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Gwon N, Jeong W, Kim JH, Oh KH, Jun JK. Effects of Intervention Timing on Health-Related Fake News: Simulation Study. JMIR Form Res 2024; 8:e48284. [PMID: 39109788 PMCID: PMC11339565 DOI: 10.2196/48284] [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: 04/18/2023] [Revised: 05/29/2024] [Accepted: 07/01/2024] [Indexed: 08/25/2024] Open
Abstract
BACKGROUND Fake health-related news has spread rapidly through the internet, causing harm to individuals and society. Despite interventions, a fenbendazole scandal recently spread among patients with lung cancer in South Korea. It is crucial to intervene appropriately to prevent the spread of fake news. OBJECTIVE This study investigated the appropriate timing of interventions to minimize the side effects of fake news. METHODS A simulation was conducted using the susceptible-infected-recovered (SIR) model, which is a representative model of the virus spread mechanism. We applied this model to the fake news spread mechanism. The parameters were set similarly to those in the digital environment, where the fenbendazole scandal occurred. NetLogo, an agent-based model, was used as the analytical tool. RESULTS Fake news lasted 278 days in the absence of interventions. As a result of adjusting and analyzing the timing of the intervention in response to the fenbendazole scandal, we found that faster intervention leads to a shorter duration of fake news (intervention at 54 days = fake news that lasted for 210 days; intervention at 16 days = fake news that lasted for 187 days; and intervention at 10 days = fake news that lasted for 157 days). However, no significant differences were observed when the intervention was performed within 10 days. CONCLUSIONS Interventions implemented within 10 days were effective in reducing the duration of the spread of fake news. Our findings suggest that timely intervention is critical for preventing the spread of fake news in the digital environment. Additionally, a monitoring system that can detect fake news should be developed for a rapid response.
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Affiliation(s)
- Nahyun Gwon
- Cancer Knowledge and Information Center, National Cancer Control Institute, National Cancer Center, Goyang, Republic of Korea
| | - Wonjeong Jeong
- Cancer Knowledge and Information Center, National Cancer Control Institute, National Cancer Center, Goyang, Republic of Korea
| | - Jee Hyun Kim
- Cancer Knowledge and Information Center, National Cancer Control Institute, National Cancer Center, Goyang, Republic of Korea
| | - Kyoung Hee Oh
- Cancer Knowledge and Information Center, National Cancer Control Institute, National Cancer Center, Goyang, Republic of Korea
| | - Jae Kwan Jun
- Cancer Knowledge and Information Center, National Cancer Control Institute, National Cancer Center, Goyang, Republic of Korea
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Nitzsche C, Simm S. Agent-based modeling to estimate the impact of lockdown scenarios and events on a pandemic exemplified on SARS-CoV-2. Sci Rep 2024; 14:13391. [PMID: 38862580 PMCID: PMC11167020 DOI: 10.1038/s41598-024-63795-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 06/03/2024] [Indexed: 06/13/2024] Open
Abstract
In actual pandemic situations like COVID-19, it is important to understand the influence of single mitigation measures as well as combinations to create most dynamic impact for lockdown scenarios. Therefore we created an agent-based model (ABM) to simulate the spread of SARS-CoV-2 in an abstract city model with several types of places and agents. In comparison to infection numbers in Germany our ABM could be shown to behave similarly during the first wave. In our model, we implemented the possibility to test the effectiveness of mitigation measures and lockdown scenarios on the course of the pandemic. In this context, we focused on parameters of local events as possible mitigation measures and ran simulations, including varying size, duration, frequency and the proportion of events. The majority of changes to single event parameters, with the exception of frequency, showed only a small influence on the overall course of the pandemic. By applying different lockdown scenarios in our simulations, we could observe drastic changes in the number of infections per day. Depending on the lockdown strategy, we even observed a delayed peak in infection numbers of the second wave. As an advantage of the developed ABM, it is possible to analyze the individual risk of single agents during the pandemic. In contrast to standard or adjusted ODEs, we observed a 21% (with masks) / 48% (without masks) increased risk for single reappearing participants on local events, with a linearly increasing risk based on the length of the events.
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Affiliation(s)
- Christian Nitzsche
- University Medicine Greifswald, Institute of Bioinformatics, Greifswald, 17489, Germany
| | - Stefan Simm
- University Medicine Greifswald, Institute of Bioinformatics, Greifswald, 17489, Germany.
- Coburg University of Applied Sciences, Institute of Bioanalysis, Coburg, Germany.
