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Gharaibeh A, Gharaibeh MA, Bataineh S, Kecerová AM. Exploring the Spatial and Temporal Patterns of Children and Adolescents with COVID-19 Infections in Slovakia during March 2020 to July 2022. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:931. [PMID: 38929548 PMCID: PMC11205471 DOI: 10.3390/medicina60060931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/11/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024]
Abstract
Background and Objectives: The COVID-19 pandemic has had a significant global impact, necessitating a comprehensive understanding of its spatiotemporal patterns. The objective of this study is to explore the spatial and temporal patterns of COVID-19 infections among five age groups (<1, 1-4, 5-9, 10-14, and 15-19 years) in 72 districts of Slovakia on a quarterly basis from March 2020 to July 2022. Material and Methods: During the study period, a total of 393,429 confirmed PCR cases of COVID-19 or positive antigen tests were recorded across all studied age groups. The analysis examined the spatiotemporal spread of COVID infections per quarter, from September 2021 to May 2022. Additionally, data on hospitalizations, intensive care unit (ICU) admissions, pulmonary ventilation (PV), and death cases were analyzed. Results: The highest number of COVID-19 infections occurred between September 2021 and May 2022, particularly in the 10-14-year-old group (68,695 cases), followed by the 15-19-year-old group (62,232 cases), while the lowest incidence was observed in the <1-year-old group (1235 cases). Out of the total confirmed PCR cases, 18,886 individuals required hospitalization, 456 needed ICU admission, 402 received pulmonary ventilation, and only 16 died. The analysis of total daily confirmed PCR cases for all regions showed two major peaks on 12 December 2021 (6114 cases) and 1 February 2022 (3889 cases). Spatial mapping revealed that during December 2021 to February 2022, the highest number of infections in all age groups were concentrated mainly in Bratislava. Moreover, temporal trends of infections within each age group, considering monthly and yearly variations, exhibited distinct spatial patterns, indicating localized outbreaks in specific regions. Conclusions: The spatial and temporal patterns of COVID-19 infections among different age groups in Slovakia showed a higher number of infections in the 10-14-year-old age group, mainly occurring in urban districts. The temporal pattern of the spread of the virus to neighboring urban and rural districts reflected the movement of infected individuals. Hospitalizations, ICU and PV admissions, and deaths were relatively low. The study highlights the need for more proactive measures to contain outbreaks promptly and ensure the resilience of healthcare systems against future pandemics.
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Affiliation(s)
- Ahmad Gharaibeh
- Teaching Department of Orthopaedics Musculoskeletal Trauma, Faculty of Medicine, University Hospital of Louise Pasteur, Pavel Jozef Safarik University, 040 11 Košice, Slovakia
| | - Mamoun A. Gharaibeh
- Department of Natural Resources and the Environment, Faculty of Agriculture, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan
| | - Siham Bataineh
- Department of Civil Engineering, Faculty of Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan;
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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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Robert A, Chapman LAC, Grah R, Niehus R, Sandmann F, Prasse B, Funk S, Kucharski AJ. Predicting subnational incidence of COVID-19 cases and deaths in EU countries. BMC Infect Dis 2024; 24:204. [PMID: 38355414 PMCID: PMC11361242 DOI: 10.1186/s12879-024-08986-x] [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: 09/01/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Recurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models. METHODS We used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios. RESULTS At a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12-50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics. DISCUSSION Our model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.
