Accommodation selection process

, 244, 252

Acquired immunodeficiency syndrome (AIDS)

, 1, 19, 62–63

Affiliation statistics

, 99–100

Ageing population

, 34

Air cargo market analysis

, 236

Airline companies

, 228–230

losses in

, 231, 233–234

Airline industry

, 80, 84–85, 132

Airline passenger market analysis

, 236

Air Navigation Safety Agency

, 231

Airport Council International (ACI)

, 231

Airport management

, 234

Airport operators

, 228–229

Antiviral drugs

, 24

Arboviral diseases

, 3

Asian flu

, 18, 61–62

Asia Pacific

, 227

Asiatic flu

, 17–18

Austria

, 178–179

Autoregressive distributed lag (ARDL)

, 105

Autoregressive integrated moving average (ARIMA)

, 105

Available seat kilometres (ASKs)

, 236

Avian flu

, 22

international tourist

, 164–166

Aviation industry

, 5–6, 226

airline companies

, 228–229

airport operators

, 228–229

government and aviation authorities

, 229, 231–233

industry-oriented analysis

, 235–236

losses

, 231–235

Nigeria

, 227

unemployment risk

, 234–235

Bartlett test of sphericity

, 246–249

BibExcel

, 97

Bibliometric analysis

, 46, 96, 106–107

affiliation statistics

, 99–100

analysis tools

, 97

chronological publication trend

, 98

citation analysis

, 100, 102–103

cluster analysis

, 104–106

leading journals

, 98–99

network analysis of publications

, 100, 102, 104

page rank analysis

, 103–104

productive authors

, 100, 102

search process

, 96–97

Bibliometrix

, 97

Biodiversity

, 36

BioNTech

, 8

Black Death

, 17–18, 147–148

Black Plague

, 129–130

Boolean operators

, 97

Brazil

, 114–116

Campos do Jordão (SP)

, 120–121

epidemiological crises

, 115

Garanhuns (PE)

, 122–123

Petropolis (RJ)

, 121–122

sanitary issues

, 116–119

São Francisco de Paula (RS)

, 123–124

tourist nature

, 119–120

urbanization

, 116–119

Bubonic plague

, 17

Business impact

, 131–132

Cambodia

, 87

Camel flu

, 19–20

Campos do Jordão (SP)

, 120–121

Capitalism

, 114

Catastrophic pandemics

, 15–16

Asian flu (1956–1958)

, 18

Black Death (1346–1353)

, 17–18

cholera

, 17

COVID-19

, 20

Ebola

, 20

HIV/AIDS

, 19

Hong Kong flu (1968)

, 18

Middle East respiratory syndrome (MERS)

, 19

Russian flu (1889–1890)

, 17–18

severe acute respiratory syndrome (SARS)

, 18–19

Spanish flu (1918)

, 18

Swine flu (2009)

, 19

Chikungunya virus (CHIKV)

, 1

China

, 63, 148–149

aviation industry

, 231

Centre for Disease Control and Prevention (CDC)

, 149–150

inbound tourism

, 84

severe acute respiratory syndrome (SARS)

, 76

World Travel and Tourism Council (WTTC)

, 163–164

Cholera

, 1, 17, 114

Citation analysis

, 100, 102–103

CitNetExplorer

, 97

Climate change

, 35–37

Clinical trails

, 4

Cluster analysis

, 51, 104, 250

crisis management

, 105–106

severe acute respiratory syndrome (SARS)

, 104–105

tourism forecasting

, 105

Comma-separated values (CSV)

, 97

Contact tracing

, 24

COVID-19

, 22–23, 65–66, 72, 116, 147–148, 170–171, 178–179

accommodation selection process

, 244, 252

aviation industry, 5. See Aviation industry

bibliometric analysis

, 46

breakout

, 32–33

business impact

, 131–132

business travel payout

, 131

clinical trails

, 8–9

demographic change

, 33–34

destination choice

, 133–134

fatality rates

, 20

France

, 242

global impact

, 129–130

global travel

, 35

gross domestic production (GDP)

