Epidemiological Characteristics and Forecast of COVID-19 Outbreak in the Republic of Kazakhstan

Semenova Yuliya; Glushkova Natalya; Pivina Lyudmila; Khismetova Zaituna; Zhunussov Yersin; Sandybaev Marat; Ivankov Alexandr

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자료유형: 학술저널

저자정보: Semenova Yuliya (Semey Medical University) Glushkova Natalya (Semey Medical University) Pivina Lyudmila (Semey Medical University) Khismetova Zaituna (Semey Medical University) Zhunussov Yersin (Semey Medical University) Sandybaev Marat (Regional Oncology Hospital) Ivankov Alexandr (Kazakh Medical University of Continuing Education)

저널정보: 대한의학회 Journal of Korean Medical Science Journal of Korean Medical Science Vol.35 No.24

발행연도: 2020.1

수록면: 1 - 12 (12page)

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Background: Coronavirus disease 2019 (COVID-19) pandemic entered Kazakhstan on 13 March 2020 and quickly spread over its territory. This study aimed at reporting on the rates of COVID-19 in the country and at making prognoses on cases, deaths, and recoveries through predictive modeling. Also, we attempted to forecast the needs in professional workforce depending on implementation of quarantine measures. Methods: We calculated both national and local incidence, mortality and case-fatality rates, and made forecast modeling via classic susceptible-exposed-infected-removed (SEIR) model. The Health Workforce Estimator tool was utilized for forecast modeling of health care workers capacity. Results: The vast majority of symptomatic patients had mild disease manifestations and the proportion of moderate disease was around 10%. According to the SEIR model, there will be 156 thousand hospitalized patients due to severe illness and 15.47 thousand deaths at the peak of an outbreak if no measures are implemented. Besides, this will substantially increase the need in professional medical workforce. Still, 50% compliance with quarantine may possibly reduce the deaths up to 3.75 thousand cases and the number of hospitalized up to 9.31 thousand cases at the peak. Conclusion: The outcomes of our study could be of interest for policymakers as they help to forecast the trends of COVID-19 outbreak, the demands for professional workforce, and to estimate the consequences of quarantine measures.

#COVID-19 #Kazakhstan #Forecast Modeling #Quarantine #Workforce

참고문헌 (33)

참고문헌 신청

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