A comparative study on the three calculation methods for reproduction numbers of COVID-19

Objective This study uses four COVID-19 outbreaks as examples to calculate and compare merits and demerits, as well as applicational scenarios, of three methods for calculating reproduction numbers. Method The epidemiological characteristics of the COVID-19 outbreaks are described. Through the definition method, the next-generation matrix-based method, and the epidemic curve and serial interval (SI)-based method, corresponding reproduction numbers were obtained and compared. Results Reproduction numbers (Reff), obtained by the definition method of the four regions, are 1.20, 1.14, 1.66, and 1.12. Through the next generation matrix method, in region H Reff = 4.30, 0.44;region P Reff = 6.5, 1.39, 0;region X Reff = 6.82, 1.39, 0;and region Z Reff = 2.99, 0.65. Time-varying reproduction numbers (Rt), which are attained by SI of onset dates, are decreasing with time. Region H reached its highest Rt = 2.8 on July 29 and decreased to Rt < 1 after August 4;region P reached its highest Rt = 5.8 on September 9 and dropped to Rt < 1 by September 14;region X had a fluctuation in the Rt and Rt < 1 after September 22;Rt in region Z reached a maximum of 1.8 on September 15 and decreased continuously to Rt < 1 on September 19. Conclusion The reproduction number obtained by the definition method is optimal in the early stage of epidemics with a small number of cases that have clear transmission chains to predict the trend of epidemics accurately. The effective reproduction number Reff, calculated by the next generation matrix, could assess the scale of the epidemic and be used to evaluate the effectiveness of prevention and control measures used in epidemics with a large number of cases. Time-varying reproduction number Rt, obtained via epidemic curve and SI, can give a clear picture of the change in transmissibility over time, but the conditions of use are more rigorous, requiring a greater sample size and clear transmission chains to perform the calculation. The rational use of the three methods for reproduction numbers plays a role in the further study of the transmissibility of COVID-19.

Generic Digital Equity Model in Education: Mobile-Assisted Personalized Learning (MAPL) through e-Modules

This study addresses the challenges most learners face in Third World and developing countries concerning education accessibility in an emergency. On the basis of the shortcomings found in a review of past studies, this scoping review introduces adapted model mobile-assisted personalized learning (MAPL), which focused on full distance learning using the personalized learning (PL) concept. This concept is rarely used in the classrooms of Third World and developing countries. MAPL is technology-integrated and customized PL but it does not depend on artificial intelligence. This model bridges the digital divide that hinders learners in accessing education by providing flexibility, regardless of weak internet reception or low bandwidth, among other hindrances, in Third World or developing countries. Learners in these countries inevitably opt for mobile devices as their preferred learning tool. MAPL is necessary and can aid underprivileged learners who are highly dependent on mobile devices. Rethinking and reforming current teaching practices are required. In this study, a pool of 60 articles from 2011 to 2021 was qualitatively synthesized. Among the articles, 29 focused on PL, 15 on mobile learning, and 16 on the potentials of MAPL. The findings indicate that MAPL could be a viable solution for achieving equity in education for every learner during full-fledged distance learning.

The neurology and neuropsychiatry of COVID-19: a systematic review and meta-analysis of the early literature reveals frequent CNS manifestations and key emerging narratives (preprint)

ObjectivesThere is accumulating evidence of the neurological and neuropsychiatric features of infection with SARS-CoV-2. In this systematic review and meta-analysis, we aimed to describe the characteristics of the early literature and estimate point prevalences for neurological and neuropsychiatric manifestations. MethodsWe searched MEDLINE, Embase, PsycInfo and CINAHL up to 18 July 2020 for randomised controlled trials, cohort studies, case-control studies, cross-sectional studies and case series. Studies reporting prevalences of neurological or neuropsychiatric symptoms were synthesised into meta-analyses to estimate pooled prevalence. Results13,292 records were screened by at least two authors to identify 215 included studies, of which there were 37 cohort studies, 15 case-control studies, 80 cross-sectional studies and 83 case series from 30 countries. 147 studies were included in the meta-analysis. The symptoms with the highest prevalence were anosmia (43.1% [35.2--51.3], n=15,975, 63 studies), weakness (40.0% [27.9--53.5], n=221, 3 studies), fatigue (37.8% [31.6--44.4], n=21,101, 67 studies), dysgeusia (37.2% [30.0--45.3], n=13,686, 52 studies), myalgia (25.1% [19.8--31.3], n=66.268, 76 studies), depression (23.0 % [11.8--40.2], n=43,128, 10 studies), headache (20.7% [95% CI 16.1--26.1], n=64,613, 84 studies), anxiety (15.9% [5.6--37.7], n=42,566, 9 studies) and altered mental status (8.2% [4.4--14.8], n=49,326, 19 studies). Heterogeneity for most clinical manifestations was high. ConclusionsNeurological and neuropsychiatric symptoms of COVID-19 in the pandemics early phase are varied and common. The neurological and psychiatric academic communities should develop systems to facilitate high-quality methodologies, including more rapid examination of the longitudinal course of neuropsychiatric complications of newly emerging diseases and their relationship to neuroimaging and inflammatory biomarkers.

