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Hong H, Eom E, Lee H, Choi S, Choi B, Kim JK. Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics. Nat Commun 2024; 15:8734. [PMID: 39384847 PMCID: PMC11464791 DOI: 10.1038/s41467-024-53095-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 09/26/2024] [Indexed: 10/11/2024] Open
Abstract
Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method.
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Affiliation(s)
- Hyukpyo Hong
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea
- Department of Mathematics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Eunjin Eom
- Department of Economic Statistics, Korea University, Sejong, 30019, Republic of Korea
| | - Hyojung Lee
- Department of Statistics, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Sunhwa Choi
- Innovation Center for Industrial Mathematics, National Institute for Mathematical Sciences, Seongnam, 13449, Republic of Korea.
| | - Boseung Choi
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
- Division of Big Data Science, Korea University, Sejong, 30019, Republic of Korea.
- College of Public Health, The Ohio State University, OH, 43210, USA.
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea.
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
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Yin Y, Ahmadianfar I, Karim FK, Elmannai H. Advanced forecasting of COVID-19 epidemic: Leveraging ensemble models, advanced optimization, and decomposition techniques. Comput Biol Med 2024; 175:108442. [PMID: 38678939 DOI: 10.1016/j.compbiomed.2024.108442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 03/25/2024] [Accepted: 04/07/2024] [Indexed: 05/01/2024]
Abstract
In the global effort to address the outbreak of the new coronavirus pneumonia (COVID-19) pandemic, accurate forecasting of epidemic patterns has become crucial for implementing successful interventions aimed at preventing and controlling the spread of the disease. The correct prediction of the course of COVID-19 outbreaks is a complex and challenging task, mainly because of the significant volatility in the data series related to COVID-19. Previous studies have been limited by the exclusive use of individual forecasting techniques in epidemic modeling, disregarding the integration of diverse prediction procedures. The lack of attention to detail in this situation can yield worse-than-ideal results. Consequently, this study introduces a novel ensemble framework that integrates three machine learning methods (kernel ridge regression (KRidge), Deep random vector functional link (dRVFL), and ridge regression) within a linear relationship (L-KRidge-dRVFL-Ridge). The optimization of this framework is accomplished through a distinctive approach, specifically adaptive differential evolution and particle swarm optimization (A-DEPSO). Moreover, an effective decomposition method, known as time-varying filter empirical mode decomposition (TVF-EMD), is employed to decompose the input variables. A feature selection technique, specifically using the light gradient boosting machine (LGBM), is also implemented to extract the most influential input variables. The daily datasets of COVID-19 collected from two countries, namely Italy and Poland, were used as the experimental examples. Additionally, all models are implemented to forecast COVID-19 at two-time horizons: 10- and 14-day ahead (t+10 and t+14). According to the results, the proposed model can yield higher correlation coefficient (R) for both case studies: Italy (t+10 = 0.965, t+14 = 0.961) and Poland (t+10 = 0.952, t+14 = 0.940) than the other models. The experimental results demonstrate that the model suggested in this paper has outstanding results in various kinds of complex epidemic prediction situations. The proposed ensemble model demonstrates exceptional accuracy and resilience, outperforming all similar models in terms of efficacy.
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Affiliation(s)
- Yingyu Yin
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China.
| | - Iman Ahmadianfar
- Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq.
| | - Faten Khalid Karim
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.BOX 84428, Riyadh 11671, Saudi Arabia.
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.BOX 84428, Riyadh 11671, Saudi Arabia.
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Galarza CRC, Sánchez OND, Pimentel JS, Bulhões R, López-Gonzales JL, Rodrigues PC. Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru. ENTROPY (BASEL, SWITZERLAND) 2024; 26:474. [PMID: 38920483 PMCID: PMC11202420 DOI: 10.3390/e26060474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/17/2024] [Accepted: 05/28/2024] [Indexed: 06/27/2024]
Abstract
Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear model to elucidate mortality patterns and assess the impact of vaccination efforts. Leveraging data from 194 provinces over 651 days, our analysis reveals heterogeneous spatial and temporal patterns in COVID-19 mortality rates. Higher vaccination coverage is associated with reduced mortality rates, emphasizing the importance of vaccination in mitigating the pandemic's impact. The findings underscore the value of spatio-temporal data analysis in understanding disease dynamics and guiding targeted public health interventions.
