resilience modeling method of airport network affected by

Research ArticleResilience Modeling Method of Airport Network Affected byGlobal Public Health Events

Jiuxia Guo 12 Xinping Zhu2 Chenxi Liu3 and ShuzhiSam Ge1

1School of Computer Science and Engineering Center for Robotics University of Electronic Science and Technology of ChinaChengdu China2Civil Aviation Flight University of China Guanghan 618307 Sichuan China3University of Washington Seattle 98195 WA USA

Correspondence should be addressed to Jiuxia Guo didiyes163com

Received 21 October 2020 Accepted 16 December 2020 Published 4 February 2021

Academic Editor Luigi Rodino

Copyright copy 2021 Jiuxia Guo et al (is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

(e COVID-19 global pandemic hit the aviation industry hard since the end of 2019 It has had an immediate dramatic impact onairport traffic and revenue Airports are the important nodes in the aviation system network and the failure of a single airport canoften affect the surrounding airports (e purpose of our analysis is to show how is the resilience and recovery of airports in theglobal public health crisis Much research on resilience can be found in air transportation networks facing natural hazards orextreme weather which focus on the robustness of the airport network(ese methods are not suitable for the global public healthevent (erefore based on the collection of existing data we combined with existing resilience measurement methods to analyzethe resilience and recovery of airports during the global public health crisis (e resilience metrics results reflect the recovery ofairports very well under different strategies Here we analyze airport network resilience by considering the performance-basedmethods We integrate some metrics such as aircraft movements passenger throughput and freight throughput in the resiliencemetrics model comparing the resilience evaluation under different preventive and control strategies which can reflect theairportrsquos recovery speed during the COVID-19 pandemic Our analysis indicates that the aviation system network deterioratessoon after the COVID-19 outbreak but the recovery level of the aviation industry depends on what measures are taken to preventand control the COVID-19 epidemic In particular the recovery of the aviation system network in Europe takes longer than inChina due to different prevention and control strategies for COVID-19 (e study proves that the emergency response ability ofthe country for the public health crisis has a significant positive impact on speeding up the recovery of the aviation system

1 Introduction

(e novel coronavirus disease (COVID-19) was the SARS-coronavirus-2 that emerged in 2019 However there are noclinically approved vaccines or specific therapeutic drugsavailable for COVID-19 at present [1] Considering theperson-to-person transmission of COVID-19 HubeiProvince was placed under lockdown approximately threeweeks after the start of the COVID-19 outbreak [2] (ensocial distancing mandatory quarantine city lockdown anda series of control methods are implemented in all regions ofChina [3] and finally the number of infected cases goesdown in May 2020 However as the time of writing (30thSept 2020) the COVID-19 has caused 1007769 global death

and 33642602 confirmed cases according to global COVID-19 outbreak statistics maintained by the Center for SystemScience and Engineering (CSS) at Johns Hopkins University[4] from the data of COVID-19 in the world the speed inwhich countries respond to pandemics directly affects thedevelopment trend of the epidemic (e research studiesprove that the countries implemented rapid governmentinterventions and strict public health measures for quar-antine and isolation and the spread of infection was suc-cessfully halted and prevented it exploding exponentially [5]Besides many governments around the world have imple-mented the active prevention and control actions such associal distancing mobility constraints pro-active testingand isolation of detected cases [6] China has gradually

HindawiMathematical Problems in EngineeringVolume 2021 Article ID 6622031 13 pageshttpsdoiorg10115520216622031

resumed domestic flights and built a relatively complete setof antiepidemic monitor system to prevent imported casessuch as the health QR code system which is proved effective[7 8]

Air traffic is characterized by high safety and securityconcerns coupled with airlines airports and air trafficcontrol departments (e aviation system network consistsof nodes (airports) with strict regulations and interdepen-dencies between vehicles and journeys and links (flights)with high degrees of freedom concerning flight path andspeed [9] Airports are an integral part of the aviationecosystem and currently face a crisis in which sudden andprolonged collapse occurs in traffic levels due to publichealth events As the public health event was a disturbancehow to deal with the impact on the aviation system after thecrisis so that system can restore resilience to reduce the lossof the aviation system

With the global public health outbreak the resilience ofthe system in several industries is considered Resilience isthe intrinsic ability of a system to adjust its functioning priorto during or following changes and disturbances In thepast the research on aviation safety management mainlyfocused on system failures or catastrophic accidents becausethese events have large economic and safety impacts Andthe disturbances of the system focus on hardware softwarehuman and environment For humans as the key elementsof aviation there are more studies about human errors [10](erefore Hollnagel et al [11] introduced the resilienceconcept into the safety science domain in 2006 ResilienceEngineering is an important tool and method of systemdevelopment and system safety in many industries such asthe ecosystem and transportation system [12 13] Intransportation networks research on resilience mainly facesnatural hazards or extreme weather [14 15] (e qualitativeresilience assessment approaches include conceptualframeworks and semiquantitative indices [16] Researchabout the resilience of the air transport system mainly fo-cuses on air traffic management (ATM) system [17 18] AndEurope had the Sharing of Authority in FailureEmergencyCondition for Resilience of Air traffic Management(SAFECORAM) project whose purpose was eventually todeal with the reallocation of tasks between residual resourcesof the system after a disturbance to minimize the system lossof global performance (ere are twelve study referencescenarios in the resilience 2050 project of Europe which area potential hazard of the ATM system such as Taxiwayincursion during take-off taxiing Big airport closure due tosnow and activation of a temporary segregated area due tonatural disaster [19]

However most research studies [20ndash22] have dealt withmodeling and estimating the resilience of an air transportnetwork effect by various disruptive events they are onlyconsidered the impact of an airport network after the in-terruption of some airport affected by the disruption and didnot consider how global disruptive events affect the globalairport network Few studies have quantified and forecastthe aviation system performance parameters such as ca-pacity costbenefit and environment under multiple pan-demic scenarios and the method proposed in the paper is

desperately needed To the date studies focused on theaviation network impacts of the COVID-19 pandemic arethe interactive dashboards developed by the InternationalCivil Aviation Organization (ICAO) and the COVID-19pandemic impact on the European air traffic network Fromthe perspective of safety management we have a newcognitive of resilience engineering (erefore global publichealth is a disturbance of the aviation system how to dealwith it so that the system can quickly recover is the firstconsidered issue of aviation Besides ICAO proposed elevenKey Performance Areas (KPAs) of aviation [23] and manyresearch studies focus on capacity safety environment andefficiency in pre-COVID-19 In the paper based on theexisting resilience metric measures methods we develop amodel of the airport network resilience following the col-lectible data of airport operation in different countrieswhich obtains the results of the rapid emergency response ofvarious national airports facing sudden global public healthevents

(e main contributions of this study are summarized asfollows First this study is studying the global public healthimpact on the airport network resilience and recoverySecond this study developed a resilience metric measuremodel based on existing resilience measures to apply to theglobal public health crisis By comparing the production dataof Chinarsquos four regional airports during the first half of 2020the resilience index of the eastern and western airports ishigher In addition to a comparison of production recoveryof airports in China and Europe the recovery of airports inmainland China is better than in Europe (e main reasonfor this is that China takes strict epidemic prevention andsurveillance system to quickly stop the spread of the virus(e resilience metric of airports in different countries showsthe prevention and control measures and policies are a veryimportant factor to recover the normal operation of theaviation system After the disturbance occurs how to quicklyrestore the system to normal levels is a problem worthy ofour consideration

(e remainder sections are structured as follows Section2 introduces the economic impact of the COVID-19 and theresilience measure model of the airport Section 3 introducesthe case study of the resilience analysis of airports in Chinaand Europe and analyses the resilience difference of differentairports Finally Section 4 provides the conclusions andoutlines the finding and future work

2 Materials and Methods

21 System Resilience Frame Generic Model (e definitionsof resilience include aspects of a system withstanding dis-turbances adapting to the disruption and recovering fromthe state of reduced performance Resilience metrics can bedivided into three types which are attribute-focused metricsdata-based indicators and performance-based methodsMost resilience metrics for transportation networks arecategorized as performance-based methods as they focus onimpacts of flows across the network during recovery ac-tivities (e properties of social system resilience can bedefined as the following [24]

2 Mathematical Problems in Engineering

Robustness strength or the ability of elements sys-tems and other units of analysis to withstand a givenlevel of stress or demand without suffering degradationor loss of functionRedundancy the extent to which elements systems orother units of analysis exist that are substitutableResourcefulness the capacity to identify problemsestablish priorities and mobilize resources whenconditions exist which threaten to disrupt some ele-ment system or other units of analysisRapidity the capacity to meet priorities and achievegoals promptly to contain losses and avoid futuredisruption

Vugrin et al [25] proposed the generic concept ofdisruption and recovery underlying performance-basedapproaches which is a time-dependent function F(t) Undernormal operating conditions the system performancemeasure F has a nominal value until the system suffering adisruption at the time t0 as illustrated in Figure 1

(e resilience formulation is the ratio of recovery to loss

R(t) F(t) minus Fmin

F0 minus Fmin (1)

Figure 1 shows the system performance deteriorates at aspecific rate over time after being affected by the impact of adisruptive event (e resilience of complex networks isinterpreted as the ability to retain performance during andafter disruptions and to return to the normal state of op-eration quickly after disruptions It has three capacitiesabsorptive capacity adaptive capacity and restorative ca-pacity Some researchers introduced resilience to the ATMsystem [17 26] Meanwhile resilience formulation is alsoused in the airport network (ere are many disruptiveevents impacting airport network operation such as badweather failure of facilities and equipment and publichealth events Deterioration of the nominal performance ofairports by the impact of COVID-19 can reflect theirresilience Ren et al [27] proposed a three-dimensionalresilience triangle model in the complex engineered systems(CESs) including defensive capability adaptive capabilityand recovery capability(erefore we can propose the three-dimensional resilience triangle model of airport networksunder the epidemic crisis which includes absorptive ca-pacity adaptive capacity and restorative capacity (seeFigure 2)