, 96

health governance measures

, 140–141

hospitality

, 5–6, 137, 139–140

income loss

, 131

infection rates

, 20

Japan

, 198–199

job losses, tourism

, 132–133

Macao. See Macao

mental health

, 134–136

Norway. See also Norway

, 182–184

symptoms

, 226

thematic model

, 136–137

tourism

, 5–6, 130–131, 137, 139–141

traveller’s risk perception

, 133–134

urbanization

, 33

vaccination development

, 3, 8–9

Vietnam

, 214, 218, 220

COVIDSafe

, 7

Crisis and disaster management

, 105–106, 178–180

Cruise Lines International Association (CLIA)

, 6–7

Cultural consumption

, 196–198

Deforestation

, 36

Democratic Republic of Congo (DRC)

, 19, 64–65

Demographic change

, 33–35

Dengue fever

, 44, 178

Destination marketing/management organizations (DMOs)

, 178, 181–182

Digital object identifier (DOI)

, 97

Disease outbreak

, 44

contributed countries

, 50–51

contributed funding sponsors

, 50

frequently cited articles

, 51

keyword analysis

, 51

methodological framework

, 46–47

productive authors

, 48–49

productive journals

, 48

research subject areas

, 49–50

Ebola virus disease (EBV)

, 1, 20, 22, 44, 64–65, 107–108, 147–148, 168, 170

Congo

, 178

West Africa

, 178

Economic depression

, 15–16

Economy cooperative actions, Macao

financial institutions

, 156

gaming concessionaires

, 154–155

hotels

, 156

large enterprises

, 155–156

universities

, 156

Emerging pathogens

, 2–3

England

, 118–119

Ethical engagement

, 249–251

European Union (EU)

, 178–179

Exclusion criteria (EC)

, 97

Experiential marketing

, 196–198

Exploratory factor analysis

, 246–249

Extracorporeal membrane oxygenation (ECMO)

, 149

Factor–cluster analysis

, 244

Food chain

, 36

Foot and mouth disease (FMD)

, 60–61, 178

France

, 117, 242, 244

Bartlett test of sphericity

, 246–249

cluster analysis

, 250

Cronbach’s alpha

, 246–249

ethical engagement

, 251–252

exploratory factor analysis

, 246–249

factor analysis

, 247–249

KMO index

, 246–249

methodology

, 244–245

physical distancing and hygiene

, 249–251

respondents characteristics

, 245–246

Varimax rotation

, 246–249

Garanhuns (PE)

, 122–123

Gephi

, 97, 100

Global economic crisis

, 163

Globalization

, 31–33

people-to-people contact and

, 37

Global tourism

, 182–183

Global travel

, 35

Global warming

, 35–36

Great Depression

, 182–183

Greater Bay Area (GBA)

, 153

Gross domestic product (GDP)

, 62, 96, 132–133, 148

Guangdong Health Code

, 153–154

Guinea

, 168

Hand and mouth disease (HMD)

, 107–108

Healthcare workers

, 38–39

Health diseases

, 130

Health governance measures

, 140–141

Health management

, 130–131

Histcite

, 97

H1N1 influenza

, 1, 96

H2N2 virus

, 61–62

H3N2 virus

, 62

Hokkaido Garden Kaido

, 200

Hong Kong flu

, 18

Hospitality

, 5–6, 44, 137, 139

innovations and digital solutions

, 7–8

Hotel industry

, 79–81, 83

Human–animal contact

, 38

Human immunodeficiency virus (HIV)

, 19, 62–63

Inbound tourism

, 84, 87–88, 90

Inclusion criteria (IC)

, 97

Income loss

, 131

Indonesia

, 164–166

Industry-oriented analysis

, 235

air cargo market analysis

, 236

airline passenger market analysis

, 236

Innovative marketing

, 197–198

Insomnia

, 138–139

Intergovernmental Panel on Climate Change (IPCC)

, 35–36

International Air Transportation Association (IATA)

