Measuring the impact of COVID-19 vaccination and immunity waning: A modelling study for Portugal

Caetano, Constantino; Morgado, Maria Luísa; Patrício, Paula; Leite, Andreia; Machado, Ausenda; Torres, A.; Pereira, João F.; Namorado, Sónia; Sottomayor, Ana; Peralta‐Santos, André; Nunes, Baltazar

doi:10.1016/j.vaccine.2022.10.007

Cited by 7 publications

(5 citation statements)

References 44 publications

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“…1A), and de nes the municipalities that had higher and increasingly above the mean incidence rates, especially in autumn-winter 2021-22 season. The sharper increase in incidence and variability patterns observed between December 2021 and February 2022 is consistent with the dissemination of the Omicron variant 33,34 , ease of public health restrictive measures that occurred after a long second and third waves of the COVID-19 pandemic, and the sense of safety created by the implementation of vaccination programmes 35 . The vast majority of municipalities located in the northern coast and adjacent regions are those who had relatively high incidence rates in that period and are classi ed in clusters 1 and 5 (Fig.…”

Section: Spatiotemporal Evolutionsupporting

confidence: 55%

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: a functional data analysis from August 2020 to March 2022

Ribeiro

Azevedo

Peralta‐Santos³

et al. 2023

Preprint

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Since the emergence of SARS-CoV-2 and the pandemic, massive amounts of daily data on incidence are being collected by governments and public health authorities, providing relevant information about the dissemination of pandemic in terms of its virological evolution and spatiotemporal distribution of cases, hospitalization, and deaths. We propose a novel approach combining functional data analysis and unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying SARS-CoV-2 incidence. We modelled the daily COVID-19 confirmed cases by municipality as a function of time using functional principal component analysis to describe their temporal evolution in order to outline the main temporal patterns of variability. Municipalities were classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. The proposed approach is applied to mainland Portugal with data collected between August 2020 and March 2022 by municipality. The results obtained discriminate northern and coastal regions from southern and hinterland, and the effects in 2020-21 from the effects in 2021-22 autumn-winter seasons. Spatiotemporal patterns and classification of municipalities agree with results reported by other works and provides proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach extends and refines existing exploratory tools for spatiotemporal analysis of public health data.

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Section: Spatiotemporal Evolutionsupporting

confidence: 55%

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: a functional data analysis from August 2020 to March 2022

Ribeiro

Azevedo

Peralta‐Santos³

et al. 2023

Preprint

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“…The temporal pattern found in the first functional PC ( Fig 5 , PC1) matches the curve shape found for the overall mean incidence evolution in Portugal (e.g., see Fig 2 ), and defines the municipalities that had higher and increasingly above the mean incidence rates, especially in autumn-winter 2021–2022. The sharper increase in incidence and variability patterns observed between December 2021 and February 2022 is consistent with the dissemination of the Omicron variant [ 52 , 53 ], the easing of restrictive public health measures that occurred after the long second and third waves of the COVID-19 pandemic, and the sense of safety created by the implementation of vaccination programs [ 32 ].…”

Section: Discussionmentioning

confidence: 81%

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022

Ribeiro,

Azevedo,

Santos

et al. 2024

PLoS ONE

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During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic’s spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020–2021 period from those in the 2021–2022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.

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“…Caetano et al ( 32 ) estimated the COVID-19 averted deaths in Portugal using a SEIR model to measure the impact of vaccination strategy. The authors adapted an age-structured SEIR deterministic model and used hospitalization data for the model calibration to measure the impact of the COVID-19 Portuguese vaccination strategy on the effective reproduction number.…”

Section: Resultsmentioning

confidence: 99%

“…It is important to note that a comparative analysis between studies is hampered by the different ways the impacts of vaccination processes are presented. Most studies estimated the impact in terms of averted COVID-19 deaths ( 20 , 25 , 26 , 28 , 32–34 , 36 , 37 ), and some of them also analyzed the averted hospitalizations (and severe hospitalizations) ( 24–26 , 28 , 34 , 37 ). Other studies investigated the COVID-19 incidence seeking to estimate the reduction in the number of cases ( 19 , 21 , 28 , 29 , 34 , 38 ).…”

Section: Resultsmentioning

confidence: 99%

What we talk about when we talk about COVID-19 vaccination campaign impact: a narrative review

Filho

Peres

Maddalena

et al. 2023

Front. Public Health

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BackgroundThe lack of precise definitions and terminological consensus about the impact studies of COVID-19 vaccination leads to confusing statements from the scientific community about what a vaccination impact study is.ObjectiveThe present work presents a narrative review, describing and discussing COVID-19 vaccination impact studies, mapping their relevant characteristics, such as study design, approaches and outcome variables, while analyzing their similarities, distinctions, and main insights.MethodsThe articles screening, regarding title, abstract, and full-text reading, included papers addressing perspectives about the impact of vaccines on population outcomes. The screening process included articles published before June 10, 2022, based on the initial papers’ relevance to this study’s research topics. The main inclusion criteria were data analyses and study designs based on statistical modelling or comparison of pre- and post-vaccination population.ResultsThe review included 18 studies evaluating the vaccine impact in a total of 48 countries, including 32 high-income countries (United States, Israel, and 30 Western European countries) and 16 low- and middle-income countries (Brazil, Colombia, and 14 Eastern European countries). We summarize the main characteristics of the vaccination impact studies analyzed in this narrative review.ConclusionAlthough all studies claim to address the impact of a vaccination program, they differ significantly in their objectives since they adopt different definitions of impact, methodologies, and outcome variables. These and other differences are related to distinct data sources, designs, analysis methods, models, and approaches.

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Measuring the impact of COVID-19 vaccination and immunity waning: A modelling study for Portugal

Cited by 7 publications

References 44 publications

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: a functional data analysis from August 2020 to March 2022

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: a functional data analysis from August 2020 to March 2022

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022

What we talk about when we talk about COVID-19 vaccination campaign impact: a narrative review

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