Several coronaviruses (CoVs) have been associated with serious health hazards in recent decades, resulting in the deaths of thousands around the globe. The recent coronavirus pandemic has emphasized the importance of discovering novel and effective antiviral medicines as quickly as possible to prevent more loss of human lives. Positive-sense RNA viruses with group spikes protruding from their surfaces and an abnormally large RNA genome enclose CoVs. CoVs have already been related to a range of respiratory infectious diseases possibly fatal to humans, such as MERS, SARS, and the current COVID-19 outbreak. As a result, effective prevention, treatment, and medications against human coronavirus (HCoV) is urgently needed. In recent years, many natural substances have been discovered with a variety of biological significance, including antiviral properties. Throughout this work, we reviewed a wide range of natural substances that interrupt the life cycles for MERS and SARS, as well as their potential application in the treatment of COVID-19.

Natural bioactive molecules: An alternative approach to the treatment and control of covid-19

BACKGROUND: COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. METHODS: An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. RESULTS: Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019-13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21-12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14-4.98) for moderate-selenium-deficient cities and 3.06 (1.49-6.27) for severe-selenium-deficient cities. CONCLUSIONS: Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

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Association between fatality rate of COVID-19 and selenium deficiency in China.

Abnormal inflammatory mediator concentrations during SARS-CoV-2 infection may represent disease severity. We aimed to assess plasma inflammatory mediator concentrations in patients with SARS-CoV-2 in Addis Ababa, Ethiopia. In this study, 260 adults: 126 hospitalized patients with confirmed COVID-19 sorted into severity groups: severe (n=68) and mild or moderate (n=58), and 134 healthy controls were enrolled. We quantified 39 plasma inflammatory mediators using multiplex ELISA. Spearman rank correlation and Mann-Whitney U test were used to identify mechanistically coupled inflammatory mediators and compare disease severity. Compared to healthy controls, patients with COVID-19 had significantly higher levels of interleukins 1α, 2, 6, 7, 8, 10 and 15, C-reactive protein (CRP), serum amyloid A (SAA), intercellular adhesion molecule 1 (ICAM-1), vascular cell adhesion protein 1 (VCAM-1), IFN-γ-inducible protein-10 (IP-10, CXCL10), macrophage inflammatory protein-1 alpha (MIP-1α, CCL3), eotaxin-3 (CCL26), interferon-gamma (IFN-γ), tumor necrosis factor-α (TNF-α), basic fibroblast growth factor (bFGF), placental growth factor (PlGF), and fms-like tyrosine kinase 1 (Flt-1). Patients with severe COVID-19 had higher IL-10 and lower macrophage-derived chemokine (MDC, CCL22) compared to the mild or moderate group (P<0.05). In the receiver operating characteristic curve, SAA, IL-6 and CRP showed strong sensitivity and specificity in predicting the severity and prognosis of COVID-19. Greater age and higher CRP had a significant association with disease severity (P<0.05). Our findings reveal that CRP, SAA, VCAM-1, CXCL10, CCL22 and IL-10 levels are promising biomarkers for COVID-19 disease severity, suggesting that plasma inflammatory mediators could be used as warning indicators of COVID-19 severity, aid in COVID-19 prognosis and treatment.

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Inflammatory mediators profile in patients hospitalized with COVID-19: A comparative study

https://doi.org/10.1016/j.ssmqr.2022.100177

Support amid uncertainty: Long COVID illness experiences and the role of online communities

An agent-based model of the dual causality between individual and collective behaviors in an epidemic.

