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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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2
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Flückiger M, Ludwig M. Spatial networks and the spread of COVID-19: results and policy implications from Germany. JAHRBUCH FUR REGIONALWISSENSCHAFTT = REVIEW OF REGIONAL RESEARCH 2023; 43:1-27. [PMID: 37520679 PMCID: PMC10183698 DOI: 10.1007/s10037-023-00185-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 03/22/2023] [Indexed: 08/01/2023]
Abstract
Spatial networks are known to be informative about the spatiotemporal transmission dynamics of COVID-19. Using district-level panel data from Germany that cover the first 22 weeks of 2020, we show that mobility, commuter and social networks all predict the spatiotemporal propagation of the epidemic. The main innovation of our approach is that it incorporates the whole network and updated information on case numbers across districts over time. We find that when disease incidence increases in network neighbouring regions, case numbers in the home district surge one week later. The magnitude of these network transmission effects is comparable to within-district transmission, illustrating the importance of networks as drivers of local disease dynamics. After the introduction of containment policies in mid-March, network transmission intensity drops substantially. Our analysis suggests that this reduction is primarily due to a change in quality-not quantity-of interregional movements. This implies that blanket mobility restrictions are not a prerequisite for containing the interregional spread of COVID-19.
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Affiliation(s)
| | - Markus Ludwig
- CESifo, Munich, Germany
- Technische Universität Braunschweig, Braunschweig, Germany
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3
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Zaremba K. Opening of hotels and ski facilities: Impact on mobility, spending, and Covid-19 outcomes. HEALTH ECONOMICS 2023; 32:1148-1180. [PMID: 36791023 DOI: 10.1002/hec.4660] [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: 02/02/2022] [Revised: 01/02/2023] [Accepted: 01/12/2023] [Indexed: 06/18/2023]
Abstract
This paper investigates how reopening hotels and ski facilities in Poland impacted tourism spending, mobility, and COVID-19 outcomes. We used administrative data from a government program that subsidizes travel to show that the policy increased the consumption of tourism services in ski resorts. By leveraging geolocation data from Facebook, we showed that ski resorts experienced a significant influx of tourists, increasing the number of local users by up to 50%. Furthermore, we confirmed an increase in the probability of meetings between pairs of users from distanced locations and users from tourist and non-tourist areas. As the policy impacted travel and gatherings, we then analyzed its effect on the diffusion of COVID-19. We found that counties with ski facilities experienced more infections after the reopening. Moreover, counties strongly connected to the ski resorts during the reopening had more subsequent cases than weakly connected counties.
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Jing F, Li Z, Qiao S, Zhang J, Olatosi B, Li X. Using geospatial social media data for infectious disease studies: a systematic review. INTERNATIONAL JOURNAL OF DIGITAL EARTH 2023; 16:130-157. [PMID: 37997607 PMCID: PMC10664840 DOI: 10.1080/17538947.2022.2161652] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 12/17/2022] [Indexed: 11/25/2023]
Abstract
Geospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022. These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood, county, state, and country. We categorized these studies into three major infectious disease research domains: surveillance, explanation, and prediction. With the assistance of advanced statistical and spatial methods, GSM data has been widely and deeply applied to these domains, particularly in surveillance and explanation domains. We further identified four knowledge gaps in terms of contextual information use, application scopes, spatiotemporal dimension, and data limitations and proposed innovation opportunities for future research. Our findings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.