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Affiliation(s)
- César Raúl Castro Galarza
- Escuela de Posgrado, Universidad Peruana Unión, Lima 15468, Peru; (C.R.C.G.); (O.N.D.S.); (J.L.L.-G.)
| | | | - Jonatha Sousa Pimentel
- Department of Statistics, Federal University of Pernambuco, Recife 50740-540, PE, Brazil
| | - Rodrigo Bulhões
- Department of Statistics, Federal University of Bahia, Salvador 40170-110, BA, Brazil; (R.B.); (P.C.R.)
| | | | - Paulo Canas Rodrigues
- Department of Statistics, Federal University of Bahia, Salvador 40170-110, BA, Brazil; (R.B.); (P.C.R.)
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Shanmugam R, Fulton L, Kruse CS, Beauvais B, Betancourt J, Pacheco G, Pradhan R, Sen K, Ramamonjiarivelo Z, Sharma A. The effect of COVID-19 on cancer incidences in the U.S. Heliyon 2024; 10:e28804. [PMID: 38601551 PMCID: PMC11004761 DOI: 10.1016/j.heliyon.2024.e28804] [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: 12/28/2023] [Revised: 03/10/2024] [Accepted: 03/25/2024] [Indexed: 04/12/2024] Open
Abstract
Fundamental data analysis assists in the evaluation of critical questions to discern essential facts and elicit formerly invisible evidence. In this article, we provide clarity into a subtle phenomenon observed in cancer incidences throughout the time of the COVID-19 pandemic. We analyzed the cancer incidence data from the American Cancer Society [1]. We partitioned the data into three groups: the pre-COVID-19 years (2017, 2018), during the COVID-19 years (2019, 2020, 2021), and the post-COVID-19 years (2022, 2023). In a novel manner, we applied principal components analysis (PCA), computed the angles between the cancer incidence vectors, and then added lognormal probability concepts in our analysis. Our analytic results revealed that the cancer incidences shifted within each era (pre, during, and post), with a meaningful change in the cancer incidences occurring in 2020, the peak of the COVID-19 era. We defined, computed, and interpreted the exceedance probability for a cancer type to have 1000 incidences in a future year among the breast, cervical, colorectal, uterine corpus, leukemia, lung & bronchus, melanoma, Hodgkin's lymphoma, prostate, and urinary cancers. We also defined, estimated, and illustrated indices for other cancer diagnoses from the vantage point of breast cancer in pre, during, and post-COVID-19 eras. The angle vectors post the COVID-19 were 72% less than pre-pandemic and 28% less than during the pandemic. The movement of cancer vectors was dynamic between these eras, and movement greatly differed by type of cancer. A trend chart of cervical cancer showed statistical anomalies in the years 2019 and 2021. Based on our findings, a few future research directions are pointed out.
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Affiliation(s)
- Ramalingam Shanmugam
- Texas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA
| | - Larry Fulton
- Boston College, Woods College of Advancing Studies, St. Mary's Hall South, Chestnut Hill, MA, 02467, USA
| | - C. Scott Kruse
- Texas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA
| | - Brad Beauvais
- Texas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA
| | - Jose Betancourt
- Texas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA
| | - Gerardo Pacheco
- Texas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA
| | - Rohit Pradhan
- Texas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA
| | - Keya Sen
- Texas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA
| | - Zo Ramamonjiarivelo
- Texas State University, School of Health Administration, Encino Hall, Room 250A, 601 University Drive, San Marcos, TX, 78666, USA
| | - Arvind Sharma
- Boston College, Woods College of Advancing Studies, St. Mary's Hall South, Chestnut Hill, MA, 02467, USA
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Maestre J, Chanfreut P, Aarons L. Constrained numerical deconvolution using orthogonal polynomials. Heliyon 2024; 10:e24762. [PMID: 38317950 PMCID: PMC10839874 DOI: 10.1016/j.heliyon.2024.e24762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 12/28/2023] [Accepted: 01/14/2024] [Indexed: 02/07/2024] Open
Abstract
In this article, we present an enhanced version of Cutler's deconvolution method to address the limitations of the original algorithm in estimating realistic input and output parameters. Cutler's method, based on orthogonal polynomials, suffers from unconstrained solutions, leading to the lack of realism in the deconvolved signals in some applications. Our proposed approach incorporates constraints using a ridge factor and Lagrangian multipliers in an iterative fashion, maintaining Cutler's iterative projection-based nature. This extension avoids the need for external optimization solvers, making it particularly suitable for applications requiring constraints on inputs and outputs. We demonstrate the effectiveness of the proposed method through two practical applications: the estimation of COVID-19 curves and the study of mavoglurant, an experimental drug. Our results show that the extended method presents a sum of squared residuals in the same order of magnitude of that of the original Cutler's method and other widely known unconstrained deconvolution techniques, but obtains instead physically plausible solutions, correcting the errors introduced by the alternative methods considered, as illustrated in our case studies.