BACKGROUND: The incubation period is a crucial index of epidemiology in understanding the spread of the emerging Coronavirus disease 2019 (COVID-19). In this study, we aimed to describe the incubation period of COVID-19 globally and in the mainland of China. METHODS: The searched studies were published from December 1, 2019 to May 26, 2021 in CNKI, Wanfang, PubMed, and Embase databases. A random-effect model was used to pool the mean incubation period. Meta-regression was used to explore the sources of heterogeneity. Meanwhile, we collected 11 545 patients in the mainland of China outside Hubei from January 19, 2020 to September 21, 2020. The incubation period fitted with the Log-normal model by the coarseDataTools package. RESULTS: A total of 3235 articles were searched, 53 of which were included in the meta-analysis. The pooled mean incubation period of COVID-19 was 6.0 days (95% confidence interval [CI] 5.6-6.5) globally, 6.5 days (95% CI 6.1-6.9) in the mainland of China, and 4.6 days (95% CI 4.1-5.1) outside the mainland of China (P = 0.006). The incubation period varied with age (P = 0.005). Meanwhile, in 11 545 patients, the mean incubation period was 7.1 days (95% CI 7.0-7.2), which was similar to the finding in our meta-analysis. CONCLUSIONS: For COVID-19, the mean incubation period was 6.0 days globally but near 7.0 days in the mainland of China, which will help identify the time of infection and make disease control decisions. Furthermore, attention should also be paid to the region- or age-specific incubation period.

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The incubation period of COVID-19: a global meta-analysis of 53 studies and a Chinese observation study of 11 545 patients.


 Background

The incubation period is a key index of epidemiology in understanding of the spread of infectious diseases and the decision-making of the disease control. However, the incubation period of the emerging COVID-19 is still unclear.
Methods

Between January 19, 2020 and September 21, 2020, we collected information on 11545 patients in Mainland China outside Hubei. The 218 patients with precise data was validation population. The incubation period was fitted with lognormal model by the coarseDataTools package in R.
Results

In 11545 patients, the mean incubation period of COVID-19 was 7.1 days (95% Confidence interval [CI], 7.0–7.2). About 5.4% of patients had precise incubation period less than 3 days, 10.2% longer than 14 days, and 2.1% longer than 21 days. There was no statistically significant difference in incubation period between male and female (P = 0.603). It was similar in the 218 patients. The mean accurate incubation period was 6.8 days (6.2–7.4). Of which, 14.7% (32/218) of patients had incubation period less than 3 days, 12.4% (27/218) longer than 14 days, and 0.9% (2/218) longer than 21 days.
Conclusions

For COVID-19, the mean incubation period is 7.1 days and 10.2% of patients developed disease 14 days after infection, which challenges the current 14-day quarantine strategy.

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Incubation Period of COVID-19 From 11545 Patients in Observation Study

Objective: To analyze the characteristics of COVID-19 case spectrum and spread intensity in different provinces in China except Hubei province. Methods: The daily incidence data and case information of COVID-19 were collected from the official websites of provincial and municipal health commissions. The morbidity rate, severity rate, case-fatality rate, and spread ratio of COVID-19 were calculated. Results: As of 20 March, 2020, a total of 12 941 cases of COVID-19 had been conformed, including 116 deaths, and the average morbidity rate, severity rate and case-fatality rate were 0.97/100 000, 13.5% and 0.90%, respectively. The morbidity rates in Zhejiang (2.12/100 000), Jiangxi (2.01/100 000) and Beijing (1.93/100 000) ranked top three. The characteristics of COVID-19 case spectrum varied from province to province. The first three provinces (autonomous region, municipality) with high severity rates were Tianjin (45.6%), Xinjiang (35.5%) and Heilongjiang (29.5%). The case-fatality rate was highest in Xinjiang (3.95%), followed by Hainan (3.57%) and Heilongjiang (2.70%). The average spread ratio was 0.98 and the spread intensity varied from province to province. Tibet had the lowest spread ratio (0), followed by Qinghai (0.20) and Guangdong (0.23). Conclusion: The intervention measures were effective in preventing the spread of COVID-19 and improved treatment effect in China. However, there were significant differences among different regions in severity, case-fatality rate and spread ratio.

Juan Geng

Papers

The incubation period of COVID-19: a global meta-analysis of 53 studies and a Chinese observation study of 11 545 patients.

Incubation Period of COVID-19 From 11545 Patients in Observation Study

[Preliminary analysis on COVID-19 case spectrum and spread intensity in different provinces in China except Hubei province]