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Jang G, Kim J, Lee Y, Son C, Ko KT, Lee H. Analysis of the impact of COVID-19 variants and vaccination on the time-varying reproduction number: statistical methods. Front Public Health 2024; 12:1353441. [PMID: 39022412 PMCID: PMC11253806 DOI: 10.3389/fpubh.2024.1353441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 06/07/2024] [Indexed: 07/20/2024] Open
Abstract
Introduction The COVID-19 pandemic has profoundly impacted global health systems, requiring the monitoring of infection waves and strategies to control transmission. Estimating the time-varying reproduction number is crucial for understanding the epidemic and guiding interventions. Methods Probability distributions of serial interval are estimated for Pre-Delta and Delta periods. We conducted a comparative analysis of time-varying reproduction numbers, taking into account population immunity and variant differences. We incorporated the regional heterogeneity and age distribution of the population, as well as the evolving variants and vaccination rates over time. COVID-19 transmission dynamics were analyzed with variants and vaccination. Results The reproduction number is computed with and without considering variant-based immunity. In addition, values of reproduction number significantly differed by variants, emphasizing immunity's importance. Enhanced vaccination efforts and stringent control measures were effective in reducing the transmission of the Delta variant. Conversely, Pre-Delta variant appeared less influenced by immunity levels, due to lower vaccination rates. Furthermore, during the Pre-Delta period, there was a significant difference between the region-specific and the non-region-specific reproduction numbers, with particularly distinct pattern differences observed in Gangwon, Gyeongbuk, and Jeju in Korea. Discussion This research elucidates the dynamics of COVID-19 transmission concerning the dominance of the Delta variant, the efficacy of vaccinations, and the influence of immunity levels. It highlights the necessity for targeted interventions and extensive vaccination coverage. This study makes a significant contribution to the understanding of disease transmission mechanisms and informs public health strategies.
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Affiliation(s)
- Geunsoo Jang
- Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu, Republic of Korea
| | - Jihyeon Kim
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| | - Yeonsu Lee
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| | - Changdae Son
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| | - Kyeong Tae Ko
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| | - Hyojung Lee
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
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2
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Park J, Yi S, Chang W, Mateu J. A spatio-temporal Dirichlet process mixture model for coronavirus disease-19. Stat Med 2023; 42:5555-5576. [PMID: 37812818 DOI: 10.1002/sim.9925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 09/12/2023] [Accepted: 09/19/2023] [Indexed: 10/11/2023]
Abstract
Understanding the spatio-temporal patterns of the coronavirus disease 2019 (COVID-19) is essential to construct public health interventions. Spatially referenced data can provide richer opportunities to understand the mechanism of the disease spread compared to the more often encountered aggregated count data. We propose a spatio-temporal Dirichlet process mixture model to analyze confirmed cases of COVID-19 in an urban environment. Our method can detect unobserved cluster centers of the epidemics, and estimate the space-time range of the clusters that are useful to construct a warning system. Furthermore, our model can measure the impact of different types of landmarks in the city, which provides an intuitive explanation of disease spreading sources from different time points. To efficiently capture the temporal dynamics of the disease patterns, we employ a sequential approach that uses the posterior distribution of the parameters for the previous time step as the prior information for the current time step. This approach enables us to incorporate time dependence into our model in a computationally efficient manner without complicating the model structure. We also develop a model assessment by comparing the data with theoretical densities, and outline the goodness-of-fit of our fitted model.
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Affiliation(s)
- Jaewoo Park
- Department of Applied Statistics, Yonsei University, Seoul, South Korea
- Department of Statistics and Data Science, Yonsei University, Seoul, South Korea
| | - Seorim Yi
- Department of Statistics and Data Science, Yonsei University, Seoul, South Korea
| | - Won Chang
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, Ohio, USA
| | - Jorge Mateu
- Department of Mathematics, University Jaume I, Castellón de la Plana, Spain
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3
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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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4
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Fierro R. Cumulative damage for multi-type epidemics and an application to infectious diseases. J Math Biol 2023; 86:47. [PMID: 36797526 PMCID: PMC9934514 DOI: 10.1007/s00285-023-01880-1] [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: 08/09/2022] [Revised: 12/04/2022] [Accepted: 01/23/2023] [Indexed: 02/18/2023]
Abstract
A continuous time multivariate stochastic model is proposed for assessing the damage of a multi-type epidemic cause to a population as it unfolds. The instants when cases occur and the magnitude of their injure are random. Thus, we define a cumulative damage based on counting processes and a multivariate mark process. For a large population we approximate the behavior of this damage process by its asymptotic distribution. Also, we analyze the distribution of the stopping times when the numbers of cases caused by the epidemic attain levels beyond certain thresholds. We focus on introducing some tools for statistical inference on the parameters related with the epidemic. In this regard, we present a general hypothesis test for homogeneity in epidemics and apply it to data of Covid-19 in Chile.