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Affiliation(s)
- Fengrui Jing
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| | - Zhenlong Li
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| | - Shan Qiao
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Jiajia Zhang
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Banky Olatosi
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Xiaoming Li
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
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5
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Fritz C, De Nicola G, Rave M, Weigert M, Khazaei Y, Berger U, Küchenhoff H, Kauermann G. Statistical modelling of COVID-19 data: Putting generalized additive models to work. STAT MODEL 2022. [DOI: 10.1177/1471082x221124628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Over the course of the COVID-19 pandemic, Generalized Additive Models (GAMs) have been successfully employed on numerous occasions to obtain vital data-driven insights. In this article we further substantiate the success story of GAMs, demonstrating their flexibility by focusing on three relevant pandemic-related issues. First, we examine the interdepency among infections in different age groups, concentrating on school children. In this context, we derive the setting under which parameter estimates are independent of the (unknown) case-detection ratio, which plays an important role in COVID-19 surveillance data. Second, we model the incidence of hospitalizations, for which data is only available with a temporal delay. We illustrate how correcting for this reporting delay through a nowcasting procedure can be naturally incorporated into the GAM framework as an offset term. Third, we propose a multinomial model for the weekly occupancy of intensive care units (ICU), where we distinguish between the number of COVID-19 patients, other patients and vacant beds. With these three examples, we aim to showcase the practical and ‘off-the-shelf’ applicability of GAMs to gain new insights from real-world data.
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Affiliation(s)
- Cornelius Fritz
- Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Giacomo De Nicola
- Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Martje Rave
- Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Maximilian Weigert
- Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Yeganeh Khazaei
- Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Ursula Berger
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Helmut Küchenhoff
- Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Göran Kauermann
- Department of Statistics, Ludwig-Maximilians-University Munich, Munich, Germany
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6
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Huang X, Wang S, Zhang M, Hu T, Hohl A, She B, Gong X, Li J, Liu X, Gruebner O, Liu R, Li X, Liu Z, Ye X, Li Z. Social media mining under the COVID-19 context: Progress, challenges, and opportunities. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION : ITC JOURNAL 2022; 113:102967. [PMID: 36035895 PMCID: PMC9391053 DOI: 10.1016/j.jag.2022.102967] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 06/17/2022] [Accepted: 08/05/2022] [Indexed: 05/21/2023]
Abstract
Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and reproducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies.
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Affiliation(s)
- Xiao Huang
- Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Siqin Wang
- School of Earth Environmental Sciences, University of Queensland, Brisbane, Queensland 4076, Australia
| | - Mengxi Zhang
- Department of Nutrition and Health Science, Ball State University, Muncie, IN 47304, USA
| | - Tao Hu
- Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA
| | - Alexander Hohl
- Department of Geography, The University of Utah, Salt Lake City, UT 84112, USA
| | - Bing She
- Institute for social research, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xi Gong
- Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA
| | - Jianxin Li
- School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia
| | - Xiao Liu
- School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia
| | - Oliver Gruebner
- Department of Geography, University of Zurich, Zürich CH-8006, Switzerland
| | - Regina Liu
- Department of Biology, Mercer University, Macon, GA 31207, USA
| | - Xiao Li
- Texas A&M Transportation Institute, Bryan, TX 77807, USA
| | - Zhewei Liu
- Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Xinyue Ye
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77840, USA
| | - Zhenlong Li
- Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208, USA
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7
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Jahn B, Friedrich S, Behnke J, Engel J, Garczarek U, Münnich R, Pauly M, Wilhelm A, Wolkenhauer O, Zwick M, Siebert U, Friede T. Authors’ response: on the role of data, statistics and decisions in a pandemic. ASTA ADVANCES IN STATISTICAL ANALYSIS 2022; 106:403-405. [PMID: 35967605 PMCID: PMC9362421 DOI: 10.1007/s10182-022-00460-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 07/09/2022] [Indexed: 11/26/2022]
Affiliation(s)
- Beate Jahn
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT – University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
| | - Sarah Friedrich
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
- Institute of Mathematics, University of Augsburg, Augsburg, Germany
| | - Joachim Behnke
- Zeppelin University Friedrichshafen, Friedrichshafen, Germany
| | - Joachim Engel
- Pädagogische Hochschule Ludwigsburg, Ludwigsburg, Germany
| | | | - Ralf Münnich
- Economic and Social Statistics, Trier University, Trier, Germany
| | - Markus Pauly
- Department of Statistics, TU Dortmund University, Dortmund, Germany
| | - Adalbert Wilhelm
- Psychology and Methods, Jacobs University Bremen, Bremen, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology & Bioinformatics, University of Rostock and Leibniz-Institute for Food Systems Biology, Technical University of Munich, Munchen, Germany