, 6–7, 131–132, 163, 226, 231

regulations and recommendations

, 229–231

International Civil Aviation Organization (ICAO)

, 6–7, 231

International Monetary Fund

, 131

International tourist

, 162–163

Istanbul Sabiha Gokcen Airport Security Commission

, 228

Istanbul Sabiha Gokcen International Airport Administration (ISG)

, 228

Japan

, 104–105

COVID-19

, 198–199

cultural consumption

, 197–198

experiential marketing

, 198, 207

Fukui Prefecture

, 204

Gunma Prefecture

, 204

Hokkaido Prefecture

, 200–201

hot springs

, 196–197

innovative marketing

, 198, 206

Kanagawa Prefecture

, 201–202

Kumamoto Prefecture

, 201

Nagano, Shizuoka

, 204–205

Niigata Prefecture

, 203

Oita Prefecture

, 201

Okayama Prefecture

, 204–205

Olympic Games

, 196

product and service industry

, 196

Tochigi and Shizuoka Prefecture

, 203

Tokyo Metropolitan

, 201–202

tourism

, 198–199

Yamagata

, 204

Job loss

, 138–139

mental health

, 134–136

tourism

, 132–133

Keyword analysis

, 51

Lassa fever

, 1, 147–148

Lockdown

, 130–131, 134–135, 217–218

Macao

, 148–149, 156, 158

community-based pandemic prevention and control

, 150–151

Crowd Control Policy

, 150

economy cooperative actions

, 154–156

entry screening and prohibition

, 151

global outbreak

, 152–153

Guaranteed Mask Supply Scheme

, 150

pre-outbreak

, 149–150

semi-lockdown

, 151–152

trifles of resumption

, 153–154

Malaria

, 114, 178

Malaysia

, 36–37

Mental health

, 134–136

Mexico

, 166

Micro enterprises (MEs)

, 178–181, 184–185

Middle Ages

, 114

Middle East respiratory syndrome (MERS)

, 1, 16–17, 19, 22, 64, 147–148, 167–168

Monetary shortfall

, 135

National Health Commission (NHC)

, 149–150

Neolithic period

, 114

Nigeria

, 147–148, 227

Nipah virus

, 1

Non-pharmaceutical interventions (NPI)

, 60

Norway

, 179

COVID-19

, 182–183

crisis and disaster management

, 179–180

global tourism

, 182–183

government and industry responses

, 185–186

life cycles

, 179–180

micro enterprises (MEs)

, 184–185, 187

past health pandemics

, 180

small and medium-sized enterprises (SMEs)

, 184–185, 187

tourism development

, 183–184

tourism mobility

, 184

tourism responses

, 180–182

Olympic Games

, 196

Organisation for Economic Co-operation and Development (OECD)

, 178–179

Pacific Asia Travel Association (PATA)

, 6–7

Page rank analysis

, 103–104

Pajek

, 100

Pandemics

, 16–17

catastrophic

, 17–20

community strategies

, 23–24

vs. COVID-19

, 66–72

human history

, 33

non-pharmaceutical interventions

, 23–24

outbreak

, 32, 34

pharmaceutical interventions

, 24

tourism mobility

, 21–23

People-to-people contact

, 37

Personal protective equipment (PPE)

, 249–251

Personal protective measures

, 24

Petropolis (RJ)

, 121–122

Pneumonia

, 20, 149

Post-COVID tourism mobility

, 10

Prisma approach

, 78–79

Public Health Emergency of International Concern (PHEIC)

, 5

Quarantine

, 23–24, 136

Re-emerging pathogens

, 2–3

Republic of Korea (ROK)

, 64

Research and development (R&D)

, 2–3

Resilience

, 6, 66–67, 71

Restaurant industry

, 136

Revenue Passenger Kilometres (RPKs)

, 236

Revenue per available room (RevPAR)

, 218–219

Rift Valley fever virus (RVFV)

, 2–3

Russian flu (1889–1890)

, 17–18

Sanitation

, 249–251

São Francisco de Paula (RS)