Figure 3 depicts system performance as a function ofrecovery decisions As shown in Figure 3 the system op-eration state includes four stages such as initial steady-stateat the time t0 disruptive state from time td to tr recover statefrom time tr to tns and nominal state Two possible pathsthat the system recovers with different actions that mayfollow are represented by the solid red line and blue line inFigure 3(e red line represents the system performance willgradually recover with action a1 the blue line represents thesystem performance to recover the nominal state with actiona2 need more time than action a1 For example the epidemicprevention measures of airport networks may be built

quickly for the passengersrsquo health safety when COVID-19outbreak Different measures are taken to recover systemperformance with different results and EUROCONTROLhas produced two scenarios to illustrate the possible impactof the COVID-19 for recovering airports operation whichare the Coordinated Measures Scenario and UncoordinatedMeasures Scenario [30]

22 Airport Network Resilience Measure Model (e pro-posed model assesses the ability of airport network resilienceand recovery using four KPAs such as airport capacitysafety environment and costbenefit respectively

(e paper mainly discussed the public health safetyimpact of the airport network (erefore the models onlyconsider two KPAs denoted by i 2 andAi A1 A21113864 1113865 capacity area safety area1113864 1113865

Following the previous studies [23] the airport capacityvariable is used to measure the influence of infrastructurequality on the performance of resilience and it includesthree key performance indicators (KPI) that is

i 1 j 3

KPIAi

j KPIA11 KPIA1

2 KPIA131113966 1113967

passengers volume freight volume aircraftmovements1113864 1113865

(2)

F (t)

F0 = F (t0)

Fmin = F (t1)

t1 t2t0 Time

Actual performance

Nominal

Figure 1 (e generic concept of disruption

Restorativecapacity

Cre

AdaptivecapacityCad

AdaptivecapacityCab

Post-epidemicPo

st-ep

idem

ic

Resiliencecapacity

During-epidemic

Figure 2 (ree-dimensional resilience triangle model of airportnetworks

Mathematical Problems in Engineering 3

And safety contains aircraft operation safety runwaysafety and health safety that is

i 2 j 1

KPIAi

j KPIA21 KPIA2

2 KPIA231113966 1113967

aircraft operation safety runway safety public health safety1113864 1113865

(3)

In [20 29] general resilience metrics (GR) for an airportnetwork is as follows

GR f RRAPIDPRAPIRPTAPLRA( 1113857

R timesRAPIRPRAPIDP

1113888 1113889 times(TAPL)minus 1

times RA

(4)

wherein R represents the robustness of the system whichquantifies the minimum measurement of performance(MOP) value between td and tns in Figure 2 td is the startingtime of affection of the system performance by the impact ofthe COVID-19 pandemic tns is the ending time of recoveryof the system performance after the end of impact COVID-19 pandemic RAPIDP represents the rapidity and perfor-mance loss of the system in the disruptive state during theperiod [td tr] which refers to the rate of performance de-cline during the disruptive state tr is the starting recovery ofthe system performance under the sustained impact of theCOVID-19 pandemic

RAPIDP MOP td( 1113857 minus MOP tr( 1113857

tr minus td

(5)

where RAPIRP describes the rate of system recovery duringthe recovery state

RAPIRP MOP tns( 1113857 minus MOP tr( 1113857

tns minus tr

(6)

TAPL is the Time Average Performance Loss in thedisruptive stage which is

TAPL 1113938

tr

tdMOP t0( 1113857 minus MOP(t)dt( 1113857

tr minus td

(7)

RA is the recovering capacity of the system which is

RA MOP tns( 1113857 minus MOP tr( 1113857

MOP t0( 1113857 minus MOP tr( 1113857

11138681113868111386811138681113868111386811138681113868

11138681113868111386811138681113868111386811138681113868 (8)

whereMOPs focus on different KPIs of the airport networksand this paper mainly considers two KPAs which is capacityarea and safety area during the pandemic of COVID-19 To

Absorptivecapacity

Adaptiverestorative capacity

Syste

m p

erfo

rman

ce

F (td)

Fmin

t0 td tr tns

Initialsteadystate

Disruptivestate

Recover state Nominal state

Perfo

rman

ce lo

st Disruptive event

Transitionpoint

Recover systemperformance with action α1


Figure 3 An illustration of the airport resilience affected by COVID-19 Source revised and upgraded based on [25 28 29]


allow for comparison the MOPs of airport networks arenormalized in the range [0 1]

MOPKPIAij

(t) log10KPI

Ai

j (t)

log10 max KPIAi

j (t)1113960 1113961isin [0 1]

t 1 m

i 1 k

j 1 n

(9)

Overall the modeling framework that examines a singleindicator affecting airports network resilience metrics can beexpressed as follows

GRKPIAij

RKPIAij

times

RAPIRPKPIAij

RAPIDPKPIAij

⎛⎜⎜⎜⎜⎝ ⎞⎟⎟⎟⎟⎠ times TAPLKPIAij

1113874 1113875minus 1

times RKPIAij

(10)

where GRAJKPI denotes the resilience metrics of key perfor-

mance indicator j of key performance area Ai aboutimpacting airport network operation

Based on the entropy weight method [31] index entropyweight can be obtained by the following formula

MOP btj1113966 1113967mtimesn

(11)

where MOP is the normalized matrix(e entropy of the j evaluation index is

Ej (minuslnm)minus 1

1113944

m

n1Ptj lnPtj (12)

where Ptj (btj1113936mt1 bij)

After the entropy of the index is defined the entropyweight of the index can be obtained

Wj 1 minus Ej

n minus 1113936nj1 Ej

0leWj le 1 1113944n

j11⎛⎝ ⎞⎠ (13)

Combining the weights of each index the airport net-work resilience metrics in a key performance area Ai can beobtained

GPKPAi 1113944

n

j1WjGR

Ai

KPIj (14)

3 Case Study

(e proposed method was applied to evaluating the airportrsquosresilience during the COVID-19 spread VariFlight [32]reported that aircraft movements handled by airports inmainland China reached a YoY drop of 75 in the first halfof 2020 which was about the level of 2001 With morebusiness travel encouraged by the effective prevention andcontrol of the COVID-19 aircraft movements in June havereturned to the level close to 2016 And the trends of aviation

operation have been highly correlated with COVID-19development especially the confirmed cases of United Stateand European countries are in the particular fast rise andcivil aviation in those countries will get worse and may takelonger to recover (see Figure 4)

31 Data (e data of this study was obtained from variousresources the detailed airport production operational dataof Mainland China was obtained from the Civil AviationAdministration of China (CAAC) and VarFlight this papermainly discusses the COVID-19 impact on China andEuropean Airports (erefore according to the airportproduction statistics data of four regions released by CAACreleased which are East region Middle (MID) region Westregion and Northeast (NE) region we analyze resiliencerecovery of the mainland airports in China during theCOVID-19 epidemic (see Table 1) Figure 5 shows the dataof aircraft movements passenger throughput and freightthroughput sharply in February because of COVID-19outbreak from Wuhan then the government of Chinaposted the lockdown city ban From March the COVID-19epidemic is gradually being effectively controlled in Chinaand the domestic flights resumed gradually until more than70 of Chinese mainland airport flights resumed in JuneDomestic passenger traffic in China already bottomed out inmid-February and the capacity offered in June was recov-ered to around 76 of last year [32] As can be seen from thecurves the airport production indicators in the easternregion recovered better than the other three regions

32 Economic Losses of Aviation due to COVID-19 (esocio-technical system consists of two parts the socialsystem and the technical system (e social system includespersonnel at different organizational levels (employeesmanagers contractors etc)(e support of the social systemis inseparable during the operation of the civil aviationsystem Human-caused disruptions are often unpredictableand inevitable in all the systems such as accidents weather-induced hazards and virus pandemic With the COVID-19outbreak the aviation system was also hit hard the traffic of18 airports in major aviation markets in Asia-Pacific and theMiddle East plummeted by 95 by the middle of April [34](e COVID-19 pandemic has had a dramatic impact onairport traffic passenger volumes reduced 60 in 2020 vis-a-vis the projected baseline and cargo traffic also declined byminus175 in the first six months of 2020 year-over-yearAirports Council International (ACI) World projects arevenue shortfall for the year 2020 exceeding $100 billion forthe airport industry at a global level [35] Under the ldquore-cessionrdquo scenario the COVID-19 may cause a loss of US $ 82trillion in the global economy within five years from theUniversity of Cambridge Judges Business School Risk Re-search Center data and it is having a huge impact on theaviation and air travel industry [36] (e impact of COVID-19 has already surpassed the 2003 SARS outbreak which hadresulted in a reduction of annual RPKs by 8 and US $6billion revenues for AsiaPacific airlines (see Figure 6) [37]


In the past eight months the COVID-19 directly causeda loss of US $74 trillion in the international and domesticregion of whole world airports from the ICAO data (seeFigure 7) ICAO [38] developed interactive dashboards tomonitor the impact of COVID-19 on civil aviation based ondata-driven mainly about operational impact economicimpact aircraft utilization and country-pair traffic Asmentioned in the International Air Transport Association(IATA) restoring air connectivity will be a key contributionto a successful and rapid recovery of the global economypost-COVID-19(e ICAOCouncil Aviation Recovery TaskForce (CART) [39] proposed 10 key principles to restart andrecover the aviation operations in a safe secure sustainableand orderly manner And states and the civil aviation in-dustry will need to commit towards building a more resilientaviation system in the longer term

