Modifying impact of environmental factors on the course of an epidemic process
- Authors: Zaitseva N.V.1, Popova A.Y.2, Kleyn S.V.1, Letyushev A.N.2, Kiryanov D.A.1, Chigvintsev V.M.1, Glukhikh M.V.1
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Affiliations:
- Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
- The Russian Medical Academy for Continuous Occupational Learning of the RF Public Healthcare Ministry
- Issue: Vol 101, No 11 (2022)
- Pages: 1274-1282
- Section: ENVIRONMENTAL HYGIENE
- Published: 26.12.2022
- URL: https://rjraap.com/0016-9900/article/view/638711
- DOI: https://doi.org/10.47470/0016-9900-2022-101-11-1274-1282
- ID: 638711
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Full Text
Abstract
Introduction. It is necessary to establish peculiarities and regularities of COVID-19 infection; this task requires further research on how to formalize and build spatial-temporal models of the infection spread. This article focuses on determining non-infectious factors that can modify the epidemic process caused by the COVID-19 coronavirus for further substantiation of integrated solutions that are necessary to ensure sanitary-epidemiological welfare of the RF population.
Materials and methods. Our study involved analyzing regularities of regional differentiation in parameters introduced into mathematical models. These models described how the epidemic process developed in RF regions depending on modifying non-infectious factors identified by modelling the dynamics of spread of SARS-CoV-2 delta strain. These modifying factors included anti-epidemic activities; sanitary-epidemiological, sociodemographic, and economic conditions in a region; weather and climate; public healthcare systems and people’s lifestyles in RF regions over 2020–2021. The dynamics of the epidemic process was modelled by using the conventional SIR-model. Relationships between parameters introduced into the model of the epidemic process and modifying regional conditions were examined by using correlation-regression analysis.
Results. The modelling made it possible to identify priority risk factors that modified COVID-19 spread authentically (p<0.05) and explained regional differences in intensity of contagion, recovery and lethality. We established that population coverage with vaccination, especially among people aged 31–40 years, had the greatest authentic positive influence on the decline of reproduction index (R0) of the virus (r=–0.37). An increase in monthly average temperatures in autumn and winter as well as over a year made for people moving faster from the susceptible to infected category (r=0.21–0.22). Growing sun insolation over a year, especially in summer, resulted in slower movement of susceptible people into the infected category (r=–0.02–(–0.23)). Next, several sanitary-epidemiological indicators authentically made the infection spread faster; they were improper working conditions (not conforming to the safety standards as per physical indicators) and ambient air quality in settlement not corresponding to the hygienic standards as per chemical indicators and noise (r=0.29–0.24). Recovery took longer in regions where alcohol consumption was comparatively higher (r=–0.32).
Limitations. The limitations of the study include modelling the epidemic process using the standard SIR model; limited set of indicators and period of analysis.
Conclusions. The existing regional differentiation in development of specific stages in the epidemic process related to the COVID-19 delta strain occurs due to complex interactions and influence exerted by modifying factors that create a certain multi-level and multi-component system. This system is able to transform the epidemic process either potentiating it or slowing it down.
Compliance with ethical standards. No approval by the committee on biomedical ethics was required to accomplish this study (it was based on free available data taken from the official statistical reports).
Contribution:
Zaitseva N.V., PopovaA.Yu. — concept and design of the study, writing text, editing;
Kleyn S.V. — concept and design of the study, collection and processing of the material, writing text, editing;
Kiryanov D.A. — concept and design of the study, collection and processing of the material, writing text;
Chigvintsev V.M., Glukhikh M.V. — collection and processing of the material, writing text.
All authors are responsible for the integrity of all parts of the manuscript and approval of the manuscript final version.
Conflict of interest. The authors declare no conflict of interest.
Acknowledgement. The study had no sponsorship.
Received: October 20, 2022 / Accepted: October 3, 2022 / Published: November 30, 2022
About the authors
Nina V. Zaitseva
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
Author for correspondence.
