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Transition model for coronavirus management

Author

Listed:
  • Antoine Djogbenou
  • Christian Gourieroux
  • Joann Jasiak
  • Paul Rilstone
  • Maygol Bandehali

Abstract

This paper examines the individual records of patients treated for COVID‐19 during the early phase of the pandemic in Ontario. We trace out daily transitions of patients through medical care of different intensity and address the right truncation in the database. We also examine the sojourn times and reveal duration dependence in the treatments for COVID‐19. The transition model is used to estimate and forecast the counts of patients treated for COVID‐19 in Ontario, while adjusting for the right truncation and right censoring in the sample. This research is based on the Public Health Ontario (PHO) data set from May 7, 2020. Un modèle de transition pour la gestion du cononavirus. Cet article examine les historiques individuels des patients traités pour le COVID‐19 au début de le pandémie en Ontario. Nous traçons les transitions journalières des patients recevant des soins médicaux d'intensités différentes et traitons la troncature à droite dans la base de données. Nous examinons également les temps de séjour et révélons une dépendance à la durée dans les traitements du COVID‐19. Le modèle de transition est utilisé pour estimer et prévoir le nombre de patients traités pour le COVID‐19 en Ontario, tout en tenant compte de la troncature à droite et de la censure à droite dans l'échantillon. Cette recherche est basée sur des données de Santé publique Ontario datant du 7 mai 2020.

Suggested Citation

  • Antoine Djogbenou & Christian Gourieroux & Joann Jasiak & Paul Rilstone & Maygol Bandehali, 2022. "Transition model for coronavirus management," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 665-704, February.
    • Handle: RePEc:wly:canjec:v:55:y:2022:i:s1:p:665-704
    DOI: 10.1111/caje.12550
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    References listed on IDEAS

    as
    1. Gourieroux, C. & Jasiak, J., 2023. "Time varying Markov process with partially observed aggregate data: An application to coronavirus," Journal of Econometrics, Elsevier, vol. 232(1), pages 35-51.
    2. Andrew Atkeson, 2020. "What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios," NBER Working Papers 26867, National Bureau of Economic Research, Inc.
    3. Victor Aguirregabiria & Jiaying Gu & Yao Luo & Pedro Mira, 2020. "A Dynamic Structural Model of Virus Diffusion and Network Production: A First Report," Working Papers wp2020_2014, CEMFI.
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