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J Korean Med Sci ; 35(24): e227, 2020 Jun 22.
Artigo em Inglês | MEDLINE | ID: mdl-32567261

RESUMO

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.


Assuntos
Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/mortalidade , Hospitalização/estatística & dados numéricos , Hospitalização/tendências , Pneumonia Viral/epidemiologia , Pneumonia Viral/mortalidade , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Betacoronavirus , COVID-19 , Criança , Pré-Escolar , Feminino , Previsões , Humanos , Incidência , Lactente , Recém-Nascido , Cazaquistão/epidemiologia , Masculino , Pessoa de Meia-Idade , Modelos Estatísticos , Pandemias , Prognóstico , SARS-CoV-2 , Índice de Gravidade de Doença , Adulto Jovem
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