Email: noemail@neicon.ru
ORCID iD: 0000-0003-2356-1145
Russian Federation
Anna Yu. Popova
The Russian Medical Academy for Continuous Occupational Learning of the RF Public Healthcare Ministry
Email: noemail@neicon.ru
ORCID iD: 0000-0003-2567-9032
Russian Federation
Svetlana V. Kleyn
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
Email: kleyn@fcrisk.ru
ORCID iD: 0000-0002-2534-5713
MD, PhD, DSci,, Head of the department of sanitary and hygienic analysis and monitoring of systemic methods Federal Scientific Center for Medical and Preventive Health Risk Management Technologies, Perm, 614045, Russian Federation.
e-mail: kleyn@fcrisk.ru
Russian FederationAleksandr N. Letyushev
The Russian Medical Academy for Continuous Occupational Learning of the RF Public Healthcare Ministry
Email: noemail@neicon.ru
ORCID iD: 0000-0002-4185-9829
Russian Federation
Dmitry A. Kiryanov
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
Email: noemail@neicon.ru
ORCID iD: 0000-0002-5406-4961
Russian Federation
Vladimir M. Chigvintsev
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
Email: noemail@neicon.ru
ORCID iD: 0000-0002-0345-3895
Russian Federation
Maxim V. Glukhikh
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies
Email: noemail@neicon.ru
ORCID iD: 0000-0002-4755-8306
Russian Federation
References
- WHO. World Health Statistics 2022: Monitoring Health for the SDGs, Sustainable Development Goals; 2022.
- UNDP. Special Report on Human Security. New Threats to Human Security in the Anthropocene: Demanding Greater Solidarity; 2022. https://doi.org/10.18356/9789210014007
- COVID-19 Excess Mortality Collaborators. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21. Lancet. 2022; 399(10334): 1513–36. https://doi.org/10.1016/S0140-6736(21)02796-3
- The Economist. The pandemic’s true death toll. Available at: https://www.economist.com/graphic-detail/coronavirus-excess-deaths-estimates
- Our World in Data. Total confirmed COVID-19 deaths. Available at: https://ourworldindata.org/grapher/covid-deaths-income
- International Science Council. Unprecedented & Unfinished: COVID-19 and Implications for National and Global Policy. Paris; 2022. https://doi.org/10.24948/2022.03
- Aburto J.M., Schöley J., Kashnitsky I., Zhang L., Rahal C., Missov T.I., et al. Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries. Int. J. Epidemiol. 2022; 51(1): 63–74. https://doi.org/10.1093/ije/dyab207
- Aburto J.M., Kashyap R., Schöley J., Angus C., Ermisch J., Mills M.C., et al. Estimating the burden of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality in England and Wales: a population-level analysis. J. Epidemiol. Community Health. 2021; 75(8): 735–40. https://doi.org/10.1136/jech-2020-215505
- Yadav S., Yadav P.K., Yadav N. Impact of COVID-19 on life expectancy at birth in India: a decomposition analysis. BMC Public Health. 2021; 21(1): 1906. https://doi.org/10.1186/s12889-021-11690-z
- Castro M.C., Gurzenda S., Turra C.M., Kim S., Andrasfay T., Goldman N. COVID-19 is not an independent cause of death. medRxiv. 2022; 2022.06.01.22275878. Preprint. https:/doi.org/10.1101/2022.06.01.22275878
- Schwartz J. Harvesting and long-term exposure effects in the relation between air pollution and mortality. Am. J. Epidemiol. 2000; 151(5): 440–8. https://doi.org/10.1093/oxfordjournals.aje.a010228
- Chan E., Cheng D., Martin J. Impact of COVID-19 on excess mortality, life expectancy, and years of life lost in the United States. PLoS One. 2021; 16(9): e0256835. https://doi.org/10.1371/journal.pone.0256835