| | | | - Uwe Siebert
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT – University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
- Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital; Harvard Medical School, Boston, MA USA
- Center for Health Decision Science and Departments of Epidemiology and Health Policy & Management, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
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8
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Berger U, Kauermann G, Küchenhoff H. Discussion on On the role of data, statistics and decisions in a pandemic: by Jahn et al (2022). ADVANCES IN STATISTICAL ANALYSIS : ASTA : A JOURNAL OF THE GERMAN STATISTICAL SOCIETY 2022; 106:387-390. [PMID: 35702376 PMCID: PMC9186273 DOI: 10.1007/s10182-022-00450-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 05/19/2022] [Indexed: 12/02/2022]
Abstract
The authors make an important contribution presenting a comprehensive and thoughtful overview about the many different aspects of data, statistics and data analyses in times of the recent COVID-19 pandemic discussing all relevant topics. The paper certainly provides a very valuable reflection of what has been done, what could have been done and what needs to be done. We contribute here with a few comments and some additional issues. We do not discuss all chapters of Jahn et al. (AStA Adv Stat Anal, 2022. 10.1007/s10182-022-00439-7), but focus on those where our personal views and experiences might add some additional aspects.
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Affiliation(s)
- Ursula Berger
- Ludwig-Maximilians-University Munich, Munich, Germany
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9
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Grimée M, Bekker-Nielsen Dunbar M, Hofmann F, Held L. Modelling the effect of a border closure between Switzerland and Italy on the spatiotemporal spread of COVID-19 in Switzerland. SPATIAL STATISTICS 2022; 49:100552. [PMID: 34786328 PMCID: PMC8579705 DOI: 10.1016/j.spasta.2021.100552] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
We present an approach to extend the endemic-epidemic (EE) modelling framework for the analysis of infectious disease data. In its spatiotemporal formulation, spatial dependencies have originally been captured by static neighbourhood matrices. These weight matrices are adjusted over time to reflect changes in spatial connectivity between geographical units. We illustrate this extension by modelling the spread of COVID-19 disease between Swiss and bordering Italian regions in the first wave of the COVID-19 pandemic. The spatial weights are adjusted with data describing the daily changes in population mobility patterns, and indicators of border closures describing the state of travel restrictions since the beginning of the pandemic. These time-dependent weights are used to fit an EE model to the region-stratified time series of new COVID-19 cases. We then adjust the weight matrices to reflect two counterfactual scenarios of border closures and draw counterfactual predictions based on these, to retrospectively assess the usefulness of border closures. Predictions based on a scenario where no closure of the Swiss-Italian border occurred increased the number of cumulative cases in Switzerland by a factor of 2.7 (10th to 90th percentile: 2.2 to 3.6) over the study period. Conversely, a closure of the Swiss-Italian border two weeks earlier than implemented would have resulted in only a 12% (8% to 18%) decrease in the number of cases and merely delayed the epidemic spread by a couple of weeks. Our study provides useful insight into modelling the effect of epidemic countermeasures on the spatiotemporal spread of COVID-19.
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Affiliation(s)
- Mathilde Grimée
- Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität München, Germany
| | - Maria Bekker-Nielsen Dunbar
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland
- Swiss School of Public Health, Switzerland
- Life Science Zurich Graduate School, Switzerland
| | - Felix Hofmann
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland
| | - Leonhard Held
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland
- Swiss School of Public Health, Switzerland
- Center for Reproducible Science, University of Zurich, Switzerland
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10
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Government Intervention, Human Mobility, and COVID-19: A Causal Pathway Analysis from 121 Countries. SUSTAINABILITY 2022. [DOI: 10.3390/su14063694] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Based on data from 121 countries, the study assesses the dynamic effect and causality path of the government epidemic prevention policies and human mobility behaviors on the growth rates of COVID-19 new cases and deaths. Our results find that both policies and behaviors influenced COVID-19 cases and deaths. The direct effect of policies on COVID-19 was more than the indirect effect. Policies influence behaviors, and behaviors react spontaneously to information. Further, masks give people a false sense of security and increase mobility. The close public transport policy increased COVID-19 new cases. We also conducted sensitivity analysis and found that some policies hold robustly, such as the policies of school closing, restrictions on gatherings, stay-at-home requirements, international travel controls, facial coverings, and vaccination. The counterfactual tests suggest that, as of early March 2021, if governments had mandated masking policies early in the epidemic, the cases and deaths would have been reduced by 18% and 14% separately. If governments had implemented vaccination policies early in the pandemic, the cases and deaths would have been reduced by 93% and 62%, respectively. Without public transportation closures, cases and deaths would have been reduced by 40% and 10%, respectively.
