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Wei F, Zhou R, Jin Z, Sun Y, Peng Z, Cai S, Chen G, Zheng K. Studying the impacts of variant evolution for a generalized age-group transmission model. PLoS One 2024; 19:e0306554. [PMID: 38968178 PMCID: PMC11226140 DOI: 10.1371/journal.pone.0306554] [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: 12/11/2023] [Accepted: 06/18/2024] [Indexed: 07/07/2024] Open
Abstract
The differences of SARS-CoV-2 variants brought the changes of transmission characteristics and clinical manifestations during the prevalence of COVID-19. In order to explore the evolution mechanisms of SARS-CoV-2 variants and the impacts of variant evolution, the classic SIR (Susceptible-Infected-Recovered) compartment model was modified to a generalized SVEIR (Susceptible-Vaccinated-Exposed-Infected-Recovered) compartment model with age-group and varying variants in this study. By using of the SVEIR model and least squares method, the optimal fittings against the surveillance data from Fujian Provincial Center for Disease Control and Prevention were performed for the five epidemics of Fujian Province. The main epidemiological characteristics such as basic reproduction number, effective reproduction number, sensitivity analysis, and cross-variant scenario investigations were extensively investigated during dynamic zero-COVID policy. The study results showed that the infectivities of the variants became fast from wild strain to the Delta variant, further to the Omicron variant. Meanwhile, the cross-variant investigations showed that the average incubation periods were shortened, and that the infection scales quickly enhanced. Further, the risk estimations with the new variants were performed without implements of the non-pharmaceutical interventions, based on the dominant variants XBB.1.9.1 and EG.5. The results of the risk estimations suggested that non-pharmaceutical interventions were necessary on the Chinese mainland for controlling severe infections and deaths, and also that the regular variant monitors were still workable against the aggressive variant evolution and the emergency of new transmission risks in the future.
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Affiliation(s)
- Fengying Wei
- School of Mathematics and Statistics, Fuzhou University, Fuzhou, Fujian, China
- Center for Applied Mathematics of Fujian Province, Fuzhou University, Fuzhou, Fujian, China
- Key Laboratory of Operations Research and Control of Universities in Fujian, Fuzhou University, Fuzhou, Fujian, China
| | - Ruiyang Zhou
- School of Mathematics and Statistics, Fuzhou University, Fuzhou, Fujian, China
| | - Zhen Jin
- Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi, China
| | - Yamin Sun
- Research Institute of Public Health, Nankai University, Tianjin, China
| | - Zhihang Peng
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Shaojian Cai
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, Fujian, China
| | - Guangmin Chen
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, Fujian, China
| | - Kuicheng Zheng
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, Fujian, China
- Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, Fujian, China
- Teaching Base of the School of Public Health of Fujian Medical University, Fuzhou, Fujian, China
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2
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Gorji H, Stauffer N, Lunati I. Emergence of the reproduction matrix in epidemic forecasting. J R Soc Interface 2024; 21:20240124. [PMID: 39081116 PMCID: PMC11289658 DOI: 10.1098/rsif.2024.0124] [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: 02/21/2024] [Accepted: 05/30/2024] [Indexed: 08/02/2024] Open
Abstract
During the recent COVID-19 pandemic, the instantaneous reproduction number, R(t), has surged as a widely used measure to target public health interventions aiming at curbing the infection rate. In analogy with the basic reproduction number that arises from the linear stability analysis, R(t) is typically interpreted as a threshold parameter that separates exponential growth (R(t) > 1) from exponential decay (R(t) < 1). In real epidemics, however, the finite number of susceptibles, the stratification of the population (e.g. by age or vaccination state), and heterogeneous mixing lead to more complex epidemic courses. In the context of the multidimensional renewal equation, we generalize the scalar R(t) to a reproduction matrix, [Formula: see text], which details the epidemic state of the stratified population, and offers a concise epidemic forecasting scheme. First, the reproduction matrix is computed from the available incidence data (subject to some a priori assumptions), then it is projected into the future by a transfer functional to predict the epidemic course. We demonstrate that this simple scheme allows realistic and accurate epidemic trajectories both in synthetic test cases and with reported incidence data from the COVID-19 pandemic. Accounting for the full heterogeneity and nonlinearity of the infection process, the reproduction matrix improves the prediction of the infection peak. In contrast, the scalar reproduction number overestimates the possibility of sustaining the initial infection rate and leads to an overshoot in the incidence peak. Besides its simplicity, the devised forecasting scheme offers rich flexibility to be generalized to time-dependent mitigation measures, contact rate, infectivity and vaccine protection.
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Affiliation(s)
- Hossein Gorji
- Laboratory for Computational Engineering, Empa, Dübendorf, Switzerland
| | - Noé Stauffer
- Laboratory for Computational Engineering, Empa, Dübendorf, Switzerland
- Chair of Computational Mathematics and Simulation Science, EPFL, Switzerland
| | - Ivan Lunati
- Laboratory for Computational Engineering, Empa, Dübendorf, Switzerland
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3
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Dorešić D, Grein S, Hasenauer J. Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data. Bioinformatics 2024; 40:i558-i566. [PMID: 38940161 PMCID: PMC11211815 DOI: 10.1093/bioinformatics/btae210] [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] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a nonlinear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared. RESULTS We propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods. AVAILABILITY AND IMPLEMENTATION Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO) available at https://github.com/ICB-DCM/pyPESTO.
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Affiliation(s)
- Domagoj Dorešić
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Stephan Grein
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Jan Hasenauer
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748 Garching, Germany
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4
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Calistri A, Francesco Roggero P, Palù G. Chaos theory in the understanding of COVID-19 pandemic dynamics. Gene 2024; 912:148334. [PMID: 38458366 DOI: 10.1016/j.gene.2024.148334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 02/28/2024] [Indexed: 03/10/2024]
Abstract
The chaos theory, a field of study in mathematics and physics, offers a unique lens through which to understand the dynamics of the COVID-19 pandemic. This theory, which deals with complex systems whose behavior is highly sensitive to initial conditions, can provide insights into the unpredictable and seemingly random nature of the pandemic's spread. In this review, we will discuss some literature data with the aim of showing how chaos theory could provide valuable perspectives in understanding the complex and dynamic nature of the COVID-19 pandemic. In particular, we will emphasize how the chaos theory can help in dissecting the unpredictable, non- linear progression of the disease, the importance of initial conditions, and the complex interactions between various factors influencing its spread. These insights are crucial for developing effective strategies to manage and mitigate the impact of the pandemic.
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Affiliation(s)
- Arianna Calistri
- Department of Molecular Medicine, University of Padova, Via A. Gabelli 63, 35121 Padova, Italy.
| | - Pier Francesco Roggero
- Department of Molecular Medicine, University of Padova, Via A. Gabelli 63, 35121 Padova, Italy.
| | - Giorgio Palù
- Department of Molecular Medicine, University of Padova, Via A. Gabelli 63, 35121 Padova, Italy.
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5
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Willem L, Abrams S, Franco N, Coletti P, Libin PJK, Wambua J, Couvreur S, André E, Wenseleers T, Mao Z, Torneri A, Faes C, Beutels P, Hens N. The impact of quality-adjusted life years on evaluating COVID-19 mitigation strategies: lessons from age-specific vaccination roll-out and variants of concern in Belgium (2020-2022). BMC Public Health 2024; 24:1171. [PMID: 38671366 PMCID: PMC11047051 DOI: 10.1186/s12889-024-18576-w] [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: 10/27/2023] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND When formulating and evaluating COVID-19 vaccination strategies, an emphasis has been placed on preventing severe disease that overburdens healthcare systems and leads to mortality. However, more conventional outcomes such as quality-adjusted life years (QALYs) and inequality indicators are warranted as additional information for policymakers. METHODS We adopted a mathematical transmission model to describe the infectious disease dynamics of SARS-COV-2, including disease mortality and morbidity, and to evaluate (non)pharmaceutical interventions. Therefore, we considered temporal immunity levels, together with the distinct transmissibility of variants of concern (VOCs) and their corresponding vaccine effectiveness. We included both general and age-specific characteristics related to SARS-CoV-2 vaccination. Our scenario study is informed by data from Belgium, focusing on the period from August 2021 until February 2022, when vaccination for children aged 5-11 years was initially not yet licensed and first booster doses were administered to adults. More specifically, we investigated the potential impact of an earlier vaccination programme for children and increased or reduced historical adult booster dose uptake. RESULTS Through simulations, we demonstrate that increasing vaccine uptake in children aged 5-11 years in August-September 2021 could have led to reduced disease incidence and ICU occupancy, which was an essential indicator for implementing non-pharmaceutical interventions and maintaining healthcare system functionality. However, an enhanced booster dose regimen for adults from November 2021 onward could have resulted in more substantial cumulative QALY gains, particularly through the prevention of elevated levels of infection and disease incidence associated with the emergence of Omicron VOC. In both scenarios, the need for non-pharmaceutical interventions could have decreased, potentially boosting economic activity and mental well-being. CONCLUSIONS When calculating the impact of measures to mitigate disease spread in terms of life years lost due to COVID-19 mortality, we highlight the impact of COVID-19 on the health-related quality of life of survivors. Our study underscores that disease-related morbidity could constitute a significant part of the overall health burden. Our quantitative findings depend on the specific setup of the interventions under review, which is open to debate or should be contextualised within future situations.
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Affiliation(s)
- Lander Willem
- Department of Family Medicine and Population Health, Antwerp, Belgium.
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium.
| | - Steven Abrams
- Department of Family Medicine and Population Health, Antwerp, Belgium
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Nicolas Franco
- Data Science Institute, Hasselt University, Hasselt, Belgium
- Namur Institute for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium
| | - Pietro Coletti
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Pieter J K Libin
- Data Science Institute, Hasselt University, Hasselt, Belgium
- Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium
- Rega Institute for Medical Research, Clinical and Epidemiological Virology, University of Leuven, Leuven, Belgium
| | - James Wambua
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Simon Couvreur
- Department of Epidemiology and public health, Sciensano, Brussel, Belgium
| | - Emmanuel André
- National Reference Centre for Respiratory Pathogens, University Hospitals Leuven, Leuven, Belgium
- Department of Microbiology, Immunology and Transplantation, University of Leuven, Leuven, Belgium
| | - Tom Wenseleers
- Laboratory of Socioecology and Social Evolution, University of Leuven, Leuven, Belgium
| | - Zhuxin Mao
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
| | - Andrea Torneri
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Christel Faes
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Philippe Beutels
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Niel Hens
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- Data Science Institute, Hasselt University, Hasselt, Belgium
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6
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Lei B, Mahajan A, Mallick B. Identifying and overcoming COVID-19 vaccination impediments using Bayesian data mining techniques. Sci Rep 2024; 14:8595. [PMID: 38615084 PMCID: PMC11016065 DOI: 10.1038/s41598-024-58902-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 04/04/2024] [Indexed: 04/15/2024] Open
Abstract
The COVID-19 pandemic has profoundly reshaped human life. The development of COVID-19 vaccines has offered a semblance of normalcy. However, obstacles to vaccination have led to substantial loss of life and economic burdens. In this study, we analyze data from a prominent health insurance provider in the United States to uncover the underlying reasons behind the inability, refusal, or hesitancy to receive vaccinations. Our research proposes a methodology for pinpointing affected population groups and suggests strategies to mitigate vaccination barriers and hesitations. Furthermore, we estimate potential cost savings resulting from the implementation of these strategies. To achieve our objectives, we employed Bayesian data mining methods to streamline data dimensions and identify significant variables (features) influencing vaccination decisions. Comparative analysis reveals that the Bayesian method outperforms cutting-edge alternatives, demonstrating superior performance.
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Affiliation(s)
- Bowen Lei
- Department of Statistics, Texas A&M University, College Station, TX, USA
| | - Arvind Mahajan
- Department of Finance, Texas A&M University, College Station, TX, USA
| | - Bani Mallick
- Department of Statistics, Texas A&M University, College Station, TX, USA.
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7
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Wheeler J, Rosengart A, Jiang Z, Tan K, Treutle N, Ionides EL. Informing policy via dynamic models: Cholera in Haiti. PLoS Comput Biol 2024; 20:e1012032. [PMID: 38683863 PMCID: PMC11081515 DOI: 10.1371/journal.pcbi.1012032] [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: 10/07/2023] [Revised: 05/09/2024] [Accepted: 03/29/2024] [Indexed: 05/02/2024] Open
Abstract
Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.