Meanwhile EUROCONTROL [30] has produced twoscenarios to illustrate the possible impact of COVID-19 forairlines and airports in all European States which are co-ordinated measures and uncoordinated measures scenarioAccording to the prediction data the coordinated measures

scenario envisages a loss of 45 of flights (5 million) in 2020while the uncoordinated measures scenario would result inthe loss of 57 of flights (62 million) Also according to theChina Aviation Daily reported [41] among the Top 150global airports the number of airports in mainland Chinawhich have reduced flights by more than 50 year-on-yearis below 20 since March 16 while the number of airports inother countries and territories has been rising rapidly closeto 110 which is the highest ever been

With the effective execution of antiepidemic measuresthe global outbreak is under control gradually Since Feb-ruary 20 the number of confirmed cases in China hascontinued to reduce and operated flights have started to risesteadily after experiencing a sustained fall (e domesticflight of China started to revive and increase from April2020 and Europe as well as the US have also graduallyreopened the domestic flight from May 2020

33 Result and Discussion According to equations (10) and(14) the airportsrsquo resilience index of these four regions in thedisruption scenario can be computed (e results are shown

Airc

ra

mov

emen

tsCu

rren

t con

firm

ed ca

ses

The United State

The United Kingdom

The United Kingdom

The United State

European Union

European Union

Japan

Japan

South korea

South korea

Mainland China(T = 75 days)

Days T0 = Total confirmed cases gt 100


Earlydevelopment

Steadilygrowing

Fastacceleration

Lateaccumulation Recovery


Figure 4 COVID-19 confirmed cases and aircraft movements compared chart [33]

Table 1 Airport production data of four regions in China

TimeAircraft movements (en thousand) Passengers throughput (million people) Freight throughput (en thousand

KG)Et Mid W NE E Mid W NE E Mid W NE

Jan 428 115 277 58 55114 1150 29889 6759 974 97 222 55Feb 13 25 74 17 9148 1463 5173 1263 532 35 86 23Mar 187 42 16 21 14427 3037 11658 1779 856 83 168 34Apr 21 63 192 2 1603 3724 12835 1898 905 94 172 36May 286 102 257 3 25428 6012 19444 2062 1013 113 202 36June 318 118 28 44 3016 7483 22463 3032 984 118 215 42July 361 134 303 55 3864 9933 2712 4523 953 114 216 38


in Table 2 Figures 8(a)ndash8(c) show the capacity recovery ofthe four regional airports in terms of aircraft movementspassenger throughput and freight throughput respectively

West region airports recover better than the other threeregions according to the resilience index of aircraft move-ments passenger throughput and freight throughput

Jan Feb Mar Apr May June July0

1 times 105

2 times 105

3 times 105

4 times 105

5 times 105A

ircra

m

ovem

ents

(flig

hts)

Time

EMID

WNE

(a)

EMID

WNE


10

20

30

40

50

60

Pass

enge

r thr

ough

put (

mill

ion

peop

le)

Time

(b)

Jan Feb Mar Apr May June July

EMID

WNE

0

20 times 105

40 times 105

60 times 105

80 times 105

10 times 106

Frei

ght t

hrou

ghpu

t(KG

)

Time

(c)

Figure 5 Airport production data of China in the first-seven month of 2020 (a) Aircraft movements (b) Passenger throughput (c) Freightthroughput

120110100

908070605040302010

Inde

x (c

risis

mon

th =

100

)

ndash3 ndash2 ndash1 0 1 2 3 4 5 6 7 8 9 10 11 12Months before and after the start of the crisis

Impact of past disease outbreaks on aviationSARS (2003)

North AmericanAirlines RPKs

Avian Flu (2005)RPKs to from and within

South-East Asia

Avian Flu (2013)Asia Pacific Airlines RPKs

Mers Flu (2015)RPKs to from and within south

korea

SARS (2003)China Domestic Market

RPKs

SARS (2003)Asia Pacefic

AirlineRPKs

Figure 6 Impact of past disease outbreaks on the aviation chart [37]


However considering the weight of each indicator overallrecovery in the eastern region airports is best during theCOVID-19 epidemic (see Figure 8(d)) Due to the difficultyof obtaining all production index data of the airport thispaper only analyzes the take-off and landing index Airportsin mainland China have classified three types by passengerthroughput which are above 30 million 10 million to 30million and 2 million to 10 million We choose eleven 30million level airports of China to study the resilience andrecovery of the airport during the COVID-19 pandemicwhich is SHA (ZSSS) PVG (ZSPD) PEK (ZBAA) CAN(ZGGG) CTU (ZUUU) CKG (ZUCK) KMG (ZPPP) XIY(ZLXY) SZX (ZGSZ) HGH (ZSHC) and NKG (ZSHJ) [42]

Besides we choose eight European airports among thetop 50 airports globally in 2019 that is LHR (EGLL) CDG(LFPG) AMS (EHAM) FRA (EDDF) MAD (LEMD) MUC(EDDM) BCN (LEBL) and LGW (EGKK) (e overseasepidemic continued to break out in March and the Euro-pean continent entered a ldquostate of emergencyrdquo InFigure 9(a) the red box shows the aircraft movements trendsof China mainland airports And the blue box shows that theaircraft movement numbers have sharply decreased due toEurope starting to post the travel restriction in April(erefore the traffic in Europe dropped to a freezing pointfrom April According to equation (10) the resilience matricabout aircraft movements of airports can be computedTables 3 and 4 show the results

Since the COVID-19 outbreak in Wuhan differentepidemic prevention strategies have been adopted in eachcountry of the world Based on SARSrsquos epidemic preventionexperience in 2003 China quickly established an epidemicprevention strategy (denotes strategy 1) and the Chineseauthorities introduced exceptional measures to stem thevirus rapid spread from quarantines and shop closures toputting entire cities of millions on lockdown (erefore theaircraft movements of large airports that above 30 million

levels of China have recovered to around 70 of the sameperiod last year in June [32] As shown in Figure 9(b) theresilience index value of aircraft movements at 11 majorairports of China was above 20 except for Beijing airport(e aircraft movements of ZGSZ and ZUUU airports havereturned to near pre-COVID-19 levels Besides ZSSSZUCK and ZSHC airports also have returned to near pre-COVID-19 levels Due to the special geographical locationand strict prevention measures of Beijing Airport (ZBAA)[43] CAAC has implemented a very strict epidemic pre-vention policy and the flight volume recovery is relativelylow

However some countries such as Europe and the UnitedStates initially lacked awareness of COVID-19 so they tookprevention and control measures slowlyWhen the epidemicoutbreaks happened globally at the end of March 2020 thenumber of confirmed cases in European countries rosefrequently European epidemic prevention measures (de-notes strategy 2) are different from Chinese which is totalimmunization and no wearing face mask at first Europeexperienced a resurgence in cases as people aremore relaxedabout social distancing during the summer months(erefore there was a trend rebound in the epidemic in thesecond half of 2020 As shown in Figure 9(a) the aircraftmovements of European major airports dropped to aminimum in April by the end of July the number of aircraftmovements was rising but growth speed was slow becausethe COVID-19 epidemic was not fully controlled and was atrisk of a second outbreak From Figure 9(b) the resilienceindex value of European major airport was below 10 and therecovery of European airports production was relativelyslow Figures 9(c) and 9(d) show different airport opera-tional scenarios by different strategies Facing the globalpublic crisis it has different results by different strategies toprevent the COVID-19 epidemic (e strategy 1 is verystrict but strategy 2 is not severe (e results of strategy 1

ndash352242254

ndash2955064176

ndash4643629102

ndash929603693

ndash1032734614

ndash2717431757

Revenueloss by airport regions(USD) in july

Africamiddot

AsiapacificEurope

Latin americaCaribbeanMiddle eastNorth america

(a)

ndash16000000000ndash14000000000ndash12000000000ndash10000000000


02000000000

1

Time

Revenueloss by airport regions each month

JanFebMarApr

MayJuneJulyAug

Reve

nue

loss

by

airp

ort r

egio

ns

() U

SD(b)

Figure 7 Airport revenuelosses of the whole world in half of 2020 (data from [40]) (a) Revenueloss in July (b) Revenueloss in eachmonth


and strategy 2 show that the airportrsquos ability to respond tosudden global public emergencies varies greatly (e fact toprove that social distancing measures and testing andtracing methods can help contain a large resurgence of theCOVID-19

34 5e Preventing and Controlling Measures in Aviation onCOVID-19 Epidemic Various countries are taking corre-sponding measures to gradually restore normal social op-erations to reduce the economic losses caused by COVID-19 Internationally in 2006 ICAO had established thePrevention and Management of Public Health Events inCivil Aviation (CAPSCA) programme to assist in

preparedness planning for the public health event that mightcause a public health emergency (e CAPSCA programmehas developed a strategy known as the Public Health Cor-ridor Concept (is strategy describes how to manage es-sential flights that allow aircraft and crew to perform withminimal additional burdens during the COVID-19 crisis[44] From the active case surveillance rapid case diagnosisandmanagement strict follow-up and quarantine of personswith close contact and issuance of guidance to prompt andeffective high-level policy decisions China established acomplete activation of the public health system [45] China isgradually resuming domestic flight operations To preventimported cases the Chinese authorities have established a setof relative comparisons complete antiepidemic measures

E MID W NE

Resilience index

0

10

20

30

40

50

60

70

Resil

ienc

e ind

ex v

alue

()

(a)

E MID W NE

Resilience index

0

20

40

60

80

100

120

140

Resil

ienc

e ind

ex v

alue

()

(b)

Resilience index

E MID W NE0

50

100

150

200

250

Resil

ienc

e ind

ex v

alue

()

(c)

Resilience index

E MID W NE0

20

40

60

80

100

Resil

ienc

e ind

ex v

alue

()

(d)

Figure 8 (e resilience index of the four regional airports in China (a) Aircraft movements (b) Passenger throughout (c) Freightthroughout (d) Airports

Table 2 (e resilience index result of four regional airports in China

Resilience index () Aircraft movement Passenger throughout Freight throughout GRKPIAij

