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Holtzapple E, Zhou G, Luo H, Tang D, Arazkhani N, Hansen C, Telmer CA, Miskov-Zivanov N. The BioRECIPE Knowledge Representation Format. ACS Synth Biol 2024; 13:2621-2624. [PMID: 39051984 PMCID: PMC11334182 DOI: 10.1021/acssynbio.4c00096] [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: 02/12/2024] [Revised: 05/22/2024] [Accepted: 07/09/2024] [Indexed: 07/27/2024]
Abstract
The BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) knowledge representation format was introduced to standardize and facilitate human-machine interaction while creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is compatible with multiple representation formats, natural language processing tools, modeling tools, and databases that are used by the systems and synthetic biology communities.
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Affiliation(s)
- Emilee Holtzapple
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Gaoxiang Zhou
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Haomiao Luo
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Difei Tang
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Niloofar Arazkhani
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Casey Hansen
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Cheryl A. Telmer
- Department
of Biological Sciences, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Natasa Miskov-Zivanov
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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2
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Chen J, Bhattacharya P, Hoops S, Machi D, Adiga A, Mortveit H, Venkatramanan S, Lewis B, Marathe M. Role of heterogeneity: National scale data-driven agent-based modeling for the US COVID-19 Scenario Modeling Hub. Epidemics 2024; 48:100779. [PMID: 39024889 DOI: 10.1016/j.epidem.2024.100779] [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: 08/15/2023] [Revised: 05/20/2024] [Accepted: 06/17/2024] [Indexed: 07/20/2024] Open
Abstract
UVA-EpiHiper is a national scale agent-based model to support the US COVID-19 Scenario Modeling Hub (SMH). UVA-EpiHiper uses a detailed representation of the underlying social contact network along with data measured during the course of the pandemic to initialize and calibrate the model. In this paper, we study the role of heterogeneity on model complexity and resulting epidemic dynamics using UVA-EpiHiper. We discuss various sources of heterogeneity that we encounter in the use of UVA-EpiHiper to support modeling and analysis of epidemic dynamics under various scenarios. We also discuss how this affects model complexity and computational complexity of the corresponding simulations. Using round 13 of the SMH as an example, we discuss how UVA-EpiHiper was initialized and calibrated. We then discuss how the detailed output produced by UVA-EpiHiper can be analyzed to obtain interesting insights. We find that despite the complexity in the model, the software, and the computation incurred to an agent-based model in scenario modeling, it is capable of capturing various heterogeneities of real-world systems, especially those in networks and behaviors, and enables analyzing heterogeneities in epidemiological outcomes between different demographic, geographic, and social cohorts. In applying UVA-EpiHiper to round 13 scenario modeling, we find that disease outcomes are different between and within states, and between demographic groups, which can be attributed to heterogeneities in population demographics, network structures, and initial immunity.
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Affiliation(s)
- Jiangzhuo Chen
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
| | | | - Stefan Hoops
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Dustin Machi
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Abhijin Adiga
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Henning Mortveit
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA; Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA
| | | | - Bryan Lewis
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA
| | - Madhav Marathe
- Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA; Department of Computer Science, University of Virginia, Charlottesville, VA, USA.
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3
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Moore S, Cavany S, Perkins TA, España GFC. Projecting the future impact of emerging SARS-CoV-2 variants under uncertainty: Modeling the initial Omicron outbreak. Epidemics 2024; 47:100759. [PMID: 38452455 DOI: 10.1016/j.epidem.2024.100759] [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: 08/18/2023] [Revised: 01/26/2024] [Accepted: 03/01/2024] [Indexed: 03/09/2024] Open
Abstract
Over the past several years, the emergence of novel SARS-CoV-2 variants has led to multiple waves of increased COVID-19 incidence. When the Omicron variant emerged, there was considerable concern about its potential impact in the winter of 2021-2022 due to its increased fitness. However, there was also considerable uncertainty regarding its likely impact due to questions about its relative transmissibility, severity, and degree of immune escape. We sought to evaluate the ability of an agent-based model to forecast incidence in the context of this emerging pathogen variant. To project COVID-19 cases and deaths in Indiana, we calibrated our model to COVID-19 hospitalizations, deaths, and test-positivity rates through November 2021, and then projected COVID-19 incidence through April 2022 under four different scenarios that covered the plausible ranges of Omicron's severity, transmissibility, and degree of immune escape. Our initial projections from December 2021 through March 2022 indicated that under a pessimistic scenario with high disease severity, the peak in weekly COVID-19 deaths in Indiana would be larger than the previous peak in December 2020. However, retrospective analyses indicate that Omicron's severity was closer to the optimistic scenario, and even though cases and hospitalizations reached a new peak, fewer deaths occurred than during the previous peak. According to our results, Omicron's rapid spread was consistent with a combination of higher transmissibility and immune escape relative to earlier variants. Our updated projections starting in January 2022 accurately predicted that cases would peak in mid-January and decline rapidly over the next several months. The performance of our projections shows that following the emergence of a new pathogen variant, models can help quantify the potential range of outbreak magnitudes and trajectories. Agent-based models are particularly useful in these scenarios because they can efficiently track individual vaccination and infection histories with multiple variants with varying degrees of cross-protection.
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Affiliation(s)
- Sean Moore
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States.
| | - Sean Cavany
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
| | - T Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Guido Felipe Camargo España
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
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Fabiani G, Evangelou N, Cui T, Bello-Rivas JM, Martin-Linares CP, Siettos C, Kevrekidis IG. Task-oriented machine learning surrogates for tipping points of agent-based models. Nat Commun 2024; 15:4117. [PMID: 38750063 PMCID: PMC11096392 DOI: 10.1038/s41467-024-48024-7] [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: 10/24/2023] [Accepted: 04/15/2024] [Indexed: 05/18/2024] Open
Abstract
We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.
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Affiliation(s)
- Gianluca Fabiani
- Modelling Engineering Risk and Complexity, Scuola Superiore Meridionale, Naples, Italy
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Nikolaos Evangelou
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Tianqi Cui
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Juan M Bello-Rivas
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | | | - Constantinos Siettos
- Dipartimento di Matematica e Applicazioni 'Renato Caccioppoli', Università degli Studi di Napoli Federico II, Naples, Italy.
| | - Ioannis G Kevrekidis
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.
- School of Medicine's Dept. of Urology, Johns Hopkins University, Baltimore, MD, USA.
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Lee S, Zabinsky ZB, Wasserheit JN, Ross JM, Chen S, Liu S. Impact of Vaccination and Nonpharmaceutical Interventions With Possible COVID-19 Viral Evolutions Using an Agent-Based Simulation. AJPM FOCUS 2024; 3:100155. [PMID: 38130803 PMCID: PMC10733698 DOI: 10.1016/j.focus.2023.100155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Introduction The COVID-19 pandemic continues with highly contagious variants and waning immunity. As the virus keeps evolving to be more infectious and immune evasive, some question whether the COVID-19 pandemic can be managed through sustainable public health measures. Methods We developed an agent-based simulation to explore the impact of COVID-19 mutations, periodic vaccinations, and nonpharmaceutical interventions on reducing COVID-19 deaths. The model is calibrated to the greater Seattle area by observing local epidemic data. We perform scenario analyses on viral mutations that change infectiousness, disease severity, and immune evasiveness from previous infections and vaccination every 6 months. The simulation is run until the end of year 2023. Results Variants with increased infectivity or increased immune evasion dominate previous strains. With enhanced immune protection from a pancoronavirus vaccine, the most optimistic periodic vaccination rate reduces average total deaths by 44.6% compared with the most pessimistic periodic vaccination rate. A strict threshold nonpharmaceutical intervention policy reduces average total deaths by 71.3% compared with an open society, whereas a moderate nonpharmaceutical intervention policy results in a 33.6% reduction. Conclusions Our findings highlight the potential benefits of pancoronavirus vaccines that offer enhanced and longer-lasting immunity. We emphasize the crucial role of nonpharmaceutical interventions in reducing COVID-19 deaths regardless of virus mutation scenarios. Owing to highly immune evasive and contagious SARS-CoV-2 variants, most scenarios in this study fail to reduce the mortality of COVID-19 to the level of influenza and pneumonia. However, our findings indicate that periodic vaccinations and a threshold nonpharmaceutical intervention policy may succeed in achieving this goal. This indicates the need for caution and vigilance in managing a continuing COVID-19 epidemic.
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Affiliation(s)
- Serin Lee
- Department of Industrial & Systems Engineering, University of Washington, Seattle, Washington
| | - Zelda B. Zabinsky
- Department of Industrial & Systems Engineering, University of Washington, Seattle, Washington
| | - Judith N. Wasserheit
- Department of Global Health, University of Washington, Seattle, Washington
- Division of Allergy & Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington
| | - Jennifer M. Ross
- Division of Allergy & Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington
| | - Shi Chen
- Department of Information Systems and Operations Management, Foster School of Business, University of Washington, Seattle, Washington
| | - Shan Liu
- Department of Industrial & Systems Engineering, University of Washington, Seattle, Washington
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Krauland MG, Roberts MS. Modeling the Impact of COVID-19 Mitigation Strategies in Pennsylvania, USA. MDM Policy Pract 2024; 9:23814683241260744. [PMID: 38911124 PMCID: PMC11191394 DOI: 10.1177/23814683241260744] [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: 09/01/2023] [Accepted: 05/09/2024] [Indexed: 06/25/2024] Open
Abstract
Purpose. To estimate the impact on mortality of nonpharmaceutical interventions (NPIs) implemented early in the COVID-19 pandemic. Methods. We implemented an agent-based modified SEIR model of COVID-19, calibrated to match death numbers reported in Pennsylvania from January 2020 to April 2021 and including representations of NPIs implemented in Pennsylvania. To investigate the impact of these strategies, we ran the calibrated model with no interventions and with varying combinations, timings, and levels of interventions. Results. The model closely replicated death outcomes data for Pennsylvania. Without NPIs, deaths in the early months of the pandemic were estimated to be much higher (67,718 deaths compared to actual 6,969). Voluntary interventions alone were relatively ineffective at decreasing mortality. Delaying implementation of interventions led to higher deaths (∼9,000 more deaths with just a 1-week delay). School closure was insufficient as a single intervention but was an important part of a combined intervention strategy. Conclusions. NPIs were effective at reducing deaths early in the COVID-19 pandemic. Agent-based models can incorporate substantial detail on infectious disease spread and the impact of mitigations. Policy Implications. The model supports the importance and effectiveness of NPIs to decrease morbidity from respiratory pathogens. This is particularly important for emerging pathogens for which no vaccines or treatments exist, but such strategies are applicable to a variety of respiratory pathogens. Highlights Nonpharmaceutical interventions were used extensively during the early period of the COVID-19 pandemic, but their use has remained controversial.Agent-based modeling of the impact of these mitigation strategies early in the COVID-19 pandemic supports the effectiveness of nonpharmaceutical interventions at decreasing mortality.Since such interventions are not specific to a particular pathogen, they can be used to protect against any respiratory pathogen, known or emerging. They can be applied rapidly when conditions warrant.