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Affiliation(s)
- J.M. Maestre
- Department of Systems and Automation Engineering, University of Seville, Spain
- Health and Pharmacy PhD program at University of Salamanca, Spain
| | - P. Chanfreut
- Department of Mechanical Engineering, Eindhoven University of Technology, the Netherlands
| | - L. Aarons
- Division of Pharmacy and Optometry, The University of Manchester, Manchester, UK
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Alpers R, Kühne L, Truong HP, Zeeb H, Westphal M, Jäckle S. Evaluation of the EsteR Toolkit for COVID-19 Decision Support: Sensitivity Analysis and Usability Study. JMIR Form Res 2023; 7:e44549. [PMID: 37368487 DOI: 10.2196/44549] [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: 12/05/2022] [Revised: 05/14/2023] [Accepted: 05/15/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND During the COVID-19 pandemic, local health authorities were responsible for managing and reporting current cases in Germany. Since March 2020, employees had to contain the spread of COVID-19 by monitoring and contacting infected persons as well as tracing their contacts. In the EsteR project, we implemented existing and newly developed statistical models as decision support tools to assist in the work of the local health authorities. OBJECTIVE The main goal of this study was to validate the EsteR toolkit in two complementary ways: first, investigating the stability of the answers provided by our statistical tools regarding model parameters in the back end and, second, evaluating the usability and applicability of our web application in the front end by test users. METHODS For model stability assessment, a sensitivity analysis was carried out for all 5 developed statistical models. The default parameters of our models as well as the test ranges of the model parameters were based on a previous literature review on COVID-19 properties. The obtained answers resulting from different parameters were compared using dissimilarity metrics and visualized using contour plots. In addition, the parameter ranges of general model stability were identified. For the usability evaluation of the web application, cognitive walk-throughs and focus group interviews were conducted with 6 containment scouts located at 2 different local health authorities. They were first asked to complete small tasks with the tools and then express their general impressions of the web application. RESULTS The simulation results showed that some statistical models were more sensitive to changes in their parameters than others. For each of the single-person use cases, we determined an area where the respective model could be rated as stable. In contrast, the results of the group use cases highly depended on the user inputs, and thus, no area of parameters with general model stability could be identified. We have also provided a detailed simulation report of the sensitivity analysis. In the user evaluation, the cognitive walk-throughs and focus group interviews revealed that the user interface needed to be simplified and more information was necessary as guidance. In general, the testers rated the web application as helpful, especially for new employees. CONCLUSIONS This evaluation study allowed us to refine the EsteR toolkit. Using the sensitivity analysis, we identified suitable model parameters and analyzed how stable the statistical models were in terms of changes in their parameters. Furthermore, the front end of the web application was improved with the results of the conducted cognitive walk-throughs and focus group interviews regarding its user-friendliness.
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Affiliation(s)
- Rieke Alpers
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Lisa Kühne
- Department of Prevention and Evaluation, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Hong-Phuc Truong
- Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, Germany
| | - Hajo Zeeb
- Department of Prevention and Evaluation, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Max Westphal
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Sonja Jäckle
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
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