Sensitivity and identifiability analysis of COVID-19 pandemic models

• Published in: Vavilovskiĭ zhurnal genetiki i selekt͡s︡ii Vol. 25; no. 1; pp. 82 - 91
• Main Authors: Krivorotko, O I, Kabanikhin, S I, Sosnovskaya, M I, Andornaya, D V
• Format: Journal Article
• Language: English
• Published: Russia (Federation) The Federal Research Center Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences 01.02.2021; Siberian Branch of the Russian Academy of Sciences, Federal Research Center Institute of Cytology and Genetics, The Vavilov Society of Geneticists and Breeders
• Subjects: covid-19; epidemiology; forecasting; identif iability; inverse problems; novosibirsk region; ordinary differential equations; Original article; parameter sensitivity; COVID-19; epidemiology; ordinary differential equations; forecasting; Novosibirsk region; identifiability; inverse problems; parameter sensitivity
• ISSN: 2500-0462; 2500-3259
• DOI: 10.18699/VJ21.010

The paper presents the results of sensitivity-based identifiability analysis of the COVID-19 pandemic spread models in the Novosibirsk region using the systems of differential equations and mass balance law. The algorithm is built on the sensitivity matrix analysis using the methods of differential and linear algebra. It allows one to determine the parameters that are the least and most sensitive to data changes to build a regularization for solving an identification problem of the most accurate pandemic spread scenarios in the region. The performed analysis has demonstrated that the virus contagiousness is identifiable from the number of daily confirmed, critical and recovery cases. On the other hand, the predicted proportion of the admitted patients who require a ventilator and the mortality rate are determined much less consistently. It has been shown that building a more realistic forecast requires adding additional information about the process such as the number of daily hospital admissions. In our study, the problems of parameter identification using additional information about the number of daily confirmed, critical and mortality cases in the region were reduced to minimizing the corresponding misfit functions. The minimization problem was solved through the differential evolution method that is widely applied for stochastic global optimization. It has been demonstrated that a more general COVID-19 spread compartmental model consisting of seven ordinary differential equations describes the main trend of the spread and is sensitive to the peaks of confirmed cases but does not qualitatively describe small statistical datasets such as the number of daily critical cases or mortality that can lead to errors in forecasting. A more detailed agent-oriented model has been able to capture statistical data with additional noise to build scenarios of COVID-19 spread in the region.