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Affiliation(s)
- Mary G. Krauland
- Department of Health Policy and Management, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark S. Roberts
- Department of Health Policy and Management, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
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7
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Lönn SL, Krauland MG, Fagan AA, Sundquist J, Sundquist K, Roberts MS, Kendler KS. The Impact of the Good Behavior Game on Risk for Drug Use Disorder in an Agent-Based Model of Southern Sweden. J Stud Alcohol Drugs 2023; 84:863-873. [PMID: 37650838 PMCID: PMC10765974 DOI: 10.15288/jsad.22-00413] [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: 12/07/2022] [Accepted: 06/25/2023] [Indexed: 09/01/2023] Open
Abstract
OBJECTIVE Drug use disorder (DUD) is a worldwide problem, and strategies to reduce its incidence are central to decreasing its burden. This investigation seeks to provide a proof of concept for the ability of agent-based modeling to predict the impact of the introduction of an effective school-based intervention, the Good Behavior Game (GBG), on reducing DUD in Scania, Sweden, primarily through increasing school achievement. METHOD We modified an existing agent-based simulation model of opioid use disorder to represent DUD in Scania County, southern Sweden. The model represents every individual in the population and is calibrated with the linked individual data from multiple sources including demographics, education, medical care, and criminal history. Risks for developing DUD were estimated from the population in Scania. Scenarios estimated the impact of introducing the GBG in schools located in disadvantaged areas. RESULTS The model accurately reflected the growth of DUD in Scania over a multiyear period and reproduced the levels of affected individuals in various socioeconomic strata over time. The GBG was estimated to improve school achievement and lower DUD registrations over time in males residing in disadvantaged areas by 10%, reflecting a decrease of 540 cases of DUD. Effects were considerably smaller in females. CONCLUSIONS This work provides support for the impact of improving school achievement on long-term risks of developing DUD. It also demonstrated the value of using simulation modeling calibrated with data from a real population to estimate the impact of an intervention applied at a population level.
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Affiliation(s)
- Sara L. Lönn
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Mary G. Krauland
- Department of Health Policy and Management, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
- Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Abigail A. Fagan
- Department of Sociology and Criminology & Law, University of Florida, Gainesville, Florida
| | - Jan Sundquist
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
- Department of Sociology and Criminology & Law, University of Florida, Gainesville, Florida
| | - Kristina Sundquist
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
- Department of Family Medicine and Community Health, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Mark S. Roberts
- Department of Health Policy and Management, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
- Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Kenneth S. Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
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Woodul RL, Delamater PL, Woodburn M. Validating model output in the absence of ground truth data: A COVID-19 case study using the Simulator of Infectious Disease Dynamics in North Carolina (SIDD-NC) model. Health Place 2023; 83:103065. [PMID: 37352616 PMCID: PMC10267499 DOI: 10.1016/j.healthplace.2023.103065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/10/2023] [Accepted: 06/07/2023] [Indexed: 06/25/2023]
Abstract
As the COVID-19 pandemic has progressed, various models have been developed to forecast changes in the outbreak and assess intervention strategies. In this study we validate the Simulator of Infectious Disease Dynamics in North Carolina (SIDD-NC) model against an ensemble of proxy-ground truth infections datasets. We assess the performance of SIDD-NC using Spearman Rank Correlation, RMSE, and percent RMSE at a state and county level. We conduct the analysis for the period of March 2020 through November 2020 as well as in shorter time increments to assess both the recreation of the pandemic curve as well as day-to-day transmission of SARS-CoV-2 within the population. We find that SIDD-NC performs well against the datasets in the ensemble, generating an estimate of infections that is robust both spatially and temporally.
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Affiliation(s)
- Rachel L Woodul
- Department of Geography, The University of North Carolina at Chapel Hill, Carolina Hall, Campus Box 3220, Chapel Hill, NC, 27599, United States; Carolina Population Center, 123 West Franklin St, Chapel Hill, NC, 27516, United States.
| | - Paul L Delamater
- Department of Geography, The University of North Carolina at Chapel Hill, Carolina Hall, Campus Box 3220, Chapel Hill, NC, 27599, United States; Carolina Population Center, 123 West Franklin St, Chapel Hill, NC, 27516, United States.
| | - Meg Woodburn
- Department of Geography, The University of North Carolina at Chapel Hill, Carolina Hall, Campus Box 3220, Chapel Hill, NC, 27599, United States.
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Tracy M, Gordis E, Strully K, Marshall BDL, Cerdá M. Applications of agent-based modeling in trauma research. PSYCHOLOGICAL TRAUMA : THEORY, RESEARCH, PRACTICE AND POLICY 2023; 15:939-950. [PMID: 36136775 PMCID: PMC10030380 DOI: 10.1037/tra0001375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Trauma, violence, and their consequences for population health are shaped by complex, intersecting forces across the life span. We aimed to illustrate the strengths of agent-based modeling (ABM), a computational approach in which population-level patterns emerge from the behaviors and interactions of simulated individuals, for advancing trauma research; Method: We provide an overview of agent-based modeling for trauma research, including a discussion of the model development process, ABM as a complement to other causal inference and complex systems approaches in trauma research, and past ABM applications in the trauma literature; Results: We use existing ABM applications to illustrate the strengths of ABM for trauma research, including incorporating interactions between individuals, simulating processes across multiple scales, examining life-course effects, testing alternate theories, comparing intervention strategies in a virtual laboratory, and guiding decision making. We also discuss the challenges of applying ABM to trauma research and offer specific suggestions for incorporating ABM into future studies of trauma and violence; Conclusion: Agent-based modeling is a useful complement to other methodological advances in trauma research. We recommend a more widespread adoption of ABM, particularly for research into patterns and consequences of individual traumatic experiences across the life course and understanding the effects of interventions that may be influenced by social norms and social network structures. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Melissa Tracy
- Department of Epidemiology and Biostatistics, University at Albany, State University of New York, 1 University Place, Rensselaer, NY 12144, United States
| | - Elana Gordis
- Department of Psychology, University at Albany, State University of New York, 1400 Washington Ave, Albany, NY 12222, United States
| | - Kate Strully
- Department of Sociology, University at Albany, State University of New York, 1400 Washington Ave, Albany, NY 12222, United States
| | - Brandon D. L. Marshall
- Department of Epidemiology, Brown University School of Public Health, 121 South Main St, Providence, RI, 02912, United States
| | - Magdalena Cerdá
- Department of Population Health, New York University Grossman School of Medicine, 180 Madison Ave, New York, NY 10016, United States
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Krauland MG, Zimmerman RK, Williams KV, Raviotta JM, Harrison LH, Williams JV, Roberts MS. Agent-based model of the impact of higher influenza vaccine efficacy on seasonal influenza burden. Vaccine X 2023; 13:100249. [PMID: 36536801 PMCID: PMC9753457 DOI: 10.1016/j.jvacx.2022.100249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 08/08/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction Current influenza vaccines have limited effectiveness. COVID-19 vaccines using mRNA technology have demonstrated very high efficacy, suggesting that mRNA vaccines could be more effective for influenza. Several such influenza vaccines are in development. FRED, an agent-based modeling platform, was used to estimate the impact of more effective influenza vaccines on seasonal influenza burden. Methods Simulations were performed using an agent-based model of influenza that included varying levels of vaccination efficacy (40-95 % effective). In some simulations, level of infectiousness and/or length of infectious period in agents with breakthrough infections was also decreased. Impact of increased and decreased levels of vaccine uptake were also modeled. Outcomes included number of symptomatic influenza cases estimated for the US. Results Highly effective vaccines significantly reduced estimated influenza cases in the model. When vaccine efficacy was increased from 40 % to a maximum of 95 %, estimated influenza cases in the US decreased by 43 % to > 99 %. The base simulation (40 % efficacy) resulted in ∼ 28 million total yearly cases in the US, while the most effective vaccine modeled (95 % efficacy) decreased estimated cases to ∼ 22,000. Discussion Highly effective vaccines could dramatically reduce influenza burden. Model estimates suggest that even modest increases in vaccine efficacy could dramatically reduce seasonal influenza disease burden.
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Affiliation(s)
- Mary G. Krauland
- Department of Health Policy and Management, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA,Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA,Corresponding author at: 7132 Public Health, 130 De Soto St, Pittsburgh, PA 15261, USA
| | - Richard K. Zimmerman
- Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Katherine V. Williams
- Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jonathan M. Raviotta
- Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Lee H. Harrison
- Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - John V. Williams
- Department of Pediatrics, School of Medicine, University of Pittsburgh and University of Pittsburgh Medical Center Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Mark S. Roberts
- Department of Health Policy and Management, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA,Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
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11
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Guo Z, Xiao G, Wang Y, Li S, Du J, Dai B, Gong L, Xiao D. Dynamic model of respiratory infectious disease transmission in urban public transportation systems. Heliyon 2023; 9:e14500. [PMID: 36967891 PMCID: PMC10034446 DOI: 10.1016/j.heliyon.2023.e14500] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 03/05/2023] [Accepted: 03/08/2023] [Indexed: 03/13/2023] Open
Abstract
During the epidemics of respiratory infectious diseases, the use of public transportation increases the risk of disease transmission. Therefore, we established a dynamic model to provide an in-depth understanding of the mechanism of epidemic spread via this route. We designed a computer program to model a rail transit system including four transit lines in a small town in which assumed 70% of the residents commute via these trams in weekdays and the remaining residents take the tram at random. The model could identify the best travel route for each passenger and the specific passengers onboard when the tram passed through each station, and simulate the dynamic spread of a respiratory pathogen as the passengers used the rail transit system. Based on the program operating, we estimated that all residents in the town were ultimately infected, including 86.6% who were infected due to the public transportation system. The remaining individuals were infected at home. As the infection rate increased, the number of infected individuals increased more rapidly. Reducing the frequency of trams, driving private cars or riding bicycles, showing nucleic acid certificates and wearing masks for passengers, etc., are effective measures for the prevention of the spread of epidemic diseases.
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Affiliation(s)
- Zuiyuan Guo
- Department of Infectious Disease Prevention and Control, PLA Northern Theater Command Center for Disease Control and Prevention, Shenyang, China
| | - Guangquan Xiao
- Department of Infectious Disease Prevention and Control, PLA Northern Theater Command Center for Disease Control and Prevention, Shenyang, China
| | - Yayu Wang
- Department of Infectious Disease Prevention and Control, PLA Northern Theater Command Center for Disease Control and Prevention, Shenyang, China
| | - Sidong Li
- Department of Infectious Disease Prevention and Control, PLA Northern Theater Command Center for Disease Control and Prevention, Shenyang, China
| | - Jianhong Du
- Training Base of Non-Commissioned Officer Specialized in Aviation Support of Naval Aeronautical University, Qingdao, China
| | - Botao Dai
- Liaoning Agricultural Development Service Center, Shenyang, China
| | - Lili Gong
- Department of Psychiatry, General Hospital of Northern Theater Command, Shenyang, China
| | - Dan Xiao
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
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Bhattacharya P, Chen J, Hoops S, Machi D, Lewis B, Venkatramanan S, Wilson ML, Klahn B, Adiga A, Hurt B, Outten J, Adiga A, Warren A, Baek YY, Porebski P, Marathe A, Xie D, Swarup S, Vullikanti A, Mortveit H, Eubank S, Barrett CL, Marathe M. Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support. THE INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2023; 37:4-27. [PMID: 38603425 PMCID: PMC9596688 DOI: 10.1177/10943420221127034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.