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Affiliation(s)
- Jesse Wheeler
- Statistics Department, University of Michigan, Ann Arbor, Michigan, United States of America
| | - AnnaElaine Rosengart
- Statistics and Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Zhuoxun Jiang
- Statistics Department, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Kevin Tan
- Wharton Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Noah Treutle
- Statistics Department, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Edward L. Ionides
- Statistics Department, University of Michigan, Ann Arbor, Michigan, United States of America
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8
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Trimarco V, Izzo R, Pacella D, Trama U, Manzi MV, Lombardi A, Piccinocchi R, Gallo P, Esposito G, Piccinocchi G, Lembo M, Morisco C, Rozza F, Santulli G, Trimarco B. Incidence of new-onset hypertension before, during, and after the COVID-19 pandemic: a 7-year longitudinal cohort study in a large population. BMC Med 2024; 22:127. [PMID: 38500180 PMCID: PMC10949764 DOI: 10.1186/s12916-024-03328-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/28/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND While the augmented incidence of diabetes after COVID-19 has been widely confirmed, controversial results are available on the risk of developing hypertension during the COVID-19 pandemic. METHODS We designed a longitudinal cohort study to analyze a closed cohort followed up over a 7-year period, i.e., 3 years before and 3 years during the COVID-19 pandemic, and during 2023, when the pandemic was declared to be over. We analyzed medical records of more than 200,000 adults obtained from a cooperative of primary physicians from January 1, 2017, to December 31, 2023. The main outcome was the new diagnosis of hypertension. RESULTS We evaluated 202,163 individuals in the pre-pandemic years and 190,743 in the pandemic years, totaling 206,857 when including 2023 data. The incidence rate of new hypertension was 2.11 (95% C.I. 2.08-2.15) per 100 person-years in the years 2017-2019, increasing to 5.20 (95% C.I. 5.14-5.26) in the period 2020-2022 (RR = 2.46), and to 6.76 (95% C.I. 6.64-6.88) in 2023. The marked difference in trends between the first and the two successive observation periods was substantiated by the fitted regression lines of two Poisson models conducted on the monthly log-incidence of hypertension. CONCLUSIONS We detected a significant increase in new-onset hypertension during the COVID-19 pandemic, which at the end of the observation period affected ~ 20% of the studied cohort, a percentage higher than the diagnosis of COVID-19 infection within the same time frame. This observation suggests that increased attention to hypertension screening should not be limited to individuals who are aware of having contracted the infection but should be extended to the entire population.
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Affiliation(s)
- Valentina Trimarco
- Department of Neuroscience, Reproductive Sciences, and Dentistry, "Federico II" University, Naples, Italy
| | - Raffaele Izzo
- Department of Advanced Biomedical Sciences, "Federico II" University, Naples, Italy
| | - Daniela Pacella
- Department of Public Health, "Federico II" University, Naples, Italy
| | - Ugo Trama
- Pharmaceutical Department of Campania Region, Naples, Italy
| | - Maria Virginia Manzi
- Department of Advanced Biomedical Sciences, "Federico II" University, Naples, Italy
| | - Angela Lombardi
- Department of Microbiology and Immunology, Fleischer Institute for Diabetes and Metabolism (FIDAM), Albert Einstein College of Medicine, New York City, NY, USA
| | | | - Paola Gallo
- Department of Neuroscience, Reproductive Sciences, and Dentistry, "Federico II" University, Naples, Italy
| | - Giovanni Esposito
- Department of Advanced Biomedical Sciences, "Federico II" University, Naples, Italy
| | - Gaetano Piccinocchi
- COMEGEN Primary Care Physicians Cooperative, Italian Society of General Medicine (SIMG), Naples, Italy
| | - Maria Lembo
- Department of Advanced Biomedical Sciences, "Federico II" University, Naples, Italy
| | - Carmine Morisco
- Department of Advanced Biomedical Sciences, "Federico II" University, Naples, Italy
- International Translational Research and Medical Education (ITME) Consortium, Academic Research Unit, Naples, Italy
- Italian Society for Cardiovascular Prevention (SIPREC), Rome, Italy
| | - Francesco Rozza
- Department of Advanced Biomedical Sciences, "Federico II" University, Naples, Italy
| | - Gaetano Santulli
- Department of Advanced Biomedical Sciences, "Federico II" University, Naples, Italy.
- International Translational Research and Medical Education (ITME) Consortium, Academic Research Unit, Naples, Italy.
- Department of Medicine, Wilf Family Cardiovascular Research Institute, Einstein-Mount Sinai Diabetes Research Center (ES-DRC), Albert Einstein College of Medicine, 1300 Morris Park Avenue, New York City, NY, 10461, USA.
| | - Bruno Trimarco
- Department of Advanced Biomedical Sciences, "Federico II" University, Naples, Italy
- International Translational Research and Medical Education (ITME) Consortium, Academic Research Unit, Naples, Italy
- Italian Society for Cardiovascular Prevention (SIPREC), Rome, Italy
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9
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Cancedda C, Cappellato A, Maninchedda L, Meacci L, Peracchi S, Salerni C, Baralis E, Giobergia F, Ceri S. Social and economic variables explain COVID-19 diffusion in European regions. Sci Rep 2024; 14:6142. [PMID: 38480771 PMCID: PMC10937953 DOI: 10.1038/s41598-024-56267-z] [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: 11/23/2022] [Accepted: 03/04/2024] [Indexed: 03/17/2024] Open
Abstract
At the beginning of 2020, Italy was the country with the highest number of COVID-19 cases, not only in Europe, but also in the rest of the world, and Lombardy was the most heavily hit region of Italy. The objective of this research is to understand which variables have determined the prevalence of cases in Lombardy and in other highly-affected European regions. We consider the first and second waves of the COVID-19 pandemic, using a set of 22 variables related to economy, population, healthcare and education. Regions with a high prevalence of cases are extracted by means of binary classifiers, then the most relevant variables for the classification are determined, and the robustness of the analysis is assessed. Our results show that the most meaningful features to identify high-prevalence regions include high number of hours spent in work environments, high life expectancy, and low number of people leaving from education and neither employed nor educated or trained.
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Affiliation(s)
- Christian Cancedda
- Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Turin, Italy
| | - Alessio Cappellato
- Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Turin, Italy
| | - Luigi Maninchedda
- Department of Management, Economics and Industrial Engineering (DIG), Politecnico di Milano, Milan, Italy
| | - Leonardo Meacci
- Department of Management, Economics and Industrial Engineering (DIG), Politecnico di Milano, Milan, Italy
| | - Sofia Peracchi
- Department of Design (DESIGN), Politecnico di Milano, Milan, Italy
| | - Claudia Salerni
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Elena Baralis
- Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Turin, Italy
| | - Flavio Giobergia
- Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Turin, Italy.
| | - Stefano Ceri
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
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10
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Heidecke J, Fuhrmann J, Barbarossa MV. A mathematical model to assess the effectiveness of test-trace-isolate-and-quarantine under limited capacities. PLoS One 2024; 19:e0299880. [PMID: 38470895 DOI: 10.1371/journal.pone.0299880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 02/17/2024] [Indexed: 03/14/2024] Open
Abstract
Diagnostic testing followed by isolation of identified cases with subsequent tracing and quarantine of close contacts-often referred to as test-trace-isolate-and-quarantine (TTIQ) strategy-is one of the cornerstone measures of infectious disease control. The COVID-19 pandemic has highlighted that an appropriate response to outbreaks of infectious diseases requires a firm understanding of the effectiveness of such containment strategies. To this end, mathematical models provide a promising tool. In this work, we present a delay differential equation model of TTIQ interventions for infectious disease control. Our model incorporates the assumption of limited TTIQ capacities, providing insights into the reduced effectiveness of testing and tracing in high prevalence scenarios. In addition, we account for potential transmission during the early phase of an infection, including presymptomatic transmission, which may be particularly adverse to a TTIQ based control. Our numerical experiments inspired by the early spread of COVID-19 in Germany demonstrate the effectiveness of TTIQ in a scenario where immunity within the population is low and pharmaceutical interventions are absent, which is representative of a typical situation during the (re-)emergence of infectious diseases for which therapeutic drugs or vaccines are not yet available. Stability and sensitivity analyses reveal both disease-dependent and disease-independent factors that impede or enhance the success of TTIQ. Studying the diminishing impact of TTIQ along simulations of an epidemic wave, we highlight consequences for intervention strategies.
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Affiliation(s)
- Julian Heidecke
- Frankfurt Institute for Advanced Studies, Frankfurt, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Heidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany
| | - Jan Fuhrmann
- Institute of Applied Mathematics, Heidelberg University, Heidelberg, Germany
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11
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Atamer Balkan B, Chang Y, Sparnaaij M, Wouda B, Boschma D, Liu Y, Yuan Y, Daamen W, de Jong MCM, Teberg C, Schachtschneider K, Sikkema RS, van Veen L, Duives D, ten Bosch QA. The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model. PLoS Comput Biol 2024; 20:e1011956. [PMID: 38547311 PMCID: PMC11003685 DOI: 10.1371/journal.pcbi.1011956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 04/09/2024] [Accepted: 02/29/2024] [Indexed: 04/11/2024] Open
Abstract
SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics-Virus Spread model (PeDViS), an individual-based model that combines pedestrian behaviour models with virus spread models incorporating direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark 'at risk' interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to the buildup of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent. This uncertainty should be considered when assessing transmission risks upon different types of human interactions in indoor spaces. We illustrated the multi-dimensionality of indoor SARS-CoV-2 transmission that emerges from the interplay of human behaviour and the spread of respiratory viruses. A modelling strategy that incorporates this in risk assessments can help inform policy makers and citizens on the safe use of indoor spaces with varying inter-human interactions.
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Affiliation(s)
- Büsra Atamer Balkan
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
| | - You Chang
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Martijn Sparnaaij
- Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands
| | - Berend Wouda
- Gamelab, Delft University of Technology, Delft, The Netherlands
| | - Doris Boschma
- Gamelab, Delft University of Technology, Delft, The Netherlands
| | - Yangfan Liu
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Yufei Yuan
- Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands
| | - Winnie Daamen
- Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands
| | - Mart C. M. de Jong
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Colin Teberg
- Steady State Scientific Computing, Chicago, Illinois, United States of America
| | | | | | - Linda van Veen
- Gamelab, Delft University of Technology, Delft, The Netherlands
| | - Dorine Duives
- Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands
| | - Quirine A. ten Bosch
- Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands
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12
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Smirnova A, Baroonian M. Reconstruction of incidence reporting rate for SARS-CoV-2 Delta variant of COVID-19 pandemic in the US. Infect Dis Model 2024; 9:70-83. [PMID: 38125200 PMCID: PMC10733106 DOI: 10.1016/j.idm.2023.12.001] [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: 09/30/2023] [Revised: 12/03/2023] [Accepted: 12/03/2023] [Indexed: 12/23/2023] Open
Abstract
In recent years, advanced regularization techniques have emerged as a powerful tool aimed at stable estimation of infectious disease parameters that are crucial for future projections, prevention, and control. Unlike other system parameters, i.e., incubation and recovery rates, the case reporting rate, Ψ, and the time-dependent effective reproduction number, R e ( t ) , are directly influenced by a large number of factors making it impossible to pre-estimate these parameters in any meaningful way. In this study, we propose a novel iteratively-regularized trust-region optimization algorithm, combined with SuSvIuIvRD compartmental model, for stable reconstruction of Ψ and R e ( t ) from reported epidemic data on vaccination percentages, incidence cases, and daily deaths. The innovative regularization procedure exploits (and takes full advantage of) a unique structure of the Jacobian and Hessian approximation for the nonlinear observation operator. The proposed inversion method is thoroughly tested with synthetic and real SARS-CoV-2 Delta variant data for different regions in the United States of America from July 9, 2021, to November 25, 2021. Our study shows that case reporting rate during the Delta wave of COVID-19 pandemic in the US is between 12% and 37%, with most states being in the range from 15% to 25%. This confirms earlier accounts on considerable under-reporting of COVID-19 cases due to the impact of "silent spreaders" and the limitations of testing.
