the outbreak. Using scope it is possible to switch perspectives and focus on a node as a target rather than on a single outbreak origin. This is an important point when advising administration of an airport on matters of pandemic response with no regard to a specific pandemic. The aim of this application of scope is to raise awareness about locations relevant for the respective airport to facilitate a faster response in case of an outbreak. Again,

Until now we have concentrated on outbreak scenarios and characterised the role of nodes in context of the outbreak. Using scope it is possible to switch perspectives and focus on a node as a target rather than on a single outbreak origin. This is an important point when advising administration of an airport on matters of pandemic response with no regard to a specific pandemic. The aim of this application of scope is to raise awareness about locations relevant for the respective airport to facilitate a faster response in case of an outbreak. Again,