ABSTRACT
Background: To inform researchers about the methodology and results of epidemic estimation studies performed for COVID-19 epidemic in Iran, we aimed to perform a rapid review. Methods: We searched for and included published articles, preprint manuscripts and reports that estimated numbers of cumulative or daily deaths or cases of COVID-19 in Iran. We found 131 studies and included 29 of them. Results: The included studies provided outputs for a total of 84 study-model/scenario combinations. Sixteen studies used 3-4 compartmental disease models. At the end of month two of the epidemic (2020-04-19), the lowest (and highest) values of predictions were 1777 (388951) for cumulative deaths, 20588 (2310161) for cumulative cases, and at the end of month four (2020-06-20), were 3590 (1819392) for cumulative deaths, and 144305 (4266964) for cumulative cases. Highest estimates of cumulative deaths (and cases) for latest date available in 2020 were 418834 on 2020-12-19 (and 41475792 on 2020-12-31). Model estimates predict an ominous course of epidemic progress in Iran. Increase in percent population using masks from the current situation to 95% might prevent 26790 additional deaths (95% confidence interval 19925-35208) by the end of year 2020. Conclusions: : Meticulousness and degree of details reported for disease modeling and statistical methods used in the included studies varied widely. Greater heterogeneity was observed regarding the results of predicted outcomes. Consideration of minimum and preferred reporting items in epidemic estimation studies might better inform future revisions of the available models and new models to be developed. Not accounting for under-reporting drives the models’ results misleading.
Subject(s)
COVID-19 , Encephalitis, ArbovirusABSTRACT
Aim: Abnormal laboratory findings have been shown to be associated with severe COVID-19. However, all aspects of this association have not been reviewed systematically. Therefore, the aim of this meta-analysis was to explore crucial laboratory parameters in severe COVID-19 infection. Methods: We performed the literature review of scientific articles indexed in electronic databases. Scientific search engines were used to perform the electronic literature search. After the removal of duplicates and selection of articles of interest, 30 studies were eligible to include. If heterogeneity was high (I2>50%), a random-effects model was applied to combine the data. Otherwise, a fixed-effects model was used.Results: A total of 5586 individuals were assessed (1555 patients with severe COVID-19 infection and 3452 with non-severe infection). Platelets, lymphocytes and serum albumin were significantly lower in severe patients while other biochemical and immunological parameters including prothrombin time, ALT, AST, total bilirubin, LDH, procalcitonin, CRP, IL-6, and IgA were significantly higher in patients with severe infection. Neutrophil and monocyte counts as well as hemoglobin level, D-dimer, hypersensitive troponin I, IL-2R, IgG and IgM levels were different between two groups; however, the difference was not statistically significant (All P-values >0.05). Conclusions: Lymphopenia, elevated liver enzymes, and high levels of inflammatory biomarkers are associated with severe COVID-19 infection.
Subject(s)
COVID-19 , LymphopeniaABSTRACT
Background It remains unclear whether a specific chest CT characteristic is associated with the clinical severity of COVID-19. This meta-analysis was performed to assess the relationship between different chest CT features and severity of clinical presentation in COVID-19.Methods PubMed, Embase, Scopus, web of science databases (WOS), Cochrane library, and Google scholar were searched up to May 19, 2020 for observational studies that assessed the relationship of different chest CT manifestations and the severity of clinical presentation in COVID-19 infection. Risk of bias assessment was evaluated applying the Newcastle-Ottawa Scale. A random-effects model or fixed-effects model, as appropriately, were used to pool results. Heterogeneity was assessed using Forest plot, Cochran's Q test, and I2. Publication bias was assessed applying Egger’s test.Results A total of 18 studies involving 3323 patients were included. Bronchial wall thickening (OR 11.64, 95% CI 1.81- 74.66) was more likely to be associated with severe cases of COVID-19 infection, followed by linear opacity (OR 3.27, 95% CI 1.10- 9.70), and GGO (OR 1.37, 95% CI 1.08- 1.73). However, there was no significant association between the presence of consolidation and severity of clinical presentation (OR 2.33, 95% CI 0.85- 6.36). Considering the lesion distribution bilateral lung involvement was more frequently associated with severe clinical presentation (OR 3.44, 95% CI 1.74- 6.79).Conclusions Our meta-analysis of observational studies suggests some specific chest CT features. The presence of these CT features can help the physicians to early and appropriately approach to the severe and fatal cases of COVID-19.