A spatial autocorrelation for modelling the spread of coronavirus infections

A spatial autocorrelation for modelling the spread of coronavirus infections Full article

Journal

SHS Web of Conferences
, E-ISSN: 2261-2424

Output data

Year: 2021, Volume: 106, Article number : 01001, Pages count : 7 DOI: 10.1051/shsconf/202110601001

Authors

Naumov Ilya ¹ , Krasnykh Sergey ¹ , Otmakhova Yuliya ²

Affiliations

1	Institute of Economics of the Ural Branch of the Russian Academy of Sciences
2	Central Economics and Mathematics Institute of the Russian Academy of Sciences

Funding (1)

Российский фонд фундаментальных исследований

20-04-60188

Spatial autocorrelation methods are used to study spatial disproportions in the socio-economic development of territories. The most common research methods are the analysis of Moran local indices, Moran global index, Getis-Ord hot spots. In this study, we used spatial autocorrelation methods to estimate COVID-19 distribution patterns. As a result of the study, we identified the formed growth poles, the epicenters of the spread of infection (St. Petersburg, Sverdlovsk and Nizhny Novgorod regions) and only emerging ones. The practical application of this methodological approach allowed us to predict further spatial directions of the spread of coronavirus infection (Vladimir, Kaluga, Smolensk, Tula, Tver, Yaroslavl, Ryazan and Leningrad regions).

Naumov I. , Krasnykh S. , Otmakhova Y.
A spatial autocorrelation for modelling the spread of coronavirus infections
SHS Web of Conferences. 2021. V.106. 01001 :1-7. DOI: 10.1051/shsconf/202110601001 OpenAlex

OpenAlex:

W3162625511

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