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Estimating Hidden Population Size of COVID-19 using Respondent-Driven Sampling Method - A Systematic Review
SeyedAhmad SeyedAlinaghi1, Arian Afzalian2, Mohsen Dashti3
1Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High-Risk Behaviors, Tehran University of Medical Sciences, Tehran, Iran.
Infectious Disorders Drug Targets
|February 1, 2024
Summary
Respondent-driven sampling (RDS) effectively estimates hidden COVID-19 cases, revealing significantly higher infection numbers than reported. This cost-effective method is crucial for future epidemic preparedness.
Area of Science:
- Epidemiology
- Public Health
- Statistical Methods
Background:
- The COVID-19 pandemic presents a global health challenge, with infection numbers often underestimated due to asymptomatic carriers.
- Accurate detection of COVID-19 cases is vital for effective treatment and prevention strategies.
- Conventional sampling methods are insufficient for reaching hidden or hard-to-reach populations.
Conclusions:
- Respondent-driven sampling (RDS) demonstrates efficacy in estimating undetected, asymptomatic COVID-19 cases.
- RDS offers a cost-effective, low-cost, and relatively trouble-free sampling method.
- The methodology holds significant value for managing probable future epidemics.
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