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Affiliation(s)
- Alexis Robert
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
| | - Lloyd A C Chapman
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
| | - Rok Grah
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Rene Niehus
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Frank Sandmann
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
- Current address: Robert Koch Institute, Berlin, Germany
| | - Bastian Prasse
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Adam J Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
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Jung Kjær L, Ward MP, Boklund AE, Larsen LE, Hjulsager CK, Kirkeby CT. Using surveillance data for early warning modelling of highly pathogenic avian influenza in Europe reveals a seasonal shift in transmission, 2016-2022. Sci Rep 2023; 13:15396. [PMID: 37717056 PMCID: PMC10505205 DOI: 10.1038/s41598-023-42660-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 09/13/2023] [Indexed: 09/18/2023] Open
Abstract
Avian influenza in wild birds and poultry flocks constitutes a problem for animal welfare, food security and public health. In recent years there have been increasing numbers of outbreaks in Europe, with many poultry flocks culled after being infected with highly pathogenic avian influenza (HPAI). Continuous monitoring is crucial to enable timely implementation of control to prevent HPAI spread from wild birds to poultry and between poultry flocks within a country. We here utilize readily available public surveillance data and time-series models to predict HPAI detections within European countries and show a seasonal shift that happened during 2021-2022. The output is models capable of monitoring the weekly risk of HPAI outbreaks, to support decision making.
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Affiliation(s)
- Lene Jung Kjær
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Michael P Ward
- Faculty of Science, Sydney School of Veterinary Science, University of Sydney, Camden, NSW, Australia
| | - Anette Ella Boklund
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars Erik Larsen
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Carsten Thure Kirkeby
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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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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Spatio-temporal model to investigate COVID-19 spread accounting for the mobility amongst municipalities. Spat Spatiotemporal Epidemiol 2023:100568. [PMCID: PMC9904848 DOI: 10.1016/j.sste.2023.100568] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
The rapid spread of COVID-19 worldwide led to the implementation of various non-pharmaceutical interventions to limit transmission and hence reduce the number of infections. Using telecom-operator-based mobility data and a spatio-temporal dynamic model, the impact of mobility on the evolution of the pandemic at the level of the 581 Belgian municipalities is investigated. By decomposing incidence, particularly into within- and between-municipality components, we noted that the global epidemic component is relatively more important in larger municipalities (e.g., cities), while the local component is more relevant in smaller (rural) municipalities. Investigation of the effect of mobility on the pandemic spread showed that reduction of mobility has a significant impact in reducing the number of new infections.
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Bekker‐Nielsen Dunbar M, Hofmann F, Held L. Session 3 of the RSS Special Topic Meeting on Covid-19 Transmission: Replies to the discussion. JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A, (STATISTICS IN SOCIETY) 2022; 185:S158-S164. [PMID: 38607908 PMCID: PMC9878005 DOI: 10.1111/rssa.12985] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/14/2024]
Affiliation(s)
| | - Felix Hofmann
- Epidemiology, Biostatistics and Prevention Institute (EBPI)University of Zurich (UZH)ZurichSwitzerland
| | - Leonhard Held
- Epidemiology, Biostatistics and Prevention Institute (EBPI)University of Zurich (UZH)ZurichSwitzerland
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Bekker‐Nielsen Dunbar M, Hofmann F, Held L. Assessing the effect of school closures on the spread of COVID-19 in Zurich. JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A, (STATISTICS IN SOCIETY) 2022; 185:S131-S142. [PMID: 38607867 PMCID: PMC9878126 DOI: 10.1111/rssa.12910] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 04/08/2022] [Indexed: 04/14/2024]
Abstract
The effect of school closure on the spread of COVID-19 has been discussed intensively in the literature and the news. To capture the interdependencies between children and adults, we consider daily age-stratified incidence data and contact patterns between age groups which change over time to reflect social distancing policy indicators. We fit a multivariate time-series endemic-epidemic model to such data from the Canton of Zurich, Switzerland and use the model to predict the age-specific incidence in a counterfactual approach (with and without school closures). The results indicate a 17% median increase of incidence in the youngest age group (0-14 year olds), whereas the relative increase in the other age groups drops to values between 2% and 3%. We argue that our approach is more informative to policy makers than summarising the effect of school closures with time-dependent effective reproduction numbers, which are difficult to estimate due to the sparsity of incidence counts within the relevant age groups.