()

E 6326 9775 24424 9009MID 2907 8347 6597 4314W 6918 12714 8559 8176NE 1583 6674 1267 1749


such as the 14-day health code which is an effect on theresumption of work and production [46] To resolutelycontain the increasing risks of imported COVID-19 casesCAAC issued the Information on International Flight Plans(Phase Five) on 12 March which all airlines shall followEuropean Union Aviation Safety Agency (EASA) and Eu-ropean Center for Disease Prevention and Control (ECDC)have developed a serial operational guidelines on 30 Junewhich purpose is to serve as an Aviation Health SafetyProtocol and to provide a source of best practice on howairport operators airplane operators conducting commer-cial and noncommercial passenger transport operations(hereon referred to as ldquoaircraft operatorsrdquo) [47] Further-more ICAO has developed this COVID-19 RecoveryPlatform to collate the forecasts guidance tools and re-sources which are needed by national regulators pursuing

pandemic responses and outlined a series of recommen-dations and measures for all levels local national and in-ternational [39]

4 Conclusions

Limited studies have been carried out to assess the resilienceof the airport network considering the impact of COVID-19events on airport operations (is paper addresses one keyresearch question using airport data in China and Europe asan example how airport network resilience varies spatiallyand temporally during the COVID-19 outbreak Based onresilience index analysis and entropy of information cal-culated this study reveals that airport resilience to publichealth events does vary depending on factors such as publichealth preventive and control strategy and airport capacity

4000

3000

2000

1000

0

Jan Feb Mar Apr May June JulyTime

Airc

ra

mov

emen

ts

ChinaEurope

RegionChinaEurope

(a)

Strategy 1

Strategy 2

00510152025

2030

2035

40

Resil

ienc

e ind

ex v

alue

ZBA

AZS

PDZS

SSZG

SZZU

UU

ZPPP

ZUCK

ZGG

G

ZSH

CZS

NJ

ZLXY

EGLL

LFPG

EHA

MED

DF

LEM

DLE

BLED

DM

EGKK

Airports

Resilience index

(b)


45000

40000

35000

30000

25000

20000

15000

10000

5000

Airc

ra

mov

emen

ts

80000

60000

40000

20000

0

Case

s by

Chin

a

Time

ZUUUZPPPZBAAZGGG

ZUCKZSHCZSNJZLXY

ZSPDZSSSZGSZ

Confirmed cases

(c)


40000

35000

30000

25000

20000

15000

10000

5000

0

Airc

ra

mov

emen

ts

60000

40000

20000

0

Case

s by

Euro

pe

Time

EHAMEDDFLOWW

LEMDEDDMLEBL

EGLLLFPG

Confirmed cases

(d)

Figure 9 (e aircraft movements of airports during the global public health crisis (a) Aircraft movements of airports (b) Resilience indexvalue of airports (c) Airport scenario at Strategy 1 (d) Airport scenario at Strategy 2


In particular the recovery of the airport network in Europetends to be relatively longer during the COVID-19 crisisthan in China due to taking different prevention and controlmeasures for COVID-19 (e emergency response ability ofthe country for the public health crisis was found to have asignificant positive impact on speeding up the recovery ofaviation Furthermore we also compared the operationalresilience measures with network performance indicatorswhich were used to analyze the robustness of the airportnetwork Due to the strict preventing and controllingmeasures of the epidemic the airportrsquos operational pro-duction in the east and west regions of China recoveredbetter than in other regions (e success of aviationrsquos re-covery today and future resilience is best achieved withcollective efforts among different regions and sectors (erigorous follow-up to the recommendations and measuresoutlined by ICAO will be required in every country

However one should note that this study also has severallimitations which should improve upon in future researchendeavors For example our current analysis did not capturethe factors such as flight punctuality COVID-19 confirmcases passenger and cargo throughput of airports in othercountries that were implemented at the airport during theCOVID-19 crisis (ese aspects of airport network resilienceshould be further studied once such data becomes availableFuture work will investigate strategies to improve theresilience of the airport network during the disruption

Using big data analytics to assess the airport network per-formance at the disruption it can support the airportmanagement agencies to take more proactive measures inemergency response and management

Data Availability

Data can be obtained from httpsansperformanceeudashboardstakeholderairport and httpwwwcaacgovcnenSY Some data can be found in the manuscript (seeTable 2)

Conflicts of Interest

(e authors declare that they have no conflicts of interest

Acknowledgments

(is research was supported in part by the China Schol-arship Council (CSC) scholarship (no 201808515167)National Science Foundation of China (no U1733105)special foundation for Local government Science andTechnology Development of China (2020ZYD094) andCivil Aviation Authority Security Capacity BuildingFunding Project (TM-2018-18-13) National Key RampDProgram of China (2018YFC0831800) Research Programsof Sichuan Science and Technology Department(17ZDYF3184)

References

[1] H Li S Liu X Yu S Tang and C Tang ldquoCoronavirus disease2019 (COVID-19) current status and future perspectivesrdquoInternational Journal of Antimicrobial Agents vol 55 no 5Article ID 105951 2020

[2] H Lau V Khosrawipour P Kocbach et al ldquo(e associationbetween international and domestic air traffic and thecoronavirus (COVID-19) outbreakrdquo Journal of Microbiol-ogy Immunology and Infection vol 53 no 3 pp 467ndash4722020

[3] Y Qiu X Chen andW Shi ldquoImpacts of social and economicfactors on the transmission of coronavirus disease 2019(COVID-19) in Chinardquo Journal of Population Economicsvol 33 no 4 pp 1127ndash1172 2020

[4] E Dong H Du and L Gardner ldquoAn interactive web-baseddashboard to track COVID-19 in real timerdquo 5e Lancet In-fectious Diseases vol 20 no 5 pp 533-534 2020

[5] R Dandekar and G Barbastathis ldquoQuantifying the effect ofquarantine control in Covid-19 infectious spread using ma-chine learning medRxivrdquo pp 1ndash13 2020

[6] J Hellewell S Abbott A Gimma et al ldquoFeasibility of con-trolling COVID-19 outbreaks by isolation of cases andcontactsrdquo 5e Lancet Global Health vol 8 no 4pp e488ndashe496 2020

[7] Z Ma Health QR Code Helps Curb Spread of Infection ChinaDaily Beijing China 2020 httpswwwchinadailyhkcomarticle124757

[8] Z Wang M Yao C Meng and C Claramunt ldquoRisk as-sessment of the overseas imported COVID-19 of ocean-goingships based on AIS and infection datardquo ISPRS InternationalJournal of Geo-Information vol 9 no 6 p 351 2020

Table 3 (e resilience computations of China airport for COVID-19-Strategy 1

Airport GR R RAPIDP (1month) RAPIRP (1month)ZBAA 16307 08698 00434 00152ZSPD 37938 09007 00497 00161ZSSS 29680 08837 01163 00230ZGSZ 36164 09086 00914 00174ZUUU 34901 09110 00890 00164ZPPP 19923 08662 01338 00235ZGGG 21349 08897 01103 00179ZUCK 29559 08891 01109 00213ZSHC 29142 08893 01107 00211ZSNJ 20974 08693 01307 00232ZLXY 20534 08559 01441 00268Note GR general resilience R robustness RAPIDP rapidity in disruptivephase RAPIRP rapidity in the recovery phase

Table 4 (e resilience computations of European airport forCOVID-19-Strategy 2

Airport GR R RAPIDP (1month) RAPIRP (1month)EGLL 05386 0813 01512 00264LFPG 12667 07936 01618 00472EHAM 10211 07982 01641 00411EDDF 09164 08365 01189 00272LEMD 06618 07516 01985 00465LEBL 09103 07266 02208 00641EDDM 06674 07261 02183 0055EGKK 08848 05615 02032 0128Note GR general resilience R robustness RAPIDP rapidity in disruptivephase RAPIRP rapidity in the recovery phase


[9] E Jenelius and L-G Mattsson ldquoResilience of transport sys-temsrdquo in Encyclopedia of TransportationElsevier AmsterdamNetherlands 2020

[10] J Rasmussen ldquoHuman error and the problem of causality inanalysis of accidentsrdquo Philosophical Transactions of the RoyalSociety of London Series B Biological Sciences vol 327no 1241 pp 449ndash460 1990

[11] E Hollnagel D D Woods and N Leveson Resilience En-gineering Concepts and Precepts Ashgate Publishing LtdFarnham UK 2006

[12] EuroControl A White Paper on Resilience Engineering forATM Cooperative Network Design EuroControl BrusselsBelgium 2009

[13] C S Holling ldquoResilience and stability of ecological systemsrdquoAnnual Review of Ecology and Systematics vol 4 no 1pp 1ndash23 1973

[14] J E Muriel-Villegas K C Alvarez-Uribe C E Patintildeo-Rodrıguez and J G Villegas ldquoAnalysis of transportationnetworks subject to natural hazards-insights from aColombian caserdquo Reliability Engineering amp System Safetyvol 152 pp 151ndash165 2016

[15] S A Markolf C Hoehne A Fraser M V Chester andB S Underwood ldquoTransportation resilience to climate changeand extreme weather events-beyond risk and robustnessrdquoTransport Policy vol 74 pp 174ndash186 2019

[16] S Hosseini K Barker and J E Ramirez-Marquez ldquoA reviewof definitions and measures of system resiliencerdquo ReliabilityEngineering amp System Safety vol 145 pp 47ndash61 2016

[17] E Filippone F Gargiulo A Errico V Di Vito andD Pascarella ldquoResilience management problem in ATMsystems as a shortest path problemrdquo Journal of Air TransportManagement vol 56 pp 57ndash65 2016

[18] A Cook H A P Blom F Lillo et al ldquoApplying complexityscience to air traffic managementrdquo Journal of Air TransportManagement vol 42 p 149 2015

