Estimating Hidden Population Size of COVID-19 using Respondent-Driven Sampling Method - A Systematic Review


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Abstract

Introduction:Currently, the ongoing COVID-19 pandemic is posing a challenge to health systems worldwide. Unfortunately, the true number of infections is underestimated due to the existence of a vast number of asymptomatic infected individual’s proportion. Detecting the actual number of COVID-19-affected patients is critical in order to treat and prevent it. Sampling of such populations, so-called hidden or hard-to-reach populations, is not possible using conventional sampling methods. The objective of this research is to estimate the hidden population size of COVID-19 by using respondent-driven sampling methods.

Methods:This study is a systematic review. We have searched online databases of PubMed, Web of Science, Scopus, Embase, and Cochrane to identify English articles published from the beginning of December 2019 to December 2022 using purpose-related keywords. The complete texts of the final chosen articles were thoroughly reviewed, and the significant findings are condensed and presented in the table

Results:Of the 7 included articles, all were conducted to estimate the actual extent of COVID- 19 prevalence in their region and provide a mathematical model to estimate the asymptomatic and undetected cases of COVID-19 amid the pandemic. Two studies stated that the prevalence of COVID-19 in their sample population was 2.6% and 2.4% in Sierra Leone and Austria, respectively. In addition, four studies stated that the actual numbers of infected cases in their sample population were significantly higher, ranging from two to 50 times higher than the recorded reports.

Conclusions:In general, our study illustrates the efficacy of RDS sampling in the estimation of undetected asymptomatic cases with high cost-effectiveness due to its relatively trouble-free and low-cost methods of sampling the population. This method would be valuable in probable future epidemics.

About the authors

SeyedAhmad SeyedAlinaghi

Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High-Risk Behaviors, Tehran University of Medical Sciences

Email: info@benthamscience.net

Arian Afzalian

School of medicine, Tehran University of Medical Sciences

Email: info@benthamscience.net

Mohsen Dashti

Department of Radiology, Tabriz University of Medical Sciences

Email: info@benthamscience.net

Afsaneh Ghasemzadeh

Department of Radiology, Tabriz University of Medical Sciences

Email: info@benthamscience.net

Zohal Parmoon

Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High-Risk Behaviors, Tehran University of Medical Sciences

Email: info@benthamscience.net

Ramin Shahidi

School of Medicine, Bushehr University of Medical Sciences

Email: info@benthamscience.net

Sanaz Varshochi

School of Medicine, Tehran University of Medical Sciences

Email: info@benthamscience.net

Ava Pashaei

Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High-Risk Behaviors, Tehran University of Medical Sciences

Email: info@benthamscience.net

Samaneh Mohammadi

Department of Health Information Technology, School of Allied Medical Sciences, Tehran University of Medical Sciences

Email: info@benthamscience.net

Fatemeh Khajeh Akhtaran

Faculty of Mathematical Sciences, Shahid Beheshti University

Email: info@benthamscience.net

Amirali Karimi

School of Medicine, Tehran University of Medical Sciences

Email: info@benthamscience.net

Khadijeh Nasiri

Department of Nursing, Khalkhal University of Medical Sciences

Email: info@benthamscience.net

Esmaeil Mehraeen

Department of Health Information Technology, Khalkhal University of Medical sciences

Author for correspondence.
Email: info@benthamscience.net

Daniel Hackett

Physical Activity, Lifestyle, Ageing and Wellbeing Faculty Research Group, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney

Email: info@benthamscience.net

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