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Affiliation(s)
- Raúl Fierro
- Instituto de Matemáticas, Pontificia Universidad Católica de Valparaíso, Casilla 4059, Valparaíso, Chile.
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5
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Briz-Redón Á, Iftimi A, Mateu J, Romero-García C. A mechanistic spatio-temporal modeling of COVID-19 data. Biom J 2023; 65:e2100318. [PMID: 35934898 DOI: 10.1002/bimj.202100318] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 03/26/2022] [Accepted: 04/02/2022] [Indexed: 01/17/2023]
Abstract
Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used. Point processes are the natural tool to perform such analyses. We analyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19) cases detected in Valencia (Spain) during the first 11 months (February 2020 to January 2021) of the pandemic. In particular, we propose a mechanistic spatio-temporal model for the first-order intensity function of the point process. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while similar studies have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations. The results suggest that there has only been a mild level of spatio-temporal interaction between cases in the study area, which to a large extent corresponds to people living in the same residential location. Extending our proposed model to larger areas could help us gain knowledge on the propagation of COVID-19 across cities with high mobility levels.
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Affiliation(s)
- Álvaro Briz-Redón
- Department of Statistics and Operations Research, University of Valencia, Spain.,Statistics Office, City Council of Valencia, Spain
| | - Adina Iftimi
- Department of Statistics and Operations Research, University of Valencia, Spain
| | - Jorge Mateu
- Department of Mathematics, University Jaume I, Spain
| | - Carolina Romero-García
- Department of Anesthesia, Critical Care and Pain Unit, General University Hospital, Spain.,Division of Research Methodology, European University of Valencia, Spain
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6
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Ota S, Kimura M. Statistical injury prediction for professional sumo wrestlers: Modeling and perspectives. PLoS One 2023; 18:e0283242. [PMID: 36930622 PMCID: PMC10022813 DOI: 10.1371/journal.pone.0283242] [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: 10/16/2022] [Accepted: 03/03/2023] [Indexed: 03/18/2023] Open
Abstract
In sumo wrestling, a traditional sport in Japan, many wrestlers suffer from injuries through bouts. In 2019, an average of 5.2 out of 42 wrestlers in the top division of professional sumo wrestling were absent in each grand sumo tournament due to injury. As the number of injury occurrences increases, professional sumo wrestling becomes less interesting for sumo fans, requiring systems to prevent future occurrences. Statistical injury prediction is a useful way to communicate the risk of injuries for wrestlers and their coaches. However, the existing statistical methods of injury prediction are not always accurate because they do not consider the long-term effects of injuries. Here, we propose a statistical model of injury occurrences for sumo wrestlers. The proposed model provides the estimated probability of the next potential injury occurrence for a wrestler. In addition, it can support making a risk-based injury prevention scenario for wrestlers. While a previous study modeled injury occurrences by using the Poisson process, we model it by using the Hawkes process to consider the long-term effect of injuries. The proposed model can also be applied to injury prediction for athletes of other sports.
