5 CONCLUSIONS
Mobile phone activity during the spring and autumn
emergencies still has been high in both cities and
country, reaching 70 % to 80 %of the pre-crisis
period. Since the spring, the behaviour and habits of
the population have changed significantly, so a
different approach is needed.
Residents' shopping habits have changed
significantly – on weekends, the number of visitors in
shopping centres has dropped significantly as people
choose to shop on weekdays instead. Human activity
at household goods stores has remained high since
spring.
People's activity decreases on weekends, while
activity on weekdays even increases slightly. At
present, introducing the same restrictions as in the
spring has failed to achieve an equivalent behavioural
change and the level of discipline.
Restrictions on weekends have increased
shopping activity on weekdays, where activity was
already much higher than on weekends. Authorities
ought to examine the possibility of applying
restrictions to all days of the week, not just holidays,
to compensate for the uneven workload of the
shopping infrastructure.
Restrictions that balance the day-to-day shopping
load should be considered to limit the number of
people who visit the store at the same time, creating
peak visits.
It is necessary to create and communicate positive
alternatives to spending weekends and holidays in
shopping centres, because restrictions alone do not
achieve the desired effect and provoke negative
reactions.
ACKNOWLEDGEMENTS
This work was supported by the University of Latvia
and LMT Ltd. [grant number 7-3/151/2].
REFERENCES
Arhipova, I., Berzins, G., Brekis, E., Opmanis, M.,
Binde, J., Steinbuka, I., Kravcova, J., 2019. Pattern
Identification by Factor Analysis for Regions with
Similar Economic Activity Based on Mobile
Communication Data. Advances in Intelligent Systems
and Computing, 886, pp.561–569.
Arhipova, I., Berzins, G., Brekis, E., Binde, J.,
Opmanis, M., Erglis, A., Ansonska, E., 2020. Mobile
phone data statistics as a dynamic proxy indicator in
assessing regional economic activity and human
commuting patterns. Expert Systems, 37(50), e12530.
Block, P., Hoffman, M., Raabe, I.J., Dowd, J. B., Rahal, C.,
Kashyap, R., 2020. Social network-based distancing
strategies to flatten the COVID-19 curve in a post-
lockdown world. Nature Human Behaviour, 4, pp.588–
596.
Chang, S., Pierson, E., Koh, P.W., Gerardin, J., Redbird, B.,
Grusky, D., Leskovec, J., 2020. Mobility network
models of COVID-19 explain inequities and inform
reopening. Nature (2020).
Ghanbari, B., 2020. On forecasting the spread of the
COVID-19 in Iran: The second wave, Chaos, Solitons
& Fractals, 140, 110176.
Mahmoudi, M. R., Baleanu, D., Mansor, Z., Tuan, B. A.,
Pho, K.-H., 2020. Fuzzy clustering method to compare
the spread rate of Covid-19 in the high risks countries.
Chaos, Solitons & Fractals, 140, 110230.
Păcurar, C.-M., Necula, B.-R., 2020. An analysis of
COVID-19 spread based on fractal interpolation and
fractal dimension, Chaos, Solitons & Fractals, 139,
110073.
SPKC. Center for Disease Prevention and Control in Latvia,
https://www.spkc.gov.lv/lv