key: cord-0947732-k37ar16m
authors: Soltanian, Ali Reza; Bashirian, Saeid; Basti, Shahin Akhondzadeh; Karami, Manoochehr; Ostovar, Afshin; Khazaei, Salman
title: Estimation of the Hidden Population with COVID-19 Disease
date: 2020-06-25
journal: Int J MCH AIDS
DOI: 10.21106/ijma.396
sha: 4964df9d53c83d8008dc42c83c27d8dcb90027ab
doc_id: 947732
cord_uid: k37ar16m

The population with emerging diseases such as COVID-19, which is used to calculate the basic reproduction number of epidemic outbreak (R(0)) 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.

In epidemiological studies, estimation of attack rate indicators such as R 0 (i.e., the basic reproduction number of epidemic outbreak) has a special place among health analysts and policymakers to estimate the rate of epidemic. 1 For example, R 0 was used in Japan 1 to measure the potential for influenza transmission, H1N1 in 2009.

Although there are different ways to estimate R 0.

2 , one of the most common forms of R 0 used in epidemiological studies to estimate epidemic viability is the equation (1),

where, AR is the percentage of the infected population and S 0 is the initial percentage of susceptible population to disease in a target population. According to Equation (1), we find that the value of S 0 cannot be easily estimated, because duration of diseases and susceptible population is exactly unknown. Therefore, the relatively accurate method for estimating the number of susceptible (S 0 ) as one of the R 0 parameters is very important. However, this estimation in studies is usually not possible and its size is estimated either based on statistical distributions. 3 or determined from previous study information. Both of these strategies may cause a bias in estimating R 0 . Now, the fundamental question is "what is the way to estimate hidden population with covid-19 disease?" Another questions is, "is it possible to estimate coronavirus susceptibility population without screening and spending exorbitant CT-scan and PCR-test costs?" Several studies 4-6 have shown that hidden population can be estimated by the network scaleup method as indirect estimation method. Therefore, we propose a network scale-up method to estimate the hidden population with COVID-19 disease.

For simplicity and adapting to Lotfi's study, 7 we can introduce the steps of estimating the hidden population of people with COVID-19 disease using the following network expansion method:

1. Estimating the average of respondents' personal network size; 2. Asking the respondents about the number of people they know in the hidden population;

i.e., the number of people with coronavirus in the hidden population known by respondents; and 3. Estimation of the hidden population size, i.e., estimation of people with coronavirus as hidden population.

Bernard et al. 4 showed that the hidden population size can be estimated according to the Equation (2),

where, T is target population or the total number of people living in a geographical area, which is determined by census information, etc.; e is the size of the total hidden population in the target group, i.e., coronavirus patients; m is the average number of people with coronavirus that each person knows on their active social network; and C is average of the active social network of respondents. Now, according to equation (1) what is unknown for us is the size of S 0 (i.e., the hidden population with coronavirus), which can be replaced to in equation (2) . Thus, we can estimate the indirect estimate of the hidden population with Covid-19 disease using network expansion method according to Equation (3),

Our proposed method can answer the controversy over the estimation of the hidden population of patients with COVID-19 disease. The network scale-up methods have already been used to estimate the hidden population of drug abuse, 7 size estimation of groups at high risk of HIV/AIDS. 6 Achieving such a population plays an important role in health policy aimed at identifying the burden of the disease within groups and populations. 

Pros and cons of estimating the reproduction number from early epidemic growth rate of influenza A (H1N1)

The R0 package: a toolbox to estimate reproduction numbers for epidemic outbreaks

The Failure of R0

Counting hard-to-count populations: the network scale-up method for public health

Hidden population size estimation from respondent-driven sampling: a network approach

Size estimation of groups at high risk of HIV/AIDS using network scale up in Kerman

Estimation of the Population of Drug Abusers Using the Network Expansion Method for Assessment of the Community in the Golhesar Village