- COVID-19 Forecasting Team. Variation in the COVID-19 infection-fatality ratio by age, time, and geography during the pre-vaccine era: a systematic analysis. Lancet. 2022; 399(10334): 1469–88. https://doi.org/10.1016/S0140-6736(21)02867-1
- Cherkasskiy B.L. Global Epidemiology [Global’naya epidemiologiya]. Moscow; 2008. (in Russian)
- Cherkasskiy B.L. The epidemic process as a system. Message 1. The structure of the epidemic process. Zhurnal mikrobiologii, epidemiologii i immunobiologii. 1985; 62(3): 45–51. (in Russian)
- Zaytseva N.V., Popova A.Yu., Alekseev V.B., Kir’yanov D.A., Chigvintsev V.M. Regional peculiarities of the epidemiological process caused by SARS-COV-2 (COVID-19), compensation for the impact of modifying factors of non-infectious genesis. Gigiena i Sanitaria (Hygiene and Sanitation, Russian journal). 2022; 101(6): 701–8. https://doi.org/10.47470/0016-9900-2022-101-6-701-708 (in Russian)
- McKendrick A. Applications of Mathematics to Medical Problems. Proc. Edinburgh Math. Soc. 1925; 44: 98–130. https://doi.org/10.1017/S0013091500034428
- Kermack W.O., McKendrick A.G. A contribution to the mathematical theory of epidemics. Proc. Royal Soc. London. 1927; A115: 700–21. https://doi.org/10.1098/rspa.1927.0118
- Li W., Zhang P., Zhao K., Zhao S. The geographical distribution and influencing factors of COVID-19 in China. Trop. Med. Infect. Dis. 2022; 7(3): 45. https://doi.org/10.3390/tropicalmed7030045
- Meo S.A., Abukhalaf A.A., Alomar A.A., Sumaya O.Y., Sami W., Shafi K.M., et al. Effect of heat and humidity on the incidence and mortality due to COVID-19 pandemic in European countries. Eur. Rev. Med. Pharmacol. Sci. 2020; 24(17): 9216–25. https://doi.org/10.26355/eurrev_202009_22874
- Khalis M., Toure A.B., El Badisy I., Khomsi K., Najmi H., Bouaddi O., et al. Relationship between Meteorological and Air Quality Parameters and COVID-19 in Casablanca Region, Morocco. Int. J. Environ. Res. Public Health. 2022; 19(9): 4989. https://doi.org/10.3390/ijerph19094989
- Islam M.M., Noor F.M. Correlation between COVID-19 and weather variables: A meta-analysis. Heliyon. 2022; 8(8): e10333. https://doi.org/10.1016/j.heliyon.2022.e10333
- Chu D.K., Akl E.A., Duda S., Solo K., Yaacoub S., Schünemann H.J. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet. 2020; 395(10242): 1973–87. https://doi.org/10.1016/S0140-6736(20)31142-9
- Zhu J., Yan W., Zhu L., Liu J. COVID-19 pandemic in BRICS countries and its association with socio-economic and demographic characteristics, health vulnerability, resources, and policy response. Infect. Dis. Poverty. 2021; 10(1): 97. https://doi.org/10.1186/s40249-021-00881-w
- Zaytseva N.V., Kleyn S.V., Glukhikh M.V., Kir’yanov D.A., Kamaltdinov M.R. Regional peculiarities of the epidemiological process caused by SARS-COV-2 (Covid-19), compensation for the impact of modifying factors of non-infectious genesis. Analiz riska zdorov’yu. 2022; (2): 4–16. https://doi.org/10.21668/health.risk/2022.2.01.eng
- Popova A.Yu., Zaytseva N.V., Onishchenko G.G., Kleyn S.V., Glukhikh M.V., Kamaltdinov M.R. Sanitary-epidemiologic determinants and potential for growth in life expectancy of the population in the Russian Federation taking into account regional differentiation. Analiz riska zdorov’yu. 2020; (1): 4–17. https://doi.org/10.21668/health.risk/2020.1.01.eng
- Wilta F., Chong A.L.C., Selvachandran G., Kotecha K., Ding W. Generalized Susceptible-Exposed-Infectious-Recovered model and its contributing factors for analysing the death and recovery rates of the COVID-19 pandemic. Appl. Soft Comput. 2022; 123: 108973. https://doi.org/10.1016/j.asoc.2022.108973
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