A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile
BackgroundOne of the main lessons of the COVID-19 pandemic is that we must prepare to face another pandemic like it. Consequently, this article aims to develop a general framework consisting of epidemiological modeling and a practical identifiability approach to assess combined vaccination and non-p...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1111641/full |
| _version_ | 1797855006969298944 |
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| author | Patricio Cumsille Patricio Cumsille Oscar Rojas-Díaz Carlos Conca Carlos Conca |
| author_facet | Patricio Cumsille Patricio Cumsille Oscar Rojas-Díaz Carlos Conca Carlos Conca |
| author_sort | Patricio Cumsille |
| collection | DOAJ |
| description | BackgroundOne of the main lessons of the COVID-19 pandemic is that we must prepare to face another pandemic like it. Consequently, this article aims to develop a general framework consisting of epidemiological modeling and a practical identifiability approach to assess combined vaccination and non-pharmaceutical intervention (NPI) strategies for the dynamics of any transmissible disease.Materials and methodsEpidemiological modeling of the present work relies on delay differential equations describing time variation and transitions between suitable compartments. The practical identifiability approach relies on parameter optimization, a parametric bootstrap technique, and data processing. We implemented a careful parameter optimization algorithm by searching for suitable initialization according to each processed dataset. In addition, we implemented a parametric bootstrap technique to accurately predict the ICU curve trend in the medium term and assess vaccination.ResultsWe show the framework's calibration capabilities for several processed COVID-19 datasets of different regions of Chile. We found a unique range of parameters that works well for every dataset and provides overall numerical stability and convergence for parameter optimization. Consequently, the framework produces outstanding results concerning quantitative tracking of COVID-19 dynamics. In addition, it allows us to accurately predict the ICU curve trend in the medium term and assess vaccination. Finally, it is reproducible since we provide open-source codes that consider parameter initialization standardized for every dataset.ConclusionThis work attempts to implement a holistic and general modeling framework for quantitative tracking of the dynamics of any transmissible disease, focusing on accurately predicting the ICU curve trend in the medium term and assessing vaccination. The scientific community could adapt it to evaluate the impact of combined vaccination and NPIs strategies for COVID-19 or any transmissible disease in any country and help visualize the potential effects of implemented plans by policymakers. In future work, we want to improve the computational cost of the parametric bootstrap technique or use another more efficient technique. The aim would be to reconstruct epidemiological curves to predict the combined NPIs and vaccination policies' impact on the ICU curve trend in real-time, providing scientific evidence to help anticipate policymakers' decisions. |
| first_indexed | 2024-04-09T20:16:34Z |
| format | Article |
| id | doaj.art-7f17a0d0d4614adc8eedff5ab1402e9a |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-09T20:16:34Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-7f17a0d0d4614adc8eedff5ab1402e9a2023-03-31T08:27:35ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-03-011110.3389/fpubh.2023.11116411111641A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in ChilePatricio Cumsille0Patricio Cumsille1Oscar Rojas-Díaz2Carlos Conca3Carlos Conca4Department of Basic Sciences, Faculty of Sciences, University of Bío-Bío, Chillán, ChileCentre for Biotechnology and Bioengineering, University of Chile, Santiago, ChileDepartment of Mathematics and Computers Science, Faculty of Science, University of Santiago of Chile, Santiago, ChileCentre for Biotechnology and Bioengineering, University of Chile, Santiago, ChileDepartment of Mathematical Engineering and Center for Mathematical Modeling, University of Chile (UMI CNRS 2807), Santiago, ChileBackgroundOne of the main lessons of the COVID-19 pandemic is that we must prepare to face another pandemic like it. Consequently, this article aims to develop a general framework consisting of epidemiological modeling and a practical identifiability approach to assess combined vaccination and non-pharmaceutical intervention (NPI) strategies for the dynamics of any transmissible disease.Materials and methodsEpidemiological modeling of the present work relies on delay differential equations describing time variation and transitions between suitable compartments. The practical identifiability approach relies on parameter optimization, a parametric bootstrap technique, and data processing. We implemented a careful parameter optimization algorithm by searching for suitable initialization according to each processed dataset. In addition, we implemented a parametric bootstrap technique to accurately predict the ICU curve trend in the medium term and assess vaccination.ResultsWe show the framework's calibration capabilities for several processed COVID-19 datasets of different regions of Chile. We found a unique range of parameters that works well for every dataset and provides overall numerical stability and convergence for parameter optimization. Consequently, the framework produces outstanding results concerning quantitative tracking of COVID-19 dynamics. In addition, it allows us to accurately predict the ICU curve trend in the medium term and assess vaccination. Finally, it is reproducible since we provide open-source codes that consider parameter initialization standardized for every dataset.ConclusionThis work attempts to implement a holistic and general modeling framework for quantitative tracking of the dynamics of any transmissible disease, focusing on accurately predicting the ICU curve trend in the medium term and assessing vaccination. The scientific community could adapt it to evaluate the impact of combined vaccination and NPIs strategies for COVID-19 or any transmissible disease in any country and help visualize the potential effects of implemented plans by policymakers. In future work, we want to improve the computational cost of the parametric bootstrap technique or use another more efficient technique. The aim would be to reconstruct epidemiological curves to predict the combined NPIs and vaccination policies' impact on the ICU curve trend in real-time, providing scientific evidence to help anticipate policymakers' decisions.https://www.frontiersin.org/articles/10.3389/fpubh.2023.1111641/fullCOVID-19predictive modelingepidemiological modelingtime delaysvaccinationpractical identifiability |
| spellingShingle | Patricio Cumsille Patricio Cumsille Oscar Rojas-Díaz Carlos Conca Carlos Conca A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile Frontiers in Public Health COVID-19 predictive modeling epidemiological modeling time delays vaccination practical identifiability |
| title | A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile |
| title_full | A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile |
| title_fullStr | A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile |
| title_full_unstemmed | A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile |
| title_short | A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile |
| title_sort | general modeling framework for quantitative tracking accurate prediction of icu and assessing vaccination for covid 19 in chile |
| topic | COVID-19 predictive modeling epidemiological modeling time delays vaccination practical identifiability |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1111641/full |
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