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Affiliation(s)
| | - Felix Hofmann
- Epidemiology, Biostatistics and Prevention Institute (EBPI)University of Zurich (UZH)ZurichSwitzerland
| | - Leonhard Held
- Epidemiology, Biostatistics and Prevention Institute (EBPI)University of Zurich (UZH)ZurichSwitzerland
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Douwes‐Schultz D, Sun S, Schmidt AM, Moodie EEM. Extended Bayesian endemic–epidemic models to incorporate mobility data into COVID‐19 forecasting. CAN J STAT 2022; 50:713-733. [PMID: 35941958 PMCID: PMC9349401 DOI: 10.1002/cjs.11723] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 04/23/2022] [Indexed: 11/09/2022]
Abstract
Forecasting the number of daily COVID‐19 cases is critical in the short‐term planning of hospital and other public resources. One potentially important piece of information for forecasting COVID‐19 cases is mobile device location data that measure the amount of time an individual spends at home. Endemic–epidemic (EE) time series models are recently proposed autoregressive models where the current mean case count is modelled as a weighted average of past case counts multiplied by an autoregressive rate, plus an endemic component. We extend EE models to include a distributed‐lag model in order to investigate the association between mobility and the number of reported COVID‐19 cases; we additionally include a weekly first‐order random walk to capture additional temporal variation. Further, we introduce a shifted negative binomial weighting scheme for the past counts that is more flexible than previously proposed weighting schemes. We perform inference under a Bayesian framework to incorporate parameter uncertainty into model forecasts. We illustrate our methods using data from four US counties.
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Affiliation(s)
- Dirk Douwes‐Schultz
- Department of Epidemiology Biostatistics and Occupational Health, McGill University Montréal Canada
| | - Shuo Sun
- Department of Epidemiology Biostatistics and Occupational Health, McGill University Montréal Canada
| | - Alexandra M. Schmidt
- Department of Epidemiology Biostatistics and Occupational Health, McGill University Montréal Canada
| | - Erica E. M. Moodie
- Department of Epidemiology Biostatistics and Occupational Health, McGill University Montréal Canada
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Zhu P, Tan X. Evaluating the effectiveness of Hong Kong's border restriction policy in reducing COVID-19 infections. BMC Public Health 2022; 22:803. [PMID: 35449094 PMCID: PMC9023047 DOI: 10.1186/s12889-022-13234-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
This study evaluates the effectiveness of Hong Kong's strict border restrictions with mainland China in curbing the transmission of COVID-19. Combining big data from Baidu Population Migration with traditional meteorological data and census data for over 200 Chinese cities, we utilize an advanced quantitative approach, namely synthetic control modeling, to produce a counterfactual "synthetic Hong Kong" without a strict border restriction policy. We then simulate infection trends under the hypothetical scenarios and compare them to actual infection numbers. Our counterfactual synthetic control model demonstrates a lower number of COVID-19 infections than the actual scenario, where strict border restrictions with mainland China were implemented from February 8 to March 6, 2020. Moreover, the second synthetic control model, which assumes a border reopen on 7 May 2020 demonstrates nonpositive effects of extending the border restriction policy on preventing and controlling infections. We conclude that the border restriction policy and its further extension may not be useful in containing the spread of COVID-19 when the virus is already circulating in the local community. Given the substantial economic and social costs, and as precautionary measures against COVID-19 becomes the new normal, countries can consider reopening borders with neighbors who have COVID-19 under control. Governments also need to closely monitor the changing epidemic situations in other countries in order to make prompt and sensible amendments to their border restriction policies.
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Affiliation(s)
- Pengyu Zhu
- Associate Professor Division of Public Policy, Hong Kong University of Science and Technology, Hong Kong SAR, China.
| | - Xinying Tan
- PhD Student in Public Policy, Division of Public Policy, Hong Kong University of Science and Technology, Hong Kong SAR, China
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