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Affiliation(s)
- Shuhei Ota
- Department of Industrial Engineering and Management, Kanagawa University, Yokohama, Kanagawa, Japan
- * E-mail:
| | - Mitsuhiro Kimura
- Department of Industrial and Systems Engineering, Hosei University, Faculty of Science & Engineering, Tokyo, Japan
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7
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Najem S, Monni S, Hatoum R, Sweidan H, Faour G, Abdallah C, Ghosn N, Hassan H, Touma J. A framework for reconstructing transmission networks in infectious diseases. APPLIED NETWORK SCIENCE 2022; 7:85. [PMID: 36567737 PMCID: PMC9761645 DOI: 10.1007/s41109-022-00525-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
In this paper, we propose a general framework for the reconstruction of the underlying cross-regional transmission network contributing to the spread of an infectious disease. We employ an autoregressive model that allows to decompose the mean number of infections into three components that describe: intra-locality infections, inter-locality infections, and infections from other sources such as travelers arriving to a country from abroad. This model is commonly used in the identification of spatiotemporal patterns in seasonal infectious diseases and thus in forecasting infection counts. However, our contribution lies in identifying the inter-locality term as a time-evolving network, and rather than using the model for forecasting, we focus on the network properties without any assumption on seasonality or recurrence of the disease. The topology of the network is then studied to get insight into the disease dynamics. Building on this, and particularly on the centrality of the nodes of the identified network, a strategy for intervention and disease control is devised.
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Affiliation(s)
- Sara Najem
- Department of Physics, American University of Beirut, Beirut, Lebanon
- Center for Advanced Mathematical Sciences, American University of Beirut, Beirut, Lebanon
| | - Stefano Monni
- Department of Physics, American University of Beirut, Beirut, Lebanon
- Department of Mathematics, American University of Beirut, Beirut, Lebanon
| | - Rola Hatoum
- Center for Advanced Mathematical Sciences, American University of Beirut, Beirut, Lebanon
| | - Hawraa Sweidan
- Epidemiological Surveillance Program, Ministry of Public Health, Beirut, Lebanon
| | - Ghaleb Faour
- National Center for Remote Sensing, National Council for Scientific Research (CNRS), Beirut, Lebanon
| | - Chadi Abdallah
- National Center for Remote Sensing, National Council for Scientific Research (CNRS), Beirut, Lebanon
| | - Nada Ghosn
- Epidemiological Surveillance Program, Ministry of Public Health, Beirut, Lebanon
| | - Hamad Hassan
- Faculty of Public Health, Lebanese University, Beirut, Lebanon
| | - Jihad Touma
- Department of Physics, American University of Beirut, Beirut, Lebanon
- Center for Advanced Mathematical Sciences, American University of Beirut, Beirut, Lebanon
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8
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Hatami F, Chen S, Paul R, Thill JC. Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192315771. [PMID: 36497846 PMCID: PMC9736132 DOI: 10.3390/ijerph192315771] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/12/2022] [Accepted: 11/24/2022] [Indexed: 05/09/2023]
Abstract
The global COVID-19 pandemic has taken a heavy toll on health, social, and economic costs since the end of 2019. Predicting the spread of a pandemic is essential to developing effective intervention policies. Since the beginning of this pandemic, many models have been developed to predict its pathways. However, the majority of these models assume homogeneous dynamics over the geographic space, while the pandemic exhibits substantial spatial heterogeneity. In addition, spatial interaction among territorial entities and variations in their magnitude impact the pandemic dynamics. In this study, we used a spatial extension of the SEIR-type epidemiological model to simulate and predict the 4-week number of COVID-19 cases in the Charlotte-Concord-Gastonia Metropolitan Statistical Area (MSA), USA. We incorporated a variety of covariates, including mobility, pharmaceutical, and non-pharmaceutical interventions, demographics, and weather data to improve the model's predictive performance. We predicted the number of COVID-19 cases for up to four weeks in the 10 counties of the studied MSA simultaneously over the time period 29 March 2020 to 13 March 2021, and compared the results with the reported number of cases using the root-mean-squared error (RMSE) metric. Our results highlight the importance of spatial heterogeneity and spatial interactions among locations in COVID-19 pandemic modeling.
