Identifying the shifting sources to predict the dynamics of COVID-19 in the U.S.

Mobility restriction is a crucial measure to control the transmission of the COVID-19. Research has shown that effective distance measured by the number of travelers instead of physical distance can capture and predict the transmission of the deadly virus. However, these efforts have been limited mainly to a single source of disease. Also, they have not been tested on finer spatial scales. Based on prior work of effective distances on the country level, we propose the multiple-source effective distance, a metric that captures the distance for the virus to propagate through the mobility network on the county level in the U.S. Then, we estimate how the change in the number of sources impacts the global mobility rate. Based on the findings, a new method is proposed to locate sources and estimate the arrival time of the virus. The new metric outperforms the original single-source effective distance in predicting the arrival time. Last, we select two potential sources and quantify the arrival time delay caused by the national emergency declaration. In doing so, we provide quantitative answers on the effectiveness of the national emergency declaration.

Early predictors of severe COVID-19 among hospitalized patients.

BACKGROUND: Limited research has been conducted on early laboratory biomarkers to identify patients with severe coronavirus disease (COVID-19). This study fills this gap to ensure appropriate treatment delivery and optimal resource utilization. METHODS: In this retrospective, multicentre, cohort study, 52 and 64 participants with severe and mild cases of COVID-19, respectively, were enrolled during January-March 2020. Least absolute shrinkage and selection operator and binary forward stepwise logistic regression were used to construct a predictive risk score. A prediction model was then developed and verified using data from four hospitals. RESULTS: Of the 50 variables assessed, eight were independent predictors of COVID-19 and used to calculate risk scores for severe COVID-19: age (odds ratio (OR = 14.01, 95% confidence interval (CI) 2.1-22.7), number of comorbidities (OR = 7.8, 95% CI 1.4-15.5), abnormal bilateral chest computed tomography images (OR = 8.5, 95% CI 4.5-10), neutrophil count (OR = 10.1, 95% CI 1.88-21.1), lactate dehydrogenase (OR = 4.6, 95% CI 1.2-19.2), C-reactive protein OR = 16.7, 95% CI 2.9-18.9), haemoglobin (OR = 16.8, 95% CI 2.4-19.1) and D-dimer levels (OR = 5.2, 95% CI 1.2-23.1). The model was effective, with an area under the receiver-operating characteristic curve of 0.944 (95% CI 0.89-0.99, p < 0.001) in the derived cohort and 0.8152 (95% CI 0.803-0.97; p < 0.001) in the validation cohort. CONCLUSION: Predictors based on the characteristics of patients with COVID-19 at hospital admission may help predict the risk of subsequent critical illness.

Vaccination intentions generate racial disparities in the societal persistence of COVID-19.

We combined survey, mobility, and infections data in greater Boston, MA to simulate the effects of racial disparities in the inclination to become vaccinated on continued infection rates and the attainment of herd immunity. The simulation projected marked inequities, with communities of color experiencing infection rates 3 times higher than predominantly White communities and reaching herd immunity 45 days later on average. Persuasion of individuals uncertain about vaccination was crucial to preventing the worst inequities but could only narrow them so far because 1/5th of Black and Latinx individuals said that they would never vaccinate. The results point to a need for well-crafted, compassionate messaging that reaches out to those most resistant to the vaccine.

SARS-CoV-2-associated acute disseminated encephalomyelitis: a systematic review of the literature.

The literature on cases of acute disseminated encephalomyelitis (ADEM) associated with SARS-CoV-2 infection has been rapidly increasing. However, the specific clinical features of ADEM associated with SARS-CoV-2 (SARS-CoV-2-ADEM) have not been previously evaluated. We screened all articles resulting from a search of PubMed and Web of Science databases looking for reports of ADEM published between December 01, 2019, and June 5, 2021. Of the 48 ADEM cases identified from 37 studies, 34 (71%) had ADEM while 14 (29%) were of AHLE. RT-PCR for SARS-CoV-2 was positive in 83% (n = 19) of patients. 26 patients (54%) were male, and 18 patients (38%) were female, with a male to female sex ratio of 1.4:1; median age was 44 (1.4-71) years. 9 patients (19%, 9/48) were children. Of the 9 children patients, their median age was 9 years (range 1.4-13 years), 6 patients (67%) were female, and 2 patients (22%) were male, with a female to male sex ratio of 3:1.39 patients (81%) was performed CSF analysis. PCR for SARS-CoV-2 tested positive in 3 patients (14%, 3/22) on CSF sample. 31 (64%) of patients had a poor outcome on discharge from hospital. Five (10%) patients died in hospital. Compared to classic ADEM, SARS-CoV-2-ADEM have a more longer duration between the onset of the antecedent infective symptoms and the start of ADEM symptoms, the older age distribution of the patients, relatively poor outcome, a lower full recovery rate, a more frequently brain lesions involved the periventricular white matter and corpus callosum, and less frequently affected the deep gray matter. Taken together, the present comprehensive review reveals that although rare, ADEM can be associated with SARS-CoV-2 infection. SARS-CoV-2-ADEM seems to share most features of classic ADEM, with moderate discrepancies from the classical ADEM.