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Affiliation(s)
- Alexandra Smirnova
- Department of Mathematics & Statistics, Georgia State University, Atlanta, USA
| | - Mona Baroonian
- Department of Mathematics & Statistics, Georgia State University, Atlanta, USA
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13
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Fu R, Liu W, Wang S, Zhao J, Cui Q, Hu Z, Zhang L, Wang F. Scenario analysis of COVID-19 dynamical variations by different social environmental factors: a case study in Xinjiang. Front Public Health 2024; 12:1297007. [PMID: 38435296 PMCID: PMC10906079 DOI: 10.3389/fpubh.2024.1297007] [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: 10/16/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
Background With the rapid advancement of the One Health approach, the transmission of human infectious diseases is generally related to environmental and animal health. Coronavirus disease (COVID-19) has been largely impacted by environmental factors regionally and globally and has significantly disrupted human society, especially in low-income regions that border many countries. However, few research studies have explored the impact of environmental factors on disease transmission in these regions. Methods We used the Xinjiang Uygur Autonomous Region as the study area to investigate the impact of environmental factors on COVID-19 variation using a dynamic disease model. Given the special control and prevention strategies against COVID-19 in Xinjiang, the focus was on social and environmental factors, including population mobility, quarantine rates, and return rates. The model performance was evaluated using the statistical metrics of correlation coefficient (CC), normalized absolute error (NAE), root mean square error (RMSE), and distance between the simulation and observation (DISO) indices. Scenario analyses of COVID-19 in Xinjiang encompassed three aspects: different population mobilities, quarantine rates, and return rates. Results The results suggest that the established dynamic disease model can accurately simulate and predict COVID-19 variations with high accuracy. This model had a CC value of 0.96 and a DISO value of less than 0.35. According to the scenario analysis results, population mobilities have a large impact on COVID-19 variations, with quarantine rates having a stronger impact than return rates. Conclusion These results provide scientific insight into the control and prevention of COVID-19 in Xinjiang, considering the influence of social and environmental factors on COVID-19 variation. The control and prevention strategies for COVID-19 examined in this study may also be useful for the control of other infectious diseases, especially in low-income regions that are bordered by many countries.
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Affiliation(s)
- Ruonan Fu
- School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Wanli Liu
- Center of Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
| | - Senlu Wang
- School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
- Center of Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
| | - Jun Zhao
- Center of Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
| | - Qianqian Cui
- School of Mathematics and Statistics, Ningxia University, Yingchuan, Ningxia, China
| | - Zengyun Hu
- School of Global Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, China
| | - Ling Zhang
- School of Global Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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14
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Cildoz M, Gaston M, Frias L, Garcia-Vicuña D, Azcarate C, Mallor F. Early detection of new pandemic waves. Control chart and a new surveillance index. PLoS One 2024; 19:e0295242. [PMID: 38346027 PMCID: PMC10861055 DOI: 10.1371/journal.pone.0295242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 11/20/2023] [Indexed: 02/15/2024] Open
Abstract
The COVID-19 pandemic highlights the pressing need for constant surveillance, updating of the response plan in post-peak periods and readiness for the possibility of new waves of the pandemic. A short initial period of steady rise in the number of new cases is sometimes followed by one of exponential growth. Systematic public health surveillance of the pandemic should signal an alert in the event of change in epidemic activity within the community to inform public health policy makers of the need to control a potential outbreak. The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with a new surveillance metric to overcome some of their difficulties in capturing the changing dynamics of the pandemic. At statistically-founded threshold values, the new measure will trigger alert signals giving early warning of the onset of a new pandemic wave. We define a new index, the weighted cumulative incidence index, based on the daily new-case count. We model the infection spread rate at two levels, inside and outside homes, which explains the overdispersion observed in the data. The seasonal component of real data, due to the public surveillance system, is incorporated into the statistical analysis. Probabilistic analysis enables the construction of a Control Chart for monitoring index variability and setting automatic alert thresholds for new pandemic waves. Both the new index and the control chart have been implemented with the aid of a computational tool developed in R, and used daily by the Navarre Government (Spain) for virus propagation surveillance during post-peak periods. Automated monitoring generates daily reports showing the areas whose control charts issue an alert. The new index reacts sooner to data trend changes preluding new pandemic waves, than the standard surveillance index based on the 14-day notification rate of reported COVID-19 cases per 100,000 population.
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Affiliation(s)
- Marta Cildoz
- Institute of Smart Cities, Public University of Navarre, Campus Arrosadia, Pamplona, Spain
| | - Martin Gaston
- Institute of Smart Cities, Public University of Navarre, Campus Arrosadia, Pamplona, Spain
| | - Laura Frias
- Institute of Smart Cities, Public University of Navarre, Campus Arrosadia, Pamplona, Spain
| | - Daniel Garcia-Vicuña
- Institute of Smart Cities, Public University of Navarre, Campus Arrosadia, Pamplona, Spain
| | - Cristina Azcarate
- Institute of Smart Cities, Public University of Navarre, Campus Arrosadia, Pamplona, Spain
| | - Fermin Mallor
- Institute of Smart Cities, Public University of Navarre, Campus Arrosadia, Pamplona, Spain
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15
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Acemoglu D, Fallah A, Giometto A, Huttenlocher D, Ozdaglar A, Parise F, Pattathil S. Optimal Adaptive Testing for Epidemic Control: Combining Molecular and Serology Tests. AUTOMATICA : THE JOURNAL OF IFAC, THE INTERNATIONAL FEDERATION OF AUTOMATIC CONTROL 2024; 160:111391. [PMID: 38282699 PMCID: PMC10810420 DOI: 10.1016/j.automatica.2023.111391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
Epidemic interventions based on surveillance testing programs are a fundamental tool to control the first stages of new epidemics, yet they are costly, invasive and rely on scarce resources, limiting their applicability. To overcome these challenges, we investigate two optimal control problems: (i) how testing needs can be minimized while maintaining the number of infected individuals below a desired threshold, and (ii) how peak infections can be minimized given a typically scarce testing budget. We find that in both cases the optimal testing policy for the well-known Susceptible-Infected-Recovered (SIR) model is adaptive, with testing rates that depend on the epidemic state, and leads to significant cost savings compared to non-adaptive policies. By using the concept of observability, we then show that a central planner can estimate the required unknown epidemic state by complementing molecular tests, which are highly sensitive but have a short detectability window, with serology tests, which are less sensitive but can detect past infections.
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Affiliation(s)
| | - Alireza Fallah
- Department of Electrical Engineering and Computer Science, UC Berkeley, United States of America
| | - Andrea Giometto
- School of Civil and Environmental Engineering, Cornell University
| | | | - Asuman Ozdaglar
- Department of Electrical Engineering and Computer Science, MIT
| | - Francesca Parise
- School of Electrical and Computer Engineering, Cornell University
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16
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Cao Y, Fang W, Chen Y, Zhang H, Ni R, Pan G. Simulating the impact of optimized prevention and control measures on the transmission of monkeypox in the United States: A model-based study. J Med Virol 2024; 96:e29419. [PMID: 38293742 DOI: 10.1002/jmv.29419] [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: 07/25/2023] [Revised: 11/24/2023] [Accepted: 01/10/2024] [Indexed: 02/01/2024]
Abstract
This study aimed to develop a modified susceptible-exposed-infected-recovered (SEIR) model to evaluate monkeypox epidemics in the United States and explore more optimized prevention and control measures. To further assess the impact of public health measures on the transmission of monkeypox, different intervention scenarios were developed based on the classic SEIR model, considering reducing contact, enhancing vaccination, diagnosis delay, and environmental transmission risk, respectively. We evaluated the impact of different measures by simulating their spread in different scenarios. During the simulation period, 8709 people were infected with monkeypox. The simulation analysis showed that: (1) the most effective measures to control monkeypox transmission during the early stage of the epidemic were reducing contact and enhancing vaccination, with cumulative infections at 51.20% and 41.90% of baseline levels, respectively; (2) shortening diagnosis time would delay the peak time of the epidemic by 96 days; and (3) the risk of environmental transmission of monkeypox virus was relatively low. This study indirectly proved the effectiveness of the prevention and control measures, such as reducing contact, enhancing vaccination, shortening diagnosis time, and low risk of environmental transmission, which also provided an important reference and containment experience for nonepidemic countries.
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Affiliation(s)
- Yawen Cao
- Department of Epidemiology and Biostatistics, Anhui Medical University, Hefei, Anhui, China
| | - Wenbin Fang
- Department of Epidemiology and Biostatistics, Anhui Medical University, Hefei, Anhui, China
| | - Yingying Chen
- Department of Epidemiology and Biostatistics, Anhui Medical University, Hefei, Anhui, China
| | - Hengchuan Zhang
- Department of Epidemiology and Biostatistics, Anhui Medical University, Hefei, Anhui, China
| | - Ruyu Ni
- Department of Epidemiology and Biostatistics, Anhui Medical University, Hefei, Anhui, China
| | - Guixia Pan
- Department of Epidemiology and Biostatistics, Anhui Medical University, Hefei, Anhui, China
- Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, Hefei, Anhui, China
- Medical Data Processing Center of School of Public Health of Anhui Medical University, Anhui Medical University, Hefei, Anhui, China
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17
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Boldea O, Alipoor A, Pei S, Shaman J, Rozhnova G. Age-specific transmission dynamics of SARS-CoV-2 during the first 2 years of the pandemic. PNAS NEXUS 2024; 3:pgae024. [PMID: 38312225 PMCID: PMC10837015 DOI: 10.1093/pnasnexus/pgae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 01/09/2024] [Indexed: 02/06/2024]
Abstract
During its first 2 years, the SARS-CoV-2 pandemic manifested as multiple waves shaped by complex interactions between variants of concern, non-pharmaceutical interventions, and the immunological landscape of the population. Understanding how the age-specific epidemiology of SARS-CoV-2 has evolved throughout the pandemic is crucial for informing policy decisions. In this article, we aimed to develop an inference-based modeling approach to reconstruct the burden of true infections and hospital admissions in children, adolescents, and adults over the seven waves of four variants (wild-type, Alpha, Delta, and Omicron BA.1) during the first 2 years of the pandemic, using the Netherlands as the motivating example. We find that reported cases are a considerable underestimate and a generally poor predictor of true infection burden, especially because case reporting differs by age. The contribution of children and adolescents to total infection and hospitalization burden increased with successive variants and was largest during the Omicron BA.1 period. However, the ratio of hospitalizations to infections decreased with each subsequent variant in all age categories. Before the Delta period, almost all infections were primary infections occurring in naive individuals. During the Delta and Omicron BA.1 periods, primary infections were common in children but relatively rare in adults who experienced either reinfections or breakthrough infections. Our approach can be used to understand age-specific epidemiology through successive waves in other countries where random community surveys uncovering true SARS-CoV-2 dynamics are absent but basic surveillance and statistics data are available.
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Affiliation(s)
- Otilia Boldea
- Department of Econometrics and OR, Tilburg School of Economics and Management, Tilburg University, Tilburg 5037 AB, The Netherlands
| | - Amir Alipoor
- Department of Econometrics and OR, Tilburg School of Economics and Management, Tilburg University, Tilburg 5037 AB, The Netherlands
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
- Columbia Climate School, Columbia University, New York, NY 10025, USA
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht 3584 CX, The Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht 3584 CE, The Netherlands
- Faculdade de Ciências, Universidade de Lisboa, Lisbon PT1749-016, Portugal
- BioISI—Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon PT1749-016, Portugal
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18
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Taleb S, Vahedian-Azimi A, Karimi L, Salim S, Mohammad F, Samhadaneh D, Singh K, Hussein NR, Ait Hssain A. Evaluation of psychological distress, burnout and structural empowerment status of healthcare workers during the outbreak of coronavirus disease (COVID-19): a cross-sectional questionnaire-based study. BMC Psychiatry 2024; 24:61. [PMID: 38254016 PMCID: PMC10804486 DOI: 10.1186/s12888-023-05088-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 08/08/2023] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND In light of several recent studies, there is evidence that the coronavirus disease 2019 (COVID-19) pandemic has caused various mental health concerns in the general population, as well as among healthcare workers (HCWs). The main aim of this study was to assess the psychological distress, burnout and structural empowerment status of HCWs during the COVID-19 outbreak, and to evaluate its predictors. METHODS This multi-center, cross-sectional web-based questionnaire survey was conducted on HCWs during the outbreak of COVID-19 from August 2020 to January 2021. HCWs working in hospitals from 48 different countries were invited to participate in an online anonymous survey that investigated sociodemographic data, psychological distress, burnout and structural empowerment (SE) based on Depression Anxiety and Stress Scale 21 (DASS-21), Maslach Burnout Inventory (MBI) and Conditions for work effectiveness questionnaire (CWEQ_II), respectively. Predictors of the total scores of DASS-21, MBI and CWEQ-II were assessed using unadjusted and adjusted binary logistic regression analysis. RESULTS Out of the 1030 HCWs enrolled in this survey, all completed the sociodemographic section (response rate 100%) A total of 730 (70.9%) HCWs completed the DASS-21 questionnaire, 852 (82.6%) completed the MBI questionnaire, and 712 (69.1%) completed the CWEQ-II questionnaire. The results indicate that 360 out of 730 responders (49.3%) reported severe or extremely severe levels of stress, anxiety, and depression. Additionally, 422 out of 851 responders (49.6%) reported a high level of burnout, while 268 out of 712 responders (37.6%) reported a high level of structural empowerment based on the DASS-21, MBI, and CWEQ-II scales, respectively. In addition, the analysis showed that HCWs working in the COVID-19 areas experienced significantly higher symptoms of severe stress, anxiety, depression and higher levels of burnout compared to those working in other areas. The results also revealed that direct work with COVID-19 patients, lower work experience, and high workload during the outbreak of COVID-19 increase the risks of negative psychological consequences. CONCLUSION Health professionals had high levels of burnout and psychological symptoms during the COVID-19 emergency. Monitoring and timely treatment of these conditions is needed.
