AUTHOR=Cumsille Patricio , Rojas-Díaz Oscar , Conca Carlos 

TITLE=A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile

JOURNAL=Frontiers in Public Health

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1111641

DOI=10.3389/fpubh.2023.1111641

ISSN=2296-2565

ABSTRACT=Background: One of the main lessons of the COVID-19 pandemic is that we must prepare to face another pandemic like it. Consequently, this paper aims to develop a general framework consisting of epidemiological modeling and a practical identifiability approach to assessing combined vaccination and non-pharmaceutical interventions (NPIs) strategies for the dynamics of any transmissible disease.

Materials and methods: Epidemiological modeling relies on delay differential equations describing time variation and transitions between suitable compartments. The practical identifiability approach relies on careful 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 UCI curve trend in the medium term and assess vaccination.

Results: We demonstrate the framework’s capabilities for several processed COVID-19 datasets of different regions of Chile. As a result, we found a unique range of parameters that works well for every dataset and provides overall numerical stability and convergence for parameter optimization. Consequently, it produces outstanding results concerning quantitative tracking of COVID-19 dynamics. In addition, it allows us to accurately predict the UCI 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.

Conclusion: This work attempts to implement a holistic and general framework for quantitative tracking of the dynamics of any transmissible disease, focusing on accurately predicting the UCI 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 efficiently. The aim would be to reconstruct epidemiological curves to predict the combined NPIs and vaccination policies’ impact on the UCI curve trend in real-time, providing scientific evidence to help anticipate policymakers’ decisions.