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Bekker-Nielsen Dunbar M. Transmission matrices used in epidemiologic modelling. Infect Dis Model 2024; 9:185-194. [PMID: 38249428 PMCID: PMC10796975 DOI: 10.1016/j.idm.2023.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 01/23/2024] Open
Abstract
Mixing matrices are included in infectious disease models to reflect transmission opportunities between population strata. These matrices were originally constructed on the basis of theoretical considerations and most of the early work in this area originates from research on sexually transferred diseases in the 80s, in response to AIDS. Later work in the 90s populated these matrices on the basis of survey data gathered to capture transmission risks for respiratory diseases. We provide an overview of developments in the construction of matrices for capturing transmission opportunities in populations. Such transmission matrices are useful for epidemiologic modelling to capture within and between stratum transmission and can be informed from theoretical mixing assumptions, informed by empirical evidence gathered through investigation as well as generated on the basis of data. Links to summary measures and threshold conditions are also provided.
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Affiliation(s)
- M. Bekker-Nielsen Dunbar
- Centre for Research on Pandemics & Society, OsloMet – Oslo Metropolitan University, HG536, Holbergs gate 1, Oslo, 0166, Norway
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Nguyen MH, Nguyen THT, Molenberghs G, Abrams S, Hens N, Faes C. The impact of national and international travel on spatio-temporal transmission of SARS-CoV-2 in Belgium in 2021. BMC Infect Dis 2023; 23:428. [PMID: 37355572 DOI: 10.1186/s12879-023-08368-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 06/02/2023] [Indexed: 06/26/2023] Open
Abstract
BACKGROUND The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread over the world and caused tremendous impacts on global health. Understanding the mechanism responsible for the spread of this pathogen and the impact of specific factors, such as human mobility, will help authorities to tailor interventions for future SARS-CoV-2 waves or newly emerging airborne infections. In this study, we aim to analyze the spatio-temporal transmission of SARS-CoV-2 in Belgium at municipality level between January and December 2021 and explore the effect of different levels of human travel on disease incidence through the use of counterfactual scenarios. METHODS We applied the endemic-epidemic modelling framework, in which the disease incidence decomposes into endemic, autoregressive and neighbourhood components. The spatial dependencies among areas are adjusted based on actual connectivity through mobile network data. We also took into account other important factors such as international mobility, vaccination coverage, population size and the stringency of restriction measures. RESULTS The results demonstrate the aggravating effect of international travel on the incidence, and simulated counterfactual scenarios further stress the alleviating impact of a reduction in national and international travel on epidemic growth. It is also clear that local transmission contributed the most during 2021, and municipalities with a larger population tended to attract a higher number of cases from neighboring areas. CONCLUSIONS Although transmission between municipalities was observed, local transmission was dominant. We highlight the positive association between the mobility data and the infection spread over time. Our study provides insight to assist health authorities in decision-making, particularly when the disease is airborne and therefore likely influenced by human movement.
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Affiliation(s)
- Minh Hanh Nguyen
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium.
| | | | - Geert Molenberghs
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- I-BioStat, Katholieke Universiteit Leuven, BE-3000, Leuven, Belgium
| | - Steven Abrams
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- Global Health Institute, University of Antwerp, BE-2000, Antwerpen, Belgium
| | - Niel Hens
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- Global Health Institute, University of Antwerp, BE-2000, Antwerpen, Belgium
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, BE-2000, Antwerpen, Belgium
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- I-BioStat, Katholieke Universiteit Leuven, BE-3000, Leuven, Belgium
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