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Affiliation(s)
- Sara Taleb
- Division of Genomics and Translational Biomedicine, College of Health and Life Science, Hamad Bin Khalifa University, Doha, Qatar
- Proteomics Core, Research department, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Amir Vahedian-Azimi
- Trauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Leila Karimi
- Behavioral Sciences Research Center, Life Style Institute, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Safa Salim
- Division of Biological and Biomedical Sciences, College of Health & Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Farhan Mohammad
- Division of Biological and Biomedical Sciences, College of Health & Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | | | - Kalpana Singh
- Nursing Midwifery Research Department, Hamad Medical Corporation, Doha, Qatar
| | | | - Ali Ait Hssain
- Medical Intensive Care Unit, Hamad General Hospital, Doha, Qatar.
- Department of Medicine, Weill Cornell Medical College, Doha, Qatar.
- College of Health and Life Science, Hamad Bin Khalifa University, Doha, Qatar.
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19
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Alòs J, Ansótegui C, Dotu I, García-Herranz M, Pastells P, Torres E. ePyDGGA: automatic configuration for fitting epidemic curves. Sci Rep 2024; 14:784. [PMID: 38191771 PMCID: PMC10774272 DOI: 10.1038/s41598-023-43958-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/30/2023] [Indexed: 01/10/2024] Open
Abstract
Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provide a model-agnostic framework for epidemic parameter fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the fitted parameters. Briefly, we have developed a Python framework that expects a Python function (epidemic model) and epidemic data and performs parameter fitting using automatic configuration. Our framework is capable of fitting parameters for any type of epidemic model, as long as it is provided as a Python function (or even in a different programming language). Moreover, we provide the code for different types of models, as well as the implementation of 4 concrete models with data to fit them. Documentation, code and examples can be found at https://ulog.udl.cat/static/doc/epidemic-gga/html/index.html .
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Affiliation(s)
- Josep Alòs
- Logic and Optimization Group, University of Lleida, Lleida, Spain.
| | - Carlos Ansótegui
- Logic and Optimization Group, University of Lleida, Lleida, Spain.
| | | | | | | | - Eduard Torres
- Logic and Optimization Group, University of Lleida, Lleida, Spain
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20
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Crocker A, Strömbom D. Susceptible-Infected-Susceptible type COVID-19 spread with collective effects. Sci Rep 2023; 13:22600. [PMID: 38114694 PMCID: PMC10730724 DOI: 10.1038/s41598-023-49949-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
Many models developed to forecast and attempt to understand the COVID-19 pandemic are highly complex, and few take collective behavior into account. As the pandemic progressed individual recurrent infection was observed and simpler susceptible-infected type models were introduced. However, these do not include mechanisms to model collective behavior. Here, we introduce an extension of the SIS model that accounts for collective behavior and show that it has four equilibria. Two of the equilibria are the standard SIS model equilibria, a third is always unstable, and a fourth where collective behavior and infection prevalence interact to produce either node-like or oscillatory dynamics. We then parameterized the model using estimates of the transmission and recovery rates for COVID-19 and present phase diagrams for fixed recovery rate and free transmission rate, and both rates fixed. We observe that regions of oscillatory dynamics exist in both cases and that the collective behavior parameter regulates their extent. Finally, we show that the system exhibits hysteresis when the collective behavior parameter varies over time. This model provides a minimal framework for explaining oscillatory phenomena such as recurring waves of infection and hysteresis effects observed in COVID-19, and other SIS-type epidemics, in terms of collective behavior.
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Affiliation(s)
- Amanda Crocker
- Department of Biology, Lafayette College, Easton, PA, 18042, USA
| | - Daniel Strömbom
- Department of Biology, Lafayette College, Easton, PA, 18042, USA.
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21
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Milanesi S, De Nicolao G. Correction of Italian under-reporting in the first COVID-19 wave via age-specific deconvolution of hospital admissions. PLoS One 2023; 18:e0295079. [PMID: 38060513 PMCID: PMC10703316 DOI: 10.1371/journal.pone.0295079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
When the COVID-19 pandemic first emerged in early 2020, healthcare and bureaucratic systems worldwide were caught off guard and largely unprepared to deal with the scale and severity of the outbreak. In Italy, this led to a severe underreporting of infections during the first wave of the spread. The lack of accurate data is critical as it hampers the retrospective assessment of nonpharmacological interventions, the comparison with the following waves, and the estimation and validation of epidemiological models. In particular, during the first wave, reported cases of new infections were strikingly low if compared with their effects in terms of deaths, hospitalizations and intensive care admissions. In this paper, we observe that the hospital admissions during the second wave were very well explained by the convolution of the reported daily infections with an exponential kernel. By formulating the estimation of the actual infections during the first wave as an inverse problem, its solution by a regularization approach is proposed and validated. In this way, it was possible to compute corrected time series of daily infections for each age class. The new estimates are consistent with the serological survey published in June 2020 by the National Institute of Statistics (ISTAT) and can be used to speculate on the total number of infections occurring in Italy during 2020, which appears to be about double the number officially recorded.
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Affiliation(s)
- Simone Milanesi
- Department of Mathematics, University of Pavia, Pavia, Italy
| | - Giuseppe De Nicolao
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Division of Infectious Diseases I, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
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22
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Jia Z, Hu J, Lian T, Qian L, Yu W, Zhang C. The impact of community nucleic acid testing on infection in residential compounds during a city-wide lockdown. Sci Rep 2023; 13:21334. [PMID: 38049496 PMCID: PMC10696007 DOI: 10.1038/s41598-023-48585-5] [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: 10/06/2022] [Accepted: 11/28/2023] [Indexed: 12/06/2023] Open
Abstract
The question of whether community nucleic acid testing contributes to an increase in infections within residential compounds has not been definitively answered. Shanghai, one of the largest cities in China, conducted city-wide community testing during its lockdown from late March to May 2022. This situation provided a unique opportunity to examine the effect of community testing on infection rates, as the lockdown largely eliminated confounding factors such as citizen mobility. In our study, based on a survey of 208 residential compounds in Shanghai and the daily infection data during the lockdown period, we found a significant correlation between community testing and infection risk in these compounds. However, after addressing potential issues of reverse causality and sampling bias, we found no significant causal link between community testing and infection risk. Furthermore, we discovered that increased awareness of mask-wearing correlated with a decrease in infections within the residential compounds during community testing. This suggests that the perceived correlation between community testing and infection risk may be confounded by residents' adherence to mask-wearing practices. Our findings emphasize the need for public health decision-makers to reinforce the importance of mask-wearing during community testing, as a means to prevent infections among citizens.
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Affiliation(s)
- Zhenzhen Jia
- School of Management, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jianqiang Hu
- School of Management, Fudan University, Shanghai, 200433, China.
| | - Teng Lian
- School of Management, Fudan University, Shanghai, 200433, China
| | - Lixian Qian
- International Business School Suzhou, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China
| | - Wen Yu
- School of Management, Fudan University, Shanghai, 200433, China
| | - Cheng Zhang
- School of Management, Fudan University, Shanghai, 200433, China
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23
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Dempsey W. ADDRESSING SELECTION BIAS AND MEASUREMENT ERROR IN COVID-19 CASE COUNT DATA USING AUXILIARY INFORMATION. Ann Appl Stat 2023; 17:2903-2923. [PMID: 38939875 PMCID: PMC11210953 DOI: 10.1214/23-aoas1744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
Coronavirus case-count data has influenced government policies and drives most epidemiological forecasts. Limited testing is cited as the key driver behind minimal information on the COVID-19 pandemic. While expanded testing is laudable, measurement error and selection bias are the two greatest problems limiting our understanding of the COVID-19 pandemic; neither can be fully addressed by increased testing capacity. In this paper, we demonstrate their impact on estimation of point prevalence and the effective reproduction number. We show that estimates based on the millions of molecular tests in the US has the same mean square error as a small simple random sample. To address this, a procedure is presented that combines case-count data and random samples over time to estimate selection propensities based on key covariate information. We then combine these selection propensities with epidemiological forecast models to construct a doubly robust estimation method that accounts for both measurement-error and selection bias. This method is then applied to estimate Indiana's active infection prevalence using case-count, hospitalization, and death data with demographic information, a statewide random molecular sample collected from April 25-29th, and Delphi's COVID-19 Trends and Impact Survey. We end with a series of recommendations based on the proposed methodology.
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Affiliation(s)
- Walter Dempsey
- DEPARTMENT OF BIOSTATISTICS, UNIVERSITY OF MICHIGAN, ANN ARBOR, MI 48109
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24
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Juneau CE, Briand AS, Collazzo P, Siebert U, Pueyo T. Effective contact tracing for COVID-19: A systematic review. GLOBAL EPIDEMIOLOGY 2023; 5:100103. [PMID: 36959868 PMCID: PMC9997056 DOI: 10.1016/j.gloepi.2023.100103] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/19/2023] [Accepted: 03/06/2023] [Indexed: 03/11/2023] Open
Abstract
Contact tracing is commonly recommended to control outbreaks of COVID-19, but its effectiveness is unclear. Following PRISMA guidelines, we searched four databases using a range of terms related to contact tracing effectiveness for COVID-19. We found 343 papers; 32 were included. All were observational or modelling studies. Observational studies (n = 14) provided consistent, very-low certainty evidence that contact tracing (alone or in combination with other interventions) was associated with better control of COVID-19 (e.g. in Hong Kong, only 1084 cases and four deaths were recorded in the first 4.5 months of the pandemic). Modelling studies (n = 18) provided consistent, high-certainty evidence that under assumptions of prompt and thorough tracing with effective quarantines, contact tracing could stop the spread of COVID-19 (e.g. by reducing the reproduction number from 2.2 to 0.57). A cautious interpretation indicates that to stop the spread of COVID-19, public health practitioners have 2-3 days from the time a new case develops symptoms to isolate the case and quarantine at least 80% of its contacts.
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Affiliation(s)
- Carl-Etienne Juneau
- Direction régionale de santé publique, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada
| | - Anne-Sara Briand
- École de santé publique, Université de Montréal, Montréal, Québec, Canada
| | - Pablo Collazzo
- Danube University Krems, Dr. Karl Dorrek-Strasse 30, 3500 Krems, Austria and IEEM Universidad de Montevideo, Lord Ponsonby 2542, 16000 Montevideo, Uruguay
| | - Uwe Siebert
- Institute for Technology Assessment, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Austria
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25
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Ashmore P, Sherwood E. An overview of COVID-19 global epidemiology and discussion of potential drivers of variable global pandemic impacts. J Antimicrob Chemother 2023; 78:ii2-ii11. [PMID: 37995358 PMCID: PMC10666997 DOI: 10.1093/jac/dkad311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2023] Open
Abstract
With a WHO-estimated excess mortality burden of 14.9 million over the course of 2020 and 2021, the COVID-19 pandemic has had a major human impact so far. It has also affected a range of disciplines, systems and practices from mathematical modelling to behavioural sciences, pharmaceutical development to health system management. This article explores these developments and, to set the scene, this paper summarizes the global epidemiology of COVID-19 from January 2020 to June 2021 and considers some potential drivers of variation.
