Estimating Hidden Population Size of COVID-19 Using Respondent- Driven Sampling Method - A Systematic Review.

INTRODUCTION: Currently, the ongoing COVID-19 pandemic is posing a challenge to health systems worldwide. Unfortunately, the true number of infections is underestimated due to the existence of a vast number of asymptomatic infected individual's proportion. Detecting the actual number of COVID-19-affected patients is critical in order to treat and prevent it. Sampling of such populations, so-called hidden or hard-to-reach populations, is not possible using conventional sampling methods. The objective of this research is to estimate the hidden population size of COVID-19 by using respondent-driven sampling methods. METHOD: This study is a systematic review. We have searched online databases of PubMed, Web of Science, Scopus, Embase, and Cochrane to identify English articles published from the beginning of December 2019 to December 2022 using purpose-related keywords. The complete texts of the final chosen articles were thoroughly reviewed, and the significant findings are condensed and presented in the table. RESULTS: Of the 7 included articles, all were conducted to estimate the actual extent of COVID- 19 prevalence in their region and provide a mathematical model to estimate the asymptomatic and undetected cases of COVID-19 amid the pandemic. Two studies stated that the prevalence of COVID-19 in their sample population was 2.6% and 2.4% in Sierra Leone and Austria, respectively. In addition, four studies stated that the actual numbers of infected cases in their sample population were significantly higher, ranging from two to 50 times higher than the recorded reports. CONCLUSIONS: In general, our study illustrates the efficacy of RDS sampling in the estimation of undetected asymptomatic cases with high cost-effectiveness due to its relatively trouble-free and low-cost methods of sampling the population. This method would be valuable in probable future epidemics.

The effect of macronutrient and micronutrient supplements on COVID-19: an umbrella review.

BACKGROUND AND AIMS: A healthy diet play an important role in the prevention and even treatment of various diseases. Proper nutrition plays an important role in boosting of immune system. These include the consumption of macronutrients such as proteins, lipids, carbohydrates, and also micronutrients including vitamins. Here, we aimed to systematically review the effects of macronutrients and micronutrients on the prevention and treatment of COVID-19. METHODS: We searched the databases of PubMed, Scopus, Embase, and Web of Science on December 23, 2023. The records were downloaded into an EndNote file, the duplicates were removed, and the studies underwent a two-phase screening process based on their title/abstracts and full texts. The included articles were screened and underwent inclusion and exclusion criteria. We included the English systematic reviews and meta-analyses that concurred with the aim of our study. The selected articles were assessed by Cochrane's Risk of Bias in Systematic Reviews for the quality check. The data of the eligible studies were extracted in a pre-designed word table and were used for the qualitative synthesis. RESULTS: A total of 28 reviews were included in this study. Most studies have shown that micronutrients are effective in morbidity and mortality controlling in viral respiratory infections such as COVID-19 but some studies have shown that micronutrients are sometimes not effective in controlling severity. On the other hand, calcifediol was by far the most successful agent in reducing intensive care needs and mortality between studies. CONCLUSION: Individuals without malnutrition had a reduced risk of SARS-CoV-2 infection and severe disease. The administration of Vitamin D is effective in reducing the morbidity and mortality of COVID-19 patients. Patients with vitamin D deficiency were more prone to experience severe infection, and they were at higher risk of morbidities and mortality. Other micronutrients such as Vitamin A, Vitamin B, and Zinc also showed some benefits in patients with COVID-19. Vitamin C showed no efficacy in COVID-19 management even in intravenous form or in high doses.

Estimating the Size of the Hidden Population of COVID-19.

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.

Estimating Methods of the Undetected Infections in the COVID-19 Outbreak: A Systematic Review.

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.