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Affiliation(s)
- Parantapa Bhattacharya
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Jiangzhuo Chen
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Stefan Hoops
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Dustin Machi
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Bryan Lewis
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | | | - Mandy L Wilson
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Aniruddha Adiga
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Benjamin Hurt
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Joseph Outten
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Abhijin Adiga
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Andrew Warren
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Young Yun Baek
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Przemyslaw Porebski
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Achla Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Dawen Xie
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Samarth Swarup
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Anil Vullikanti
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Henning Mortveit
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Eng. Systems and Environment, University of Virginia, Charlottesville, VA, USA
| | - Stephen Eubank
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Christopher L Barrett
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA
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13
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Gopalappa C, Balasubramanian H, Haas PJ. A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases- application to sexually transmitted infections. Infect Dis Model 2022; 8:84-100. [PMID: 36632177 PMCID: PMC9827035 DOI: 10.1016/j.idm.2022.12.003] [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: 10/23/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 12/25/2022] Open
Abstract
Background A model that jointly simulates infectious diseases with common modes of transmission can serve as a decision-analytic tool to identify optimal intervention combinations for overall disease prevention. In the United States, sexually transmitted infections (STIs) are a huge economic burden, with a large fraction of the burden attributed to HIV. Data also show interactions between HIV and other sexually transmitted infections (STIs), such as higher risk of acquisition and progression of co-infections among persons with HIV compared to persons without. However, given the wide range in prevalence and incidence burdens of STIs, current compartmental or agent-based network simulation methods alone are insufficient or computationally burdensome for joint disease modeling. Further, causal factors for higher risk of coinfection could be both behavioral (i.e., compounding effects of individual behaviors, network structures, and care behaviors) and biological (i.e., presence of one disease can biologically increase the risk of another). However, the data on the fraction attributed to each are limited. Methods We present a new mixed agent-based compartmental (MAC) framework for jointly modeling STIs. It uses a combination of a new agent-based evolving network modeling (ABENM) technique for lower-prevalence diseases and compartmental modeling for higher-prevalence diseases. As a demonstration, we applied MAC to simulate lower-prevalence HIV in the United States and a higher-prevalence hypothetical Disease 2, using a range of transmission and progression rates to generate burdens replicative of the wide range of STIs. We simulated sexual transmissions among heterosexual males, heterosexual females, and men who have sex with men (men only and men and women). Setting the biological risk of co-infection to zero, we conducted numerical analyses to evaluate the influence of behavioral factors alone on disease dynamics. Results The contribution of behavioral factors to risk of coinfection was sensitive to disease burden, care access, and population heterogeneity and mixing. The contribution of behavioral factors was generally lower than observed risk of coinfections for the range of hypothetical prevalence studied here, suggesting potential role of biological factors, that should be investigated further specific to an STI. Conclusions The purpose of this study is to present a new simulation technique for jointly modeling infectious diseases that have common modes of transmission but varying epidemiological features. The numerical analysis serves as proof-of-concept for the application to STIs. Interactions between diseases are influenced by behavioral factors, are sensitive to care access and population features, and are likely exacerbated by biological factors. Social and economic conditions are among key drivers of behaviors that increase STI transmission, and thus, structural interventions are a key part of behavioral interventions. Joint modeling of diseases helps comprehensively simulate behavioral and biological factors of disease interactions to evaluate the true impact of common structural interventions on overall disease prevention. The new simulation framework is especially suited to simulate behavior as a function of social determinants, and further, to identify optimal combinations of common structural and disease-specific interventions.
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14
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Smedemark-Margulies N, Walters R, Zimmermann H, Laird L, van der Loo C, Kaushik N, Caceres R, van de Meent JW. Probabilistic program inference in network-based epidemiological simulations. PLoS Comput Biol 2022; 18:e1010591. [PMID: 36342957 PMCID: PMC9671460 DOI: 10.1371/journal.pcbi.1010591] [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: 12/12/2021] [Revised: 11/17/2022] [Accepted: 09/21/2022] [Indexed: 11/09/2022] Open
Abstract
Accurate epidemiological models require parameter estimates that account for mobility patterns and social network structure. We demonstrate the effectiveness of probabilistic programming for parameter inference in these models. We consider an agent-based simulation that represents mobility networks as degree-corrected stochastic block models, whose parameters we estimate from cell phone co-location data. We then use probabilistic program inference methods to approximate the distribution over disease transmission parameters conditioned on reported cases and deaths. Our experiments demonstrate that the resulting models improve the quality of fit in multiple geographies relative to baselines that do not model network topology.
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Affiliation(s)
- Niklas Smedemark-Margulies
- Khoury College of Computer Science, Northeastern University, Boston, Massachusetts, United States of America
| | - Robin Walters
- Khoury College of Computer Science, Northeastern University, Boston, Massachusetts, United States of America
| | - Heiko Zimmermann
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
| | - Lucas Laird
- Khoury College of Computer Science, Northeastern University, Boston, Massachusetts, United States of America
- MIT Lincoln Laboratory, Lexington, Massachusetts, United States of America
| | | | - Neela Kaushik
- MIT Lincoln Laboratory, Lexington, Massachusetts, United States of America
| | - Rajmonda Caceres
- MIT Lincoln Laboratory, Lexington, Massachusetts, United States of America
| | - Jan-Willem van de Meent
- Khoury College of Computer Science, Northeastern University, Boston, Massachusetts, United States of America
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
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15
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Can a Two-Dose Influenza Vaccine Regimen Better Protect Older Adults? An Agent-Based Modeling Study. Vaccines (Basel) 2022; 10:vaccines10111799. [DOI: 10.3390/vaccines10111799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 11/16/2022] Open
Abstract
Older adults (age ≥ 65) are at high risk of influenza morbidity and mortality. This study evaluated the impact of a hypothetical two-dose influenza vaccine regimen per season to reduce symptomatic flu cases by providing preseason (first dose) and mid-season (second dose) protection to offset waning vaccine effectiveness (VE). The Framework for Reconstructing Epidemiological Dynamics (FRED), an agent-based modeling platform, was used to compare typical one-dose vaccination to a two-dose vaccination strategy. Primary models incorporated waning VE of 10% per month and varied influenza season timing (December through March) to estimate cases and hospitalizations in older adults. Additional scenarios modeled reductions in uptake and VE of the second dose, and overall waning. In seasons with later peaks, two vaccine doses had the largest potential to reduce cases (14.4% with February peak, 18.7% with March peak) and hospitalizations (13.1% with February peak, 16.8% with March peak). Reductions in cases and hospitalizations still resulted but decreased when 30% of individuals failed to receive a second dose, second dose VE was reduced, or overall waning was reduced to 7% per month. Agent-based modeling indicates that two influenza vaccine doses could decrease cases and hospitalizations in older individuals. The highest impact occurred in the more frequently observed late-peak seasons. The beneficial impact of the two-dose regimen persisted despite model scenarios of reduced uptake of the second dose, decreased VE of the second dose, or overall VE waning.
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16
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Ghoroghi A, Rezgui Y, Wallace R. Impact of ventilation and avoidance measures on SARS-CoV-2 risk of infection in public indoor environments. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:156518. [PMID: 35688237 PMCID: PMC9172255 DOI: 10.1016/j.scitotenv.2022.156518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/12/2022] [Accepted: 06/02/2022] [Indexed: 05/19/2023]
Abstract
BACKGROUND The literature includes many studies which individually assess the efficacy of protective measures against the spread of the SARS-CoV-2 virus. This study considers the high infection risk in public buildings and models the quality of the indoor environment, related safety measures, and their efficacy in preventing the spread of the SARS-CoV-2 virus. METHODS Simulations are created that consider protective factors such as hand hygiene, face covering and engagement with Covid-19 vaccination programs in reducing the risk of infection in a university foyer. Furthermore, a computational fluid dynamics model is developed to simulate and analyse the university foyer under three ventilation regimes. The probability of transmission was measured across different scenarios. FINDINGS Estimates suggest that the Delta variant requires the air change rate to be increased >1000 times compared to the original strain, which is practically not feasible. Consequently, appropriate hygiene practices, such as wearing masks, are essential to reducing secondary infections. A comparison of different protective factors in simulations found the overall burden of infections resulting from indoor contact depends on (i) face mask adherence, (ii) quality of the ventilation system, and (iii) other hygiene practices. INTERPRETATION Relying on ventilation, whether natural, mechanical, or mixed, is not sufficient alone to mitigate the risk of aerosol infections. This is due to the internal configuration of the indoor space in terms of (i) size and number of windows, their location and opening frequency, as well as the position of the air extraction and supply inlets, which often induce hotspots with stagnating air, (ii) the excessive required air change rate. Hence, strict reliance on proper hygiene practices, namely adherence to face coverings and hand sanitising, are essential. Consequently, face mask adherence should be emphasized and promoted by policymakers for public health applications. Similar research may need to be conducted using a similar approach on the Omicron (B.1.1.529) variant.
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Affiliation(s)
- Ali Ghoroghi
- School of Engineering, Cardiff University, Cardiff, UK.
| | - Yacine Rezgui
- School of Engineering, Cardiff University, Cardiff, UK
| | - Ruth Wallace
- School of Engineering, Cardiff University, Cardiff, UK
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17
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España G, Cucunubá ZM, Diaz H, Cavany S, Castañeda N, Rodriguez L. Designing school reopening in the COVID-19 pre-vaccination period in Bogotá, Colombia: A modeling study. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000467. [PMID: 36962406 PMCID: PMC10021412 DOI: 10.1371/journal.pgph.0000467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 05/17/2022] [Indexed: 11/18/2022]
Abstract
The COVID-19 pandemic has affected millions of people around the world. In Colombia, 1.65 million cases and 43,495 deaths were reported in 2020. Schools were closed in many places around the world to slow down the spread of SARS-CoV-2. In Bogotá, Colombia, most of the public schools were closed from March 2020 until the end of the year. School closures can exacerbate poverty, particularly in low- and middle-income countries. To reconcile these two priorities in health and fighting poverty, we estimated the impact of school reopening for in-person instruction in 2021. We used an agent-based model of SARS-CoV-2 transmission calibrated to the daily number of deaths. The model includes schools that represent private and public schools in terms of age, enrollment, location, and size. We simulated school reopening at different capacities, assuming a high level of face-mask use, and evaluated the impact on the number of deaths in the city. We also evaluated the impact of reopening schools based on grade and multidimensional poverty index. We found that school at 35% capacity, assuming face-mask adherence at 75% in>8 years of age, had a small impact on the number of deaths reported in the city during a third wave. The increase in deaths was smallest when only pre-kinder was opened, and largest when secondary school was opened. At larger capacities, the impact on the number of deaths of opening pre-kinder was below 10%. In contrast, reopening other grades above 50% capacity substantially increased the number of deaths. Reopening schools based on their multidimensional poverty index resulted in a similar impact, irrespective of the level of poverty of the schools that were reopened. The impact of schools reopening was lower for pre-kinder grades and the magnitude of additional deaths associated with school reopening can be minimized by adjusting capacity in older grades.