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Affiliation(s)
- Faizeh Hatami
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Shi Chen
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Rajib Paul
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Jean-Claude Thill
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- Correspondence:
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9
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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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10
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Modeling Long-Range Dynamic Correlations of Words in Written Texts with Hawkes Processes. ENTROPY 2022; 24:e24070858. [PMID: 35885082 PMCID: PMC9316552 DOI: 10.3390/e24070858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/19/2022] [Accepted: 06/21/2022] [Indexed: 02/01/2023]
Abstract
It has been clarified that words in written texts are classified into two groups called Type-I and Type-II words. The Type-I words are words that exhibit long-range dynamic correlations in written texts while the Type-II words do not show any type of dynamic correlations. Although the stochastic process of yielding Type-II words has been clarified to be a superposition of Poisson point processes with various intensities, there is no definitive model for Type-I words. In this study, we introduce a Hawkes process, which is known as a kind of self-exciting point process, as a candidate for the stochastic process that governs yielding Type-I words; i.e., the purpose of this study is to establish that the Hawkes process is useful to model occurrence patterns of Type-I words in real written texts. The relation between the Hawkes process and an existing model for Type-I words, in which hierarchical structures of written texts are considered to play a central role in yielding dynamic correlations, will also be discussed.
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11
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Dayaratna KD, Gonshorowski D, Kolesar M. Hierarchical Bayesian spatio-temporal modeling of COVID-19 in the United States. J Appl Stat 2022; 50:2663-2680. [PMID: 37529567 PMCID: PMC10388819 DOI: 10.1080/02664763.2022.2069232] [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: 12/15/2020] [Accepted: 04/18/2022] [Indexed: 10/18/2022]
Abstract
We examine the impact of economic, demographic, and mobility-related factors have had on the transmission of COVID-19 in 2020. While many models in the academic literature employ linear/generalized linear models, few contributions exist that incorporate spatial analysis, which is useful for understanding factors influencing the proliferation of the disease before the introduction of vaccines. We utilize a Poisson generalized linear model coupled with a spatial autoregressive structure to do so. Our analysis yields a number of insights including that, in some areas of the country, the counterintuitive result that staying at home can lead to increased disease proliferation. Additionally, we find some positive effects from increased gathering at grocery stores, negative effects of visiting retail stores and workplaces, and even small effects on visiting parks highlighting the complexities travel and migration have on the transmission of diseases.
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Affiliation(s)
| | - Drew Gonshorowski
- Center for Data Analysis, The Heritage Foundation, Washington, DC, USA
| | - Mary Kolesar
- Mathematics Department, Harvard University, Cambridge, MA, USA
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12
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Selvamuthu D, Tardelli P. Infinite-server systems with Hawkes arrivals and Hawkes services. QUEUEING SYSTEMS 2022; 101:329-351. [PMID: 35505993 PMCID: PMC9052194 DOI: 10.1007/s11134-022-09813-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 04/02/2022] [Accepted: 04/06/2022] [Indexed: 06/14/2023]
Abstract
This paper is devoted to the study of the number of customers in infinite-server systems driven by Hawkes processes. In these systems, the self-exciting arrival process is assumed to be represented by a Hawkes process and the self-exciting service process by a state-dependent Hawkes process (sdHawkes process). Under some suitable conditions, for the Hawkes / sdHawkes / ∞ system, the Markov property of the system is derived. The joint time-dependent distribution of the number of customers in the system, the arrival intensity and the server intensity is characterized by a system of differential equations. Then, the time-dependent results are also deduced for the M / sdHawkes / ∞ system.
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Affiliation(s)
- Dharmaraja Selvamuthu
- Department of Mathematics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016 India
| | - Paola Tardelli
- Department of Industrial and Information Engineering and Economics, University of L’Aquila, Piazzale E. Pontieri, 67100 Roio, Italy
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13
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Wang H, Xie L, Xie Y, Cuozzo A, Mak S. Sequential Change-Point Detection for Mutually Exciting Point Processes. Technometrics 2022. [DOI: 10.1080/00401706.2022.2054862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Haoyun Wang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Liyan Xie
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Yao Xie
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Alex Cuozzo
- Department of Statistical Science, Duke University, Durham, NC
| | - Simon Mak
- Department of Statistical Science, Duke University, Durham, NC
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14
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Macías RZ, Gutiérrez-Pulido H, Arroyo EAG, González AP. Geographical network model for COVID-19 spread among dynamic epidemic regions. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:4237-4259. [PMID: 35341296 DOI: 10.3934/mbe.2022196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Pandemic due to SARS-CoV-2 (COVID-19) has affected to world in several aspects: high number of confirmed cases, high number of deaths, low economic growth, among others. Understanding of spatio-temporal dynamics of the virus is helpful and necessary for decision making, for instance to decide where, whether and how, non-pharmaceutical intervention policies are to be applied. This point has not been properly addressed in literature since typical strategies do not consider marked differences on the epidemic spread across country or large territory. Those strategies assume similarities and apply similar interventions instead. This work is focused on posing a methodology where spatio-temporal epidemic dynamics is captured by means of dividing a territory in time-varying epidemic regions, according to geographical closeness and infection level. In addition, a novel Lagrangian-SEIR-based model is posed for describing the dynamic within and between those regions. The capabilities of this methodology for identifying local outbreaks and reproducing the epidemic curve are discussed for the case of COVID-19 epidemic in Jalisco state (Mexico). The contagions from July 31, 2020 to March 31, 2021 are analyzed, with monthly adjustments, and the estimates obtained at the level of the epidemic regions present satisfactory results since Relative Root Mean Squared Error RRMSE is below 15% in most of regions, and at the level of the whole state outstanding with RRMSE below 5%.