[19] R Palumbo A Errico D Pascarella F Gargiulo andE Filippone ldquoModeling approach for resilience engineeringof the future ATM systemrdquo in Proceedings of the 15th AIAAAviation Technology Integration and Operations ConferenceDallas TX USA June 2015

[20] Y Wang J Zhan X Xu L Li P Chen and M HansenldquoMeasuring the resilience of an airport networkrdquo ChineseJournal of Aeronautics vol 32 no 12 pp 2694ndash2705 2019

[21] L Zhou and Z Chen ldquoMeasuring the performance of airportresilience to severe weather eventsrdquoTransportation Research PartD Transport and Environment vol 83 Article ID 102362 2020

[22] M Janic ldquoModelling the resilience friability and costs of anair transport network affected by a large-scale disruptiveeventrdquo Transportation Research Part A Policy and Practicevol 71 pp 1ndash16 2015

[23] ICAO Manual on Global Performance of the Air NavigationSystem International Civil Aviation Organization MontrealCanada 2009

[24] M Bruneau S E Chang R T Eguchi et al ldquoA framework toquantitatively assess and enhance the seismic resilience ofcommunitiesrdquo Earthquake Spectra vol 19 no 4 pp 733ndash7522003

[25] E D Vugrin M A Turnquist and N J K Brown ldquoOptimalrecovery sequencing for enhanced resilience and servicerestoration in transportation networksrdquo International Journalof Critical Infrastructures vol 10 no 3 pp 218ndash246 2014

[26] Z Jaksic and M Janic ldquoModeling resilience of the ATC (airtraffic control) sectorsrdquo Journal of Air Transport Managementvol 89 pp 1ndash20 2020

[27] F Ren T Zhao J Jiao and Y Hu ldquoResilience optimization forcomplex engineered systems based on the multi-dimensionalresilience conceptrdquo IEEE Access vol 5 pp 19352ndash19362 2017

[28] D Henry and J Emmanuel Ramirez-Marquez ldquoGenericmetrics and quantitative approaches for system resilience as afunction of timerdquo Reliability Engineering amp System Safetyvol 99 pp 114ndash122 2012

[29] C Nan and G Sansavini ldquoA quantitative method for assessingresilience of interdependent infrastructuresrdquo Reliability En-gineering amp System Safety vol 157 pp 35ndash53 2017

[30] EuroControl Aviation RecoveryndashImportance of a Coordinatedapproach EuroControl Brussels Belgium 2020

[31] Y Liu and J Cui ldquoIdentification of hazard sources in pre-fabricated building construction by entropy weight methodrdquoIOP Conference Series Earth and Environmental Sciencevol 560 no 1 Article ID 012073 2020

[32] VariFlight VariFlight Civil Aviation Report VariFlight HefeiChina 2020

[33] VariFlight COVID-19 Outbreak Analysis of Global AviationOperation VariFlight Hefei China 2020

[34] Flightglobal ldquoTraffic at Asia-Pacific airports hits ldquorock bot-tomrdquo ACIrdquo 2020 httpswwwflightglobalcomair-transporttraffic-at-asia-pacific-airports-hits-rock-bottom-aci138010article

[35] P Villard ldquoCOVID-19 Tailoring airport charges to supportthe economic recovery of airports ACIrdquo 2020 httpsblogaciaerocovid-19-tailoring-airport-charges-to-support-the-economic-recovery-of-airports

[36] S Nagarajan ldquo$82 trillion over 5 years Cambridge studycounts the cost of cornavirus the World economic forumCOVID action platformrdquo 2020 httpswwwweforumorgagenda202005coronavirus-covid19-pandemic-econamy-money-depression-recession

[37] B Pearce COVID-19 Updated Impact Assessment IATAEconomics Montreal Canada 2020

[38] ICAO ldquoEconomic developmentrdquo International Civil AviationOrganization Montreal Canada 2020 httpswwwicaointsustainabilityPagesCOVID-19-Air-Traffic-Dashboardaspx

[39] ICAO Council Aviation Recovery Task Force (CART) ReportInternational Civil Aviation Organization Montreal Canada2020

[40] ICAO ldquoStatement on the second meeting of the InternationalHealth Regulations (2005) Emergency Committee regardingthe outbreak of novel coronavirus (2019-nCoV)rdquo 2020 httpswwwwhointnewsitem30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov)

[41] China Aviation Daily ldquoImpacts of the epidemic on global civilaviation industryrdquo Aviation Daily Beijing China 2020httpwwwchinaaviationdailycomnews7777851html

[42] VariFlight ldquoGlobal airport and airline punctuality report forthe first half of 2020rdquo VariFlight Hefei China 2020

[43] Beijing CDPC Prevention and Control Measures of Covid-19in Beijing China Beijing Centers for Disease Control andPrevention Beijing China 2020

[44] ICAO ldquoCollaborative arrangement for the prevention andmanagement of public health events in civil aviation-CAPSCArdquo International Civil Aviation Organization Mon-treal Canada 2020 httpswwwicaointsafetyCAPSCAPagesAbout-CAPSCAaspx

[45] T-L Xu M-Y Ao X Zhou et al ldquoChinarsquos practice to preventand control COVID-19 in the context of large population


movementrdquo Infectious Diseases of Poverty vol 9 no 1pp 1ndash14 2020

[46] China Center for Disease Control Technical Guidance forPrevention and Control of COVID-19 China Center forDisease Control Beijing China 2020

[47] EASA COVID-19 Aviation Health Safety Protocol Opera-tional Guidelines for the Management of Air Passengers andAviation Personnel European Union Aviation Safety AgencyCologne Germany 2020


resumed domestic flights and built a relatively complete setof antiepidemic monitor system to prevent imported casessuch as the health QR code system which is proved effective[7 8]

Air traffic is characterized by high safety and securityconcerns coupled with airlines airports and air trafficcontrol departments (e aviation system network consistsof nodes (airports) with strict regulations and interdepen-dencies between vehicles and journeys and links (flights)with high degrees of freedom concerning flight path andspeed [9] Airports are an integral part of the aviationecosystem and currently face a crisis in which sudden andprolonged collapse occurs in traffic levels due to publichealth events As the public health event was a disturbancehow to deal with the impact on the aviation system after thecrisis so that system can restore resilience to reduce the lossof the aviation system

With the global public health outbreak the resilience ofthe system in several industries is considered Resilience isthe intrinsic ability of a system to adjust its functioning priorto during or following changes and disturbances In thepast the research on aviation safety management mainlyfocused on system failures or catastrophic accidents becausethese events have large economic and safety impacts Andthe disturbances of the system focus on hardware softwarehuman and environment For humans as the key elementsof aviation there are more studies about human errors [10](erefore Hollnagel et al [11] introduced the resilienceconcept into the safety science domain in 2006 ResilienceEngineering is an important tool and method of systemdevelopment and system safety in many industries such asthe ecosystem and transportation system [12 13] Intransportation networks research on resilience mainly facesnatural hazards or extreme weather [14 15] (e qualitativeresilience assessment approaches include conceptualframeworks and semiquantitative indices [16] Researchabout the resilience of the air transport system mainly fo-cuses on air traffic management (ATM) system [17 18] AndEurope had the Sharing of Authority in FailureEmergencyCondition for Resilience of Air traffic Management(SAFECORAM) project whose purpose was eventually todeal with the reallocation of tasks between residual resourcesof the system after a disturbance to minimize the system lossof global performance (ere are twelve study referencescenarios in the resilience 2050 project of Europe which area potential hazard of the ATM system such as Taxiwayincursion during take-off taxiing Big airport closure due tosnow and activation of a temporary segregated area due tonatural disaster [19]

However most research studies [20ndash22] have dealt withmodeling and estimating the resilience of an air transportnetwork effect by various disruptive events they are onlyconsidered the impact of an airport network after the in-terruption of some airport affected by the disruption and didnot consider how global disruptive events affect the globalairport network Few studies have quantified and forecastthe aviation system performance parameters such as ca-pacity costbenefit and environment under multiple pan-demic scenarios and the method proposed in the paper is

desperately needed To the date studies focused on theaviation network impacts of the COVID-19 pandemic arethe interactive dashboards developed by the InternationalCivil Aviation Organization (ICAO) and the COVID-19pandemic impact on the European air traffic network Fromthe perspective of safety management we have a newcognitive of resilience engineering (erefore global publichealth is a disturbance of the aviation system how to dealwith it so that the system can quickly recover is the firstconsidered issue of aviation Besides ICAO proposed elevenKey Performance Areas (KPAs) of aviation [23] and manyresearch studies focus on capacity safety environment andefficiency in pre-COVID-19 In the paper based on theexisting resilience metric measures methods we develop amodel of the airport network resilience following the col-lectible data of airport operation in different countrieswhich obtains the results of the rapid emergency response ofvarious national airports facing sudden global public healthevents

(e main contributions of this study are summarized asfollows First this study is studying the global public healthimpact on the airport network resilience and recoverySecond this study developed a resilience metric measuremodel based on existing resilience measures to apply to theglobal public health crisis By comparing the production dataof Chinarsquos four regional airports during the first half of 2020the resilience index of the eastern and western airports ishigher In addition to a comparison of production recoveryof airports in China and Europe the recovery of airports inmainland China is better than in Europe (e main reasonfor this is that China takes strict epidemic prevention andsurveillance system to quickly stop the spread of the virus(e resilience metric of airports in different countries showsthe prevention and control measures and policies are a veryimportant factor to recover the normal operation of theaviation system After the disturbance occurs how to quicklyrestore the system to normal levels is a problem worthy ofour consideration

(e remainder sections are structured as follows Section2 introduces the economic impact of the COVID-19 and theresilience measure model of the airport Section 3 introducesthe case study of the resilience analysis of airports in Chinaand Europe and analyses the resilience difference of differentairports Finally Section 4 provides the conclusions andoutlines the finding and future work