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Affiliation(s)
- Guido España
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Zulma M. Cucunubá
- MRC Centre for Global Infectious Disease Analysis, J-IDA, Imperial College London, London, United Kingdom
- Departamento de Epidemiología Clínica y Bioestadística, Pontificia Universidad Javeriana, Bogotá, Colombia
| | | | - Sean Cavany
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, Indiana, United States of America
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Majid F, Gray M, Deshpande AM, Ramakrishnan S, Kumar M, Ehrlich S. Non-Pharmaceutical Interventions as Controls to mitigate the spread of epidemics: An analysis using a spatiotemporal PDE model and COVID-19 data. ISA TRANSACTIONS 2022; 124:215-224. [PMID: 33736890 DOI: 10.1016/j.isatra.2021.02.038] [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: 08/05/2020] [Revised: 02/08/2021] [Accepted: 02/22/2021] [Indexed: 06/12/2023]
Abstract
We investigate the spatiotemporal dynamics and control of an epidemic using a partial differential equation (PDE) based Susceptible-Latent-Infected-Recovered (SLIR) model. We first validate the model using empirical COVID-19 data corresponding to a period of 45 days from the state of Ohio, United States. Upon optimizing the model parameters in the learning phase of the analysis using actual infection data from a period of the first 30 days, we then find that the model output closely tracks the actual data for the next 15 days. Next, we introduce a control input into the model to represent the Non-Pharmaceutical Intervention of social distancing. Implementing the control using two distinct schemes, we find that in both cases the control input is able to significantly mitigate the infection spread. In addition to opening a novel pathway towards the characterization, analysis and implementation of Non-Pharmaceutical Interventions across multiple geographical scales using Control frameworks, our results highlight the importance of first-principles based PDE models in understanding the spatiotemporal dynamics of epidemics triggered by novel pathogens.
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Affiliation(s)
- Faray Majid
- Department of Mechanical and Materials Engineering, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH, USA.
| | - Michael Gray
- Department of Mechanical and Industrial Engineering, University of Minnesota Duluth, Duluth, MN, USA.
| | - Aditya M Deshpande
- Department of Mechanical and Materials Engineering, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH, USA.
| | - Subramanian Ramakrishnan
- Department of Mechanical and Industrial Engineering, University of Minnesota Duluth, Duluth, MN, USA.
| | - Manish Kumar
- Department of Mechanical and Materials Engineering, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH, USA.
| | - Shelley Ehrlich
- Cincinnati Children's Hospital Medical Center, Division of Biostatistics and Epidemiology, University of Cincinnati College of Medicine, OH, USA.
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19
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Krauland MG, Galloway DD, Raviotta JM, Zimmerman RK, Roberts MS. Impact of Low Rates of Influenza on Next-Season Influenza Infections. Am J Prev Med 2022; 62:503-510. [PMID: 35305778 PMCID: PMC8866158 DOI: 10.1016/j.amepre.2021.11.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 11/05/2021] [Accepted: 11/30/2021] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Interventions to curb the spread of COVID-19 during the 2020-2021 influenza season essentially eliminated influenza during that season. Given waning antibody titers over time, future residual population immunity against influenza will be reduced. The implication for the subsequent 2021-2022 influenza season is unknown. METHODS An agent-based model of influenza implemented in the Framework for Reconstructing Epidemiological Dynamics simulation platform was used to estimate cases and hospitalizations over 2 successive influenza seasons. The impact of reduced residual immunity owing to protective measures in the first season was estimated over varying levels of similarity (cross-immunity) between influenza strains over the seasons. RESULTS When cross-immunity between first- and second-season strains was low, a decreased first season had limited impact on second-season cases. High levels of cross-immunity resulted in a greater impact on the second season. This impact was modified by the transmissibility of strains in the 2 seasons. The model estimated a possible increase of 13.52%-46.95% in cases relative to that in a normal season when strains have the same transmissibility and 40%-50% cross-immunity in a season after a very low one. CONCLUSIONS Given the light 2020-2021 influenza season, cases may increase by as much as 50% in 2021-2022, although the increase could be much less, depending on cross-immunity from past infection and transmissibility of strains. Enhanced vaccine coverage or continued interventions to reduce transmission could reduce this high season. Young children may have a higher risk in 2021-2022 owing to limited exposure to infection in the previous year.
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Affiliation(s)
- Mary G Krauland
- Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania; Public Health Dynamics Laboratory, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania.
| | - David D Galloway
- Public Health Dynamics Laboratory, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jonathan M Raviotta
- Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Richard K Zimmerman
- Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Mark S Roberts
- Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania; Public Health Dynamics Laboratory, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
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20
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Wood F, Warrington A, Naderiparizi S, Weilbach C, Masrani V, Harvey W, Ścibior A, Beronov B, Grefenstette J, Campbell D, Nasseri SA. Planning as Inference in Epidemiological Dynamics Models. Front Artif Intell 2022; 4:550603. [PMID: 35434605 PMCID: PMC9009395 DOI: 10.3389/frai.2021.550603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 10/25/2021] [Indexed: 01/10/2023] Open
Abstract
In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models. The kind of inference tasks undertaken include computing the posterior distribution over controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Among other things, we illustrate the use of a probabilistic programming language that automates inference in existing simulators. Neither the full capabilities of this tool for automating inference nor its utility for planning is widely disseminated at the current time. Timely gains in understanding about how such simulation-based models and inference automation tools applied in support of policy-making could lead to less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.
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Affiliation(s)
- Frank Wood
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
- Associate Academic Member and Canada CIFAR AI Chair, Mila Institute, Montreal, QC, Canada
| | - Andrew Warrington
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Saeid Naderiparizi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Christian Weilbach
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Vaden Masrani
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - William Harvey
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Adam Ścibior
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Boyan Beronov
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | | | | | - S. Ali Nasseri
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
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21
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Yang Z, Zhang J, Gao S, Wang H. Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:689. [PMID: 35055511 PMCID: PMC8775888 DOI: 10.3390/ijerph19020689] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/03/2022] [Accepted: 01/05/2022] [Indexed: 12/28/2022]
Abstract
The spread of viruses essentially occurs through the interaction and contact between people, which is closely related to the network of interpersonal relationships. Based on the epidemiological investigations of 1218 COVID-19 cases in eight areas of China, we use text analysis, social network analysis and visualization methods to construct a dynamic contact network of the epidemic. We analyze the corresponding demographic characteristics, network indicators, and structural characteristics of this network. We found that more than 65% of cases are likely to be infected by a strong relationship, and nearly 40% of cases have family members infected at the same time. The overall connectivity of the contact network is low, but there are still some clustered infections. In terms of the degree distribution, most cases' degrees are concentrated between 0 and 2, which is relatively low, and only a few ones have a higher degree value. The degree distribution also conforms to the power law distribution, indicating the network is a scale-free network. There are 17 cases with a degree greater than 10, and these cluster infections are usually caused by local transmission. The first implication of this research is we find that the COVID-19 spread is closely related to social structures by applying computational sociological methods for infectious disease studies; the second implication is to confirm that text analysis can quickly visualize the spread trajectory at the beginning of an epidemic.
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Affiliation(s)
- Zhangbo Yang
- School of Humanities and Social Science, Xi’an Jiaotong University, Xi’an 710049, China;
- Institute for Empirical Social Science Research, Xi’an Jiaotong University, Xi’an 710049, China
| | - Jiahao Zhang
- School of Social Development and Public Policy, Fudan University, Shanghai 200433, China
| | - Shanxing Gao
- School of Management, Xi’an Jiaotong University, Xi’an 710049, China; (S.G.); (H.W.)
| | - Hui Wang
- School of Management, Xi’an Jiaotong University, Xi’an 710049, China; (S.G.); (H.W.)
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22
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Najmi A, Nazari S, Safarighouzhdi F, Miller EJ, MacIntyre R, Rashidi TH. Easing or tightening control strategies: determination of COVID-19 parameters for an agent-based model. TRANSPORTATION 2022; 49:1265-1293. [PMID: 34276105 PMCID: PMC8275455 DOI: 10.1007/s11116-021-10210-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 05/09/2023]
Abstract
Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression. We also show that social distancing and facemask usage are complementary and discuss their interactive effects in detail.
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Affiliation(s)
- Ali Najmi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Sahar Nazari
- School of Engineering, Macquarie University, Sydney, Australia
- School of Chemical Engineering, University of New South Wales, Sydney, NSW Australia
| | - Farshid Safarighouzhdi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Eric J. Miller
- Department of Civil & Mineral Engineering, University of Toronto, 35 St. George Street, Room 305A, Toronto, ON M5S 1A4 Canada
| | - Raina MacIntyre
- Arizona State University College of Health Solutions, Phoenix, AZ USA
- Faculty of Medicine, Kirby Institute, The University of New South Wales, Sydney, NSW Australia
| | - Taha H. Rashidi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
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23
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Artificial Intelligence in Medicine: Modeling the Dynamics of Infectious Diseases. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Ren X, Weisel CP, Georgopoulos PG. Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:11950. [PMID: 34831706 PMCID: PMC8618648 DOI: 10.3390/ijerph182211950] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 10/28/2021] [Accepted: 11/09/2021] [Indexed: 12/12/2022]
Abstract
COVID-19 created an unprecedented global public health crisis during 2020-2021. The severity of the fast-spreading infection, combined with uncertainties regarding the physical and biological processes affecting transmission of SARS-CoV-2, posed enormous challenges to healthcare systems. Pandemic dynamics exhibited complex spatial heterogeneities across multiple scales, as local demographic, socioeconomic, behavioral and environmental factors were modulating population exposures and susceptibilities. Before effective pharmacological interventions became available, controlling exposures to SARS-CoV-2 was the only public health option for mitigating the disease; therefore, models quantifying the impacts of heterogeneities and alternative exposure interventions on COVID-19 outcomes became essential tools informing policy development. This study used a stochastic SEIR framework, modeling each of the 21 New Jersey counties, to capture important heterogeneities of COVID-19 outcomes across the State. The models were calibrated using confirmed daily deaths and SQMC optimization and subsequently applied in predictive and exploratory modes. The predictions achieved good agreement between modeled and reported death data; counterfactual analysis was performed to assess the effectiveness of layered interventions on reducing exposures to SARS-CoV-2 and thereby fatality of COVID-19. The modeling analysis of the reduction in exposures to SARS-CoV-2 achieved through concurrent social distancing and face-mask wearing estimated that 357 [IQR (290, 429)] deaths per 100,000 people were averted.
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Affiliation(s)
- Xiang Ren
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA; (X.R.); (C.P.W.)
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08854, USA
| | - Clifford P. Weisel
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA; (X.R.); (C.P.W.)
- Department of Environmental and Occupational Health and Justice, Rutgers School of Public Health, Piscataway, NJ 08854, USA
- Department of Environmental Sciences, Rutgers University, New Brunswick, NJ 08901, USA
| | - Panos G. Georgopoulos
- Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA; (X.R.); (C.P.W.)
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08854, USA
- Department of Environmental and Occupational Health and Justice, Rutgers School of Public Health, Piscataway, NJ 08854, USA
- Department of Environmental Sciences, Rutgers University, New Brunswick, NJ 08901, USA
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The Residential Population Generator (RPGen): Parameterization of Residential, Demographic, and Physiological Data to Model Intraindividual Exposure, Dose, and Risk. TOXICS 2021; 9:toxics9110303. [PMID: 34822694 PMCID: PMC8625086 DOI: 10.3390/toxics9110303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/03/2021] [Accepted: 11/09/2021] [Indexed: 11/17/2022]
Abstract
Exposure to chemicals is influenced by associations between the individual's location and activities as well as demographic and physiological characteristics. Currently, many exposure models simulate individuals by drawing distributions from population-level data or use exposure factors for single individuals. The Residential Population Generator (RPGen) binds US surveys of individuals and households and combines the population with physiological characteristics to create a synthetic population. In general, the model must be supported by internal consistency; i.e., values that could have come from a single individual. In addition, intraindividual variation must be representative of the variation present in the modeled population. This is performed by linking individuals and similar households across income, location, family type, and house type. Physiological data are generated by linking census data to National Health and Nutrition Examination Survey data with a model of interindividual variation of parameters used in toxicokinetic modeling. The final modeled population data parameters include characteristics of the individual's community (region, state, urban or rural), residence (size of property, size of home, number of rooms), demographics (age, ethnicity, income, gender), and physiology (body weight, skin surface area, breathing rate, cardiac output, blood volume, and volumes for body compartments and organs). RPGen output is used to support user-developed chemical exposure models that estimate intraindividual exposure in a desired population. By creating profiles and characteristics that determine exposure, synthetic populations produced by RPGen increases the ability of modelers to identify subgroups potentially vulnerable to chemical exposures. To demonstrate application, RPGen is used to estimate exposure to Toluene in an exposure modeling case example.
