Title:Estimating Methods of the Undetected Infections in the COVID-19
Outbreak: A Systematic Review
Volume: 23
Issue: 4
Author(s): Esmaeil Mehraeen, Zahra Pashaei, Fatemeh Khajeh Akhtaran, Mohsen Dashti, Arian Afzalian, Afsaneh Ghasemzadeh, Pooria Asili, Mohammad Saeed Kahrizi, Maryam Mirahmad, Ensiyeh Rahimi, Parisa Matini, Amir Masoud Afsahi, Omid Dadras and SeyedAhmad SeyedAlinaghi*
Affiliation:
- Iranian
Research Center for HIV/AIDS, Iranian Institute for Reduction of High-Risk Behaviors, Tehran University of Medical
Sciences, Tehran, Iran
Keywords:
Undetected infections, COVID-19, sampling, snowball sampling, network-based sampling, clinical symptoms.
Abstract:
Introduction: The accurate number of COVID-19 cases is essential knowledge to control an
epidemic. Currently, one of the most important obstacles in estimating the exact number of COVID-19
patients is the absence of typical clinical symptoms in a large number of people, called asymptomatic
infections. In this systematic review, we included and evaluated the studies mainly focusing on the
prediction of undetected COVID-19 incidence and mortality rates as well as the reproduction numbers,
utilizing various mathematical models.
Methods: This systematic review aims to investigate the estimating methods of undetected infections
in the COVID-19 outbreak. Databases of PubMed, Web of Science, Scopus, Cochrane, and Embase,
were searched for a combination of keywords. Applying the inclusion/exclusion criteria, all retrieved
English literature by April 7, 2022, were reviewed for data extraction through a two-step screening
process; first, titles/abstracts, and then full-text. This study is consistent with the PRISMA checklist.
Results: In this study, 61 documents were retrieved using a systematic search strategy. After an initial
review of retrieved articles, 6 articles were excluded and the remaining 55 articles met the inclusion
criteria and were included in the final review. Most of the studies used mathematical models to estimate
the number of underreported asymptomatic infected cases, assessing incidence and prevalence
rates more precisely. The spread of COVID-19 has been investigated using various mathematical models.
The output statistics were compared with official statistics obtained from different countries. Although
the number of reported patients was lower than the estimated numbers, it appeared that the
mathematical calculations could be a useful measure to predict pandemics and proper planning.
Conclusion: In conclusion, our study demonstrates the effectiveness of mathematical models in unraveling
the true burden of the COVID-19 pandemic in terms of more precise, and accurate infection and
mortality rates, and reproduction numbers, thus, statistical mathematical modeling could be an effective
tool for measuring the detrimental global burden of pandemic infections. Additionally, they could
be a really useful method for future pandemics and would assist the healthcare and public health systems
with more accurate and valid information.
