Funder: OUCRU (Wellcome funding)
Principal Investigator: Marc Choisy
Location of activity: Hanoi and Ho Chi Minh City, Viet Nam
Collaborators: Dr Pham Quang Thai, Epidemiology Department, National Institute of Hygiene and Epidemiology (NIHE), Hanoi and Ho Chi Minh City CDC.
The purpose of this study is to develop a mathematical model of ICU burden in time and space. The model is calibrated with data collected in Viet Nam when available or with data published from other countries (in particular, what concerns risk factors of severe cases). It takes into account age contact structure and population mobility data as inferred from the analysis of Facebook data.
Such a model of ICU burden is required by the National Steering Committee for COVID-19 response. Such a model will be used to assist fast decision making if a crisis occurs. As such, the model is an original enough piece of research to be published in itself as it allows to model in great detail the distribution of the durations in the various epidemiological states. The framework of the model is generic enough to be applied to other contexts than Viet Nam and other diseases than COVID-19.
- Predict the ICU burden in space and time with and without relocation of critical equipment from hospital to hospital.
- Look for policies (including quarantine, lock-down and stay-at-home, potentially with different implementations by locality and age class) that minimise the ICU burden using Optimal Control Theory.
Outputs to date:
- Richard Q, Alizon S, Choisy M, Sofonea M, Djidjou-Demasse R. Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic. PLOS Computational Biology [Internet] 2021;17(3):e1008776. Available from: https://doi.org/10.1371/journal.pcbi.1008776
- Djidjou-Demasse R, Michalakis Y, Choisy M, Sofonea M, Alizon S. Optimal COVID-19 epidemic control until vaccine deployment. [Internet] 2020;Available from: https://doi.org/10.1101/2020.04.02.20049189
- Software: R package for discrete-time non-Markovian simulations [Internet]. GitHub. 2021;Available from: https://github.com/thinhong/cpp_training