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11
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Fritz C, Dorigatti E, Rügamer D. Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany. Sci Rep 2022; 12:3930. [PMID: 35273252 PMCID: PMC8913758 DOI: 10.1038/s41598-022-07757-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/23/2022] [Indexed: 12/11/2022] Open
Abstract
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resources. In this context, several experts have called for the necessity to account for human mobility to explain the spread of COVID-19. Existing approaches often apply standard models of the respective research field, frequently restricting modeling possibilities. For instance, most statistical or epidemiological models cannot directly incorporate unstructured data sources, including relational data that may encode human mobility. In contrast, machine learning approaches may yield better predictions by exploiting these data structures yet lack intuitive interpretability as they are often categorized as black-box models. We propose a combination of both research directions and present a multimodal learning framework that amalgamates statistical regression and machine learning models for predicting local COVID-19 cases in Germany. Results and implications: the novel approach introduced enables the use of a richer collection of data types, including mobility flows and colocation probabilities, and yields the lowest mean squared error scores throughout the observational period in the reported benchmark study. The results corroborate that during most of the observational period more dispersed meeting patterns and a lower percentage of people staying put are associated with higher infection rates. Moreover, the analysis underpins the necessity of including mobility data and showcases the flexibility and interpretability of the proposed approach.
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Affiliation(s)
- Cornelius Fritz
- Department of Statistics, Ludwig Maximilian Universität, München, Germany
| | - Emilio Dorigatti
- Department of Statistics, Ludwig Maximilian Universität, München, Germany
- Institute for Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - David Rügamer
- Department of Statistics, Ludwig Maximilian Universität, München, Germany.
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12
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Shepherd HER, Atherden FS, Chan HMT, Loveridge A, Tatem AJ. Domestic and international mobility trends in the United Kingdom during the COVID-19 pandemic: an analysis of facebook data. Int J Health Geogr 2021; 20:46. [PMID: 34863206 PMCID: PMC8643186 DOI: 10.1186/s12942-021-00299-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 11/08/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which resulted in changes to mobility across different regions. An understanding of how these policies impacted travel patterns over time and at different spatial scales is important for designing effective strategies, future pandemic planning and in providing broader insights on the population geography of the country. Crowd level data on mobile phone usage can be used as a proxy for population mobility patterns and provide a way of quantifying in near-real time the impact of social distancing measures on changes in mobility. METHODS Here we explore patterns of change in densities, domestic and international flows and co-location of Facebook users in the UK from March 2020 to March 2021. RESULTS We find substantial heterogeneities across time and region, with large changes observed compared to pre-pademic patterns. The impacts of periods of lockdown on distances travelled and flow volumes are evident, with each showing variations, but some significant reductions in co-location rates. Clear differences in multiple metrics of mobility are seen in central London compared to the rest of the UK, with each of Scotland, Wales and Northern Ireland showing significant deviations from England at times. Moreover, the impacts of rapid changes in rules on international travel to and from the UK are seen in substantial fluctuations in traveller volumes by destination. CONCLUSIONS While questions remain about the representativeness of the Facebook data, previous studies have shown strong correspondence with census-based data and alternative mobility measures, suggesting that findings here are valuable for guiding strategies.