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Affiliation(s)
- Polly Ashmore
- Health Education England, Stewart House, 32 Russell Square, London WC1B 5DN, UK
| | - Emma Sherwood
- Health Education England, Stewart House, 32 Russell Square, London WC1B 5DN, UK
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26
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Raimúndez E, Fedders M, Hasenauer J. Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes. iScience 2023; 26:108083. [PMID: 37867942 PMCID: PMC10589897 DOI: 10.1016/j.isci.2023.108083] [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: 04/05/2023] [Revised: 07/16/2023] [Accepted: 09/25/2023] [Indexed: 10/24/2023] Open
Abstract
Bayesian inference is an important method in the life and natural sciences for learning from data. It provides information about parameter and prediction uncertainties. Yet, generating representative samples from the posterior distribution is often computationally challenging. Here, we present an approach that lowers the computational complexity of sample generation for dynamical models with scaling, offset, and noise parameters. The proposed method is based on the marginalization of the posterior distribution. We provide analytical results for a broad class of problems with conjugate priors and show that the method is suitable for a large number of applications. Subsequently, we demonstrate the benefit of the approach for applications from the field of systems biology. We report an improvement up to 50 times in the effective sample size per unit of time. As the scheme is broadly applicable, it will facilitate Bayesian inference in different research fields.
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Affiliation(s)
- Elba Raimúndez
- Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- Technische Universität München, Center for Mathematics, Garching, Germany
| | - Michael Fedders
- Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Jan Hasenauer
- Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- Technische Universität München, Center for Mathematics, Garching, Germany
- Helmholtz Zentrum München - German Research Center for Environmental Health, Computational Health Center, Neuherberg, Germany
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27
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Saviano M, Fierro A, Liccardo A. A deterministic compartmental model for the transition between variants in the spread of Covid-19 in Italy. PLoS One 2023; 18:e0293416. [PMID: 37963148 PMCID: PMC10645303 DOI: 10.1371/journal.pone.0293416] [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: 04/27/2023] [Accepted: 10/08/2023] [Indexed: 11/16/2023] Open
Abstract
We propose a deterministic epidemic model to describe the transition between two variants of the same virus, through the combination of a series of realistic mechanisms such as partial cross immunity, waning immunity for vaccinated individuals and a novel data-based algorithm to describe the average immunological status of the population. The model is validated on the evolution of Covid-19 in Italy, during the period in which the transition between Delta and Omicron variant occurred, with very satisfactory agreement with the experimental data. According to our model, if the vaccine efficacy had been equal against Delta and Omicron variant infections, the transition would have been smoothed and the epidemic would have gone extinct. This circumstance confirms the fundamental role of vaccines in combating the epidemic, and the importance of identifying vaccines capable of intercepting new variants.
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Affiliation(s)
- Mario Saviano
- Physics Department, Università degli Studi di Napoli ‘Federico II’, Napoli, Italy
| | - Annalisa Fierro
- Consiglio Nazionale delle Ricerche (CNR), Institute Superconductors, Oxides and other Innovative Materials and Devices (SPIN), Napoli, Italy
| | - Antonella Liccardo
- Physics Department, Università degli Studi di Napoli ‘Federico II’, Napoli, Italy
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28
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Ospina-Aguirre C, Soriano-Paños D, Olivar-Tost G, Galindo-González CC, Gómez-Gardeñes J, Osorio G. Effects of human mobility on the spread of Dengue in the region of Caldas, Colombia. PLoS Negl Trop Dis 2023; 17:e0011087. [PMID: 38011274 PMCID: PMC10703399 DOI: 10.1371/journal.pntd.0011087] [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: 01/16/2023] [Revised: 12/07/2023] [Accepted: 09/21/2023] [Indexed: 11/29/2023] Open
Abstract
According to the World Health Organization (WHO), dengue is the most common acute arthropod-borne viral infection in the world. The spread of dengue and other infectious diseases is closely related to human activity and mobility. In this paper we analyze the effect of introducing mobility restrictions as a public health policy on the total number of dengue cases within a population. To perform the analysis, we use a complex metapopulation in which we implement a compartmental propagation model coupled with the mobility of individuals between the patches. This model is used to investigate the spread of dengue in the municipalities of Caldas (CO). Two scenarios corresponding to different types of mobility restrictions are applied. In the first scenario, the effect of restricting mobility is analyzed in three different ways: a) limiting the access to the endemic node but allowing the movement of its inhabitants, b) restricting the diaspora of the inhabitants of the endemic node but allowing the access of outsiders, and c) a total isolation of the inhabitants of the endemic node. In this scenario, the best simulation results are obtained when specific endemic nodes are isolated during a dengue outbreak, obtaining a reduction of up to 2.5% of dengue cases. Finally, the second scenario simulates a total isolation of the network, i.e., mobility between nodes is completely limited. We have found that this control measure increases the number of total dengue cases in the network by 2.36%.
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Affiliation(s)
- Carolina Ospina-Aguirre
- ABCDynamics, Facultad de ciencias exactas y naturales, Universidad Nacional de Colombia - Sede Manizales, Manizales, Colombia
- Departamento de electrónica y automatización, Universidad Autonoma de Manizales, Manizales, Colombia
| | - David Soriano-Paños
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- GOTHAM lab, Institute for Biocomputation & Physics of Complex Systems (BIFI), Zaragoza, España
| | - Gerard Olivar-Tost
- Departamento de Ciencias Naturales y Tecnología, Universidad de Aysén, Coyhaique, Chile
| | - Cristian C. Galindo-González
- Percepción y Control Inteligente (PCI), Departamento de Ingeniería Eléctrica, Electrónica y Computación, Universidad Nacional de Colombia - Sede Manizales, Manizales, Colombia
| | - Jesús Gómez-Gardeñes
- Departamento de Física de la Materia Condensada Facultad de Ciencias, Universidad de Zaragoza, Zaragoza, España
- GOTHAM lab, Institute for Biocomputation & Physics of Complex Systems (BIFI), Zaragoza, España
| | - Gustavo Osorio
- Percepción y Control Inteligente (PCI), Departamento de Ingeniería Eléctrica, Electrónica y Computación, Universidad Nacional de Colombia - Sede Manizales, Manizales, Colombia
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29
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Huang D, He F, Liu W. Using geospatial trajectories to explore how the COVID-19 pandemic affects the associations between environmental attributes and runnability of park trails. Health Place 2023; 84:103145. [PMID: 37976914 DOI: 10.1016/j.healthplace.2023.103145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/30/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023]
Abstract
The worldwide coronavirus disease 2019 (COVID-19) pandemic and the associated social distancing measures have produced alterations in park visits of individuals, as well as their park-based physical activity (e.g. running exercise). Although studies on the influence of the COVID-19 pandemic on changes in running activity patterns are becoming an emerging focus, less is known about how these changes are related to the environmental attributes of parks before and after the pandemic, the knowledge of which is essential to planning green infrastructure that better supports physical activities. Therefore, we employed a volunteered geographic information approach to investigate the runnability of park trails in Shenzhen, utilizing self-tracking routes from Strava, in order to uncover the associations between trail characteristics and park features with the running intensity before and after the pandemic. Multilevel regression model analyses revealed that trail network connectivity was the only environmental attribute indicating consistent and positive associations with running intensity. Blue space density was positively correlated with running intensity in urban parks but indicated no significant association in forest parks before the pandemic. In the pre-pandemic era, population density was positively related to running intensity in urban and forest parks. However, after the pandemic, the associations between running behaviours and population density remained positive in forest parks but turned insignificant in urban parks. The outbreak of the pandemic also altered the influence of other park features (e.g. park shape and trail density) on running intensity. The evidence-based knowledge provides planners with significant insights into pandemic-resilient park planning for the post-COVID era.
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Affiliation(s)
- Dengkai Huang
- School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China; State Key Laboratory of Subtropical Building and Urban Science, Shenzhen, China; Lab for Optimizing Design of Built Environment, Shenzhen University, Shenzhen, China; Institute of Beautiful China, Shenzhen University, Shenzhen, 518060, China
| | - Fang He
- School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China; Institute of Beautiful China, Shenzhen University, Shenzhen, 518060, China; Shenzhen Meidao Landscape Architecture and Urban Planning and Design Institute, Shenzhen, 518000, China
| | - Wenjie Liu
- School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China; Institute of Beautiful China, Shenzhen University, Shenzhen, 518060, China.
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30
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Idisi OI, Yusuf TT, Owolabi KM, Ojokoh BA. A bifurcation analysis and model of Covid-19 transmission dynamics with post-vaccination infection impact. HEALTHCARE ANALYTICS (NEW YORK, N.Y.) 2023; 3:100157. [PMID: 36941830 PMCID: PMC10007718 DOI: 10.1016/j.health.2023.100157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/01/2023] [Accepted: 03/04/2023] [Indexed: 03/19/2023]
Abstract
SARS COV-2 (Covid-19) has imposed a monumental socio-economic burden worldwide, and its impact still lingers. We propose a deterministic model to describe the transmission dynamics of Covid-19, emphasizing the effects of vaccination on the prevailing epidemic. The proposed model incorporates current information on Covid-19, such as reinfection, waning of immunity derived from the vaccine, and infectiousness of the pre-symptomatic individuals into the disease dynamics. Moreover, the model analysis reveals that it exhibits the phenomenon of backward bifurcation, thus suggesting that driving the model reproduction number below unity may not suffice to drive the epidemic toward extinction. The model is fitted to real-life data to estimate values for some of the unknown parameters. In addition, the model epidemic threshold and equilibria are determined while the criteria for the stability of each equilibrium solution are established using the Metzler approach. A sensitivity analysis of the model is performed based on the Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCCs) approaches to illustrate the impact of the various model parameters and explore the dependency of control reproduction number on its constituents parameters, which invariably gives insight on what needs to be done to contain the pandemic effectively. The foregoing notwithstanding, the contour plots of the control reproduction number concerning some of the salient parameters indicate that increasing vaccination coverage and decreasing vaccine waning rate would remarkably reduce the value of the reproduction number below unity, thus facilitating the possible elimination of the disease from the population. Finally, the model is solved numerically and simulated for different scenarios of disease outbreaks with the findings discussed.
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Affiliation(s)
- Oke I Idisi
- Department of Mathematical Sciences, Federal University of Technology, Akure, P.M.B. 704, Ondo State, Nigeria
| | - Tunde T Yusuf
- Department of Mathematical Sciences, Federal University of Technology, Akure, P.M.B. 704, Ondo State, Nigeria
| | - Kolade M Owolabi
- Department of Mathematical Sciences, Federal University of Technology, Akure, P.M.B. 704, Ondo State, Nigeria
| | - Bolanle A Ojokoh
- Department of Information Systems, Federal University of Technology, Akure, P.M.B. 704, Ondo State, Nigeria
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31
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Hota AR, Maitra U, Elokda E, Bolognani S. Learning to Mitigate Epidemic Risks: A Dynamic Population Game Approach. DYNAMIC GAMES AND APPLICATIONS 2023; 13:1106-1129. [PMID: 38098859 PMCID: PMC10716085 DOI: 10.1007/s13235-023-00529-4] [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: 08/18/2023] [Indexed: 12/17/2023]
Abstract
We present a dynamic population game model to capture the behavior of a large population of individuals in presence of an infectious disease or epidemic. Individuals can be in one of five possible infection states at any given time: susceptible, asymptomatic, symptomatic, recovered and unknowingly recovered, and choose whether to opt for vaccination, testing or social activity with a certain degree. We define the evolution of the proportion of agents in each epidemic state, and the notion of best response for agents that maximize long-run discounted expected reward as a function of the current state and policy. We further show the existence of a stationary Nash equilibrium and explore the transient evolution of the disease states and individual behavior under a class of evolutionary learning dynamics. Our results provide compelling insights into how individuals evaluate the trade-off among vaccination, testing and social activity under different parameter regimes, and the impact of different intervention strategies (such as restrictions on social activity) on vaccination and infection prevalence.