2 Materials and Methods

21 System Resilience Frame Generic Model (e definitionsof resilience include aspects of a system withstanding dis-turbances adapting to the disruption and recovering fromthe state of reduced performance Resilience metrics can bedivided into three types which are attribute-focused metricsdata-based indicators and performance-based methodsMost resilience metrics for transportation networks arecategorized as performance-based methods as they focus onimpacts of flows across the network during recovery ac-tivities (e properties of social system resilience can bedefined as the following [24]






F0 minus Fmin (1)







i 1 j 3

KPIAi

j KPIA11 KPIA1

2 KPIA131113966 1113967


(2)

F (t)

F0 = F (t0)

Fmin = F (t1)

t1 t2t0 Time

Actual performance

Nominal


Restorativecapacity

Cre

AdaptivecapacityCad

AdaptivecapacityCab

Post-epidemicPo

st-ep

idem

ic

Resiliencecapacity

During-epidemic




i 2 j 1

KPIAi

j KPIA21 KPIA2

2 KPIA231113966 1113967


(3)



R timesRAPIRPRAPIDP


times RA

(4)



tr minus td

(5)



tns minus tr

(6)


TAPL 1113938

tr


tr minus td

(7)



MOP t0( 1113857 minus MOP tr( 1113857

11138681113868111386811138681113868111386811138681113868

11138681113868111386811138681113868111386811138681113868 (8)


Absorptivecapacity


Syste

m p

erfo

rman

ce

F (td)

Fmin

t0 td tr tns

Initialsteadystate

Disruptivestate


Perfo

rman

ce lo

st Disruptive event

Transitionpoint






MOPKPIAij

(t) log10KPI

Ai

j (t)

log10 max KPIAi

j (t)1113960 1113961isin [0 1]

t 1 m

i 1 k

j 1 n

(9)


GRKPIAij

RKPIAij

times

RAPIRPKPIAij

RAPIDPKPIAij


1113874 1113875minus 1

times RKPIAij

(10)





(11)



1113944

m

n1Ptj lnPtj (12)



Wj 1 minus Ej


0leWj le 1 1113944n

j11⎛⎝ ⎞⎠ (13)


GPKPAi 1113944

n

j1WjGR

Ai

KPIj (14)

3 Case Study











Airc

ra

mov

emen

tsCu

rren

t con

firm

ed ca

ses

The United State

The United Kingdom

The United Kingdom

The United State

European Union

European Union

Japan

Japan

South korea

South korea




Earlydevelopment

Steadilygrowing

Fastacceleration












1 times 105

2 times 105

3 times 105

4 times 105

5 times 105A

ircra

m

ovem

ents

(flig

hts)

Time

EMID

WNE

(a)

EMID

WNE


10

20

30

40

50

60

Pass

enge

r thr

ough

put (

mill

ion

peop

le)

Time

(b)


EMID

WNE

0

20 times 105

40 times 105

60 times 105

80 times 105

10 times 106

Frei

ght t

hrou

ghpu

t(KG

)

Time

(c)


120110100

908070605040302010

Inde

x (c

risis

mon

th =

100

)





South-East Asia



korea


RPKs


AirlineRPKs








ndash352242254

ndash2955064176

ndash4643629102

ndash929603693

ndash1032734614

ndash2717431757


Africamiddot

AsiapacificEurope


(a)



02000000000

1

Time


JanFebMarApr

MayJuneJulyAug

Reve

nue

loss

by

airp

ort r

egio

ns

() U

SD(b)






E MID W NE

Resilience index

0

10

20

30

40

50

60

70

Resil

ienc

e ind

ex v

alue

()

(a)

E MID W NE

Resilience index

0

20

40

60

80

100

120

140

Resil

ienc

e ind

ex v

alue

()

(b)

Resilience index

E MID W NE0

50

100

150

200

250

Resil

ienc

e ind

ex v

alue

()

(c)

Resilience index

E MID W NE0

20

40

60

80

100

Resil

ienc

e ind

ex v

alue

()

(d)




()

E 6326 9775 24424 9009MID 2907 8347 6597 4314W 6918 12714 8559 8176NE 1583 6674 1267 1749




4 Conclusions


4000

3000

2000

1000

0


Airc

ra

mov

emen

ts

ChinaEurope

RegionChinaEurope

(a)

Strategy 1

Strategy 2

00510152025

2030

2035

40

Resil

ienc

e ind

ex v

alue

ZBA

AZS

PDZS

SSZG

SZZU

UU

ZPPP

ZUCK

ZGG

G

ZSH

CZS

NJ

ZLXY

EGLL

LFPG

EHA

MED

DF

LEM

DLE

BLED

DM

EGKK

Airports

Resilience index

(b)


45000

40000

35000

30000

25000

20000

15000

10000

5000

Airc

ra

mov

emen

ts

80000

60000

40000

20000

0

Case

s by

Chin

a

Time

ZUUUZPPPZBAAZGGG

ZUCKZSHCZSNJZLXY

ZSPDZSSSZGSZ

Confirmed cases

(c)


40000

35000

30000

25000

20000

15000

10000

5000

0

Airc

ra

mov

emen

ts

60000

40000

20000

0

Case

s by

Euro

pe

Time

EHAMEDDFLOWW

LEMDEDDMLEBL

EGLLLFPG

Confirmed cases

(d)






Data Availability




Acknowledgments


References




























































F0 minus Fmin (1)







i 1 j 3

KPIAi

j KPIA11 KPIA1

2 KPIA131113966 1113967


(2)

F (t)

F0 = F (t0)

Fmin = F (t1)

t1 t2t0 Time

Actual performance

Nominal


Restorativecapacity

Cre

AdaptivecapacityCad

AdaptivecapacityCab

Post-epidemicPo

st-ep

idem

ic

Resiliencecapacity

During-epidemic




i 2 j 1

KPIAi

j KPIA21 KPIA2

2 KPIA231113966 1113967


(3)



R timesRAPIRPRAPIDP


times RA

(4)



tr minus td

(5)



tns minus tr

(6)


TAPL 1113938

tr


tr minus td

(7)



MOP t0( 1113857 minus MOP tr( 1113857

11138681113868111386811138681113868111386811138681113868

11138681113868111386811138681113868111386811138681113868 (8)


Absorptivecapacity


Syste

m p

erfo

rman

ce

F (td)

Fmin

t0 td tr tns

Initialsteadystate

Disruptivestate


Perfo

rman

ce lo

st Disruptive event

Transitionpoint






MOPKPIAij

(t) log10KPI

Ai

j (t)

log10 max KPIAi

j (t)1113960 1113961isin [0 1]

t 1 m

i 1 k

j 1 n

(9)


GRKPIAij

RKPIAij

times

RAPIRPKPIAij

RAPIDPKPIAij


1113874 1113875minus 1

times RKPIAij

(10)





(11)



1113944

m

n1Ptj lnPtj (12)



Wj 1 minus Ej


0leWj le 1 1113944n

j11⎛⎝ ⎞⎠ (13)


GPKPAi 1113944

n

j1WjGR

Ai

KPIj (14)

3 Case Study











Airc

ra

mov

emen

tsCu

rren

t con

firm

ed ca

ses

The United State

The United Kingdom

The United Kingdom

The United State

European Union

European Union

Japan

Japan

South korea

South korea




Earlydevelopment

Steadilygrowing

Fastacceleration












1 times 105

2 times 105

3 times 105

4 times 105

5 times 105A

ircra

m

ovem

ents

(flig

hts)

Time

EMID

WNE

(a)

EMID

WNE


10

20

30

40

50

60

Pass

enge

r thr

ough

put (

mill

ion

peop

le)

Time

(b)


EMID

WNE

0

20 times 105

40 times 105

60 times 105

80 times 105

10 times 106

Frei

ght t

hrou

ghpu

t(KG

)

Time

(c)


120110100

908070605040302010

Inde

x (c

risis

mon

th =

100

)





South-East Asia



korea


RPKs


AirlineRPKs








ndash352242254

ndash2955064176

ndash4643629102

ndash929603693

ndash1032734614

ndash2717431757


Africamiddot

AsiapacificEurope


(a)



02000000000

1

Time


JanFebMarApr

MayJuneJulyAug

Reve

nue

loss

by

airp

ort r

egio

ns

() U

SD(b)






E MID W NE

Resilience index

0

10

20

30

40

50

60

70

Resil

ienc

e ind

ex v

alue

()

(a)

E MID W NE

Resilience index

0

20

40

60

80

100

120

140

Resil

ienc

e ind

ex v

alue

()

(b)

Resilience index

E MID W NE0

50

100

150

200

250

Resil

ienc

e ind

ex v

alue

()

(c)

Resilience index

E MID W NE0

20

40

60

80

100

Resil

ienc

e ind

ex v

alue

()

(d)




()

E 6326 9775 24424 9009MID 2907 8347 6597 4314W 6918 12714 8559 8176NE 1583 6674 1267 1749




4 Conclusions


4000

3000

2000

1000

0


Airc

ra

mov

emen

ts

ChinaEurope

RegionChinaEurope

(a)

Strategy 1

Strategy 2

00510152025

2030

2035

40

Resil

ienc

e ind

ex v

alue

ZBA

AZS

PDZS

SSZG

SZZU

UU

ZPPP

ZUCK

ZGG

G

ZSH

CZS

NJ

ZLXY

EGLL

LFPG

EHA

MED

DF

LEM

DLE

BLED

DM

EGKK

Airports

Resilience index

(b)


45000

40000

35000

30000

25000

20000

15000

10000

5000

Airc

ra

mov

emen

ts

80000

60000

40000

20000

0

Case

s by

Chin

a

Time

ZUUUZPPPZBAAZGGG

ZUCKZSHCZSNJZLXY

ZSPDZSSSZGSZ

Confirmed cases

(c)