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Affiliation(s)
- Roman Zúñiga Macías
- Universidad de Guadalajara, CUCEI, Blvd. Marcelino García Barragán 1421, 44430, Guadalajara, Jal., México
| | - Humberto Gutiérrez-Pulido
- Universidad de Guadalajara, CUCEI, Blvd. Marcelino García Barragán 1421, 44430, Guadalajara, Jal., México
| | | | - Abel Palafox González
- Universidad de Guadalajara, CUCEI, Blvd. Marcelino García Barragán 1421, 44430, Guadalajara, Jal., México
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15
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Aguilar-Madera CG, Espinosa-Paredes G, Herrera-Hernández EC, Briones Carrillo JA, Valente Flores-Cano J, Matías-Pérez V. The spreading of Covid-19 in Mexico: A diffusional approach. RESULTS IN PHYSICS 2021; 27:104555. [PMID: 34312590 PMCID: PMC8294753 DOI: 10.1016/j.rinp.2021.104555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/09/2021] [Accepted: 07/13/2021] [Indexed: 06/13/2023]
Abstract
In this work, we analyze the spreading of Covid-19 in Mexico using the spatial SEIRD epidemiologic model. We use the information of the 32 regions (States) that conform the country, such as population density, verified infected cases, and deaths in each State. We extend the SEIRD compartmental epidemiologic with diffusion mechanisms in the exposed and susceptible populations. We use the Fickian law with the diffusion coefficient proportional to the population density to encompass the diffusion effects. The numerical results suggest that the epidemiologic model demands time-dependent parameters to incorporate non-monotonous behavior in the actual data in the global dynamic. The diffusional model proposed in this work has great potential in predicting the virus spreading on different scales, i.e., local, national, and between countries, since the complete reduction in people mobility is impossible.
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Affiliation(s)
- Carlos G Aguilar-Madera
- Universidad Autónoma de Nuevo León, Facultad de Ciencias de la Tierra, C.P. 67700, Linares, Mexico
| | - Gilberto Espinosa-Paredes
- Universidad Autónoma Metropolitana-Iztapalapa, Área de Ingeniería en Recursos Energéticos, CDMX 09340, Mexico
| | - E C Herrera-Hernández
- Centro de Investigación y Estudios de Posgrado, Facultad de Ciencias Químicas, Universidad Autónoma de San Luis Potosí, Av. Dr. Manuel Nava 6, Zona Universitaria, 78210 San Luis Potosí, Mexico
| | - Jorge A Briones Carrillo
- Universidad Autónoma de Nuevo León, Facultad de Ciencias de la Tierra, C.P. 67700, Linares, Mexico
| | - J Valente Flores-Cano
- Universidad Autónoma de Nuevo León, Facultad de Ciencias de la Tierra, C.P. 67700, Linares, Mexico
| | - Víctor Matías-Pérez
- Universidad Autónoma de Nuevo León, Facultad de Ciencias de la Tierra, C.P. 67700, Linares, Mexico
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16
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Rahman MM, Paul KC, Hossain MA, Ali GGMN, Rahman MS, Thill JC. Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:72420-72450. [PMID: 34786314 PMCID: PMC8545207 DOI: 10.1109/access.2021.3079121] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 05/07/2021] [Indexed: 05/19/2023]
Abstract
The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implemented citywide, regionally or nationally are affecting virus transmission, people's travel patterns, and air quality. Many studies have been conducted to predict the diffusion of the COVID-19 disease, assess the impacts of the pandemic on human mobility and on air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This literature review aims to analyze the results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 viral transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also explores the spatio-temporal aspects of lockdown and confinement measures on coronavirus diffusion, human mobility, and air quality. Additionally, we discuss policy implications, which will be helpful for policy makers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.
