Deep learning approaches to mid-term forecasting of social-economic and demographic effects of a pandemic

Devyatkin, Dmitry; Otmakhova, Yulia; Usenko, Natalia; Sochenkov, Ilya; Budzko, Vladimir

doi:10.1016/j.procs.2021.06.020

Cited by 1 publication

(3 citation statements)

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“…Модель обучается на различных данных, включая ежедневные случаи заболеваемости и смертности, возрастную структуру заболевших, транспортную доступность регионов и уровень безработицы. Экспериментальная оценка моделей показывает, что демографические показатели и показатели здравоохранения могут значительно повысить точность прогнозирования среднесрочных экономических показателей [29].…”

Section: эпидемиологический блок имитационных моделейunclassified

“…Эти вызовы требуют от мирового сообщества разработки как мер реагирования, так и предвидения новых угроз. Поэтому создание современных инструментов прогнозирования различных показателей интенсивности воздействия пандемии становится важным и актуальным для рассмотрения и оценки межрегиональных различий [29]. Одним из инструментов анализа и прогнозирования социально-экономических последствий пандемии могут выступать агент-ориентированные модели (АОМ), в которых имитируется поведение децентрализованных самообучающихся агентов, имеющих собственные цели и возможности.…”

Section: Introductionunclassified

“…Для поддержки экономики страны принимают антикризисные меры, которые нацелены, в первую очередь, на преодоление серьезных последствий в самых уязвимых секторах для того чтобы не допустить «пробуксовки» после эпидемии. В конкретном случае рассматривается ситуация, которая разворачивалась вокруг эпидемии, вызванной вирусом COVID-19 [29,32] Следующий пример -агентная модель, которая создавалась для воспроизведения надежных прогнозов оценки последствий эпидемии лихорадки «Эбола» в 2014-2015 годах в Либерии. В модели использованы демографические особенности населения в соответствии с данными переписи.…”

Section: Introductionunclassified

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Approaches to Assessing the Socio-Economic Consequences of the COVID-19 Pandemic Using Computer Simulation

Evdokimov

2022

Artificial Societies

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In the context of the coronavirus pandemic, there is an increasing need to develop methods for scientifically based assessment of the consequences both at the level of the country's economy and at the regional level. One of the acute problems of the development of the Russian economy in the context of the coronavirus pandemic is the conflict between measures to protect the life and health of people and the fall in economic activity. To support the economy, countries are taking anti-crisis measures, which are aimed primarily at overcoming serious consequences in the most vulnerable sectors. As part of the study, to assess the socio-economic consequences of the epidemic and reproduce forecasts, modern simulation tools are used - agent-based modeling. Agent-based models allow you to use software of various classes, including neural networks, mathematical models, 3D-4D add-ons and other technologies that can visualize the results of scenario predictive estimates and computational experiments. The aim of the study is to develop methods and techniques for forecasting and scenario modeling of the socio-economic consequences of viral epidemics. For the study, a detailed statistical and analytical database was formed, adaptive blocks were developed with the possibility of additional inclusion of indicators. The software implementation included three functional blocks: demographic, economic and epidemiological, as well as three categories of agents within each subject of the Russian Federation with individual characteristics based on accepted world practice. The software tool chosen to implement the research objectives is the platform for creating agent-based models "AnyLogic". The study was carried out on the example of the following subjects of the Russian Federation: Murmansk region, Krasnodar region, Sverdlovsk, Samara and Voronezh regions. Based on the results of the study, an architecture of an agent-based model was developed, which makes it possible to evaluate restrictive measures and regulations in terms of the socio-economic consequences of a pandemic. As a result of the study, methods and algorithms for agent-based modeling of the socio-economic consequences of viral epidemics were developed, taking into account spatial and communicative interactions. To fulfill the objectives of the study, at the first stage, an analysis of scientific methods for forecasting and building various models for assessing the consequences of macroeconomic decisions and models for the spread of viral epidemics was carried out. At the second stage, an agent-based model was developed, which took into account structured and unstructured information, including the socio-demographic and economic characteristics of the regions, such as morbidity and mortality, employment rates, as well as measures taken by the regions to counter the spread of COVID-19. In terms of social interaction between agents, the study implemented a dynamic multi-relational (MRN) social network of agents, the structure of which changes during the introduction of quarantine measures that limit the degree of interaction between them. The introduction of different specific values of individual characteristics within a population of agents of the same type makes it possible to assess the socio-economic consequences of viral epidemics with the maximum degree of detail - at the level of individuals. Further development of this area of research will include refinement of the developed model for analyzing the consequences of the spread of viral epidemics in terms of the socio-economic development of territorial systems based on the obtained forecast scenarios.

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Section: эпидемиологический блок имитационных моделейunclassified

Section: Introductionunclassified