40000

35000

30000

25000

20000

15000

10000

5000

0

Airc

ra

mov

emen

ts

60000

40000

20000

0

Case

s by

Euro

pe

Time

EHAMEDDFLOWW

LEMDEDDMLEBL

EGLLLFPG

Confirmed cases

(d)






Data Availability




Acknowledgments


References

























































i 2 j 1

KPIAi

j KPIA21 KPIA2

2 KPIA231113966 1113967


(3)



R timesRAPIRPRAPIDP


times RA

(4)



tr minus td

(5)



tns minus tr

(6)


TAPL 1113938

tr


tr minus td

(7)



MOP t0( 1113857 minus MOP tr( 1113857

11138681113868111386811138681113868111386811138681113868

11138681113868111386811138681113868111386811138681113868 (8)


Absorptivecapacity


Syste

m p

erfo

rman

ce

F (td)

Fmin

t0 td tr tns

Initialsteadystate

Disruptivestate


Perfo

rman

ce lo

st Disruptive event

Transitionpoint






MOPKPIAij

(t) log10KPI

Ai

j (t)

log10 max KPIAi

j (t)1113960 1113961isin [0 1]

t 1 m

i 1 k

j 1 n

(9)


GRKPIAij

RKPIAij

times

RAPIRPKPIAij

RAPIDPKPIAij


1113874 1113875minus 1

times RKPIAij

(10)





(11)



1113944

m

n1Ptj lnPtj (12)



Wj 1 minus Ej


0leWj le 1 1113944n

j11⎛⎝ ⎞⎠ (13)


GPKPAi 1113944

n

j1WjGR

Ai

KPIj (14)

3 Case Study











Airc

ra

mov

emen

tsCu

rren

t con

firm

ed ca

ses

The United State

The United Kingdom

The United Kingdom

The United State

European Union

European Union

Japan

Japan

South korea

South korea




Earlydevelopment

Steadilygrowing

Fastacceleration












1 times 105

2 times 105

3 times 105

4 times 105

5 times 105A

ircra

m

ovem

ents

(flig

hts)

Time

EMID

WNE

(a)

EMID

WNE


10

20

30

40

50

60

Pass

enge

r thr

ough

put (

mill

ion

peop

le)

Time

(b)


EMID

WNE

0

20 times 105

40 times 105

60 times 105

80 times 105

10 times 106

Frei

ght t

hrou

ghpu

t(KG

)

Time

(c)


120110100

908070605040302010

Inde

x (c

risis

mon

th =

100

)





South-East Asia



korea


RPKs


AirlineRPKs








ndash352242254

ndash2955064176

ndash4643629102

ndash929603693

ndash1032734614

ndash2717431757


Africamiddot

AsiapacificEurope


(a)



02000000000

1

Time


JanFebMarApr

MayJuneJulyAug

Reve

nue

loss

by

airp

ort r

egio

ns

() U

SD(b)






E MID W NE

Resilience index

0

10

20

30

40

50

60

70

Resil

ienc

e ind

ex v

alue

()

(a)

E MID W NE

Resilience index

0

20

40

60

80

100

120

140

Resil

ienc

e ind

ex v

alue

()

(b)

Resilience index

E MID W NE0

50

100

150

200

250

Resil

ienc

e ind

ex v

alue

()

(c)

Resilience index

E MID W NE0

20

40

60

80

100

Resil

ienc

e ind

ex v

alue

()

(d)




()

E 6326 9775 24424 9009MID 2907 8347 6597 4314W 6918 12714 8559 8176NE 1583 6674 1267 1749




4 Conclusions


4000

3000

2000

1000

0


Airc

ra

mov

emen

ts

ChinaEurope

RegionChinaEurope

(a)

Strategy 1

Strategy 2

00510152025

2030

2035

40

Resil

ienc

e ind

ex v

alue

ZBA

AZS

PDZS

SSZG

SZZU

UU

ZPPP

ZUCK

ZGG

G

ZSH

CZS

NJ

ZLXY

EGLL

LFPG

EHA

MED

DF

LEM

DLE

BLED

DM

EGKK

Airports

Resilience index

(b)


45000

40000

35000

30000

25000

20000

15000

10000

5000

Airc

ra

mov

emen

ts

80000

60000

40000

20000

0

Case

s by

Chin

a

Time

ZUUUZPPPZBAAZGGG

ZUCKZSHCZSNJZLXY

ZSPDZSSSZGSZ

Confirmed cases

(c)


40000

35000

30000

25000

20000

15000

10000

5000

0

Airc

ra

mov

emen

ts

60000

40000

20000

0

Case

s by

Euro

pe

Time

EHAMEDDFLOWW

LEMDEDDMLEBL

EGLLLFPG

Confirmed cases

(d)






Data Availability




Acknowledgments


References

























































MOPKPIAij

(t) log10KPI

Ai

j (t)

log10 max KPIAi

j (t)1113960 1113961isin [0 1]

t 1 m

i 1 k

j 1 n

(9)


GRKPIAij

RKPIAij

times

RAPIRPKPIAij

RAPIDPKPIAij


1113874 1113875minus 1

times RKPIAij

(10)





(11)



1113944

m

n1Ptj lnPtj (12)



Wj 1 minus Ej


0leWj le 1 1113944n

j11⎛⎝ ⎞⎠ (13)


GPKPAi 1113944

n

j1WjGR

Ai

KPIj (14)

3 Case Study











Airc

ra

mov

emen

tsCu

rren

t con

firm

ed ca

ses

The United State

The United Kingdom

The United Kingdom

The United State

European Union

European Union

Japan

Japan

South korea

South korea




Earlydevelopment

Steadilygrowing

Fastacceleration












1 times 105

2 times 105

3 times 105

4 times 105

5 times 105A

ircra

m

ovem

ents

(flig

hts)

Time

EMID

WNE

(a)

EMID

WNE


10

20

30

40

50

60

Pass

enge

r thr

ough

put (

mill

ion

peop

le)

Time

(b)


EMID

WNE

0

20 times 105

40 times 105

60 times 105

80 times 105

10 times 106

Frei

ght t

hrou

ghpu

t(KG

)

Time

(c)


120110100

908070605040302010

Inde

x (c

risis

mon

th =

100

)





South-East Asia



korea


RPKs


AirlineRPKs








ndash352242254

ndash2955064176

ndash4643629102

ndash929603693

ndash1032734614

ndash2717431757


Africamiddot

AsiapacificEurope


(a)



02000000000

1

Time


JanFebMarApr

MayJuneJulyAug

Reve

nue

loss

by

airp

ort r

egio

ns

() U

SD(b)






E MID W NE

Resilience index

0

10

20

30

40

50

60

70

Resil

ienc

e ind

ex v

alue

()

(a)

E MID W NE

Resilience index

0

20

40

60

80

100

120

140

Resil

ienc

e ind

ex v

alue

()

(b)

Resilience index

E MID W NE0

50

100

150

200

250

Resil

ienc

e ind

ex v

alue

()

(c)

Resilience index

E MID W NE0

20

40

60

80

100

Resil

ienc

e ind

ex v

alue

()

(d)




()

E 6326 9775 24424 9009MID 2907 8347 6597 4314W 6918 12714 8559 8176NE 1583 6674 1267 1749




4 Conclusions


4000

3000

2000

1000

0


Airc

ra

mov

emen

ts

ChinaEurope

RegionChinaEurope

(a)

Strategy 1

Strategy 2

00510152025

2030

2035

40

Resil

ienc

e ind

ex v

alue

ZBA

AZS

PDZS

SSZG

SZZU

UU

ZPPP

ZUCK

ZGG

G

ZSH

CZS

NJ

ZLXY

EGLL

LFPG

EHA

MED

DF

LEM

DLE

BLED

DM

EGKK

Airports

Resilience index

(b)


45000

40000

35000

30000

25000

20000

15000

10000

5000

Airc

ra

mov

emen

ts

80000

60000

40000

20000

0

Case

s by

Chin

a

Time

ZUUUZPPPZBAAZGGG

ZUCKZSHCZSNJZLXY

ZSPDZSSSZGSZ

Confirmed cases

(c)


40000

35000

30000

25000

20000

15000

10000

5000

0

Airc

ra

mov

emen

ts

60000

40000

20000

0

Case

s by

Euro

pe

Time

EHAMEDDFLOWW

LEMDEDDMLEBL

EGLLLFPG

Confirmed cases

(d)






Data Availability




Acknowledgments


References





























































Airc

ra

mov

emen

tsCu

rren

t con

firm

ed ca

ses

The United State

The United Kingdom

The United Kingdom

The United State

European Union

European Union

Japan

Japan

South korea

South korea




Earlydevelopment

Steadilygrowing

Fastacceleration












1 times 105

2 times 105

3 times 105

4 times 105

5 times 105A

ircra

m

ovem

ents

(flig

hts)

Time

EMID

WNE

(a)

EMID

WNE


10

20

30

40

50

60

Pass

enge

r thr

ough

put (

mill

ion

peop

le)

Time

(b)


EMID

WNE

0

20 times 105

40 times 105

60 times 105

80 times 105

10 times 106

Frei

ght t

hrou

ghpu

t(KG

)

Time

(c)


120110100

908070605040302010

Inde

x (c

risis

mon

th =

100

)





South-East Asia



korea


RPKs


AirlineRPKs








ndash352242254

ndash2955064176

ndash4643629102

ndash929603693

ndash1032734614

ndash2717431757


Africamiddot

AsiapacificEurope


(a)



02000000000

1

Time


JanFebMarApr

MayJuneJulyAug

Reve

nue

loss

by

airp

ort r

egio

ns

() U

SD(b)






E MID W NE

Resilience index

0

10

20

30

40

50

60

70

Resil

ienc

e ind

ex v

alue

()

(a)

E MID W NE

Resilience index

0

20

40

60

80

100

120

140

Resil

ienc

e ind

ex v

alue

()