, 123–124

Saudi Arabia

, 64, 167–168

Sauna tour

, 200–201

SciMAT

, 97

Scopus database

, 46–47, 96–97

Search process

, 96–97

Seasonal autoregressive integrated moving average (SARIMA)

, 105

Semi-lockdown

, 151–152

Service innovation

, 198

Severe acute respiratory syndrome (SARS)

, 1, 8, 18–19, 22, 63, 147–148, 163–164

airline industry

, 80, 84–85

article selection

, 78–79

Asian tourism industry

, 87–91

cluster analysis

, 104–105

destinations

, 84

gross domestic product (GDP)

, 163–164

hotel industry

, 79–81, 83

inbound tourism

, 84, 87–88, 90

inclusion criteria

, 77–78

outbreak

, 15–16, 77

personal protection equipment (PPEs)

, 215

previous pandemic

, 215

psychological effects

, 76

research database

, 78–79

statistics

, 76–77

Singapore

, 61–62

Sitkis

, 97

Small and medium-sized enterprises (SMEs)

, 151–152, 178–181, 184–185

Social distancing

, 23, 134–135

Social isolation

, 136

Socio-demographics

, 252

South Korea

, 167–168

Spanish flu

, 18, 61, 114

Spring General Offensive of 2020

, 217

Stay-at-home policy

, 134–135

Swine flu

, 19, 63–64, 166–167

vaccine

, 3

Swine influenza virus (SIV)

, 63

Swine-origin influenza virus (S-OIV)

, 63

Tableau

, 46–47

Taiwan

, 84

Terrorism

, 84

Thailand

, 164–166

Thematic model

, 136–137

Tourism

, 15–16, 44–46, 161–162

Brazil

, 119–120

forecasting

, 105

frequently cited articles

, 51

inbound

, 84, 87–88, 90

industry

, 6

innovations and digital solutions

, 7–8

Japan

, 198–199

job losses

, 132–133

mobility

, 3, 5, 10

Norway

, 180

resilience

, 66–67, 71

South Asia

, 60–61

stakeholders

, 141

Tourist destination safety

, 140–141

Traveller’s risk perception

, 133–134

Travel-related variables

, 244

Turkey

, 233–234

Uganda

, 65

United Nations Educational, Scientific and Cultural Organization (UNESCO)

, 8

United Nations World Tourism Organization (UNWTO)

, 5, 163, 226

United States

, 61–62

Urbanization

, 33, 116, 119

Vaccination

, 4, 24

Varimax rotation

, 246–249

Vector-borne viral diseases

, 44

Vietnam

, 63, 213–214

COVID-19

, 218, 220

data collection

, 216

government and hospitality

, 216–218

methodology

, 216

road to recovery

, 221

severe acute respiratory syndrome (SARS)

, 216

tourism businesses

, 216–218

Virus outbreaks

, 1, 60, 64

acquired immunodeficiency syndrome (AIDS)

, 62–63

Asian countries

, 3

Asian flu

, 61–62

Ebola

, 64–65

Hong Kong flu

, 62

human immunodeficiency virus (HIV)

, 62–63

Middle East respiratory syndrome (MERS)

, 64

safe travel and tourism

, 6–7

severe acute respiratory syndrome (SARS)

, 63

South Asia

, 60–61

Spanish flu

, 61

swine flu

, 63–64

tourism mobility and

, 3–5

Zika virus (ZIKV)

, 65–66

VOSviewer

, 46–47, 97

World economy

, 129–130

World Health Organization (WHO)

, 2–3, 76, 178–179

airlines industry

, 227–228

climate change

, 36

COVID-19

, 5, 32

HIV/AIDS

, 19

Hong Kong flu

, 62

Middle East respiratory syndrome (MERS)

, 19–20

World Travel and Tourism Council (WTTC)

, 6–7, 163–164, 182–183

Wuhan

, 149

Yellow fever (YF)

, 1, 178

Zika virus (ZIKV)

, 1, 65–66, 168

Zydus Cadila

, 8