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

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Abstract

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.

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Most cited references 87

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The effect of large-scale anti-contagion policies on the COVID-19 pandemic

Solomon Hsiang, Daniel L Allen, Sebastien Annan-Phan … (2020)

Governments around the world are responding to the coronavirus disease 2019 (COVID-19) pandemic1, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with unprecedented policies designed to slow the growth rate of infections. Many policies, such as closing schools and restricting populations to their homes, impose large and visible costs on society; however, their benefits cannot be directly observed and are currently understood only through process-based simulations2-4. Here we compile data on 1,700 local, regional and national non-pharmaceutical interventions that were deployed in the ongoing pandemic across localities in China, South Korea, Italy, Iran, France and the United States. We then apply reduced-form econometric methods, commonly used to measure the effect of policies on economic growth5,6, to empirically evaluate the effect that these anti-contagion policies have had on the growth rate of infections. In the absence of policy actions, we estimate that early infections of COVID-19 exhibit exponential growth rates of approximately 38% per day. We find that anti-contagion policies have significantly and substantially slowed this growth. Some policies have different effects on different populations, but we obtain consistent evidence that the policy packages that were deployed to reduce the rate of transmission achieved large, beneficial and measurable health outcomes. We estimate that across these 6 countries, interventions prevented or delayed on the order of 61 million confirmed cases, corresponding to averting approximately 495 million total infections. These findings may help to inform decisions regarding whether or when these policies should be deployed, intensified or lifted, and they can support policy-making in the more than 180 other countries in which COVID-19 has been reported7.

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The deadly coronaviruses: The 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China

Yongshi Yang, Fujun Peng, Runsheng Wang … (2020)

The 2019-nCoV is officially called SARS-CoV-2 and the disease is named COVID-19. This viral epidemic in China has led to the deaths of over 1800 people, mostly elderly or those with an underlying chronic disease or immunosuppressed state. This is the third serious Coronavirus outbreak in less than 20 years, following SARS in 2002–2003 and MERS in 2012. While human strains of Coronavirus are associated with about 15% of cases of the common cold, the SARS-CoV-2 may present with varying degrees of severity, from flu-like symptoms to death. It is currently believed that this deadly Coronavirus strain originated from wild animals at the Huanan market in Wuhan, a city in Hubei province. Bats, snakes and pangolins have been cited as potential carriers based on the sequence homology of CoV isolated from these animals and the viral nucleic acids of the virus isolated from SARS-CoV-2 infected patients. Extreme quarantine measures, including sealing off large cities, closing borders and confining people to their homes, were instituted in January 2020 to prevent spread of the virus, but by that time much of the damage had been done, as human-human transmission became evident. While these quarantine measures are necessary and have prevented a historical disaster along the lines of the Spanish flu, earlier recognition and earlier implementation of quarantine measures may have been even more effective. Lessons learned from SARS resulted in faster determination of the nucleic acid sequence and a more robust quarantine strategy. However, it is clear that finding an effective antiviral and developing a vaccine are still significant challenges. The costs of the epidemic are not limited to medical aspects, as the virus has led to significant sociological, psychological and economic effects globally. Unfortunately, emergence of SARS-CoV-2 has led to numerous reports of Asians being subjected to racist behavior and hate crimes across the world.

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Demographic science aids in understanding the spread and fatality rates of COVID-19

Jennifer Dowd, Liliana Andriano, David M. Brazel … (2020)

Governments around the world must rapidly mobilize and make difficult policy decisions to mitigate the coronavirus disease 2019 (COVID-19) pandemic. Because deaths have been concentrated at older ages, we highlight the important role of demography, particularly, how the age structure of a population may help explain differences in fatality rates across countries and how transmission unfolds. We examine the role of age structure in deaths thus far in Italy and South Korea and illustrate how the pandemic could unfold in populations with similar population sizes but different age structures, showing a dramatically higher burden of mortality in countries with older versus younger populations. This powerful interaction of demography and current age-specific mortality for COVID-19 suggests that social distancing and other policies to slow transmission should consider the age composition of local and national contexts as well as intergenerational interactions. We also call for countries to provide case and fatality data disaggregated by age and sex to improve real-time targeted forecasting of hospitalization and critical care needs.

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Author and article information

Contributors

André Alves:

ORCID: https://orcid.org/0000-0002-8979-8906

andrejoelalves@campus.ul.pt

Nuno Marques da Costa:

ORCID: https://orcid.org/0000-0003-4859-9668

nunocosta@campus.ul.pt

Paulo Morgado:

ORCID: https://orcid.org/0000-0002-3220-4943

paulo@campus.ul.pt

Eduarda Marques da Costa:

ORCID: https://orcid.org/0000-0001-5070-3562

eduarda.costa@campus.ul.pt

Journal

Journal ID (nlm-ta): Int J Health Geogr

Journal ID (iso-abbrev): Int J Health Geogr

Title: International Journal of Health Geographics

Publisher: BioMed Central (London )

ISSN (Electronic): 1476-072X

Publication date (Electronic): 6 April 2023

Publication date PMC-release: 6 April 2023

Publication date Collection: 2023

Volume: 22

Electronic Location Identifier: 8

Affiliations

[1 ]GRID grid.9983.b, ISNI 0000 0001 2181 4263, Centre of Geographical Studies, Institute of Geography and Spatial Planning, , University of Lisbon, ; 1600-276, Lisbon, Portugal

[2 ]Associate Laboratory TERRA, 1349-017 Lisbon, Portugal

Author information

André Alves https://orcid.org/0000-0002-8979-8906

Nuno Marques da Costa https://orcid.org/0000-0003-4859-9668

Paulo Morgado https://orcid.org/0000-0002-3220-4943

Eduarda Marques da Costa https://orcid.org/0000-0001-5070-3562

Article

Publisher ID: 329

DOI: 10.1186/s12942-023-00329-4

PMC ID: 10078027

PubMed ID: 37024965

SO-VID: 13e16279-5afb-4d64-99fe-d13a590f4afd

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 15 February 2023

Date accepted : 28 March 2023

Funding

Funded by: Fundação para a Ciência e a Tecnologia, Portugal

Award ID: 596685735

Award ID: 613765655

Custom metadata

ScienceOpen disciplines: Public health

Keywords: covid-19,health determinants,gis,multicriteria decision analysis,non-pharmacological interventions,spatial-based policies,spatiotemporal analysis

Data availability:

ScienceOpen disciplines: Public health

Keywords: covid-19, health determinants, gis, multicriteria decision analysis, non-pharmacological interventions, spatial-based policies, spatiotemporal analysis

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

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Novel Coronavirus Disease COVID-19

Most cited references 87

The effect of large-scale anti-contagion policies on the COVID-19 pandemic

The deadly coronaviruses: The 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China

Demographic science aids in understanding the spread and fatality rates of COVID-19

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