(b)

Resilience index

E MID W NE0

50

100

150

200

250

Resil

ienc

e ind

ex v

alue

()

(c)

Resilience index

E MID W NE0

20

40

60

80

100

Resil

ienc

e ind

ex v

alue

()

(d)




()

E 6326 9775 24424 9009MID 2907 8347 6597 4314W 6918 12714 8559 8176NE 1583 6674 1267 1749




4 Conclusions


4000

3000

2000

1000

0


Airc

ra

mov

emen

ts

ChinaEurope

RegionChinaEurope

(a)

Strategy 1

Strategy 2

00510152025

2030

2035

40

Resil

ienc

e ind

ex v

alue

ZBA

AZS

PDZS

SSZG

SZZU

UU

ZPPP

ZUCK

ZGG

G

ZSH

CZS

NJ

ZLXY

EGLL

LFPG

EHA

MED

DF

LEM

DLE

BLED

DM

EGKK

Airports

Resilience index

(b)


45000

40000

35000

30000

25000

20000

15000

10000

5000

Airc

ra

mov

emen

ts

80000

60000

40000

20000

0

Case

s by

Chin

a

Time

ZUUUZPPPZBAAZGGG

ZUCKZSHCZSNJZLXY

ZSPDZSSSZGSZ

Confirmed cases

(c)


40000

35000

30000

25000

20000

15000

10000

5000

0

Airc

ra

mov

emen

ts

60000

40000

20000

0

Case

s by

Euro

pe

Time

EHAMEDDFLOWW

LEMDEDDMLEBL

EGLLLFPG

Confirmed cases

(d)






Data Availability




Acknowledgments


References



























































1 times 105

2 times 105

3 times 105

4 times 105

5 times 105A

ircra

m

ovem

ents

(flig

hts)

Time

EMID

WNE

(a)

EMID

WNE


10

20

30

40

50

60

Pass

enge

r thr

ough

put (

mill

ion

peop

le)

Time

(b)


EMID

WNE

0

20 times 105

40 times 105

60 times 105

80 times 105

10 times 106

Frei

ght t

hrou

ghpu

t(KG

)

Time

(c)


120110100

908070605040302010

Inde

x (c

risis

mon

th =

100

)





South-East Asia



korea


RPKs


AirlineRPKs








ndash352242254

ndash2955064176

ndash4643629102

ndash929603693

ndash1032734614

ndash2717431757


Africamiddot

AsiapacificEurope


(a)



02000000000

1

Time


JanFebMarApr

MayJuneJulyAug

Reve

nue

loss

by

airp

ort r

egio

ns

() U

SD(b)






E MID W NE

Resilience index

0

10

20

30

40

50

60

70

Resil

ienc

e ind

ex v

alue

()

(a)

E MID W NE

Resilience index

0

20

40

60

80

100

120

140

Resil

ienc

e ind

ex v

alue

()

(b)

Resilience index

E MID W NE0

50

100

150

200

250

Resil

ienc

e ind

ex v

alue

()

(c)

Resilience index

E MID W NE0

20

40

60

80

100

Resil

ienc

e ind

ex v

alue

()

(d)




()

E 6326 9775 24424 9009MID 2907 8347 6597 4314W 6918 12714 8559 8176NE 1583 6674 1267 1749




4 Conclusions


4000

3000

2000

1000

0


Airc

ra

mov

emen

ts

ChinaEurope

RegionChinaEurope

(a)

Strategy 1

Strategy 2

00510152025

2030

2035

40

Resil

ienc

e ind

ex v

alue

ZBA

AZS

PDZS

SSZG

SZZU

UU

ZPPP

ZUCK

ZGG

G

ZSH

CZS

NJ

ZLXY

EGLL

LFPG

EHA

MED

DF

LEM

DLE

BLED

DM

EGKK

Airports

Resilience index

(b)


45000

40000

35000

30000

25000

20000

15000

10000

5000

Airc

ra

mov

emen

ts

80000

60000

40000

20000

0

Case

s by

Chin

a

Time

ZUUUZPPPZBAAZGGG

ZUCKZSHCZSNJZLXY

ZSPDZSSSZGSZ

Confirmed cases

(c)


40000

35000

30000

25000

20000

15000

10000

5000

0

Airc

ra

mov

emen

ts

60000

40000

20000

0

Case

s by

Euro

pe

Time

EHAMEDDFLOWW

LEMDEDDMLEBL

EGLLLFPG

Confirmed cases

(d)






Data Availability




Acknowledgments


References





























































ndash352242254

ndash2955064176

ndash4643629102

ndash929603693

ndash1032734614

ndash2717431757


Africamiddot

AsiapacificEurope


(a)



02000000000

1

Time


JanFebMarApr

MayJuneJulyAug

Reve

nue

loss

by

airp

ort r

egio

ns

() U

SD(b)






E MID W NE

Resilience index

0

10

20

30

40

50

60

70

Resil

ienc

e ind

ex v

alue

()

(a)

E MID W NE

Resilience index

0

20

40

60

80

100

120

140

Resil

ienc

e ind

ex v

alue

()

(b)

Resilience index

E MID W NE0

50

100

150

200

250

Resil

ienc

e ind

ex v

alue

()

(c)

Resilience index

E MID W NE0

20

40

60

80

100

Resil

ienc

e ind

ex v

alue

()

(d)




()

E 6326 9775 24424 9009MID 2907 8347 6597 4314W 6918 12714 8559 8176NE 1583 6674 1267 1749




4 Conclusions


4000

3000

2000

1000

0


Airc

ra

mov

emen

ts

ChinaEurope

RegionChinaEurope

(a)

Strategy 1

Strategy 2

00510152025

2030

2035

40

Resil

ienc

e ind

ex v

alue

ZBA

AZS

PDZS

SSZG

SZZU

UU

ZPPP

ZUCK

ZGG

G

ZSH

CZS

NJ

ZLXY

EGLL

LFPG

EHA

MED

DF

LEM

DLE

BLED

DM

EGKK

Airports

Resilience index

(b)


45000

40000

35000

30000

25000

20000

15000

10000

5000

Airc

ra

mov

emen

ts

80000

60000

40000

20000

0

Case

s by

Chin

a

Time

ZUUUZPPPZBAAZGGG

ZUCKZSHCZSNJZLXY

ZSPDZSSSZGSZ

Confirmed cases

(c)


40000

35000

30000

25000

20000

15000

10000

5000

0

Airc

ra

mov

emen

ts

60000

40000

20000

0

Case

s by

Euro

pe

Time

EHAMEDDFLOWW

LEMDEDDMLEBL

EGLLLFPG

Confirmed cases

(d)






Data Availability




Acknowledgments


References



























































E MID W NE

Resilience index

0

10

20

30

40

50

60

70

Resil

ienc

e ind

ex v

alue

()

(a)

E MID W NE

Resilience index

0

20

40

60

80

100

120

140

Resil

ienc

e ind

ex v

alue

()

(b)

Resilience index

E MID W NE0

50

100

150

200

250

Resil

ienc

e ind

ex v

alue

()

(c)

Resilience index

E MID W NE0

20

40

60

80

100

Resil

ienc

e ind

ex v

alue

()

(d)




()

E 6326 9775 24424 9009MID 2907 8347 6597 4314W 6918 12714 8559 8176NE 1583 6674 1267 1749




4 Conclusions


4000

3000

2000

1000

0


Airc

ra

mov

emen

ts

ChinaEurope

RegionChinaEurope

(a)

Strategy 1

Strategy 2

00510152025

2030

2035

40

Resil

ienc

e ind

ex v

alue

ZBA

AZS

PDZS

SSZG

SZZU

UU

ZPPP

ZUCK

ZGG

G

ZSH

CZS

NJ

ZLXY

EGLL

LFPG

EHA

MED

DF

LEM

DLE

BLED

DM

EGKK

Airports

Resilience index

(b)


45000

40000

35000

30000

25000

20000

15000

10000

5000

Airc

ra

mov

emen

ts

80000

60000

40000

20000

0

Case

s by

Chin

a

Time

ZUUUZPPPZBAAZGGG

ZUCKZSHCZSNJZLXY

ZSPDZSSSZGSZ

Confirmed cases

(c)


40000

35000

30000

25000

20000

15000

10000

5000

0

Airc

ra

mov

emen

ts

60000

40000

20000

0

Case

s by

Euro

pe

Time

EHAMEDDFLOWW

LEMDEDDMLEBL

EGLLLFPG

Confirmed cases

(d)






Data Availability




Acknowledgments


References


























































4 Conclusions


4000

3000

2000

1000

0


Airc

ra

mov

emen

ts

ChinaEurope

RegionChinaEurope

(a)

Strategy 1

Strategy 2

00510152025

2030

2035

40

Resil

ienc

e ind

ex v

alue

ZBA

AZS

PDZS

SSZG

SZZU

UU

ZPPP

ZUCK

ZGG

G

ZSH

CZS

NJ

ZLXY

EGLL

LFPG

EHA

MED

DF

LEM

DLE

BLED

DM

EGKK

Airports

Resilience index

(b)


45000

40000

35000

30000

25000

20000

15000

10000

5000

Airc

ra

mov

emen

ts

80000

60000

40000

20000

0

Case

s by

Chin

a

Time

ZUUUZPPPZBAAZGGG

ZUCKZSHCZSNJZLXY

ZSPDZSSSZGSZ

Confirmed cases

(c)


40000

35000

30000

25000

20000

15000

10000

5000

0

Airc

ra

mov

emen

ts

60000

40000

20000

0

Case

s by

Euro

pe

Time

EHAMEDDFLOWW

LEMDEDDMLEBL

EGLLLFPG

Confirmed cases

(d)






Data Availability




Acknowledgments


References



























































Data Availability




Acknowledgments


References
























































resilience modeling method of airport network affected by

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