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3
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Kumaresan V, Balachandar N, Poole SF, Myers LJ, Varghese P, Washington V, Jia Y, Lee VS. Fitting and validation of an agent-based model for COVID-19 case forecasting in workplaces and universities. PLoS One 2023; 18:e0283517. [PMID: 36952500 PMCID: PMC10035834 DOI: 10.1371/journal.pone.0283517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 03/10/2023] [Indexed: 03/25/2023] Open
Abstract
COVID-19 forecasting models have been critical in guiding decision-making on surveillance testing, social distancing, and vaccination requirements. Beyond influencing public health policies, an accurate COVID-19 forecasting model can impact community spread by enabling employers and university leaders to adapt worksite policies and practices to contain or mitigate outbreaks. While many such models have been developed for COVID-19 forecasting at the national, state, county, or city level, only a few models have been developed for workplaces and universities. Furthermore, COVID-19 forecasting models have rarely been validated against real COVID-19 case data. Here we present the systematic parameter fitting and validation of an agent-based compartment model for the forecasting of daily COVID-19 cases in single-site workplaces and universities with real-world data. Our approaches include manual fitting, where initial model parameters are chosen based on historical data, and automated fitting, where parameters are chosen based on candidate case trajectory simulations that result in best fit to prevalence estimation data. We use a 14-day fitting window and validate our approaches on 7- and 14-day testing windows with real COVID-19 case data from one employer. Our manual and automated fitting approaches accurately predicted COVID-19 case trends and outperformed the baseline model (no parameter fitting) across multiple scenarios, including a rising case trajectory (RMSLE values: 2.627 for baseline, 0.562 for manual fitting, 0.399 for automated fitting) and a decreasing case trajectory (RMSLE values: 1.155 for baseline, 0.537 for manual fitting, 0.778 for automated fitting). Our COVID-19 case forecasting model allows decision-makers at workplaces and universities to proactively respond to case trend forecasts, mitigate outbreaks, and promote safety.
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Affiliation(s)
- Vignesh Kumaresan
- Verily Life Sciences, South San Francisco, California, United States of America
| | | | - Sarah F. Poole
- Verily Life Sciences, South San Francisco, California, United States of America
| | - Lance J. Myers
- Verily Life Sciences, South San Francisco, California, United States of America
| | - Paul Varghese
- Verily Life Sciences, South San Francisco, California, United States of America
| | - Vindell Washington
- Verily Life Sciences, South San Francisco, California, United States of America
| | - Yugang Jia
- Verily Life Sciences, South San Francisco, California, United States of America
| | - Vivian S. Lee
- Verily Life Sciences, South San Francisco, California, United States of America
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McCue-Weil L, Knight M, Driscoll M, Jenkins P, Sorensen J. A Case Study on the Practical Use of Low-Fidelity Modeling to Mitigate the Spread of COVID-19 Amongst the Underserved Farmworker Community. LA MATEMATICA 2023; 2:223-241. [PMID: 36852263 PMCID: PMC9946281 DOI: 10.1007/s44007-023-00043-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 12/30/2022] [Accepted: 01/13/2023] [Indexed: 02/24/2023]
Abstract
In March of 2020, with the full magnitude of the COVID-19 pandemic yet to be seen, Costa and Martin released a report through the Economic Policy Institute noting that "To prevent infections and the spread of COVID-19 on farms, farm employers should be planning and implementing safety measures to protect their employees" (Costa D, Martin P, Coronavirus and farmworkers: farm employment, safety issues, and the H-2A guestworker program, Economic Policy Institute, https://www.epi.org/publication/coronavirus-and-farmworkers-h-2a/, 2020). The report goes on to provide multiple observations recognizing the seasonal nature of farm work, effects increased unemployment may have on the workforce, industry dependence on H-2A visa farm workers, impact school closings would have on worker availability, and includes recommendations for safety equipment, social distancing, as well as worker housing and transportation. This paper focuses on the worker housing component of those recommendations and describes an effort to rapidly develop and deploy a computationally efficient, web-based, low-fidelity mathematical model of COVID-19 spread in dormitory style housing to support education and mitigation strategies for the historically underserved farmworker community.
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Affiliation(s)
- Leigh McCue-Weil
- grid.22448.380000 0004 1936 8032Department of Mechanical Engineering, George Mason University, Fairfax, VA USA
| | - Margaret Knight
- grid.27860.3b0000 0004 1936 9684University of California, Davis, CA USA
| | - Maryellen Driscoll
- Northeast Center for Occupational Health and Safety, Cooperstown, NY USA
| | - Paul Jenkins
- Northeast Center for Occupational Health and Safety, Cooperstown, NY USA
| | - Julie Sorensen
- Northeast Center for Occupational Health and Safety, Cooperstown, NY USA
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Botz J, Wang D, Lambert N, Wagner N, Génin M, Thommes E, Madan S, Coudeville L, Fröhlich H. Modeling approaches for early warning and monitoring of pandemic situations as well as decision support. Front Public Health 2022; 10:994949. [PMID: 36452960 PMCID: PMC9702983 DOI: 10.3389/fpubh.2022.994949] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 10/21/2022] [Indexed: 11/15/2022] Open
Abstract
The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.
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Affiliation(s)
- Jonas Botz
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Danqi Wang
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
| | | | | | | | | | - Sumit Madan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Department of Computer Science, University of Bonn, Bonn, Germany
| | | | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
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6
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Kühn MJ, Abele D, Binder S, Rack K, Klitz M, Kleinert J, Gilg J, Spataro L, Koslow W, Siggel M, Meyer-Hermann M, Basermann A. Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany. BMC Infect Dis 2022; 22:333. [PMID: 35379190 PMCID: PMC8978163 DOI: 10.1186/s12879-022-07302-9] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 03/21/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. METHODS We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. RESULTS We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics. CONCLUSIONS Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent.
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Affiliation(s)
- Martin J Kühn
- Institute for Software Technology, German Aerospace Center, Cologne, Germany.
| | - Daniel Abele
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Sebastian Binder
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
| | - Kathrin Rack
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Margrit Klitz
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Jan Kleinert
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Jonas Gilg
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Luca Spataro
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Wadim Koslow
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Martin Siggel
- Institute for Software Technology, German Aerospace Center, Cologne, Germany
| | - Michael Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.
| | - Achim Basermann
- Institute for Software Technology, German Aerospace Center, Cologne, Germany.
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