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Affiliation(s)
- Ashish R. Hota
- Department of Electrical Engineering, IIT Kharagpur, Kharagpur, West Bengal 721302 India
| | - Urmee Maitra
- Department of Electrical Engineering, IIT Kharagpur, Kharagpur, West Bengal 721302 India
| | - Ezzat Elokda
- Automatic Control Laboratory, ETH Zürich, 8092 Zürich, Switzerland
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32
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Depero LE, Bontempi E. Comparing the spreading characteristics of monkeypox (MPX) and COVID-19: Insights from a quantitative model. ENVIRONMENTAL RESEARCH 2023; 235:116521. [PMID: 37419200 DOI: 10.1016/j.envres.2023.116521] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/07/2023] [Accepted: 06/27/2023] [Indexed: 07/09/2023]
Abstract
Climate change is acknowledged to directly affect not only the environment, economy, and society but also the transmission dynamics of infectious diseases, thereby impacting public health. The recent experiences with the spread of SARS-CoV-2 and Monkeypox have highlighted the complex and interconnected nature of infectious diseases, which are strongly linked to various determinants of health. Considering these challenges, adopting a new vision such as the trans-disciplinary approach appears to be imperative. This paper proposes a new theory about viruses' spread, based on a biological model, accounting for the optimisation of energy and material resources for organisms' survival and reproduction in the environment. The approach applies Kleiber's law scaling theory, originally developed in biology, to model community dynamics in cities. A simple equation can be used to model pathogen spread without accounting for each species' physiology by leveraging the superlinear scaling of variables with population size. This general theory offers several advantages, including the ability to explain the rapid and surprising spread of both SARS-CoV-2 and Monkeypox. The proposed model shows similarities in the spreading processes of both viruses, based on the resulting scaling factors, and opens new avenues for research. By fostering cooperation and integrating knowledge from different disciplines to effectively tackle the multifaceted dimensions of disease outbreaks, we can work towards preventing future health emergencies.
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Affiliation(s)
- L E Depero
- INSTM and Chemistry for Technologies Laboratory, Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38, 25123, Brescia, Italy
| | - E Bontempi
- INSTM and Chemistry for Technologies Laboratory, Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38, 25123, Brescia, Italy.
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33
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Peshkovskaya A, Galkin S. Health behavior in Russia during the COVID-19 pandemic. Front Public Health 2023; 11:1276291. [PMID: 37849726 PMCID: PMC10577229 DOI: 10.3389/fpubh.2023.1276291] [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: 08/11/2023] [Accepted: 09/07/2023] [Indexed: 10/19/2023] Open
Abstract
In this article, we report results from a nationwide survey on pandemic-related health behavior in Russia. A total of 2,771 respondents aged 18 to 82 were interviewed between January 21 and March 3, 2021. The survey included questions on perceived vulnerability to coronavirus, prevention-related health behavior, readiness for vaccination, and general awareness about COVID-19. Descriptive data showed that 21.2% of respondents reported high vulnerability to the coronavirus, and 25% expressed fear. Moreover, 38.7% of the surveyed individuals reported low trust in vaccination efficacy, and 57.5% were unwilling to take a vaccine, which was much higher than the official data. Based on the evidence obtained, four types of health behavior during the pandemic were constructed. Rational (29.3%) and denying (28.6%) behaviors prevailed in men, while women were found to more likely behave with a vaccine-hesitant demeanor (35.7%). Educational background affected the proportion of respondents with the denying type of health behavior, who were also of younger age. The rational behavioral type was found to be more common among respondents aged above 50 years and prevailed as well among individuals with university degrees. The middle-aged population of Russia was highly compliant with prevention-related health practices; however, vaccine hesitancy was also high among them. Furthermore, health behaviors varied significantly across the Federal Districts of Russia. We are convinced that our results contribute to existing public health practices and may help improve communication campaigns to cause positive health behaviors.
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Affiliation(s)
- Anastasia Peshkovskaya
- Tomsk State University, Tomsk, Russia
- Mental Health Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia
| | - Stanislav Galkin
- Tomsk State University, Tomsk, Russia
- Mental Health Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia
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34
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Matsuda T, Ueda Y, Uemoto K, Yamashita Y, Takei Y, Wakayama T, Sakata M, Okumura K, Matsushita N, Hanai R. [Questionnaire Survey of Hands-on Seminar Using a Web Conferencing System in the COVID-19 Pandemic]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023; 79:932-940. [PMID: 37495539 DOI: 10.6009/jjrt.2023-1354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
The purpose of this study was to evaluate the effectiveness of a hands-on seminar using a Web conferencing system, based on the post-event questionnaires of the face-to-face and online seminars of the hands-on seminar. For participants to feel realistic training in the online seminars, four educational videos explaining the procedure of the practical skill were created. We compared results of questionnaires acquired from participants after the face-to-face and online seminars. The questions about expectation, comprehension, satisfaction level, and lecture time for the seminars were graded on a 5-point scale. The higher the scores, the higher the rating, except for lecture time. A score of 3 was appropriate for the lecture time, with a higher score indicating that the seminar felt longer and a lower score indicating that the seminar felt shorter. In the evaluation of classroom lectures, such as expectation, comprehension, and satisfaction level for the seminars, there were no significant differences between the face-to-face and online seminars, and both achieved high scores of 4 or more. There was a significant difference in the evaluation of lecture time for classroom lectures, with participants feeling that it was too short in the face-to-face but just right in the online. In all evaluations for hands-on training and discussion, there were no significant differences between the face-to-face and online seminars, and both achieved high scores of 4 or more and time was short. It was concluded that our proposed online seminar approach could achieve a high level of evaluation as face-to-face seminars.
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Affiliation(s)
| | - Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute
| | | | - Yumie Yamashita
- Department of Medical Technology, Osaka General Medical Center
| | - Yoshiki Takei
- Department of Radiology, Kindai University Nara Hospital
| | | | - Motonori Sakata
- Department of Central Radiology, Osaka Metropolitan University Hospital
| | - Keisuke Okumura
- Center for Radiology and Radiation Oncology, Kobe University Hospital
| | | | - Ryo Hanai
- Department of Radiology, Nara Prefectural General Medical Center
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35
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Tradigo G, Das JK, Vizza P, Roy S, Guzzi PH, Veltri P. Strategies and Trends in COVID-19 Vaccination Delivery: What We Learn and What We May Use for the Future. Vaccines (Basel) 2023; 11:1496. [PMID: 37766172 PMCID: PMC10535057 DOI: 10.3390/vaccines11091496] [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: 08/21/2023] [Revised: 09/03/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Vaccination has been the most effective way to control the outbreak of the COVID-19 pandemic. The numbers and types of vaccines have reached considerable proportions, even if the question of vaccine procedures and frequency still needs to be resolved. We have come to learn the necessity of defining vaccination distribution strategies with regard to COVID-19 that could be used for any future pandemics of similar gravity. In fact, vaccine monitoring implies the existence of a strategy that should be measurable in terms of input and output, based on a mathematical model, including death rates, the spread of infections, symptoms, hospitalization, and so on. This paper addresses the issue of vaccine diffusion and strategies for monitoring the pandemic. It provides a description of the importance and take up of vaccines and the links between procedures and the containment of COVID-19 variants, as well as the long-term effects. Finally, the paper focuses on the global scenario in a world undergoing profound social and political change, with particular attention on current and future health provision. This contribution would represent an example of vaccination experiences, which can be useful in other pandemic or epidemiological contexts.
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Affiliation(s)
- Giuseppe Tradigo
- Department of Computer Science, eCampus University, 22060 Novedrate, Italy;
| | - Jayanta Kumar Das
- Longitudinal Studies Section, Translation Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA;
| | - Patrizia Vizza
- Department of Surgical and Medical Science, Magna Græcia University, 88100 Catanzaro, Italy;
| | - Swarup Roy
- Network Reconstruction & Analysis (NetRA) Lab, Department of Computer Applications, Sikkim University, Gangtok 737102, India;
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Science, Magna Græcia University, 88100 Catanzaro, Italy;
| | - Pierangelo Veltri
- Department of Computer Science, Modelling, Electronics and Systems, University of Calabria, 87036 Rende, Italy;
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36
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Wang N, Xue J, Xu T, Li H, Liu B. A weapon to fight against pervasive Omicron: systematic actions transiting to pre-COVID normal. Front Public Health 2023; 11:1204275. [PMID: 37744521 PMCID: PMC10512254 DOI: 10.3389/fpubh.2023.1204275] [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: 04/12/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
The Coronavirus Disease-2019 (COVID-19) pandemic is not just a health crisis but also a social crisis. Confronted with the resurgence of variants with massive infections, the triggered activities from personal needs may promote the spread, which should be considered in risk management. Meanwhile, it is important to ensure that the policy responses on citizen life to a lower level. In the face of Omicron mutations, we need to sum up the control experience accumulated, adapting strategies in the dynamic coevolution process while balancing life resumption and pandemic control, to meet challenges of future crises. We collected 46 cases occurring between 2021 and 2022, mainly from China, but also including five relevant cases from other countries around the world. Based on case studies, we combine micro-view individual needs/behaviors with macro-view management measures linking Maslow's hierarchy of needs with the transmission chain of Omicron clusters. The proposed loophole chain could help identify both individual and management loopholes in the spread of the virus. The systematic actions that were taken have effectively combated these ubiquitous vulnerabilities at lower costs and lesser time. In the dynamic coevolution process, the Chinese government has made effective and more socially acceptable prevention policies while meeting the divergent needs of the entire society at the minimum costs. Systematic actions do help maintain the balance between individuals' satisfaction and pandemic containment. This implies that risk management policies should reasonably consider individual needs and improve the cooperation of various stakeholders with targeted flexible measures, securing both public health and life resumption.
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Affiliation(s)
- Na Wang
- School of Public Administration, Jilin University, Changchun, China
| | - Jia Xue
- School of Public Administration, Jilin University, Changchun, China
| | - Tianjiao Xu
- School of International Studies, Renmin University of China, Beijing, China
| | - Huijie Li
- School of Public Administration, Jilin University, Changchun, China
| | - Bo Liu
- School of Literature and Law, Northeast Forestry University of China, Harbin, China
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37
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Gibson GC, Reich NG, Sheldon D. REAL-TIME MECHANISTIC BAYESIAN FORECASTS OF COVID-19 MORTALITY. Ann Appl Stat 2023; 17:1801-1819. [PMID: 38983109 PMCID: PMC11229176 DOI: 10.1214/22-aoas1671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
The COVID-19 pandemic emerged in late December 2019. In the first six months of the global outbreak, the U.S. reported more cases and deaths than any other country in the world. Effective modeling of the course of the pandemic can help assist with public health resource planning, intervention efforts, and vaccine clinical trials. However, building applied forecasting models presents unique challenges during a pandemic. First, case data available to models in real time represent a nonstationary fraction of the true case incidence due to changes in available diagnostic tests and test-seeking behavior. Second, interventions varied across time and geography leading to large changes in transmissibility over the course of the pandemic. We propose a mechanistic Bayesian model that builds upon the classic compartmental susceptible-exposed-infected-recovered (SEIR) model to operationalize COVID-19 forecasting in real time. This framework includes nonparametric modeling of varying transmission rates, nonparametric modeling of case and death discrepancies due to testing and reporting issues, and a joint observation likelihood on new case counts and new deaths; it is implemented in a probabilistic programming language to automate the use of Bayesian reasoning for quantifying uncertainty in probabilistic forecasts. The model has been used to submit forecasts to the U.S. Centers for Disease Control through the COVID-19 Forecast Hub under the name MechBayes. We examine the performance relative to a baseline model as well as alternate models submitted to the forecast hub. Additionally, we include an ablation test of our extensions to the classic SEIR model. We demonstrate a significant gain in both point and probabilistic forecast scoring measures using MechBayes, when compared to a baseline model, and show that MechBayes ranks as one of the top two models out of nine which regularly submitted to the COVID-19 Forecast Hub for the duration of the pandemic, trailing only the COVID-19 Forecast Hub ensemble model of which which MechBayes is a part.