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Affiliation(s)
- Harry E R Shepherd
- School of Biological Sciences, University of Southampton, Southampton, UK
| | - Florence S Atherden
- Ocean and Earth Science, National Oceanography Centre, University of Southampton, Southampton, UK
| | | | - Alexandra Loveridge
- Ocean and Earth Science, National Oceanography Centre, University of Southampton, Southampton, UK
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
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13
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Boumahdi I, Zaoujal N, Fadlallah A. Is there a relationship between industrial clusters and the prevalence of COVID-19 in the provinces of Morocco? REGIONAL SCIENCE POLICY & PRACTICE 2021; 13:138-157. [PMID: 38607820 PMCID: PMC8014607 DOI: 10.1111/rsp3.12407] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 01/21/2021] [Accepted: 02/21/2021] [Indexed: 05/19/2023]
Abstract
The main objective of this paper is to verify if there is a relationship between industrial agglomeration and the prevalence of COVID-19 and its diffusion within and between the provinces of Morocco. To do so, we used spatial exploratory analysis and spatial econometrics to show that the preponderance of industrial activity in a province has a significant effect on the number of active COVID-19 cases in that province. On the other hand, we have shown that the spatial diffusion of this effect is not significant, which indicates the appropriateness of the lockdown implemented. We have also shown that age, socio-economic deficits and habitat conditions are not significant determinants in the onset and spread of the pandemic.
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Affiliation(s)
- Ilyes Boumahdi
- National Institute of Statistic and Applied EconomyMorocco
| | - Nouzha Zaoujal
- National Institute of Statistic and Applied EconomyMorocco
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14
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Müller SA, Balmer M, Charlton W, Ewert R, Neumann A, Rakow C, Schlenther T, Nagel K. Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data. PLoS One 2021; 16:e0259037. [PMID: 34710158 DOI: 10.1101/2021.02.27.21252583] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 10/12/2021] [Indexed: 05/24/2023] Open
Abstract
Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines a well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. It is validated against the infection dynamics in Berlin (Germany). The model can be used to understand the contributions of different activity types to the infection dynamics over time. It predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reproduction number R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%. Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions.
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Affiliation(s)
- Sebastian A Müller
- Transport Systems Planning and Transport Telematics, TU Berlin, Berlin, Germany
| | | | - William Charlton
- Transport Systems Planning and Transport Telematics, TU Berlin, Berlin, Germany
| | - Ricardo Ewert
- Transport Systems Planning and Transport Telematics, TU Berlin, Berlin, Germany
| | | | - Christian Rakow
- Transport Systems Planning and Transport Telematics, TU Berlin, Berlin, Germany
| | - Tilmann Schlenther
- Transport Systems Planning and Transport Telematics, TU Berlin, Berlin, Germany
| | - Kai Nagel
- Transport Systems Planning and Transport Telematics, TU Berlin, Berlin, Germany
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Kosfeld R, Mitze T, Rode J, Wälde K. The Covid-19 containment effects of public health measures: A spatial difference-in-differences approach. JOURNAL OF REGIONAL SCIENCE 2021; 61:799-825. [PMID: 34230689 PMCID: PMC8250984 DOI: 10.1111/jors.12536] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 03/30/2021] [Indexed: 05/05/2023]
Abstract
The paper studies the containment effects of public health measures to curb the spread of Covid-19 during the first wave of the pandemic in spring 2020 in Germany. To identify the effects of six compound sets of public health measures, we employ a spatial difference-in-differences approach. We find that contact restrictions, mandatory wearing of face masks and closure of schools substantially contributed to flattening the infection curve. The significance of the impact of restaurant closure does not prove to be robust. No incremental effect is evidenced for closure of establishments and the shutdown of nonessential retail stores.
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Affiliation(s)
| | - Timo Mitze
- Department of Business and EconomicsUniversity of Southern DenmarkOdenseDenmark
| | - Johannes Rode
- Department of Law and EconomicsTechnische Universität DarmstadtDarmstadtGermany
| | - Klaus Wälde
- Gutenberg School of Management and EconomicsJohannes‐Gutenberg‐Universität Mainz, CESifo, IZAMainzGermany
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