Estimating the Size of the Hidden Population of Covid-19 Publisher Pubmed



Mehraeen E¹ ; Akhtaran FK² ; Faridrohani MR² ; Afzalian A³ ; Mojdeganlou H⁴ ; Ghanbari Z⁵ ; Fathzadeh Y⁵ ; Gholizadeh M⁵ ; Seyedalinaghi S⁶ ; Hackett D⁷ Show Affiliations
Source: Infectious Disorders - Drug Targets Published:2024

Abstract

Introduction: An asymptomatic population has the same infection as symptomatic individuals, so these individuals can unknowingly spread the virus. It is not possible to predict the rate of epidemic growth by considering only the identified isolated or hospitalized population. In this study, we want to estimate the size of the COVID-19 population, based on information derived from patients visiting medical centers. So, individuals who do not receive a formal diagnosis in those medical centers can be considered as hidden. Methodology: To estimate the Bayesian size of the hidden population of COVID-19 a respondentdriven sampling (RDS) method was used. Twenty-three people infected with COVID-19 seeds and who had positive PCR test results were selected as seeds. These participants were asked whether any of their friends and acquaintances who had COVID-19 did not visit a medical center or hid their illness. Access to other patients was gained through friendship and kinship, hence allowing the sampling process to proceed. Results: Out of 23 selected seeds, only 15 seeds remained in the sample and the rest were excluded due to not participating in the further sampling process. After 5 waves, 50 people with COVID-19 who had hidden their disease and were not registered in the official statistics were included in the sample. It was estimated that 12,198 people were infected with COVID-19 in Khalkhal city in 2022. This estimate was much higher than recorded in the official COVID-19 statistics. Conclusions: The study findings indicate that the estimated 'true' numbers of COVID-19 patients in one town in Iran were significantly higher compared to the official numbers. The RDS method can help capture the potential size of infections in further pandemics or outbreaks globally. © 2024 Bentham Science Publishers.