Estimation of the Hidden Population with COVID-19 Disease

Authors

  • Ali Reza Soltanian, PhD Department of Biostatistics, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
  • Saeid Bashirian, PhD Department of Public Health, School of Public Health, Hamadan University of Medical Sciences. Iran
  • Shahin Akhondzadeh Basti, PhD Psychiatric Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
  • Manoochehr Karami, PhD Social Determinants of Health Research Center, Hamadan University of Medical Sciences, Iran
  • Afshin Ostovar, MD, PhD Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran
  • Salman Khazaei, PhD Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran

DOI:

https://doi.org/10.21106/ijma.396

Keywords:

Corona virus, Covid-19, Epidemiology , Hidden population, Attack rate

Abstract

The population with emerging diseases such as COVID-19, which is used to calculate the basic reproduction number of epidemic outbreak (R0) cannot be simply observed. In this article, we have proposed a method for estimating the hidden population of people with COVID-19 disease. Knowing the number of people with COVID-19 disease is very important for health policy. The provision of medical equipment (e.g., masks, alcohol, ventilators, medication, etc.), the reopening of schools and universities, the start of tourism and public gatherings, the provision of medical staff and preventive planning depend on the number of patients with the disease. Therefore, it is very important to estimate the number of patients.

 

Copyright © 2020 Soltanian et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0.

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Publication History

Issue

Vol. 9 No. 2

Section

Letter to the Editor

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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