Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies

BACKGROUND

COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection.

METHODS

We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination.

RESULTS

Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions.

CONCLUSIONS

This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.

Characteristics and specifications of dashboards developed for the COVID-19 pandemic: a scoping review

Aim

The use of information-based solutions such as dashboards is on the rise for taking fact-based actions against the COVID-19 crisis. This scoping review aimed to comprehensively investigate COVID-19 dashboards from different technical perspectives.

Subject and methods

Three main bibliographic databases, PubMed, Web of Science, and Scopus, were searched on 28 August 2021 to retrieve relevant studies. Arksey and O'Malley's (Int J Soc Res Methodol 8(1):19-32, 2005) methodological framework and the enhanced version of this methodology developed by Levac et al. (Implement Sci 5(1):1-9, 2010) were adopted for conducting this review.

Results

In total, 26 articles were included. The COVID-19 dashboards mainly focused on the infected (n = 25), deceased (n = 17), and recovered cases (n = 13), as well as the performed test (n = 10). Most of the dashboards were interactive, with public accessibility targeting various user groups. While some dashboards were both informative and supportive (38%), most were mainly informative (92%). The dashboard data were generally analyzed using simple techniques (58%) and delivered through web-based applications (88%).

Conclusion

Dashboards can help immediately manage, analyze, and summarize a huge amount of information about a COVID-19 outbreak. The findings revealed that the developed COVID-19 dashboards share more or less analogous characteristics that could lay the groundwork for designing and developing dashboards for any other pandemic.

Informing the Design of Data Visualization Tools to Monitor the COVID-19 Pandemic in Portugal: A Web-Delphi Participatory Approach

Due to the large amount of data generated by new technologies and information systems in the health arena, health dashboards have become increasingly popular as data visualization tools which stimulate visual perception capabilities. Although the importance of involving users is recognized in dashboard design, a limited number of studies have combined participatory methods with visualization options. This study proposes a novel approach to inform the design of data visualization tools in the COVID-19 context. With the objective of understanding which visualization formats should be incorporated within dashboards for the COVID-19 pandemic, a specifically designed Web-Delphi process was developed to understand the preferences and views of the public in general regarding distinct data visualization formats. The design of the Delphi process aimed at considering not only the theory-based evidence regarding input data and visualization formats but also the perception of final users. The developed approach was implemented to select appropriate data visualization formats to present information commonly used in public web-based COVID-19 dashboards. Forty-seven individuals completed a two-round Web-Delphi process that was launched through a snowball approach. Most respondents were young and highly educated and expressed to prefer distinct visualisation formats for different types of indicators. The preferred visualization formats from the participants were used to build a redesigned version of the official DGS COVID-19 dashboard used in Portugal. This study provides insights into data visualization selection literature, as well as shows how a Delphi process can be implemented to assist the design of public health dashboards.

Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data

The spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this paper captures the association between changes in mobility patterns through time and the corresponding COVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate a strong relationship between mobility data and COVID-19 incidence, suggesting that more mobility is associated with more COVID-19 cases. Methodological procedures can be summarized in a multiple linear regression with a time moving window. Model validation demonstrate good forecast accuracy, particularly when we consider the cumulative number of cases. Based on this premise, it is possible to estimate and predict future evolution of the number of COVID-19 cases using near real-time information of population mobility.

PandemonCAT: Monitoring the COVID-19 Pandemic in Catalonia, Spain

BACKGROUND

The principal objective of this paper is to introduce an online interactive application that helps in real-time monitoring of the COVID-19 pandemic in Catalonia, Spain (PandemonCAT).

METHODS

This application is designed as a collection of user-friendly dashboards using open-source R software supported by the Shiny package.

RESULTS

PandemonCAT reports accumulated weekly updates of COVID-19 dynamics in a geospatial interactive platform for individual basic health areas (ABSs) of Catalonia. It also shows on a georeferenced map the evolution of vaccination campaigns representing the share of population with either one or two shots of the vaccine, for populations of different age groups. In addition, the application reports information about environmental and socioeconomic variables and also provides an interactive interface to visualize monthly public mobility before, during, and after the lockdown phases. Finally, we report the smoothed standardized COVID-19 infected cases and mortality rates on maps of basic health areas ABSs and regions of Catalonia. These smoothed rates allow the user to explore geographic patterns in incidence and mortality rates. The visualization of the variables that could have some influence on the spatiotemporal dynamics of the pandemic is demonstrated.

CONCLUSIONS

We believe the addition of these new dimensions, which is the key innovation of our project, will improve the current understanding of the spread and the impact of COVID-19 in the community. This application can be used as an open tool for consultation by the public of Catalonia and Spain in general. It could also have implications in facilitating the visualization of public health data, allowing timely interpretation due to the unpredictable nature of the pandemic.

Data Science and Knowledge Discovery

Nowadays, Data Science (DS) is gaining a relevant impact on the community. The most recent developments in Computer Science, such as advances in Machine and Deep Learning, Big Data, Knowledge Discovery, and Data Analytics, have triggered the development of several innovative solutions (e.g., approaches, methods, models, or paradigms). It is a trending topic with many application possibilities and motivates the researcher to conduct experiments in these most diverse areas. This issue created an opportunity to expose some of the most relevant achievements in the Knowledge Discovery and Data Science field and contribute to such subjects as Health, Smart Homes, Social Humanities, Government, among others. The relevance of this field can be easily observed by its current achieved numbers: thirteen research articles, one technical note, and forty-six authors from fifteen nationalities.