# The Role of the Tourism Network in the Coordination of Pandemic Control Measures

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### 2.1. Tourism Networks: Structure and Performance

#### 2.2. Evolutionary Games and Applications to Tourism

#### 2.3. Collective risk Dilemma to Control Pandemic Risk

#### 2.4. Hypotheses Development

**Hypothesis 1**

**(H1).**

**Hypothesis 2**

**(H2).**

## 3. Materials and Methods

#### 3.1. The Model

_{i}, which is a dichotomous variable defining two possibilities: being a cooperator (C), where s

_{i}= 1, or being a defector (D), where s

_{i}= 0. Cooperators adopt the pandemic control measures while defectors do not. Cooperators will suffer the income loss derived from the application of measures whereas defectors do not.

^{K}is the number of cooperators in a certain group

^{K}, the expected pay-off of a player i belonging to group K in each time-step is:

^{K}≥ mN, the expected pay-off of a defector is the highest (normalized as 1), but if C

^{K}< mN, the expected pay-off is 1 − r < 1. In the case of being a cooperator, the pay-off decreases by c

_{i}in [0,1], which represents the percentage of income lost in the case of cooperating. This cost is not constant throughout players. By assumption, it will be larger for stakeholders that have a greater dependence on tourism.

#### 3.2. Data-Based Tourism Network

#### 3.3. Model Setup

_{i}, induced by a cooperation strategy was based on the dependence on tourism of each region. To this aim, we calculated the nights spent at tourist accommodation establishments per inhabitant in 2019 and used the value as a proxy of the economic cost for the region if there was a lockdown. The distribution of these values ranges in a wide spectrum (from 0.10 to 78.59), although the average rate is 4.11 nights per inhabitant. We use c

_{i}equal to this rate divided by 100. Then, tourism-dependent regions present a higher ci than those that are not.

## 4. Results

## 5. Discussion

#### 5.1. Theoretical Implications

#### 5.2. Practical Implications

#### 5.3. Limitations

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

**Figure A1.**Robustness analysis of cooperation levels in the collective risk dilemma model for different group sizes.

**Figure A2.**Robustness analysis of cooperation levels in the collective risk dilemma model for different minimum percentages of cooperation.

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**Figure 1.**The final level of cooperation in the collective risk dilemma model for different initial cooperation (initial Cs) and risk levels (r) under four scenarios.

Parameter | Value | Parameter | Value |
---|---|---|---|

Population size (Z) | 280 (NUTS2 regions) | Monte Carlo runs | 30 |

Time steps (T) | 200 | Risk value (r) | [0,1] |

Income loss (c_{i}) | From NUTS2 data | Initial Cs | [0,1] |

Group size (N) | {4, 8, 20} | Group threshold (m) | {0.5, 0.7} |

Network edges | 441 (after pruning) |

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**MDPI and ACS Style**

Hernández, J.M.; Bulchand-Gidumal, J.; Chica, M.
The Role of the Tourism Network in the Coordination of Pandemic Control Measures. *Sustainability* **2022**, *14*, 16188.
https://doi.org/10.3390/su142316188

**AMA Style**

Hernández JM, Bulchand-Gidumal J, Chica M.
The Role of the Tourism Network in the Coordination of Pandemic Control Measures. *Sustainability*. 2022; 14(23):16188.
https://doi.org/10.3390/su142316188

**Chicago/Turabian Style**

Hernández, Juan M., Jacques Bulchand-Gidumal, and Manuel Chica.
2022. "The Role of the Tourism Network in the Coordination of Pandemic Control Measures" *Sustainability* 14, no. 23: 16188.
https://doi.org/10.3390/su142316188