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Latkowski R, Dunin-Kȩplicz B. An Agent-Based Covid-19 Simulator: Extending Covasim to the Polish Context. PROCEDIA COMPUTER SCIENCE 2021; 192:3607-3616. [PMID: 34630753 PMCID: PMC8486257 DOI: 10.1016/j.procs.2021.09.134] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Governments all over the world make their best to fight with Covid-19 pandemic as effectively as possible. Therefore, we observed a growing need of trustworthy data-intensive systems supporting administration in validating their policy decisions. ProMES, the Covasim-based multiagent pandemic simulator, may serve as such a system, adjusted to the specificity of living, working and social conditions in Poland. The main role of ProMES is to evaluate and compare strategies for reducing Covid-19 transmissions. The strategies include time- and region-dependent combinations of nonpharmaceutical coronavirus-related individual and state interventions, tests and vaccinations. Ultimately, ProMES is meant to serve as a part of data/knowledge intensive decision support system, enhancing administrative reactivity as well as pro-activity in preventing the spread of the coronavirus. This paper reports a work in progress.
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Abstract
The study of epidemics is useful for not only understanding outbreaks and trying to limit their adverse effects, but also because epidemics are related to social phenomena such as government instability, crime, poverty, and inequality. One approach for studying epidemics is to simulate their spread through populations. In this work, we describe an integrated multi-dimensional approach to epidemic simulation, which encompasses: (1) a theoretical framework for simulation and analysis; (2) synthetic population (digital twin) generation; (3) (social contact) network construction methods from synthetic populations, (4) stylized network construction methods; and (5) simulation of the evolution of a virus or disease through a social network. We describe these aspects and end with a short discussion on simulation results that inform public policy.
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Decouttere C, De Boeck K, Vandaele N. Advancing sustainable development goals through immunization: a literature review. Global Health 2021; 17:95. [PMID: 34446050 PMCID: PMC8390056 DOI: 10.1186/s12992-021-00745-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 07/23/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Immunization directly impacts health (SDG3) and brings a contribution to 14 out of the 17 Sustainable Development Goals (SDGs), such as ending poverty, reducing hunger, and reducing inequalities. Therefore, immunization is recognized to play a central role in reaching the SDGs, especially in low- and middle-income countries (LMICs). Despite continuous interventions to strengthen immunization systems and to adequately respond to emergency immunization during epidemics, the immunization-related indicators for SDG3 lag behind in sub-Saharan Africa. Especially taking into account the current Covid19 pandemic, the current performance on the connected SDGs is both a cause and a result of this. METHODS We conduct a literature review through a keyword search strategy complemented with handpicking and snowballing from earlier reviews. After title and abstract screening, we conducted a qualitative analysis of key insights and categorized them according to showing the impact of immunization on SDGs, sustainability challenges, and model-based solutions to these challenges. RESULTS We reveal the leveraging mechanisms triggered by immunization and position them vis-à-vis the SDGs, within the framework of Public Health and Planetary Health. Several challenges for sustainable control of vaccine-preventable diseases are identified: access to immunization services, global vaccine availability to LMICs, context-dependent vaccine effectiveness, safe and affordable vaccines, local/regional vaccine production, public-private partnerships, and immunization capacity/capability building. Model-based approaches that support SDG-promoting interventions concerning immunization systems are analyzed in light of the strategic priorities of the Immunization Agenda 2030. CONCLUSIONS In general terms, it can be concluded that relevant future research requires (i) design for system resilience, (ii) transdisciplinary modeling, (iii) connecting interventions in immunization with SDG outcomes, (iv) designing interventions and their implementation simultaneously, (v) offering tailored solutions, and (vi) model coordination and integration of services and partnerships. The research and health community is called upon to join forces to activate existing knowledge, generate new insights and develop decision-supporting tools for Low-and Middle-Income Countries' health authorities and communities to leverage immunization in its transformational role toward successfully meeting the SDGs in 2030.
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Affiliation(s)
- Catherine Decouttere
- KU Leuven, Access-To-Medicines research Center, Naamsestraat 69, Leuven, Belgium
| | - Kim De Boeck
- KU Leuven, Access-To-Medicines research Center, Naamsestraat 69, Leuven, Belgium
| | - Nico Vandaele
- KU Leuven, Access-To-Medicines research Center, Naamsestraat 69, Leuven, Belgium
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España G, Cavany S, Oidtman R, Barbera C, Costello A, Lerch A, Poterek M, Tran Q, Wieler A, Moore S, Perkins TA. Impacts of K-12 school reopening on the COVID-19 epidemic in Indiana, USA. Epidemics 2021; 37:100487. [PMID: 34425301 PMCID: PMC8366048 DOI: 10.1016/j.epidem.2021.100487] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 04/26/2021] [Accepted: 08/13/2021] [Indexed: 12/21/2022] Open
Abstract
In the United States, schools closed in March 2020 due to COVID-19 and began reopening in August 2020, despite continuing transmission of SARS-CoV-2. In states where in-person instruction resumed at that time, two major unknowns were the capacity at which schools would operate, which depended on the proportion of families opting for remote instruction, and adherence to face-mask requirements in schools, which depended on cooperation from students and enforcement by schools. To determine the impact of these conditions on the statewide burden of COVID-19 in Indiana, we used an agent-based model calibrated to and validated against multiple data types. Using this model, we quantified the burden of COVID-19 on K-12 students, teachers, their families, and the general population under alternative scenarios spanning three levels of school operating capacity (50 %, 75 %, and 100 %) and three levels of face-mask adherence in schools (50 %, 75 %, and 100 %). Under a scenario in which schools operated remotely, we projected 45,579 (95 % CrI: 14,109–132,546) infections and 790 (95 % CrI: 176–1680) deaths statewide between August 24 and December 31. Reopening at 100 % capacity with 50 % face-mask adherence in schools resulted in a proportional increase of 42.9 (95 % CrI: 41.3–44.3) and 9.2 (95 % CrI: 8.9–9.5) times that number of infections and deaths, respectively. In contrast, our results showed that at 50 % capacity with 100 % face-mask adherence, the number of infections and deaths were 22 % (95 % CrI: 16 %–28 %) and 11 % (95 % CrI: 5 %–18 %) higher than the scenario in which schools operated remotely. Within this range of possibilities, we found that high levels of school operating capacity (80–95 %) and intermediate levels of face-mask adherence (40–70 %) resulted in model behavior most consistent with observed data. Together, these results underscore the importance of precautions taken in schools for the benefit of their communities.
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Affiliation(s)
- Guido España
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, United States.
| | - Sean Cavany
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, United States
| | - Rachel Oidtman
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, United States
| | - Carly Barbera
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, United States
| | - Alan Costello
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, United States
| | - Anita Lerch
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, United States
| | - Marya Poterek
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, United States
| | - Quan Tran
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, United States
| | - Annaliese Wieler
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, United States
| | - Sean Moore
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, United States
| | - T Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, United States.
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Abstract
Pandemics have presented new challenges for public transport organisers and operators. New diseases (e.g., influenza H1N1, severe acute respiratory syndrome—SARS, as well as, more recently, SARS-CoV-2) increase the need for new protection measures to prevent epidemic outbreaks in public transport infrastructure. The authors’ goal is to present a set of actions in the area of public transport that are adjusted to different levels of epidemic development. The goal goes back to the following question: how can the highest possible level of passenger safety be ensured and the losses suffered by urban public transport companies kept as low as possible? The sets of pro-active measures for selected epidemic scenarios presented in the article may offer support to local authorities and public transport operators. In the next steps, it is important to develop and implement tools for public transport management to ensure safety and tackle epidemic hazards.
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Sobkowicz P, Sobkowicz A. Agent Based Model of Anti-Vaccination Movements: Simulations and Comparison with Empirical Data. Vaccines (Basel) 2021; 9:809. [PMID: 34451934 PMCID: PMC8402338 DOI: 10.3390/vaccines9080809] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/12/2021] [Accepted: 07/13/2021] [Indexed: 01/21/2023] Open
Abstract
Background: A realistic description of the social processes leading to the increasing reluctance to various forms of vaccination is a very challenging task. This is due to the complexity of the psychological and social mechanisms determining the positioning of individuals and groups against vaccination and associated activities. Understanding the role played by social media and the Internet in the current spread of the anti-vaccination (AV) movement is of crucial importance. Methods: We present novel, long-term Big Data analyses of Internet activity connected with the AV movement for such different societies as the US and Poland. The datasets we analyzed cover multiyear periods preceding the COVID-19 pandemic, documenting the behavior of vaccine related Internet activity with high temporal resolution. To understand the empirical observations, in particular the mechanism driving the peaks of AV activity, we propose an Agent Based Model (ABM) of the AV movement. The model includes the interplay between multiple driving factors: contacts with medical practitioners and public vaccination campaigns, interpersonal communication, and the influence of the infosphere (social networks, WEB pages, user comments, etc.). The model takes into account the difference between the rational approach of the pro-vaccination information providers and the largely emotional appeal of anti-vaccination propaganda. Results: The datasets studied show the presence of short-lived, high intensity activity peaks, much higher than the low activity background. The peaks are seemingly random in size and time separation. Such behavior strongly suggests a nonlinear nature for the social interactions driving the AV movement instead of the slow, gradual growth typical of linear processes. The ABM simulations reproduce the observed temporal behavior of the AV interest very closely. For a range of parameters, the simulations result in a relatively small fraction of people refusing vaccination, but a slight change in critical parameters (such as willingness to post anti-vaccination information) may lead to a catastrophic breakdown of vaccination support in the model society, due to nonlinear feedback effects. The model allows the effectiveness of strategies combating the anti-vaccination movement to be studied. An increase in intensity of standard pro-vaccination communications by government agencies and medical personnel is found to have little effect. On the other hand, focused campaigns using the Internet and social media and copying the highly emotional and narrative-focused format used by the anti-vaccination activists can diminish the AV influence. Similar effects result from censoring and taking down anti-vaccination communications by social media platforms. The benefit of such tactics might, however, be offset by their social cost, for example, the increased polarization and potential to exploit it for political goals, or increased 'persecution' and 'martyrdom' tropes.
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Affiliation(s)
- Pawel Sobkowicz
- NOMATEN Centre of Excellence, National Centre for Nuclear Resarch, 05-400 Otwock-Świerk, Poland
| | - Antoni Sobkowicz
- National Information Processing Institute OPI, 00-608 Warsaw, Poland;
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Najmi A, Nazari S, Safarighouzhdi F, Miller EJ, MacIntyre R, Rashidi TH. Easing or tightening control strategies: determination of COVID-19 parameters for an agent-based model. TRANSPORTATION 2021; 49:1265-1293. [PMID: 34276105 DOI: 10.1101/2020.06.20.20135186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 05/28/2023]
Abstract
Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression. We also show that social distancing and facemask usage are complementary and discuss their interactive effects in detail.
