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.
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.
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.
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.

Human trafficking has long-lasting implications for the well-being of trafficked people, families, and affected communities. Prevention and intervention efforts, however, have been stymied by a lack of information on the scale and scope of the problem. Because trafficked people are mostly hidden from view, traditional methods of establishing prevalence can be prohibitively expensive in the recruitment, participation, and retention of survey participants. Also, trafficked people are not randomly distributed in the general population. Researchers have therefore begun to apply methods previously used in public health research and other fields on hard-to-reach populations to measure the prevalence of human trafficking. In this topical review, we examine how these prevalence methods used for hard-to-reach populations can be used to measure the prevalence of human trafficking. These methods include network-based approaches, such as respondent-driven sampling and the network scale-up method, and venue-based methods. Respondent-driven sampling is useful, for example, when little information about the trafficked population has been produced and when an adequate sampling frame does not exist. The network scale-up method is unique in that it does not target the hidden population directly. The implications of our work internationally include the need for documenting and validating the various prevalence estimation methods in the United States in a more robust way than was done in existing efforts. In providing this roadmap for estimating the prevalence of human trafficking, our overarching goal is to promote the equitable treatment and overall well-being of the socially disadvantaged populations who disproportionately experience human trafficking.