Department of Basic Sciences, Faculty of Sciences, University of Bío-Bío, Chillán, Chile.
Centre for Biotechnology and Bioengineering, University of Chile, Santiago, Chile.
Front Public Health. 2023 Mar 31;11:1111641. doi: 10.3389/fpubh.2023.1111641. eCollection 2023.
One 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.
Epidemiological 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.
We 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.
This 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.
COVID-19 大流行的主要教训之一是，我们必须做好准备应对另一场类似的大流行。因此，本文旨在开发一个包含流行病学建模和实用可识别性方法的通用框架，以评估任何传染病动力学的联合疫苗接种和非药物干预（NPI）策略。
本工作的流行病学建模依赖于描述时间变化和合适隔室之间转换的时滞微分方程。实用可识别性方法依赖于参数优化、参数bootstrap 技术和数据处理。我们根据每个处理的数据集搜索合适的初始化来实现了一个精心设计的参数优化算法。此外，我们还实现了一个参数bootstrap 技术，以准确预测中期 ICU 曲线趋势并评估疫苗接种效果。
我们展示了该框架对智利不同地区的几个处理后的 COVID-19 数据集的校准能力。我们找到了一个独特的参数范围，它对每个数据集都有很好的效果，并为参数优化提供了整体数值稳定性和收敛性。因此，该框架在 COVID-19 动力学的定量跟踪方面取得了出色的结果。此外，它还允许我们准确预测中期 ICU 曲线趋势并评估疫苗接种效果。最后，它是可重复的，因为我们提供了开源代码，其中考虑了针对每个数据集标准化的参数初始化。
本工作试图为任何传染病动力学的定量跟踪实施一个整体和通用的建模框架，重点是准确预测中期 ICU 曲线趋势并评估疫苗接种效果。科学界可以根据该框架评估 COVID-19 或任何传染病的联合疫苗接种和 NPI 策略的影响，并帮助决策者可视化实施计划的潜在效果。在未来的工作中，我们希望提高参数 bootstrap 技术的计算成本或使用另一种更有效的技术。目标是重建流行病学曲线，以实时预测联合 NPI 和疫苗接种政策对 ICU 曲线趋势的影响，为决策者提供科学依据，帮助他们做出决策。