Self-Reported Taste and Smell Disorders in Patients with COVID-19: Distinct Features in China (preprint)

Background: Last December 2019, a cluster of viral pneumonia cases identified as coronavirus disease 2019 (COVID-19), was reported in Wuhan, China. We aimed to explore the frequencies of nasal symptoms in patients with COVID-19, including loss of smell and taste, as well as their presentation as the first symptom of the disease and their association with the severity of COVID-19.Methods: In this retrospective study, 1,206 laboratory-confirmed COVID-19 patients were included and followed-up by telephone call one month after discharged from Tongji Hospital, Wuhan. Demographic data, laboratory values, comorbidities, symptoms, and numerical rating scale scores (0-10) of nasal symptoms were extracted from the hospital medical records, and confirmed or reevaluated by the telephone follow-up. Results: From COVID-19 patients (N = 1,172) completing follow-up, 199 (17%) subjects had severe COVID-19 and 342 (29.2%) reported nasal symptoms. The most common nasal symptom was loss of taste (20.6%, median score = 6), while 11.4% had loss of smell (median score = 5). The incidence of nasal symptom including loss of smell and loss of taste as the first onset symptom was <1% in COVID-19 patients. Loss of smell or taste scores showed no correlation with the scores of other nasal symptoms. Loss of taste scores, but not loss of smell scores, were significantly increased in severe vs. non-severe COVID-19 patients. Interleukin (IL)-6 and lactose dehydrogenase (LDH) serum levels positively correlated with loss of taste scores. About 80% of COVID-19 patients recovered from smell and taste dysfunction in 2 weeks.Conclusions: In the Wuhan COVID-19 cohort, only 1 out of 10 hospital admitted patients had loss of smell while 1 out 5 reported loss of taste which was associated to severity of COVID-19. Most patients recovered smell and taste dysfunctions in 2 weeks.

Development and External Evaluation of Predictions Models for Mortality of COVID-19 Patients using Machine Learning Method (preprint)

Background To develop and evaluate the prognostic machine-learning model for mortality in patients with coronavirus disease 2019 (COVID-19).Methods Clinical data of confirmed COVID-19 were retrospectively collected from Wuhan between 18th January and 29th March 2020. Gradient Boosting Decision Tree (GBDT), logistic regression (LR) model, and simplified LR with selected 5 features (LR-5) model were built to predict the mortality of COVID-19. 5-fold area under curve (AUC), accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated and compared between models.Results A total of 2,924 patients were included in the final analysis, 257(8.8%) of whom died during hospitalization and 2,667 (91.2%) survived. There were 21(0.7%) mild cases, 2,051(70.1%) moderate case, 779(26.6%) severe cases, and 73(2.5%) critically severe cases of COVID-19 on admission. The overall 5-fold AUC was observed highest in GBDT model (0.941), followed by LR (0.928) and LR-5 (0.913). The diagnostic accuracy were 0.889 in GBDT, 0.868 in LR and 0.887 in LR-5. GBDT model also showed the highest sensitivity (0.899) and speciality (0.889). The NPV of all three models exceeded 97%, while the PPV were relatively low in all models, 0.381 for LR, 0.402 for LR-5 and 0.432 for GBDT. In subgroups analysis with severe cases only, GBDT model also performed the best with a accuracy of 0.799 and 5-fold AUC (0.918).Conclusion The finding revealed that mortality prediction performance of the GBDT was superior to the LR models in confirmed cases of COVID-19, regardless of disease severity.

Self-reported taste and smell disorders in patients with COVID-19: distinct features in China (preprint)

ObjectivesWe aimed to explore the frequencies of nasal symptoms in patients with COVID-19, including loss of smell and taste, as well as their presentation as the first symptom of the disease and their association with the severity of COVID-19. MethodsIn this retrospective study, 1,206 laboratory-confirmed COVID-19 patients were included and followed-up by telephone call one month after discharged from Tongji Hospital, Wuhan. Demographic data, laboratory values, comorbidities, symptoms, and numerical rating scale scores (0-10) of nasal symptoms were extracted from the hospital medical records, and confirmed or reevaluated by the telephone follow-up. ResultsFrom COVID-19 patients (N = 1,172) completing follow-up, 199 (17%) subjects had severe COVID-19 and 342 (29.2%) reported nasal symptoms. The most common nasal symptom was loss of taste (20.6%, median score = 6), while 11.4% had loss of smell (median score = 5). The incidence of nasal symptom including loss of smell and loss of taste as the first onset symptom was <1% in COVID-19 patients. Loss of smell or taste scores showed no correlation with the scores of other nasal symptoms. Loss of taste scores, but not loss of smell scores, were significantly increased in severe vs. non-severe COVID-19 patients. Interleukin (IL)-6 and lactose dehydrogenase (LDH) serum levels positively correlated with loss of taste scores. About 80% of COVID-19 patients recovered from smell and taste dysfunction in 2 weeks. ConclusionIn the Wuhan COVID-19 cohort, only 1 out of 10 hospital admitted patients had loss of smell while 1 out 5 reported loss of taste which was associated to severity of COVID-19. Most patients recovered smell and taste dysfunctions in 2 weeks.

Discovery of Aptamers Targeting Receptor-Binding Domain of the SARS-CoV-2 Spike Glycoprotein (preprint)

The World Health Organization has declared the outbreak of a novel coronavirus (SARS-CoV-2 or 2019-nCoV) as a global pandemic. However, the mechanisms behind the coronavirus infection are not yet fully understood, nor are there any targeted treatments or vaccines. In this study, we identified high-binding-affinity aptamers targeting SARS-CoV-2 RBD, using an ACE2 competition-based aptamer selection strategy and a machine learning screening algorithm. The K d values of the optimized CoV2-RBD-1C and CoV2-RBD-4C aptamers against RBD were 5.8 nM and 19.9 nM, respectively. Simulated interaction modeling, along with competitive with experiments, suggests that two aptamers may have partially identical binding sites at ACE2 on SARS-CoV-2 RBD. These aptamers present an opportunity for generating new probes for recognition of SARS-CoV-2, and could provide assistance in the diagnosis and treatment of SARS-CoV-2 while providing a new tool for in-depth study of the mechanisms behind the coronavirus infection.