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Affiliation(s)
- Ali Najmi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Sahar Nazari
- School of Engineering, Macquarie University, Sydney, Australia
- School of Chemical Engineering, University of New South Wales, Sydney, NSW Australia
| | - Farshid Safarighouzhdi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
| | - Eric J Miller
- Department of Civil & Mineral Engineering, University of Toronto, 35 St. George Street, Room 305A, Toronto, ON M5S 1A4 Canada
| | - Raina MacIntyre
- Arizona State University College of Health Solutions, Phoenix, AZ USA
- Faculty of Medicine, Kirby Institute, The University of New South Wales, Sydney, NSW Australia
| | - Taha H Rashidi
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
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McQuaid CF, Vassall A, Cohen T, Fiekert K, White RG. The impact of COVID-19 on TB: a review of the data. Int J Tuberc Lung Dis 2021; 25:436-446. [PMID: 34049605 PMCID: PMC8171247 DOI: 10.5588/ijtld.21.0148] [Citation(s) in RCA: 147] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 03/23/2021] [Indexed: 12/12/2022] Open
Abstract
Early in the COVID-19 pandemic, models predicted hundreds of thousands of additional TB deaths as a result of health service disruption. To date, empirical evidence on the effects of COVID-19 on TB outcomes has been limited. Here we summarise the evidence available at a country level, identifying broad mechanisms by which COVID-19 may modify TB burden and mitigation efforts. From the data, it is clear that there have been substantial disruptions to TB health services and an increase in vulnerability to TB. Evidence for changes in Mycobacterium tuberculosis transmission is limited, and it remains unclear how the resources required and available for the TB response have changed. To advocate for additional funding to mitigate the impact of COVID-19 on the global TB burden, and to efficiently allocate resources for the TB response, requires a significant improvement in the TB data available.
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Affiliation(s)
- C F McQuaid
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine (LSHTM), London, UK
| | - A Vassall
- Department of Global Health Development, Faculty of Public Health and Policy, LSHTM, London, UK
| | - T Cohen
- Yale School of Public Health, Laboratory of Epidemiology and Public Health, New Haven, CT, USA
| | - K Fiekert
- KNCV Tuberculosefonds, The Hague, the Netherlands
| | - R G White
- TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine (LSHTM), London, UK
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Lee S, Zabinsky ZB, Wasserheit JN, Kofsky SM, Liu S. COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society. Med Decis Making 2021; 41:419-429. [PMID: 33733933 DOI: 10.1177/0272989x211003081] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.
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Affiliation(s)
- Serin Lee
- Department of Industrial & Systems Engineering, University of Washington, Seattle, WA, USA
| | - Zelda B Zabinsky
- Department of Industrial & Systems Engineering, University of Washington, Seattle, WA, USA
| | - Judith N Wasserheit
- Department of Global Health, Department of Allergy and Infectious Diseases, University of Washington, Seattle, WA, USA
| | | | - Shan Liu
- Department of Industrial & Systems Engineering, University of Washington, Seattle, WA, USA
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Hunter E, Kelleher JD. Using a hybrid agent-based and equation based model to test school closure policies during a measles outbreak. BMC Public Health 2021; 21:499. [PMID: 33711969 PMCID: PMC7953375 DOI: 10.1186/s12889-021-10513-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 02/26/2021] [Indexed: 11/29/2022] Open
Abstract
Background In order to be prepared for an infectious disease outbreak it is important to know what interventions will or will not have an impact on reducing the outbreak. While some interventions might have a greater effect in mitigating an outbreak, others might only have a minor effect but all interventions will have a cost in implementation. Estimating the effectiveness of an intervention can be done using computational modelling. In particular, comparing the results of model runs with an intervention in place to control runs where no interventions were used can help to determine what interventions will have the greatest effect on an outbreak. Methods To test the effects of a school closure policy on the spread of an infectious disease (in this case measles) we run simulations closing schools based on either the proximity of the town to the initial outbreak or the centrality of the town within the network of towns in the simulation. To do this we use a hybrid model that combines an agent-based model with an equation-based model. In our analysis, we use three measures to compare the effects of different intervention strategies: the total number of model runs leading to an outbreak, the total number of infected agents, and the geographic spread of outbreaks. Results Our results show that closing down the schools in the town where an outbreak begins and the town with the highest in degree centrality provides the largest reduction in percent of runs leading to an outbreak as well as a reduction in the geographic spread of the outbreak compared to only closing down the town where the outbreak begins. Although closing down schools in the town with the closest proximity to the town where the outbreak begins also provides a reduction in the chance of an outbreak, we do not find the reduction to be as large as when the schools in the high in degree centrality town are closed. Conclusions Thus we believe that focusing on high in degree centrality towns during an outbreak is important in reducing the overall size of an outbreak.
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Affiliation(s)
| | - John D Kelleher
- Technological University Dublin, Grangegorman, Dublin, Ireland
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Machi D, Bhattacharya P, Hoops S, Chen J, Mortveit H, Venkatramanan S, Lewis B, Wilson M, Fadikar A, Maiden T, Barrett CL, Marathe MV. Scalable Epidemiological Workflows to Support COVID-19 Planning and Response. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.02.23.21252325. [PMID: 33655263 PMCID: PMC7924288 DOI: 10.1101/2021.02.23.21252325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The COVID-19 global outbreak represents the most significant epidemic event since the 1918 influenza pandemic. Simulations have played a crucial role in supporting COVID-19 planning and response efforts. Developing scalable workflows to provide policymakers quick responses to important questions pertaining to logistics, resource allocation, epidemic forecasts and intervention analysis remains a challenging computational problem. In this work, we present scalable high performance computing-enabled workflows for COVID-19 pandemic planning and response. The scalability of our methodology allows us to run fine-grained simulations daily, and to generate county-level forecasts and other counter-factual analysis for each of the 50 states (and DC), 3140 counties across the USA. Our workflows use a hybrid cloud/cluster system utilizing a combination of local and remote cluster computing facilities, and using over 20,000 CPU cores running for 6-9 hours every day to meet this objective. Our state (Virginia), state hospital network, our university, the DOD and the CDC use our models to guide their COVID-19 planning and response efforts. We began executing these pipelines March 25, 2020, and have delivered and briefed weekly updates to these stakeholders for over 30 weeks without interruption.
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Koshy-Chenthittayil S, Mendes P, Laubenbacher R. Optimization of Agent-Based Models Through Coarse-Graining: A Case Study in Microbial Ecology. LETTERS IN BIOMATHEMATICS 2021; 8:167-178. [PMID: 36590333 PMCID: PMC9802647 DOI: 10.30707/lib8.1.1647878866.083342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Optimization and control are important objectives across biology and biomedicine, and mathematical models are a key enabling technology. This paper reports a computational study of model-based multi-objective optimization in the setting of microbial ecology, using agent-based models. This modeling framework is well-suited to the field, but is not amenable to standard control-theoretic approaches. Furthermore, due to computational complexity, simulation-based optimization approaches are often challenging to implement. This paper presents the results of an approach that combines control-dependent coarse-graining with Pareto optimization, applied to two models of multi-species bacterial biofilms. It shows that this approach can be successful for models whose computational complexity prevents effective simulation-based optimization.
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Affiliation(s)
| | - Pedro Mendes
- Center for Quantitative Medicine and Center for Cell Analysis and Modeling, University of Connecticut Health Center
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Data Needs in Opioid Systems Modeling: Challenges and Future Directions. Am J Prev Med 2021; 60:e95-e105. [PMID: 33272714 PMCID: PMC8061725 DOI: 10.1016/j.amepre.2020.08.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 08/05/2020] [Accepted: 08/17/2020] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The opioid crisis is a pervasive public health threat in the U.S. Simulation modeling approaches that integrate a systems perspective are used to understand the complexity of this crisis and analyze what policy interventions can best address it. However, limitations in currently available data sources can hamper the quantification of these models. METHODS To understand and discuss data needs and challenges for opioid systems modeling, a meeting of federal partners, modeling teams, and data experts was held at the U.S. Food and Drug Administration in April 2019. This paper synthesizes the meeting discussions and interprets them in the context of ongoing simulation modeling work. RESULTS The current landscape of national-level quantitative data sources of potential use in opioid systems modeling is identified, and significant issues within data sources are discussed. Major recommendations on how to improve data sources are to: maintain close collaboration among modeling teams, enhance data collection to better fit modeling needs, focus on bridging the most crucial information gaps, engage in direct and regular interaction between modelers and data experts, and gain a clearer definition of policymakers' research questions and policy goals. CONCLUSIONS This article provides an important step in identifying and discussing data challenges in opioid research generally and opioid systems modeling specifically. It also identifies opportunities for systems modelers and government agencies to improve opioid systems models.
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Mistry D, Litvinova M, Pastore Y Piontti A, Chinazzi M, Fumanelli L, Gomes MFC, Haque SA, Liu QH, Mu K, Xiong X, Halloran ME, Longini IM, Merler S, Ajelli M, Vespignani A. Inferring high-resolution human mixing patterns for disease modeling. Nat Commun 2021; 12:323. [PMID: 33436609 PMCID: PMC7803761 DOI: 10.1038/s41467-020-20544-y] [Citation(s) in RCA: 112] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 12/08/2020] [Indexed: 01/29/2023] Open
Abstract
Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.
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Affiliation(s)
- Dina Mistry
- Institute for Disease Modeling, Global Health Division, Bill and Melinda Gates Foundation, Seattle, WA, USA
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Maria Litvinova
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
- ISI Foundation, Turin, Italy
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Ana Pastore Y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | | | - Marcelo F C Gomes
- Fiocruz, Scientific Computing Program, Grupo de Métodos Analíticos em Vigilância Epidemiológica, Rio de Janeiro, Brazil
| | - Syed A Haque
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Quan-Hui Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Kunpeng Mu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Xinyue Xiong
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - M Elizabeth Halloran
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Ira M Longini
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | | | - Marco Ajelli
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
- ISI Foundation, Turin, Italy.
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Artificial Intelligence in Medicine: Modeling the Dynamics of Infectious Diseases. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_317-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Renardy M, Eisenberg M, Kirschner D. Predicting the second wave of COVID-19 in Washtenaw County, MI. J Theor Biol 2020; 507:110461. [PMID: 32866493 PMCID: PMC7455546 DOI: 10.1016/j.jtbi.2020.110461] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/10/2020] [Accepted: 08/24/2020] [Indexed: 01/11/2023]
Abstract
The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, which is home to University of Michigan and Eastern Michigan University, and in close proximity to Detroit, MI, a major epicenter of the epidemic in Michigan. We apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact networks based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons or long term care facilities). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases of COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. We find that delaying reopening does not reduce the magnitude of the second peak of cases, but only delays it. Reducing levels of casual contact, on the other hand, both delays and lowers the second peak. Through simulations and sensitivity analyses, we explore mechanisms driving the magnitude and timing of a second wave of infections upon re-opening. We find that the most significant factors are workplace and casual contacts and protective measures taken by infected individuals who have sought care. This model can be adapted to other US counties using synthetic population databases and data specific to those regions.
