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1.
Ethics Med Public Health ; 28: 100891, 2023 Jun.
Article in English | MEDLINE | ID: covidwho-2305655

ABSTRACT

Background: As Covid-19 spread rapidly, many countries implemented a strict shelter-in-place to "flatten the curve" and build capacity to treat in the absence of effective preventative therapies or treatments. Policymakers and public health officials must balance the positive health effects of lockdowns with economic, social, and psychological costs. This study examined the economic impacts of state and county level restrictions during the 2020 Covid-19 pandemic for two regions of Georgia. Methods: Taking unemployment data from the Opportunity Insights Economic Tracker with mandate information from various sites, we examined trends before and after a mandate's implementation and relaxation using joinpoint regression. Results: We found mandates with the largest impact on unemployment claims rates were the shelters-in-place (SIPs) and closures of non-essential businesses. Specific to our study, mandates had an effect where first implemented, i.e., if the state implemented an SIP after the county, the state-wide SIP had no additional measurable effect on claims rates. School closures had a consistent impact on increasing unemployment claims rates, but to a lesser degree than SIPs or business closures. While closing businesses did have a deleterious effect, implementing social distancing for businesses and restricting gatherings did not. Notably, the Coastal region was less affected than the Metro Area. Additionally, our findings indicate that race ethnicity may be a larger predictor of adverse economic effects than education, poverty level, or geographic area. Conclusions: Our findings coincided with other studies in some areas but showed differences in what indicators may best predict adverse effects and that coastal communities may not always be as impacted as other regions in a state. Ultimately, the most restrictive measures consistently had the largest negative economic impacts. Social distancing and mask mandates can be effective for containment while mitigating the economic impacts of strict SIPs and business closures.

2.
Chinese Pharmacological Bulletin ; 37(8):1037-1041, 2021.
Article in Chinese | EMBASE | ID: covidwho-1818309

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. The life cycle of SARS-CoV-2 is not clear, which is one of the reasons that only Remdesivir has been approved by FDA for treating COVID-19. Although some new vaccines have been a- vailable, the quick mutations of SARS-CoV-2 affect the effectiveness of vaccines, calling for further assessment of the persistence and safety of vaccines. Therefore, drug treatment and prevention are still effective ways to deal with the epidemic of SARS-CoV-2. The article briefly summarizes the molecular mechanism of SARS-CoV-2 entry based on the existing literature. This virus enters the cell through two main ways, that is, spike protein mediating membrane fusion with plasma membrane or endosome membrane. According to the targets, the article summarizes the reported inhibitors of SARS-CoV-2 entry into cells, aiming to provide a reference for following research and clinical application of anti-SARS-CoV-2 drugs.

3.
J. Inverse Ill-Posed Probl. ; : 22, 2022.
Article in English | Web of Science | ID: covidwho-1793455

ABSTRACT

A novel optimization algorithm for stable parameter estimation and forecasting from limited incidence data for an emerging outbreak is proposed. The algorithm combines a compartmental model of disease progression with iteratively regularized predictor-corrector numerical scheme aimed at the reconstruction of case reporting ratio, transmission rate, and effective reproduction number. The algorithm is illustrated with real data on COVID-19 pandemic in the states of Georgia and New York, USA. The techniques of functional data analysis are applied for uncertainty quantification in extracted parameters and in future projections of new cases.

4.
Fields Institute Communications ; 85:85-137, 2022.
Article in English | Scopus | ID: covidwho-1707811

ABSTRACT

Mathematical models have been widely used to understand the dynamics of the ongoing coronavirus disease 2019 (COVID-19) pandemic as well as to predict future trends and assess intervention strategies. The asynchronicity of infection patterns during this pandemic illustrates the need for models that can capture dynamics beyond a single-peak trajectory to forecast the worldwide spread and for the spread within nations and within other sub-regions at various geographic scales. Here, we demonstrate a five-parameter sub-epidemic wave modeling framework that provides a simple characterization of unfolding trajectories of COVID-19 epidemics that are progressing across the world at different spatial scales. We calibrate the model to daily reported COVID-19 incidence data to generate six sequential weekly forecasts for five European countries and five hotspot states within the United States. The sub-epidemic approach captures the rise to an initial peak followed by a wide range of post-peak behavior, ranging from a typical decline to a steady incidence level to repeated small waves for sub-epidemic outbreaks. We show that the sub-epidemic model outperforms a three-parameter Richards model, in terms of calibration and forecasting performance, and yields excellent short- and intermediate-term forecasts that are not attainable with other single-peak transmission models of similar complexity. Overall, this approach predicts that a relaxation of social distancing measures would result in continuing sub-epidemics and ongoing endemic transmission. We illustrate how this view of the epidemic could help data scientists and policymakers better understand and predict the underlying transmission dynamics of COVID-19, as early detection of potential sub-epidemics can inform model-based decisions for tighter distancing controls. © 2022, Springer Nature Switzerland AG.

