TY - JOUR

T1 - Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm

AU - Krivorotko, Olga

AU - Sosnovskaia, Mariia

AU - Vashchenko, Ivan

AU - Kerr, Cliff

AU - Lesnic, Daniel

N1 - Funding Information: The data analysis part (section 2 ) is supported by the Russian Foundation for Basic Research and Royal Society (project no. 21-51-10 003 ). The agent-based mathematical model construction and analysis of numerical results (sections 3, 4, 5 ) was supported by the Russian Science Foundation (project no. 18-71-10 044 ) and the Royal Society IEC∖R2∖202 020 – International Exchanges 2020 Cost Share between UK and Russia. Authors would like to thank Professor Sergey Kabanikhin and Doctor Alexey Romanyukha for the problem analysis and fruitful discussions. Also we would like to thank Yan Reznichenko for language help. Publisher Copyright: © 2021 The Authors

PY - 2022/3

Y1 - 2022/3

N2 - This paper uses Covasim, an agent-based model (ABM) of COVID-19, to evaluate and scenarios of epidemic spread in New York State (USA) and the UK. Epidemiological parameters such as contagiousness (virus transmission rate), initial number of infected people, and probability of being tested depend on the region's demographic and geographical features, the containment measures introduced; they are calibrated to data about COVID-19 spread in the region of interest. At the first stage of our study, epidemiological data (numbers of people tested, diagnoses, critical cases, hospitalizations, and deaths) for each of the mentioned regions were analyzed. The data were characterized in terms of seasonality, stationarity, and dependency spaces, and were extrapolated using machine learning techniques to specify unknown epidemiological parameters of the model. At the second stage, the Optuna optimizer based on the tree Parzen estimation method for objective function minimization was applied to determine the model's unknown parameters. The model was validated with the historical data of 2020. The modeled results of COVID-19 spread in New York State and the UK have demonstrated that if the level of testing and containment measures is preserved, the number of positive cases in New York State remain the same during March of 2021, while in the UK it will reduce.

AB - This paper uses Covasim, an agent-based model (ABM) of COVID-19, to evaluate and scenarios of epidemic spread in New York State (USA) and the UK. Epidemiological parameters such as contagiousness (virus transmission rate), initial number of infected people, and probability of being tested depend on the region's demographic and geographical features, the containment measures introduced; they are calibrated to data about COVID-19 spread in the region of interest. At the first stage of our study, epidemiological data (numbers of people tested, diagnoses, critical cases, hospitalizations, and deaths) for each of the mentioned regions were analyzed. The data were characterized in terms of seasonality, stationarity, and dependency spaces, and were extrapolated using machine learning techniques to specify unknown epidemiological parameters of the model. At the second stage, the Optuna optimizer based on the tree Parzen estimation method for objective function minimization was applied to determine the model's unknown parameters. The model was validated with the historical data of 2020. The modeled results of COVID-19 spread in New York State and the UK have demonstrated that if the level of testing and containment measures is preserved, the number of positive cases in New York State remain the same during March of 2021, while in the UK it will reduce.

KW - Agent-based modeling

KW - Coronavirus data analysis

KW - COVID-19

KW - Epidemiology

KW - Forecasting scenarios

KW - Interventions analysis

KW - Optimization

KW - Parameter identification

KW - Reproduction number

UR - http://www.scopus.com/inward/record.url?scp=85120487208&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/3ad41ca9-4642-3ee3-9d1f-08f9440dcfca/

U2 - 10.1016/j.idm.2021.11.004

DO - 10.1016/j.idm.2021.11.004

M3 - Article

C2 - 34869960

AN - SCOPUS:85120487208

VL - 7

SP - 30

EP - 44

JO - Infectious Disease Modelling

JF - Infectious Disease Modelling

SN - 2468-2152

IS - 1

ER -