# Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. Importance of Spatial and Spatiotemporal Analysis

#### 1.2. Spatial Analysis Models

#### 1.3. Spatiotemporal Analysis Models

## 2. Materials and Methods

#### 2.1. Study Area

^{2}.

#### 2.2. Data Collection and Processing

#### 2.2.1. Global, Local, and Hybrid Spatial Analysis Models

#### 2.2.2. Spatiotemporal Cluster Analysis

## 3. Results

#### 3.1. Selected Important Dates for Spatial Analysis

^{2}. Another was the anticipation of the containment period, which began at midnight of 25 December. Mandatory telework was established, and it was decided that nightclubs, bars, kindergartens, and daycare centres would be closed. Finally, authorities started requiring a negative test for access to tourist establishments and local accommodation, weddings and baptisms, corporate events, cultural shows, and sports venues.

#### 3.2. Hotspot Analysis (Getis-Ord Gi*) with Inverse Euclidean Distance

#### 3.3. Hotspot Analysis (Getis-Ord Gi*) with Commuting Weight Matrix

#### 3.4. Cluster and Outlier Analysis—Anselin Local Moran’s

#### 3.5. Hybrid Analysis

#### 3.6. Spatiotemporal Cluster Analysis

## 4. Discussion

_{t}), showed that the transmission of the virus in Portugal had begun as early as 21 February [75]. The spread of COVID-19 presents high spatiotemporal heterogeneity at various scales, which results from geographical specificities derived from sociodemographic and economic structures as well as connectivity between municipalities [76]; this makes the vulnerability to contagion anisotropic [77].

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Administrative divisions of mainland Portugal and the country’s situation in the world context. Special highlight is given to the Metropolitan Area of Porto (MAP) and the Metropolitan Area of Lisbon (MAL).

**Figure 2.**Population density (

**a**) and percentage of individuals who work or study outside their residence municipality (

**b**) in 2011.

**Figure 6.**Municipalities’ hotspots of SARS-CoV-2 infection cases for 15 September 2020 (

**a**), 14 October 2020 (

**b**), 13 November 2020 (

**c**), 31 December 2020 (

**d**), and 18 January 2021 (

**e**). Analysis performed using the Getis-Ord Gi* algorithm and inverse Euclidian distance as neighbouring factor.

**Figure 7.**Municipalities’ hotspots of SARS-CoV-2 infection cases for 15 September 2020 (

**a**), 14 October 2020 (

**b**), 13 November 2020 (

**c**), 31 December 2020 (

**d**), and 18 January 2021 (

**e**). Analysis performed using the Getis-Ord Gi* algorithm and commuting weight matrix as neighbouring factor.

**Figure 8.**Municipalities’ clusters and outliers of SARS-CoV-2 infection cases for 15 September 2020 (

**a**), 14 October 2020 (

**b**), 13 November 2020 (

**c**), 31 December 2020 (

**d**), and 18 January 2021 (

**e**). Analysis performed using the Anselin Local Moran’s algorithm and inverse Euclidian distance as neighbouring factor.

**Figure 9.**Hybrid spatial autocorrelation analysis of SARS-CoV-2 infection cases for 15 September 2020 (

**a**), 14 October 2020 (

**b**), 13 November 2020 (

**c**), 31 December 2020 (

**d**), and 18 January 2021 (

**e**).

**Figure 10.**Municipalities’ spatiotemporal hotspot patterns for two time bins: 2 × 14 days (

**a**) and 3 × 14 days (

**b**).

Model | Method | Reference |
---|---|---|

Spatial analysis models | Global Moran’s Index | [19,20,21,22,23] |

Moran’s Scatterplots | [24] | |

Hotspot Analysis (Getis-Ord Gi*) | [22,24,25] | |

Kernel Density Estimation | [22] | |

Anselin Local Moran’s Index | [22] | |

Spatiotemporal analysis models | Discrete Poisson Spatial Scan Statistic | [20] |

Analysis of Variance (ANOVA) | [26] | |

Mann–Kendall | [27,28,29,30] |

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Silva, M.; Betco, I.; Capinha, C.; Roquette, R.; Viana, C.M.; Rocha, J.
Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal. *Sustainability* **2022**, *14*, 10370.
https://doi.org/10.3390/su141610370

**AMA Style**

Silva M, Betco I, Capinha C, Roquette R, Viana CM, Rocha J.
Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal. *Sustainability*. 2022; 14(16):10370.
https://doi.org/10.3390/su141610370

**Chicago/Turabian Style**

Silva, Melissa, Iuria Betco, César Capinha, Rita Roquette, Cláudia M. Viana, and Jorge Rocha.
2022. "Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal" *Sustainability* 14, no. 16: 10370.
https://doi.org/10.3390/su141610370