5.
Journal of Integrative Agriculture ; 20(11):III-III, 2021.
Article in English | Web of Science | ID: covidwho-1489835
6.
IEEE Access ; 2021.
Article in English | Scopus | ID: covidwho-1225647

ABSTRACT

Eye state evaluation is crucial for vision-based driver fatigue detection. With the outbreak of COVID-19, many proposed models for eye location and state evaluation based on facial landmarks are unreliable due to mask coverings. In this paper, we proposed a robust facial landmark location model for eye location and state evaluation. First, we develop an existing lightweight face alignment model for eye key point locations that is robust in large poses. Then, to develop the performance of our model in a complex driving environment such as an environment with mask coverings, changing illumination, etc., we design a method to augment the training data set based on the original landmark data set without any extra cost. Finally, some facial landmarks around the eyes are extracted, and the eye aspect ratio (EAR) is introduced to evaluate the eye state based on eye key points. The experiment shows that our model achieves significantly improved landmark location performance on a driving simulation data set due to data augmentation. We tested our model on the BioID data set to measure the eye state evaluation performance, and the results showed that our model obtained satisfactory performance with an accuracy of approximately 97.7%. Further testing on the driving simulation data set shows that our model is robust in different driving scenarios with an average accuracy of approximately 93.9%. CCBY

7.
BMC Infectious Diseases ; 21(1):397, 2021.
Article in English | MEDLINE | ID: covidwho-1209891

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) has emerged as a major global health threat with a great number of deaths worldwide. Despite abundant data on that many COVID-19 patients also displayed kidney disease, there is limited information available about the recovery of kidney disease after discharge. METHODS: Retrospective and prospective cohort study to patients with new-onset kidney disease during the COVID-19 hospitalization, admitted between January 28 to February 26, 2020. The median follow-up was 4 months after discharge. The follow-up patients were divided into the recovery group and non-recovery group. Descriptive statistics and between-groups comparison were used. RESULTS: In total, 143 discharged patients with new-onset kidney disease during the COVID-19 hospitalization were included. Patients had a median age was 64 (IQR, 51-70) years, and 59.4% of patients were men. During 4-months median follow-up, 91% (130 of 143) patients recovered from kidney disease, and 9% (13 of 143) patients haven't recovered. The median age of patients in the non-recovery group was 72 years, which was significantly higher than the median age of 62 years in the recovery group. Discharge serum creatinine was significantly higher in the non-recovery group than in the recovery group. CONCLUSIONS: Most of the new-onset kidney diseases during hospitalization of COVID-19 patients recovered 4 months after discharge. We recommend that COVID-19 patients with new-onset kidney disease be followed after discharge to assess kidney recovery, especially elderly patients or patients with high discharge creatinine.

9.
Journal of Integrative Agriculture ; 19(12):2946-2964, 2020.
Article in English | Web of Science | ID: covidwho-1003173

ABSTRACT

Given the sudden outbreak of the COVID-19 pandemic, a timely study on the impacts of and policy response to the pandemic on rural poverty in China is critically important because China has aimed to completely eradicate extreme poverty by the end of 2020. This paper uses data from the latest round of a nationally representative household panel survey to examine the impacts of the pandemic on rural poverty in China. Our data show that 11.9% of sample households were ever officially registered as poor households between 2013 and 2019, and this poverty incidence fell to 2.7% by the end of 2019. In the middle February of 2020, 23% of the households who have graduated from poverty since 2013 perceived that they would fall back into poverty due to the COVID-19. Among those never poor households, 7.1% perceived that they would possibly fall into poverty due to the pandemic. Results from both descriptive and multivariate analyses consistently show the interruptions that the pandemic caused in off-farm employment is an important channel that led households to perceive of falling back into or falling into poverty. We also find households in the bottom four quintiles when ranked in terms of household income per capita are much more likely to perceive themselves of falling back into or falling into poverty during this pandemic than those in the richest quintile. Meanwhile, our results show that the education and age of household heads, as well as being from Hubei Province matter in explaining household perception about falling back into or falling into poverty in some cases but not all. The paper concludes with a set of policy responses that China has taken to mitigate the impact of COVID-19 pandemic on poverty alleviation.

