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Ribeiro M, Azevedo L, Santos AP, Pinto Leite P, Pereira MJ. Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022. PLoS One 2024; 19:e0297772. [PMID: 38300912 PMCID: PMC10833534 DOI: 10.1371/journal.pone.0297772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 01/12/2024] [Indexed: 02/03/2024] Open
Abstract
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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Affiliation(s)
- Manuel Ribeiro
- CERENA, DER, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Leonardo Azevedo
- CERENA, DER, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - André Peralta Santos
- Direção de Serviços de Informação e Análise, Direção-Geral da Saúde, Lisbon, Portugal
- NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Pedro Pinto Leite
- Direção de Serviços de Informação e Análise, Direção-Geral da Saúde, Lisbon, Portugal
| | - Maria João Pereira
- CERENA, DER, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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Espinosa O, Mora L, Sanabria C, Ramos A, Rincón D, Bejarano V, Rodríguez J, Barrera N, Álvarez-Moreno C, Cortés J, Saavedra C, Robayo A, Franco OH. Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review. Syst Rev 2024; 13:30. [PMID: 38229123 PMCID: PMC10790449 DOI: 10.1186/s13643-023-02411-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 12/04/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). METHODOLOGY PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. FINDINGS In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. INTERPRETATION The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.
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Affiliation(s)
- Oscar Espinosa
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia.
| | - Laura Mora
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Cristian Sanabria
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Antonio Ramos
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Duván Rincón
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Valeria Bejarano
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Jhonathan Rodríguez
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Nicolás Barrera
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | | | - Jorge Cortés
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Carlos Saavedra
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Adriana Robayo
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Oscar H Franco
- University Medical Center Utrecht, Utrecht University & Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, USA
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Molla J, Farhang-Sardroodi S, Moyles IR, Heffernan JM. Pharmaceutical and non-pharmaceutical interventions for controlling the COVID-19 pandemic. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230621. [PMID: 38126062 PMCID: PMC10731327 DOI: 10.1098/rsos.230621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
Disease spread can be affected by pharmaceutical interventions (such as vaccination) and non-pharmaceutical interventions (such as physical distancing, mask-wearing and contact tracing). Understanding the relationship between disease dynamics and human behaviour is a significant factor to controlling infections. In this work, we propose a compartmental epidemiological model for studying how the infection dynamics of COVID-19 evolves for people with different levels of social distancing, natural immunity and vaccine-induced immunity. Our model recreates the transmission dynamics of COVID-19 in Ontario up to December 2021. Our results indicate that people change their behaviour based on the disease dynamics and mitigation measures. Specifically, they adopt more protective behaviour when mandated social distancing measures are in effect, typically concurrent with a high number of infections. They reduce protective behaviour when vaccination coverage is high or when mandated contact reduction measures are relaxed, typically concurrent with a reduction of infections. We demonstrate that waning of infection and vaccine-induced immunity are important for reproducing disease transmission in autumn 2021.
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Affiliation(s)
- Jeta Molla
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Ontario, Canada
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Ontario, Canada
| | - Suzan Farhang-Sardroodi
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Ontario, Canada
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Ontario, Canada
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Iain R. Moyles
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Ontario, Canada
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Ontario, Canada
| | - Jane M. Heffernan
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Ontario, Canada
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Ontario, Canada
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Hastenreiter Filho HN, Peres IT, Maddalena LG, Baião FA, Ranzani OT, Hamacher S, Maçaira PM, Bozza FA. What we talk about when we talk about COVID-19 vaccination campaign impact: a narrative review. Front Public Health 2023; 11:1126461. [PMID: 37250083 PMCID: PMC10211334 DOI: 10.3389/fpubh.2023.1126461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 04/06/2023] [Indexed: 05/31/2023] Open
Abstract
Background The 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. Objective The 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. Methods The 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. Results The 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. Conclusion Although 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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Affiliation(s)
- Horácio N. Hastenreiter Filho
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
- School of Management, Federal University of Bahia, Salvador, Brazil
| | - Igor T. Peres
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Lucas G. Maddalena
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fernanda A. Baião
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Otavio T. Ranzani
- Barcelona Institute for Global Health, Barcelona, Spain
- Pulmonary Division, Heart Institute, Faculty of Medicine, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Silvio Hamacher
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Paula M. Maçaira
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fernando A. Bozza
- National Institute of Infectious Disease Evandro Chagas, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil
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