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Mathematical Models for COVID-19 Pandemic: A Comparative Analysis. J Indian Inst Sci 2020; 100:793-807. [PMID: 33144763 PMCID: PMC7596173 DOI: 10.1007/s41745-020-00200-6] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 09/14/2020] [Indexed: 12/17/2022]
Abstract
COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years. Its economic, social and health impact continues to grow and is likely to end up as one of the worst global disasters since the 1918 pandemic and the World Wars. Mathematical models have played an important role in the ongoing crisis; they have been used to inform public policies and have been instrumental in many of the social distancing measures that were instituted worldwide. In this article, we review some of the important mathematical models used to support the ongoing planning and response efforts. These models differ in their use, their mathematical form and their scope.
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Adiga A, Dubhashi D, Lewis B, Marathe M, Venkatramanan S, Vullikanti A. Models for COVID-19 Pandemic: A Comparative Analysis. ARXIV 2020:arXiv:2009.10014v1. [PMID: 32995366 PMCID: PMC7523122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Abstract
COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years. Its economic, social and health impact continues to grow and is likely to end up as one of the worst global disasters since the 1918 pandemic and the World Wars. Mathematical models have played an important role in the ongoing crisis; they have been used to inform public policies and have been instrumental in many of the social distancing measures that were instituted worldwide. In this article we review some of the important mathematical models used to support the ongoing planning and response efforts. These models differ in their use, their mathematical form and their scope.
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Affiliation(s)
- Aniruddha Adiga
- BIOCOMPLEXITY INSTITUTE AND INITITIATIVE, UNIVERSITY OF VIRGINIA
| | - Devdatt Dubhashi
- DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, CHALMERS UNIVERSITY
| | - Bryan Lewis
- BIOCOMPLEXITY INSTITUTE AND INITITIATIVE, UNIVERSITY OF VIRGINIA
| | - Madhav Marathe
- BIOCOMPLEXITY INSTITUTE AND INITITIATIVE, UNIVERSITY OF VIRGINIA
- DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF VIRGINIA
| | | | - Anil Vullikanti
- BIOCOMPLEXITY INSTITUTE AND INITITIATIVE, UNIVERSITY OF VIRGINIA
- DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF VIRGINIA
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Krauland MG, Frankeny RJ, Lewis J, Brink L, Hulsey EG, Roberts MS, Hacker KA. Development of a Synthetic Population Model for Assessing Excess Risk for Cardiovascular Disease Death. JAMA Netw Open 2020; 3:e2015047. [PMID: 32870312 PMCID: PMC7489828 DOI: 10.1001/jamanetworkopen.2020.15047] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 06/10/2020] [Indexed: 12/26/2022] Open
Abstract
Importance Evaluating the association of social determinants of health with chronic diseases at the population level requires access to individual-level factors associated with disease, which are rarely available for large populations. Synthetic populations are a possible alternative for this purpose. Objective To construct and validate a synthetic population that statistically mimics the characteristics and spatial disease distribution of a real population, using real and synthetic data. Design, Setting, and Participants This population-based decision analytical model used data for Allegheny County, Pennsylvania, collected from January 2015 to December 2016, to build a semisynthetic population based on the synthetic population used by the modeling and simulation platform FRED (A Framework for Reconstructing Epidemiological Dynamics). Disease status was assigned to this population using health insurer claims data from the 3 major insurance providers in the county or from the National Health and Nutrition Examination Survey. Biological, social, and other variables were also obtained from the National Health Interview Survey, Allegheny County, and public databases. Data analysis was performed from November 2016 to February 2020. Exposures Risk of cardiovascular disease (CVD) death. Main Outcomes and Measures Difference between expected and observed CVD death risk. A validated risk equation was used to estimate CVD death risk. Results The synthetic population comprised 1 188 112 individuals with demographic characteristics similar to those of the 2010 census population in the same county. In the synthetic population, the mean (SD) age was 40.6 (23.3) years, and 622 997 were female individuals (52.4%). Mean (SD) observed 4-year rate of excess CVD death risk at the census tract level was -40 (523) per 100 000 persons. The correlation of social determinant data with difference between expected and observed CVD death risk indicated that income- and education-based social determinants were associated with risk. Estimating improved social determinants of health and biological factors associated with disease did not entirely remove the excess in CVD death rates. That is, a 20% improvement in the most significant determinants still resulted in 105 census tracts with excess CVD death risk, which represented 24% of the county population. Conclusions and Relevance The results of this study suggest that creating a geographically explicit synthetic population from real and synthetic data is feasible and that synthetic populations are useful for modeling disease in large populations and for estimating the outcome of interventions.
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Affiliation(s)
- Mary G. Krauland
- Department of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania
- Public Health Dynamics Laboratory, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania
| | - Robert J. Frankeny
- Public Health Dynamics Laboratory, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania
| | - Josh Lewis
- Allegheny County Department of Health, Pittsburgh, Pennsylvania
| | - LuAnn Brink
- Allegheny County Department of Health, Pittsburgh, Pennsylvania
| | - Eric G. Hulsey
- Allegheny County Department of Health, Pittsburgh, Pennsylvania
| | - Mark S. Roberts
- Department of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania
- Public Health Dynamics Laboratory, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania
| | - Karen A. Hacker
- Department of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania
- Allegheny County Department of Health, Pittsburgh, Pennsylvania
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Brennan A, Buckley C, Vu TM, Probst C, Nielsen A, Bai H, Broomhead T, Greenfield T, Kerr W, Meier PS, Rehm J, Shuper P, Strong M, Purshouse RC. Introducing CASCADEPOP: an open-source sociodemographic simulation platform for us health policy appraisal. INTERNATIONAL JOURNAL OF MICROSIMULATION 2020; 13:21-60. [PMID: 33884027 DOI: 10.34196/ijm.00217] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Largescale individual-level and agent-based models are gaining importance in health policy appraisal and evaluation. Such models require the accurate depiction of the jurisdiction's population over extended time periods to enable modeling of the development of non-communicable diseases under consideration of historical, sociodemographic developments. We developed CASCADEPOP to provide a readily available sociodemographic micro-synthesis and microsimulation platform for US populations. The micro-synthesis method used iterative proportional fitting to integrate data from the US Census, the American Community Survey, the Panel Study of Income Dynamics, Multiple Cause of Death Files, and several national surveys to produce a synthetic population aged 12 to 80 years on 01/01/1980 for five states (California, Minnesota, New York, Tennessee, and Texas) and the US. Characteristics include individuals' age, sex, race/ethnicity, marital/employment/parental status, education, income and patterns of alcohol use as an exemplar health behavior. The microsimulation simulates individuals' sociodemographic life trajectories over 35 years to 31/12/2015 accounting for population developments including births, deaths, and migration. Results comparing the 1980 micro-synthesis against observed data shows a successful depiction of state and US population characteristics and of drinking. Comparing the microsimulation over 30 years with Census data also showed the successful simulation of sociodemographic developments. The CASCADEPOP platform enables modelling of health behaviors across individuals' life courses and at a population level. As it contains a large number of relevant sociodemographic characteristics it can be further developed by researchers to build US agent-based models and microsimulations to examine health behaviors, interventions, and policies.
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Affiliation(s)
- Alan Brennan
- School of Health and Related Research, University of Sheffield (ScHARR), 30 Regent Street, Sheffield, S1 4DA, UK
| | - Charlotte Buckley
- Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK
| | - Tuong Manh Vu
- School of Health and Related Research, University of Sheffield (ScHARR), 30 Regent Street, Sheffield, S1 4DA, UK
| | - Charlotte Probst
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health (CAMH), 33 Russell Street, Toronto, On M5S 2S1, Canada
| | - Alexandra Nielsen
- Alcohol Research Group (ARG), Public Health Institute, 6001 Shellmound St, Emeryville, CA, 94608, USA
| | - Hao Bai
- Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK
| | - Thomas Broomhead
- School of Clinical Dentistry, University of Sheffield, 19 Claremont Crescent, Sheffield, S10 2TA, UK
| | - Thomas Greenfield
- Alcohol Research Group (ARG), Public Health Institute, 6001 Shellmound St, Emeryville, CA, 94608, USA
| | - William Kerr
- Alcohol Research Group (ARG), Public Health Institute, 6001 Shellmound St, Emeryville, CA, 94608, USA
| | - Petra S Meier
- School of Health and Related Research; University of Sheffield (ScHARR), 30 Regent Street, Sheffield, S1 4DA, UK
| | - JüRgen Rehm
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health (CAMH), 33 Russell Street, Toronto, ON M5S 2S1, Canada
| | - Paul Shuper
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health (CAMH), 33 Russell Street, Toronto, ON M5S 2S1, Canada
| | - Mark Strong
- School of Health and Related Research; University of Sheffield (ScHARR), 30 Regent Street, Sheffield, S1 4DA, UK
| | - Robin C Purshouse
- Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK
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Renardy M, Kirschner D. Predicting the second wave of COVID-19 in Washtenaw County, MI. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.07.06.20147223. [PMID: 32676613 PMCID: PMC7359538 DOI: 10.1101/2020.07.06.20147223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Marissa Renardy and Denise Kirschner University of Michigan Medical School The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. These heteroge- neous patterns are also observed within individual states, with patches of concentrated outbreaks. Data is being generated daily at all of these spatial scales, and answers to questions regarded re- opening strategies are desperately needed. Mathematical modeling is useful in exactly these cases, and using modeling at a county scale may be valuable to further predict disease dynamics for the purposes of public health interventions. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, the home to University of Michigan, Eastern Michigan University, and Google, as well as serving as a sister city to Detroit, MI where there has been a serious outbreak. Here, we apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact net- works based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We also perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases on COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. Through simulations and sensitivity analyses, we explore mechanisms driving magnitude and timing of a second wave of infections upon re-opening. This model can be adapted to other US counties using synthetic population databases and data specific to those regions.
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Renardy M, Kirschner DE. A Framework for Network-Based Epidemiological Modeling of Tuberculosis Dynamics Using Synthetic Datasets. Bull Math Biol 2020; 82:78. [PMID: 32535697 DOI: 10.1007/s11538-020-00752-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 05/25/2020] [Indexed: 11/28/2022]
Abstract
We present a framework for discrete network-based modeling of TB epidemiology in US counties using publicly available synthetic datasets. We explore the dynamics of this modeling framework by simulating the hypothetical spread of disease over 2 years resulting from a single active infection in Washtenaw County, MI. We find that for sufficiently large transmission rates that active transmission outweighs reactivation, disease prevalence is sensitive to the contact weight assigned to transmissions between casual contacts (that is, contacts that do not share a household, workplace, school, or group quarter). Workplace and casual contacts contribute most to active disease transmission, while household, school, and group quarter contacts contribute relatively little. Stochastic features of the model result in significant uncertainty in the predicted number of infections over time, leading to challenges in model calibration and interpretation of model-based predictions. Finally, predicted infections were more localized by household location than would be expected if they were randomly distributed. This modeling framework can be refined in later work to study specific county and multi-county TB epidemics in the USA.
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Affiliation(s)
- Marissa Renardy
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Denise E Kirschner
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA.