10.
Clin Microbiol Infect ; 26(9): 1171-1177, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-622944

ABSTRACT

BACKGROUND: Corticosteroids are commonly used as adjuvant therapy for acute respiratory distress syndrome by many clinicians because of their perceived anti-inflammatory effects. However, for patients with severe viral pneumonia, the corticosteroid treatment is highly controversial. OBJECTIVES: The purpose of this review is to systematically evaluate the effect and potential mechanism of corticosteroid administration in pandemic viral pneumonia. SOURCES: We comprehensively searched all manuscripts on corticosteroid therapy for influenza, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and SARS coronavirus 2 (SARS-CoV-2) viral pneumonia from the PubMed, EMBASE, Web of Science and Cochrane Library databases. CONTENT: We systematically summarized the effects of corticosteroid therapy for pandemic viral pneumonia and the potential mechanism of action for corticosteroids in coronavirus disease 2019 (COVID-19). IMPLICATIONS: Observational studies showed that corticosteroid treatment was associated with increased mortality and nosocomial infections for influenza and delayed virus clearance for SARS-CoV and MERS-CoV. Limited data on corticosteroid therapy for COVID-19 were reported. Corticosteroids were used in about a fifth of patients (670/2995, 22.4%). Although clinical observational studies reported the improvement in symptoms and oxygenation for individuals with severe COVID-19 who received corticosteroid therapy, case fatality rate in the corticosteroid group was significantly higher than that in the non-corticosteroid group (69/443, 15.6% versus 56/1310, 4.3%). Compared individuals with non-severe disease, those with severe disease were more likely to receive corticosteroid therapy (201/382, 52.6% versus 201/1310, 15.3%). Although there is no evidence that corticosteroid therapy reduces mortality in people with COVID-19, some improvements in clinical symptoms and oxygenation were reported in some clinical observational studies. Excessive inflammatory response and lymphopenia might be critical factors associated with severity of and mortality from COVID-19. Sufficiently powered randomized controlled trials with rigorous inclusion/exclusion criteria and standardized dose and duration of corticosteroids are needed to verify the effectiveness and safety of corticosteroid therapy.


Subject(s)
Adrenal Cortex Hormones/adverse effects , Adrenal Cortex Hormones/therapeutic use , COVID-19 Drug Treatment , Pneumonia, Viral/drug therapy , COVID-19/mortality , Coronavirus Infections/drug therapy , Cross Infection , Humans , Influenza, Human/drug therapy , Influenza, Human/mortality , Observational Studies as Topic , Pneumonia, Viral/mortality , Severe Acute Respiratory Syndrome/drug therapy
11.
Infect Dis Model ; 5: 256-263, 2020.
Article in English | MEDLINE | ID: covidwho-865

ABSTRACT

The initial cluster of severe pneumonia cases that triggered the COVID-19 epidemic was identified in Wuhan, China in December 2019. While early cases of the disease were linked to a wet market, human-to-human transmission has driven the rapid spread of the virus throughout China. The Chinese government has implemented containment strategies of city-wide lockdowns, screening at airports and train stations, and isolation of suspected patients; however, the cumulative case count keeps growing every day. The ongoing outbreak presents a challenge for modelers, as limited data are available on the early growth trajectory, and the epidemiological characteristics of the novel coronavirus are yet to be fully elucidated. We use phenomenological models that have been validated during previous outbreaks to generate and assess short-term forecasts of the cumulative number of confirmed reported cases in Hubei province, the epicenter of the epidemic, and for the overall trajectory in China, excluding the province of Hubei. We collect daily reported cumulative confirmed cases for the 2019-nCoV outbreak for each Chinese province from the National Health Commission of China. Here, we provide 5, 10, and 15 day forecasts for five consecutive days, February 5th through February 9th, with quantified uncertainty based on a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model. Our most recent forecasts reported here, based on data up until February 9, 2020, largely agree across the three models presented and suggest an average range of 7409-7496 additional confirmed cases in Hubei and 1128-1929 additional cases in other provinces within the next five days. Models also predict an average total cumulative case count between 37,415 and 38,028 in Hubei and 11,588-13,499 in other provinces by February 24, 2020. Mean estimates and uncertainty bounds for both Hubei and other provinces have remained relatively stable in the last three reporting dates (February 7th - 9th). We also observe that each of the models predicts that the epidemic has reached saturation in both Hubei and other provinces. Our findings suggest that the containment strategies implemented in China are successfully reducing transmission and that the epidemic growth has slowed in recent days.

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