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Affiliation(s)
- Graham C. Gibson
- School of Public Health and Health Sciences, University of Massachusetts Amherst
| | - Nicholas G. Reich
- School of Public Health and Health Sciences, University of Massachusetts Amherst
| | - Daniel Sheldon
- College of Information and Computer Sciences, University of Massachusetts Amherst
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38
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Clauß K, Kuehn C. Self-adapting infectious dynamics on random networks. CHAOS (WOODBURY, N.Y.) 2023; 33:093110. [PMID: 37695925 DOI: 10.1063/5.0149465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 08/21/2023] [Indexed: 09/13/2023]
Abstract
Self-adaptive dynamics occurs in many fields of research, such as socio-economics, neuroscience, or biophysics. We consider a self-adaptive modeling approach, where adaptation takes place within a set of strategies based on the history of the state of the system. This leads to piecewise deterministic Markovian dynamics coupled to a non-Markovian adaptive mechanism. We apply this framework to basic epidemic models (SIS, SIR) on random networks. We consider a co-evolutionary dynamical network where node-states change through the epidemics and network topology changes through the creation and deletion of edges. For a simple threshold base application of lockdown measures, we observe large regions in parameter space with oscillatory behavior, thereby exhibiting one of the most reduced mechanisms leading to oscillations. For the SIS epidemic model, we derive analytic expressions for the oscillation period from a pairwise closed model, which is validated with numerical simulations for random uniform networks. Furthermore, the basic reproduction number fluctuates around one indicating a connection to self-organized criticality.
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Affiliation(s)
- Konstantin Clauß
- Department of Mathematics, Technical University of Munich, 85748 Garching bei München, Germany
| | - Christian Kuehn
- Department of Mathematics, Technical University of Munich, 85748 Garching bei München, Germany
- Complexity Science Hub Vienna, 1070 Vienna, Austria
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39
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Montesinos-López JC, Daza-Torres ML, García YE, Herrera C, Bess CW, Bischel HN, Nuño M. Bayesian sequential approach to monitor COVID-19 variants through test positivity rate from wastewater. mSystems 2023; 8:e0001823. [PMID: 37489897 PMCID: PMC10469603 DOI: 10.1128/msystems.00018-23] [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: 01/10/2023] [Accepted: 05/01/2023] [Indexed: 07/26/2023] Open
Abstract
Deployment of clinical testing on a massive scale was an essential control measure for curtailing the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the magnitude of the COVID-19 (coronavirus disease 2019) pandemic during its waves. As the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementation of vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 antigen tests reduced the demand for mass SARS-CoV-2 testing. Unfortunately, reductions in testing and test reporting rates also reduced the availability of public health data to support decision-making. This paper proposes a sequential Bayesian approach to estimate the COVID-19 test positivity rate (TPR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. The proposed modeling framework was applied to WW surveillance data from two WW treatment plants in California; the City of Davis and the University of California, Davis campus. TPR estimates are used to compute thresholds for WW data using the Centers for Disease Control and Prevention thresholds for low (<5% TPR), moderate (5%-8% TPR), substantial (8%-10% TPR), and high (>10% TPR) transmission. The effective reproductive number estimates are calculated using TPR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. IMPORTANCE We propose a statistical model to correlate WW with TPR to monitor COVID-19 trends and to help overcome the limitations of relying only on clinical case detection. We pose an adaptive scheme to model the nonautonomous nature of the prolonged COVID-19 pandemic. The TPR is modeled through a Bayesian sequential approach with a beta regression model using SARS-CoV-2 RNA concentrations measured in WW as a covariable. The resulting model allows us to compute TPR based on WW measurements and incorporates changes in viral transmission dynamics through an adaptive scheme.
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Affiliation(s)
| | - Maria L. Daza-Torres
- Department of Public Health Sciences, University of California Davis, Davis, California, USA
| | - Yury E. García
- Department of Public Health Sciences, University of California Davis, Davis, California, USA
| | - César Herrera
- Department of Mathematics, Purdue University, West Lafayette, Indiana, USA
| | - C. Winston Bess
- Department of Civil and Environmental Engineering, University of California Davis, Davis, California, USA
| | - Heather N. Bischel
- Department of Civil and Environmental Engineering, University of California Davis, Davis, California, USA
| | - Miriam Nuño
- Department of Public Health Sciences, University of California Davis, Davis, California, USA
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40
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Sciannameo V, Azzolina D, Lanera C, Acar AŞ, Corciulo MA, Comoretto RI, Berchialla P, Gregori D. Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models. Healthcare (Basel) 2023; 11:2363. [PMID: 37628560 PMCID: PMC10454512 DOI: 10.3390/healthcare11162363] [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: 07/08/2023] [Revised: 08/06/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023] Open
Abstract
The COVID-19 outbreak involved a spread of prediction efforts, especially in the early pandemic phase. A better understanding of the epidemiological implications of the different models seems crucial for tailoring prevention policies. This study aims to explore the concordance and discrepancies in outbreak prediction produced by models implemented and used in the first wave of the epidemic. To evaluate the performance of the model, an analysis was carried out on Italian pandemic data from February 24, 2020. The epidemic models were fitted to data collected at 20, 30, 40, 50, 60, 70, 80, 90, and 98 days (the entire time series). At each time step, we made predictions until May 31, 2020. The Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) were calculated. The GAM model is the most suitable parameterization for predicting the number of new cases; exponential or Poisson models help predict the cumulative number of cases. When the goal is to predict the epidemic peak, GAM, ARIMA, or Bayesian models are preferable. However, the prediction of the pandemic peak could be made carefully during the early stages of the epidemic because the forecast is affected by high uncertainty and may very likely produce the wrong results.
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Affiliation(s)
- Veronica Sciannameo
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
- Center of Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torino, 10124 Turin, Italy;
| | - Danila Azzolina
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
- Department of Environmental and Preventive Sciences, University of Ferrara, 44121 Ferrara, Italy
| | - Corrado Lanera
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
| | | | - Maria Assunta Corciulo
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
| | - Rosanna Irene Comoretto
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
- Department of Public Health and Pediatrics, University of Torino, 10124 Turin, Italy
| | - Paola Berchialla
- Center of Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torino, 10124 Turin, Italy;
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
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41
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Ning X, Guan J, Li XA, Wei Y, Chen F. Physics-Informed Neural Networks Integrating Compartmental Model for Analyzing COVID-19 Transmission Dynamics. Viruses 2023; 15:1749. [PMID: 37632091 PMCID: PMC10459488 DOI: 10.3390/v15081749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/03/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023] Open
Abstract
Modelling and predicting the behaviour of infectious diseases is essential for early warning and evaluating the most effective interventions to prevent significant harm. Compartmental models produce a system of ordinary differential equations (ODEs) that are renowned for simulating the transmission dynamics of infectious diseases. However, the parameters in compartmental models are often unknown, and they can even change over time in the real world, making them difficult to determine. This study proposes an advanced artificial intelligence approach based on physics-informed neural networks (PINNs) to estimate time-varying parameters from given data for the compartmental model. Our proposed PINNs method captures the complex dynamics of COVID-19 by integrating a modified Susceptible-Exposed-Infectious-Recovered-Death (SEIRD) compartmental model with deep neural networks. Specifically, we modelled the system of ODEs as one network and the time-varying parameters as another network to address significant unknown parameters and limited data. Such structure of the PINNs method is in line with the prior epidemiological correlations and comprises the mismatch between available data and network output and the residual of ODEs. The experimental findings on real-world reported data data have demonstrated that our method robustly and accurately learns the dynamics and forecasts future states. Moreover, as more data becomes available, our proposed PINNs method can be successfully extended to other regions and infectious diseases.
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Affiliation(s)
- Xiao Ning
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Nanjing 210096, China
| | - Jinxing Guan
- Center for Global Health, Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Xi-An Li
- Ceyear Technology Co., Ltd., 98 Xiangjiang Road, Qingdao 266000, China
| | - Yongyue Wei
- Center for Global Health, Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Public Health and Epidemic Preparedness and Response Center, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
| | - Feng Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Nanjing 210096, China
- Center for Global Health, Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
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42
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Wang C, Mustafa S. A data-driven Markov process for infectious disease transmission. PLoS One 2023; 18:e0289897. [PMID: 37561743 PMCID: PMC10414655 DOI: 10.1371/journal.pone.0289897] [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: 02/13/2023] [Accepted: 07/27/2023] [Indexed: 08/12/2023] Open
Abstract
The 2019 coronavirus pandemic exudes public health and socio-economic burden globally, raising an unprecedented concern for infectious diseases. Thus, describing the infectious disease transmission process to design effective intervention measures and restrict its spread is a critical scientific issue. We propose a level-dependent Markov model with infinite state space to characterize viral disorders like COVID-19. The levels and states in this model represent the stages of outbreak development and the possible number of infectious disease patients. The transfer of states between levels reflects the explosive transmission process of infectious disease. A simulation method with heterogeneous infection is proposed to solve the model rapidly. After that, simulation experiments were conducted using MATLAB according to the reported data on COVID-19 published by Johns Hopkins. Comparing the simulation results with the actual situation shows that our proposed model can well capture the transmission dynamics of infectious diseases with and without imposed interventions and evaluate the effectiveness of intervention strategies. Further, the influence of model parameters on transmission dynamics is analyzed, which helps to develop reasonable intervention strategies. The proposed approach extends the theoretical study of mathematical modeling of infectious diseases and contributes to developing models that can describe an infinite number of infected persons.
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Affiliation(s)
- Chengliang Wang
- College of Economics and Management, Beijing University of Technology, Beijing, China
| | - Sohaib Mustafa
- College of Economics and Management, Beijing University of Technology, Beijing, China
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43
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Marmor Y, Abbey A, Shahar Y, Mokryn O. Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model. Sci Rep 2023; 13:12955. [PMID: 37563358 PMCID: PMC10415258 DOI: 10.1038/s41598-023-39817-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 07/31/2023] [Indexed: 08/12/2023] Open
Abstract
Interaction-driven modeling of diseases over real-world contact data has been shown to promote the understanding of the spread of diseases in communities. This temporal modeling follows the path-preserving order and timing of the contacts, which are essential for accurate modeling. Yet, other important aspects were overlooked. Various airborne pathogens differ in the duration of exposure needed for infection. Also, from the individual perspective, Covid-19 progression differs between individuals, and its severity is statistically correlated with age. Here, we enrich an interaction-driven model of Covid-19 and similar airborne viral diseases with (a) meetings duration and (b) personal disease progression. The enriched model enables predicting outcomes at both the population and the individual levels. It further allows predicting individual risk of engaging in social interactions as a function of the virus characteristics and its prevalence in the population. We further showed that the enigmatic nature of asymptomatic transmission stems from the latent effect of the network density on this transmission and that asymptomatic transmission has a substantial impact only in sparse communities.
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Affiliation(s)
- Yanir Marmor
- Information Systems, University of Haifa, Haifa, Israel
| | - Alex Abbey
- Information Systems, University of Haifa, Haifa, Israel
| | - Yuval Shahar
- Software and Information Systems Engineering, Ben Gurion University, Beer Sheva, Israel
| | - Osnat Mokryn
- Information Systems, University of Haifa, Haifa, Israel.
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44
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Azzolina D, Lanera C, Comoretto R, Francavilla A, Rosi P, Casotto V, Navalesi P, Gregori D. Automatic Forecast of Intensive Care Unit Admissions: The Experience During the COVID-19 Pandemic in Italy. J Med Syst 2023; 47:84. [PMID: 37542644 PMCID: PMC10404188 DOI: 10.1007/s10916-023-01982-9] [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: 08/08/2022] [Accepted: 07/21/2023] [Indexed: 08/07/2023]
Abstract
The experience of the COVID-19 pandemic showed the importance of timely monitoring of admissions to the ICU admissions. The ability to promptly forecast the epidemic impact on the occupancy of beds in the ICU is a key issue for adequate management of the health care system.Despite this, most of the literature on predictive COVID-19 models in Italy has focused on predicting the number of infections, leaving trends in ordinary hospitalizations and ICU occupancies in the background.This work aims to present an ETS approach (Exponential Smoothing Time Series) time series forecasting tool for admissions to the ICU admissions based on ETS models. The results of the forecasting model are presented for the regions most affected by the epidemic, such as Veneto, Lombardy, Emilia-Romagna, and Piedmont.The mean absolute percentage errors (MAPE) between observed and predicted admissions to the ICU admissions remain lower than 11% for all considered geographical areas.In this epidemiological context, the proposed ETS forecasting model could be suitable to monitor, in a timely manner, the impact of COVID-19 disease on the health care system, not only during the early stages of the pandemic but also during the vaccination campaign, to quickly adapt possible preventive interventions.