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Worden L, Wannier R, Blumberg S, Ge AY, Rutherford GW, Porco TC. Estimation of effects of contact tracing and mask adoption on COVID-19 transmission in San Francisco: a modeling study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.06.09.20125831. [PMID: 32577672 PMCID: PMC7302226 DOI: 10.1101/2020.06.09.20125831] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The current COVID-19 pandemic has spurred concern about what interventions may be effective at reducing transmission. The city and county of San Francisco imposed a shelter-in-place order in March 2020, followed by use of a contact tracing program and a policy requiring use of cloth face masks. We used statistical estimation and simulation to estimate the effectiveness of these interventions in San Francisco. We estimated that self-isolation and other practices beginning at the time of San Francisco's shelter-in-place order reduced the effective reproduction number of COVID-19 by 35.4% (95% CI, -20.1%-81.4%). We estimated the effect of contact tracing on the effective reproduction number to be a reduction of approximately 44% times the fraction of cases that are detected, which may be modest if the detection rate is low. We estimated the impact of cloth mask adoption on reproduction number to be approximately 8.6%, and note that the benefit of mask adoption may be substantially greater for essential workers and other vulnerable populations, residents return to circulating outside the home more often. We estimated the effect of those interventions on incidence by simulating counterfactual scenarios in which contact tracing was not adopted, cloth masks were not adopted, and neither contact tracing nor cloth masks was adopted, and found increases in case counts that were modest, but relatively larger than the effects on reproduction numbers. These estimates and model results suggest that testing coverage and timing of testing and contact tracing may be important, and that modest effects on reproduction numbers can nonetheless cause substantial effects on case counts over time.
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Affiliation(s)
- Lee Worden
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
| | - Rae Wannier
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Seth Blumberg
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
| | - Alex Y Ge
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - George W Rutherford
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Travis C Porco
- Francis I. Proctor Foundation, University of California, San Francisco, CA, USA
- Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
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Rhea S, Hilscher R, Rineer JI, Munoz B, Jones K, Endres-Dighe SM, DiBiase LM, Sickbert-Bennett EE, Weber DJ, MacFarquhar JK, Dubendris H, Bobashev G. Creation of a Geospatially Explicit, Agent-based Model of a Regional Healthcare Network with Application to Clostridioides difficile Infection. Health Secur 2020; 17:276-290. [PMID: 31433281 DOI: 10.1089/hs.2019.0021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Agent-based models (ABMs) describe and simulate complex systems comprising unique agents, or individuals, while accounting for geospatial and temporal variability among dynamic processes. ABMs are increasingly used to study healthcare-associated infections (ie, infections acquired during admission to a healthcare facility), including Clostridioides difficile infection, currently the most common healthcare-associated infection in the United States. The overall burden and transmission dynamics of healthcare-associated infections, including C difficile infection, may be influenced by community sources and movement of people among healthcare facilities and communities. These complex dynamics warrant geospatially explicit ABMs that extend beyond single healthcare facilities to include entire systems (eg, hospitals, nursing homes and extended care facilities, the community). The agents in ABMs can be built on a synthetic population, a model-generated representation of the actual population with associated spatial (eg, home residence), temporal (eg, change in location over time), and nonspatial (eg, sociodemographic features) attributes. We describe our methods to create a geospatially explicit ABM of a major regional healthcare network using a synthetic population as microdata input. We illustrate agent movement in the healthcare network and the community, informed by patient-level medical records, aggregate hospital discharge data, healthcare facility licensing data, and published literature. We apply the ABM output to visualize agent movement in the healthcare network and the community served by the network. We provide an application example of the ABM to C difficile infection using a natural history submodel. We discuss the ABM's potential to detect network areas where disease risk is high; simulate and evaluate interventions to protect public health; adapt to other geographic locations and healthcare-associated infections, including emerging pathogens; and meaningfully translate results to public health practitioners, healthcare providers, and policymakers.
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Affiliation(s)
- Sarah Rhea
- Sarah Rhea, DVM, PhD, is a Research Epidemiologist, Center for Applied Public Health Research; Rainer Hilscher, PhD, is a Research Data Scientist, Center for Data Science; James I. Rineer, MS, is Director, Geospatial Science and Technology; Breda Munoz, PhD, is a Research Statistician, Center for Applied Public Health Research; Kasey Jones, MS, is a Research Data Scientist, Center for Data Science; and Stacy M. Endres-Dighe, MPH, is a Research Epidemiologist, Center for Applied Public Health Research; all at RTI International, Research Triangle Park, NC
| | - Rainer Hilscher
- Sarah Rhea, DVM, PhD, is a Research Epidemiologist, Center for Applied Public Health Research; Rainer Hilscher, PhD, is a Research Data Scientist, Center for Data Science; James I. Rineer, MS, is Director, Geospatial Science and Technology; Breda Munoz, PhD, is a Research Statistician, Center for Applied Public Health Research; Kasey Jones, MS, is a Research Data Scientist, Center for Data Science; and Stacy M. Endres-Dighe, MPH, is a Research Epidemiologist, Center for Applied Public Health Research; all at RTI International, Research Triangle Park, NC
| | - James I Rineer
- Sarah Rhea, DVM, PhD, is a Research Epidemiologist, Center for Applied Public Health Research; Rainer Hilscher, PhD, is a Research Data Scientist, Center for Data Science; James I. Rineer, MS, is Director, Geospatial Science and Technology; Breda Munoz, PhD, is a Research Statistician, Center for Applied Public Health Research; Kasey Jones, MS, is a Research Data Scientist, Center for Data Science; and Stacy M. Endres-Dighe, MPH, is a Research Epidemiologist, Center for Applied Public Health Research; all at RTI International, Research Triangle Park, NC
| | - Breda Munoz
- Sarah Rhea, DVM, PhD, is a Research Epidemiologist, Center for Applied Public Health Research; Rainer Hilscher, PhD, is a Research Data Scientist, Center for Data Science; James I. Rineer, MS, is Director, Geospatial Science and Technology; Breda Munoz, PhD, is a Research Statistician, Center for Applied Public Health Research; Kasey Jones, MS, is a Research Data Scientist, Center for Data Science; and Stacy M. Endres-Dighe, MPH, is a Research Epidemiologist, Center for Applied Public Health Research; all at RTI International, Research Triangle Park, NC
| | - Kasey Jones
- Sarah Rhea, DVM, PhD, is a Research Epidemiologist, Center for Applied Public Health Research; Rainer Hilscher, PhD, is a Research Data Scientist, Center for Data Science; James I. Rineer, MS, is Director, Geospatial Science and Technology; Breda Munoz, PhD, is a Research Statistician, Center for Applied Public Health Research; Kasey Jones, MS, is a Research Data Scientist, Center for Data Science; and Stacy M. Endres-Dighe, MPH, is a Research Epidemiologist, Center for Applied Public Health Research; all at RTI International, Research Triangle Park, NC
| | - Stacy M Endres-Dighe
- Sarah Rhea, DVM, PhD, is a Research Epidemiologist, Center for Applied Public Health Research; Rainer Hilscher, PhD, is a Research Data Scientist, Center for Data Science; James I. Rineer, MS, is Director, Geospatial Science and Technology; Breda Munoz, PhD, is a Research Statistician, Center for Applied Public Health Research; Kasey Jones, MS, is a Research Data Scientist, Center for Data Science; and Stacy M. Endres-Dighe, MPH, is a Research Epidemiologist, Center for Applied Public Health Research; all at RTI International, Research Triangle Park, NC
| | - Lauren M DiBiase
- Lauren M. DiBiase, MS, is Associate Director, Infection Prevention, University of North Carolina Medical Center, Chapel Hill, NC
| | - Emily E Sickbert-Bennett
- Emily E. Sickbert-Bennett, PhD, MS, is Director, Infection Prevention, University of North Carolina Hospitals, Chapel Hill, NC
| | - David J Weber
- David J. Weber, MD, MPH, is Professor of Medicine, Pediatrics and Epidemiology, UNC School of Medicine and UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill
| | - Jennifer K MacFarquhar
- Jennifer K. MacFarquhar, MPH, is a Career Epidemiology Field Officer, Center for Preparedness and Response, Centers for Disease Control and Prevention, Atlanta, GA, and Communicable Disease Branch, Division of Public Health, North Carolina Department of Health and Human Services, Raleigh, NC
| | - Heather Dubendris
- Heather Dubendris, MSPH, is an Epidemiologist, Division of Public Health, North Carolina Department of Health and Human Services, Raleigh, NC
| | - Georgiy Bobashev
- Georgiy Bobashev, PhD, MSc, is an RTI Fellow, RTI International, and Professor of Statistics and Biostatistics, North Carolina State University, Raleigh, NC
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Brett T, Ajelli M, Liu QH, Krauland MG, Grefenstette JJ, van Panhuis WG, Vespignani A, Drake JM, Rohani P. Detecting critical slowing down in high-dimensional epidemiological systems. PLoS Comput Biol 2020; 16:e1007679. [PMID: 32150536 PMCID: PMC7082051 DOI: 10.1371/journal.pcbi.1007679] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 03/19/2020] [Accepted: 01/23/2020] [Indexed: 01/05/2023] Open
Abstract
Despite medical advances, the emergence and re-emergence of infectious diseases continue to pose a public health threat. Low-dimensional epidemiological models predict that epidemic transitions are preceded by the phenomenon of critical slowing down (CSD). This has raised the possibility of anticipating disease (re-)emergence using CSD-based early-warning signals (EWS), which are statistical moments estimated from time series data. For EWS to be useful at detecting future (re-)emergence, CSD needs to be a generic (model-independent) feature of epidemiological dynamics irrespective of system complexity. Currently, it is unclear whether the predictions of CSD-derived from simple, low-dimensional systems-pertain to real systems, which are high-dimensional. To assess the generality of CSD, we carried out a simulation study of a hierarchy of models, with increasing structural complexity and dimensionality, for a measles-like infectious disease. Our five models included: i) a nonseasonal homogeneous Susceptible-Exposed-Infectious-Recovered (SEIR) model, ii) a homogeneous SEIR model with seasonality in transmission, iii) an age-structured SEIR model, iv) a multiplex network-based model (Mplex) and v) an agent-based simulator (FRED). All models were parameterised to have a herd-immunity immunization threshold of around 90% coverage, and underwent a linear decrease in vaccine uptake, from 92% to 70% over 15 years. We found evidence of CSD prior to disease re-emergence in all models. We also evaluated the performance of seven EWS: the autocorrelation, coefficient of variation, index of dispersion, kurtosis, mean, skewness, variance. Performance was scored using the Area Under the ROC Curve (AUC) statistic. The best performing EWS were the mean and variance, with AUC > 0.75 one year before the estimated transition time. These two, along with the autocorrelation and index of dispersion, are promising candidate EWS for detecting disease emergence.
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Affiliation(s)
- Tobias Brett
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Marco Ajelli
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
- Bruno Kessler Foundation, Trento, Italy
| | - Quan-Hui Liu
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
- College of Computer Science, Sichuan University, Chengdu, China
| | - Mary G. Krauland
- University of Pittsburgh, Department of Health Policy and Management, Pittsburgh, Pennsylvania, United States of America
| | - John J. Grefenstette
- University of Pittsburgh, Department of Health Policy and Management, Pittsburgh, Pennsylvania, United States of America
| | - Willem G. van Panhuis
- University of Pittsburgh, Department of Epidemiology, Pittsburgh, Pennsylvania, United States of America
- University of Pittsburgh, Department of Biomedical Informatics, Pittsburgh, Pennsylvania, United States of America
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
- ISI Foundation, Turin, Italy
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia, United States of America
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