Dynamic Bayesian networks in solving the tasks of strategic and operational planning to hold back the pandemic
Abstract. The use of government measures to hold back the pandemic requires effective solution of operational and strategic planning tasks and coverage of the entire population of the country without exception. This study aims to identify the interrelationships between the indicators of the epidemic process and the indicators of the state`s response to hold back the COVID-19 pandemic. A sequence of anti-epidemic measures has been identified that contributes to reducing the incidence of the population and increasingthe economic potential of the country. The study was carried out on the example of Russia. Weekly statistical data for the period from January to December 2020 were used on indicators characterizing the development of the COVID-19 epidemic process and the implementation of state anti-epidemic measures (source - Oxford COVID-19 Government Response Tracker). A dynamic Bayesian network is constructed. When training the network, the algorithm "a variation on Ghada Trabelsi`s dynamic max-min hill climbing" was used. The calculations were performed usingthe dbnR library of the R programming language. The followingtypes of relationships have been identified between indicators of the development of the COVID-19 epidemic process and government measures to contain the pandemic: 1) short-term; 2) long-term; 3) self-influence. State anti-epidemic measures form a complex dynamic structure that determines the patterns of their impact on curbing the increase in the incidence of COVID-19 in the Russian population. The epidemic process is characterized by a memory effect: changes in the morbidity and mortality of the population at the current moment will cause changes in the indicators of this process in the future. Effective implementation of state anti-epidemic measures helps to reduce the increase in morbidity and mortality from COVID-19. At the same time, it is necessary to observe a strict sequence of anti-epidemic measures: start with the implementation of measures in the field of healthcare, then gradually connect restrictive state measures, then - measures of state economic support for the population. Special attention should be paid to the implementation of two types of activities: 1) restrictions on holding meetings; 2) economic support for the population, as they are significantly influenced by various factors. At the same time, the restriction on the movement of public transport does not have a significant impact on the morbidity and mortality of the population from COVID-19.
Keywords: Bayesian network, epidemic process, operational planning, state anti-epidemic measures Highlights:
- the sequence and interrelationships between the indicators of the epidemic process and the factors of the state`s response to hold back the COVID-19 pandemic in Russia are determined;
- the presence of a memory effect in the conduct of state anti-epidemic measures has been revealed, indicatingthat their effective implementation atthe moment will contribute to reducingthe growth of morbidity and mortality in the long term and the growth of the country`s economic potential;
- it is necessary to observe a strict sequence of anti-epidemic measures: start with the implementation of measures in the field of healthcare, then gradually connect restrictive state measures, then measures of state economic support for the population and business;
- in orderto reduce the negative impact of the consequences of the pandemic on the country`s economy, it is recommended to strictly monitor the socio-economic well-being of the population and businesses to prevent the risk of their bankruptcy.
Oksana M. Kulikova - Siberian State Automobile and Highway University, Omsk, Russia, Natalia S. Veremchuk - Siberian State Automobile and Highway University, Omsk, Russia