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Affiliation(s)
- Danila Azzolina
- Department of Environmental and Preventive Sciences, University of Ferrara, Ferrara, Italy
- Unit of Biostatistics, Epidemiology, and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, Padova, 35131, Italy
| | - Corrado Lanera
- Unit of Biostatistics, Epidemiology, and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, Padova, 35131, Italy
| | - Rosanna Comoretto
- Department of Public Health and Pediatrics, University of Turin, Turin, Italy
| | - Andrea Francavilla
- Unit of Biostatistics, Epidemiology, and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, Padova, 35131, Italy
| | - Paolo Rosi
- Institute of Anaesthesia and Intensive Care, Padua University Hospital, Padua, Italy
- Department of Medicine (DIMED), University of Padua, Padua, Italy
| | - Veronica Casotto
- Unit of Biostatistics, Epidemiology, and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, Padova, 35131, Italy
| | - Paolo Navalesi
- Institute of Anaesthesia and Intensive Care, Padua University Hospital, Padua, Italy
- Department of Medicine (DIMED), University of Padua, Padua, Italy
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology, and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Via Loredan, 18, Padova, 35131, Italy.
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45
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Batistela CM, Correa DPF, Bueno ÁM, Piqueira JRC. SIRSi-vaccine dynamical model for the Covid-19 pandemic. ISA TRANSACTIONS 2023; 139:391-405. [PMID: 37217378 PMCID: PMC10186248 DOI: 10.1016/j.isatra.2023.05.008] [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: 09/29/2022] [Revised: 04/17/2023] [Accepted: 05/12/2023] [Indexed: 05/24/2023]
Abstract
Covid-19, caused by severe acute respiratory syndrome coronavirus 2, broke out as a pandemic during the beginning of 2020. The rapid spread of the disease prompted an unprecedented global response involving academic institutions, regulatory agencies, and industries. Vaccination and nonpharmaceutical interventions including social distancing have proven to be the most effective strategies to combat the pandemic. In this context, it is crucial to understand the dynamic behavior of the Covid-19 spread together with possible vaccination strategies. In this study, a susceptible-infected-removed-sick model with vaccination (SIRSi-vaccine) was proposed, accounting for the unreported yet infectious. The model considered the possibility of temporary immunity following infection or vaccination. Both situations contribute toward the spread of diseases. The transcritical bifurcation diagram of alternating and mutually exclusive stabilities for both disease-free and endemic equilibria were determined in the parameter space of vaccination rate and isolation index. The existing equilibrium conditions for both points were determined in terms of the epidemiological parameters of the model. The bifurcation diagram allowed us to estimate the maximum number of confirmed cases expected for each set of parameters. The model was fitted with data from São Paulo, the state capital of SP, Brazil, which describes the number of confirmed infected cases and the isolation index for the considered data window. Furthermore, simulation results demonstrate the possibility of periodic undamped oscillatory behavior of the susceptible population and the number of confirmed cases forced by the periodic small-amplitude oscillations in the isolation index. The main contributions of the proposed model are as follows: A minimum effort was required when vaccination was combined with social isolation, while additionally ensuring the existence of equilibrium points. The model could provide valuable information for policymakers, helping define disease prevention mitigation strategies that combine vaccination and non-pharmaceutical interventions, such as social distancing and the use of masks. In addition, the SIRSi-vaccine model facilitated the qualitative assessment of information regarding the unreported infected yet infectious cases, while considering temporary immunity, vaccination, and social isolation index.
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Affiliation(s)
| | - Diego P F Correa
- Federal University of ABC - UFABC - São Bernardo do Campo, SP, Brazil.
| | - Átila M Bueno
- Polytechnic School of University of São Paulo, São Paulo, SP, Brazil.
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46
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Bhatkar S, Ma M, Zsolway M, Tarafder A, Doniach S, Bhanot G. Asymmetry in the peak in Covid-19 daily cases and the pandemic R-parameter. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.23.23292960. [PMID: 37546829 PMCID: PMC10402219 DOI: 10.1101/2023.07.23.23292960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Within the context of the standard SIR model of pandemics, we show that the asymmetry in the peak in recorded daily cases during a pandemic can be used to infer the pandemic R-parameter. Using only daily data for symptomatic, confirmed cases, we derive a universal scaling curve that yields: (i) reff, the pandemic R-parameter; (ii) Leff, the effective latency, the average number of days an infected individual is able to infect others and (iii) α , the probability of infection per contact between infected and susceptible individuals. We validate our method using an example and then apply it to estimate these parameters for the first phase of the SARS-Cov-2/Covid-19 pandemic for several countries where there was a well separated peak in identified infected daily cases. The extension of the SIR model developed in this paper differentiates itself from earlier studies in that it provides a simple method to make an a-posteriori estimate of several useful epidemiological parameters, using only data on confirmed, identified cases. Our results are general and can be applied to any pandemic.
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Affiliation(s)
- Sayali Bhatkar
- Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai 40005, India
| | - Mingyang Ma
- Department of Physics and Astronomy, Rutgers University, Piscataway, NJ, 08854, USA
| | - Mary Zsolway
- School of Arts and Sciences, Rutgers University, Piscataway, NJ, 08854, USA
| | - Ayush Tarafder
- School of Arts and Sciences, Rutgers University, Piscataway, NJ, 08854, USA
| | - Sebastian Doniach
- Department of Applied Physics, Stanford University, Stanford, CA 94305, USA
| | - Gyan Bhanot
- Department of Physics and Astronomy, Rutgers University, Piscataway, NJ, 08854, USA
- Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ, 08854, USA
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47
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Diebner HH. Spatio-Temporal Patterns of the SARS-CoV-2 Epidemic in Germany. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1137. [PMID: 37628167 PMCID: PMC10453630 DOI: 10.3390/e25081137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/27/2023]
Abstract
Results from an explorative study revealing spatio-temporal patterns of the SARS-CoV-2/ COVID-19 epidemic in Germany are presented. We dispense with contestable model assumptions and show the intrinsic spatio-temporal patterns of the epidemic dynamics. The analysis is based on COVID-19 incidence data, which are age-stratified and spatially resolved at the county level, provided by the Federal Government's Public Health Institute of Germany (RKI) for public use. Although the 400 county-related incidence time series shows enormous heterogeneity, both with respect to temporal features as well as spatial distributions, the counties' incidence curves organise into well-distinguished clusters that coincide with East and West Germany. The analysis is based on dimensionality reduction, multidimensional scaling, network analysis, and diversity measures. Dynamical changes are captured by means of difference-in-difference methods, which are related to fold changes of the effective reproduction numbers. The age-related dynamical patterns suggest a considerably stronger impact of children, adolescents and seniors on the epidemic activity than previously expected. Besides these concrete interpretations, the work mainly aims at providing an atlas for spatio-temporal patterns of the epidemic, which serves as a basis to be further explored with the expertise of different disciplines, particularly sociology and policy makers. The study should also be understood as a methodological contribution to getting a handle on the unusual complexity of the COVID-19 pandemic.
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Affiliation(s)
- Hans H Diebner
- Department of Medical Informatics, Biometry and Epidemiology, Ruhr-Universität Bochum, 44780 Bochum, Germany
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48
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Berman S, D'Souza G, Osborn J, Myers M. Comparison of homemade mask designs based on calculated infection risk, using actual COVID-19 infection scenarios. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:14811-14826. [PMID: 37679160 DOI: 10.3934/mbe.2023663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
During pandemics such as COVID-19, shortages of approved respirators necessitate the use of alternative masks, including homemade designs. The effectiveness of the masks is often quantified in terms of the ability to filter particles. However, to formulate public policy the efficacy of the mask in reducing the risk of infection for a given population is considerably more useful than its filtration efficiency (FE). The effect of the mask on the infection profile is complicated to estimate as it depends strongly upon the behavior of the affected population. A recently introduced tool known as the dynamic-spread model is well suited for performing population-specific risk assessment. The dynamic-spread model was used to simulate the performance of a variety of mask designs (all used for source control only) in different COVID-19 scenarios. The efficacy of different masks was found to be highly scenario dependent. Switching from a cotton T-shirt of 8% FE to a 3-layer cotton-gauze-cotton mask of 44% FE resulted in a decrease in number of new infections of about 30% in the New York State scenario and 60% in the Harris County, Texas scenario. The results are valuable to policy makers for quantifying the impact upon the infection rate for different intervention strategies, e.g., investing resources to provide the community with higher-filtration masks.
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Affiliation(s)
- Shayna Berman
- Division of Applied Mechanics, U. S. FDA/CDRH, 10903 New Hampshire Avenue, Silver Spring 20993, MD, USA
| | - Gavin D'Souza
- Division of Applied Mechanics, U. S. FDA/CDRH, 10903 New Hampshire Avenue, Silver Spring 20993, MD, USA
| | - Jenna Osborn
- Division of Applied Mechanics, U. S. FDA/CDRH, 10903 New Hampshire Avenue, Silver Spring 20993, MD, USA
| | - Matthew Myers
- Division of Applied Mechanics, U. S. FDA/CDRH, 10903 New Hampshire Avenue, Silver Spring 20993, MD, USA
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49
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Sinha A, Sangeet S, Roy S. Evolution of Sequence and Structure of SARS-CoV-2 Spike Protein: A Dynamic Perspective. ACS OMEGA 2023; 8:23283-23304. [PMID: 37426203 PMCID: PMC10324094 DOI: 10.1021/acsomega.3c00944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 06/01/2023] [Indexed: 07/11/2023]
Abstract
Novel coronavirus (SARS-CoV-2) enters its host cell through a surface spike protein. The viral spike protein has undergone several modifications/mutations at the genomic level, through which it modulated its structure-function and passed through several variants of concern. Recent advances in high-resolution structure determination and multiscale imaging techniques, cost-effective next-generation sequencing, and development of new computational methods (including information theory, statistical methods, machine learning, and many other artificial intelligence-based techniques) have hugely contributed to the characterization of sequence, structure, function of spike proteins, and its different variants to understand viral pathogenesis, evolutions, and transmission. Laying on the foundation of the sequence-structure-function paradigm, this review summarizes not only the important findings on structure/function but also the structural dynamics of different spike components, highlighting the effects of mutations on them. As dynamic fluctuations of three-dimensional spike structure often provide important clues for functional modulation, quantifying time-dependent fluctuations of mutational events over spike structure and its genetic/amino acidic sequence helps identify alarming functional transitions having implications for enhanced fusogenicity and pathogenicity of the virus. Although these dynamic events are more difficult to capture than quantifying a static, average property, this review encompasses those challenging aspects of characterizing the evolutionary dynamics of spike sequence and structure and their implications for functions.
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50
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Palma G, Caprioli D, Mari L. Epidemic Management via Imperfect Testing: A Multi-criterial Perspective. Bull Math Biol 2023; 85:66. [PMID: 37296314 PMCID: PMC10255952 DOI: 10.1007/s11538-023-01172-1] [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: 02/14/2023] [Accepted: 05/27/2023] [Indexed: 06/12/2023]
Abstract
Diagnostic testing may represent a key component in response to an ongoing epidemic, especially if coupled with containment measures, such as mandatory self-isolation, aimed to prevent infectious individuals from furthering onward transmission while allowing non-infected individuals to go about their lives. However, by its own nature as an imperfect binary classifier, testing can produce false negative or false positive results. Both types of misclassification are problematic: while the former may exacerbate the spread of disease, the latter may result in unnecessary isolation mandates and socioeconomic burden. As clearly shown by the COVID-19 pandemic, achieving adequate protection for both people and society is a crucial, yet highly challenging task that needs to be addressed in managing large-scale epidemic transmission. To explore the trade-offs imposed by diagnostic testing and mandatory isolation as tools for epidemic containment, here we present an extension of the classical Susceptible-Infected-Recovered model that accounts for an additional stratification of the population based on the results of diagnostic testing. We show that, under suitable epidemiological conditions, a careful assessment of testing and isolation protocols can contribute to epidemic containment, even in the presence of false negative/positive results. Also, using a multi-criterial framework, we identify simple, yet Pareto-efficient testing and isolation scenarios that can minimize case count, isolation time, or seek a trade-off solution for these often contrasting epidemic management objectives.
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Affiliation(s)
- Giuseppe Palma
- Institute of Nanotechnology, National Research Council, Campus Ecotekne, Via Monteroni, 73100 Lecce, LE Italy
| | - Damiano Caprioli
- Department of Astronomy & Astrophysics, E. Fermi Institute, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637 USA
| | - Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, MI Italy
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