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Affiliation(s)
- Md. Mokhlesur Rahman
- The William States Lee College of EngineeringUniversity of North Carolina at CharlotteCharlotteNC28223USA
- Department of Urban and Regional PlanningKhulna University of Engineering and Technology (KUET)Khulna9203Bangladesh
| | - Kamal Chandra Paul
- Department of Electrical and Computer EngineeringThe William States Lee College of EngineeringUniversity of North Carolina at CharlotteCharlotteNC28223USA
| | - Md. Amjad Hossain
- Department of Computer Science, Mathematics and EngineeringShepherd UniversityShepherdstownWV25443USA
| | - G. G. Md. Nawaz Ali
- Department of Applied Computer ScienceUniversity of CharlestonCharlestonWV25304USA
| | - Md. Shahinoor Rahman
- Department of Earth and Environmental SciencesNew Jersey City UniversityJersey CityNJ07305USA
| | - Jean-Claude Thill
- Department of Geography and Earth SciencesSchool of Data ScienceUniversity of North Carolina at CharlotteCharlotteNC28223USA
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17
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Garetto M, Leonardi E, Torrisi GL. A time-modulated Hawkes process to model the spread of COVID-19 and the impact of countermeasures. ANNUAL REVIEWS IN CONTROL 2021; 51:551-563. [PMID: 33746561 PMCID: PMC7953674 DOI: 10.1016/j.arcontrol.2021.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/23/2021] [Accepted: 02/27/2021] [Indexed: 05/04/2023]
Abstract
Motivated by the recent outbreak of coronavirus (COVID-19), we propose a stochastic model of epidemic temporal growth and mitigation based on a time-modulated Hawkes process. The model is sufficiently rich to incorporate specific characteristics of the novel coronavirus, to capture the impact of undetected, asymptomatic and super-diffusive individuals, and especially to take into account time-varying counter-measures and detection efforts. Yet, it is simple enough to allow scalable and efficient computation of the temporal evolution of the epidemic, and exploration of what-if scenarios. Compared to traditional compartmental models, our approach allows a more faithful description of virus specific features, such as distributions for the time spent in stages, which is crucial when the time-scale of control (e.g., mobility restrictions) is comparable to the lifetime of a single infection. We apply the model to the first and second wave of COVID-19 in Italy, shedding light onto several effects related to mobility restrictions introduced by the government, and to the effectiveness of contact tracing and mass testing performed by the national health service.
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Affiliation(s)
- Michele Garetto
- Università degli Studi di Torino, C.so Svizzera 185, Torino, Italy
| | - Emilio Leonardi
- Politecnico di Torino, C.so Duca degli Abruzzi 24, Torino, Italy
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18
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Safta C, Ray J, Sargsyan K. Characterization of partially observed epidemics through Bayesian inference: application to COVID-19. COMPUTATIONAL MECHANICS 2020; 66:1109-1129. [PMID: 33041410 PMCID: PMC7538372 DOI: 10.1007/s00466-020-01897-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 07/29/2020] [Indexed: 06/10/2023]
Abstract
We demonstrate a Bayesian method for the "real-time" characterization and forecasting of partially observed COVID-19 epidemic. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov chain Monte Carlo technique. The data used in this study was sourced before the arrival of the second wave of infection in July 2020. The proposed modeling approach, when applied at the country level, generally provides accurate forecasts at the regional, state and country level. The epidemiological model detected the flattening of the curve in California, after public health measures were instituted. The method also detected different disease dynamics when applied to specific regions of New Mexico.
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Affiliation(s)
| | - Jaideep Ray
- Sandia National Laboratories, Livermore, USA
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