The Global case-fatality rate of COVID-19 has been declining disproportionately between top vaccinated countries and the rest of the world (preprint)

Globally 58.83% human population received at least one dose of the COVID-19 vaccines as of 5 January 2021. COVID-19 vaccination rollout is progressing at varied rates globally and data on the impact of mass vaccination on infection and case-fatality rates require definition. We compared the global reported cumulative case-fatality rate (rCFR) between top-20 countries with COVID-19 vaccination rates (>125 doses/100 people) and the rest of the world, before and after commencement of vaccination programmes. We considered the 28th day of receiving the first vaccine in the world as a cut-off to compare the pre-vaccine period (Jan 1, 2020 - Jan 5, 2021) and the post-vaccine period (Jan 6, 2021 - Jan 5, 2022). We used a Generalized linear mixed model (GLMM) with a beta distribution to investigate the association between the CFR and potential predictors of each country and reported the relative risk (RR) of each variable. The mean rCFR of COVID-19 in the top-20 countries with vaccination rates was 1.83 (95% CI: 1.24-2.43) on 5 Jan 2021 and 1.18 (95% CI: 0.73-1.62) on 5 Jan 2022. The CFR for the rest of the world on 5 Jan 2021 was 2.32 (95% CI: 1.86-2.79) and 2.20 (95% CI: 1.86-2.55) on 5 January 2022. In Sub-Saharan Africa, the CFR remained roughly unchanged at 1.97 (95% CI: 1.59-2.35) on 5 Jan 2021 and 1.98 (95% CI:1.58-2.37) on 5 Jan 2022. The GLMM showed vaccination (/100 population) (RR:0.37) and Stringency Index (RR:0.88) were strong protective factors for the country's COVID-19 CFR indicating that both vaccination and lockdown measures help in the reduction of COVID-19 CFR. The rCFR of COVID-19 continues to decline, although at a disproportionate rate between top vaccinated countries and the rest of the world. Vaccine equity and faster roll-out across the world is critically important in reducing COVID-19 transmission and CFR.

Depression and Anxiety among University Students: a Comparison Between COVID-19 Pandemic Panic Period and Post-Panic Period in Bangladesh (preprint)

Background: The current COVID-19 pandemic is the biggest public health concern. It harmed everyone, both physically and mentally. Because of panic situations in COVID-19 pandemic, students all over the world, including those in Bangladesh, are suffering from depression and anxiety. Considering this, we aimed to assess psycho-emotional changes of the university students through investigating their level of depression and anxiety effects during panic and post-panic period of COVID-19 pandemic in Bangladesh. Methods: : Cross-sectional online surveys were conducted among university students in Bangladesh from April to July 2020 (panic period, n=170) and then from August to November 2020 (post-panic period, n=170). The PHQ-9 and GAD-7 questionnaires were used to assess respondents' depression and anxiety levels, respectively. We used continuous scores to assess the severity of depression and anxiety symptoms. We also computed binary depression and anxiety scores. Multivariable logistic regression models were used to analyze the data. Results: : The proportion of depression symptoms was 49.4% during the panic period and 52.4% after the panic period. Anxiety symptoms were experienced by 38.2% of students during the panic period, and this percentage was nearly identical in the post-panic interval. Depression levels increased in the post-panic period and urban students have significantly (P< 0.05) higher levels of depression and anxiety than their counterparts. Female students also exhibited significantly more anxious symptoms (p=0.002) than male. Depression symptoms significantly vary by family types, students place of residence whereas students age, gender, education, family head's occupation, time period and family economic condition found no significant association with the depression. Conclusions: : Students during the post-panic period have a higher prevalence of depression and anxiety symptoms than during the panic period. Although the difference was small, it was still concerning for university students in Bangladesh because it interfered with their academic life.

Transition in Learning Approach for Undergraduate Medical Students of Bangladesh in Covid 19 Pandemic: A Situation Analysis (preprint)

Background and Objectives: The outbreak of Covid 19 pandemic has fundamentally transformed the landscape of medical education system upside down worldwide. And this unanticipated transition of medical education without any pre build infrastructure has made this altered prospect more challenging in Bangladesh. Though many countries across the world utilize web based learning along with traditional approach, but medical students of Bangladesh are mostly unfamiliar and unaccustomed with this newly imposed online learning avenue. Therefore, this study has evaluated the familiarity, usage, attitude of students towards online class and figured out the barriers witnessed by students in web based learning in Bangladesh prospect. Methods: This cross sectional, questionnaire based study was conducted in medical colleges across Bangladesh. A questionnaire linked to google form were distributed to undergraduate medical students all over Bangladesh through different social platforms. Students who showed interest, filled up the questionnaire with consent and submitted voluntarily. Those answered questionnaires were automatically stored in Google drive in a specific email ID. After completion of data collection, all data were transferred in a spread sheet and statistical analysis was done. Results: A total of 1709 students participated in this study willingly from around Bangladesh. Among the respondents 45.1% were satisfied with online class. Though most of the students (45.8%) think online class in medical education is not effective like traditional lectures but many of them (47.4%) agrees to the point that online class should have complementary role in medical education. One of the strong attitude of medical students regarding web based learning revealed in this study that, most of them undoubtedly in unison (49.5% disagree, 30.3% strongly disagree) with that web based learning can never replace traditional lecture class in medical education. 77.2% students responded that web based learning is interactive. 54.9% students pointed out that they experienced interrupted internet connections with low internet speed during class time which is a barrier to WBL. 83.2% of the respondents complained about facing audio visual problem during online class which is attributed to the poor network connectivity. Most students in Bangladesh (74.8%) found online classes costly and 53.8% of the students needed technical supports for continued online class. Conclusion: This study finding can suggest a potential reform for medical education system of Bangladesh addressing the obstacles and expectations of students which can execute a fruitful web based learning in Bangladesh.

Adverse effects of COVID-19 vaccination: machine learning and statistical approach to identify and classify incidences of morbidity and post-vaccination reactogenicity (preprint)

Good vaccine safety and reliability are essential to prevent infectious disease spread. A small but significant number of apparent adverse reactions to the new COVID-19 vaccines have been reported. Here, we aim to identify possible common causes for such adverse reactions with a view to enabling strategies that reduce patient risk by using patient data to classify and characterise patients those at risk of such reactions. We examined patient medical histories and data documenting post-vaccination effects and outcomes. The data analyses were conducted by different statistical approaches followed by a set of machine learning classification algorithms. In most cases, similar features were significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, allergic history, taking other medications, type-2 diabetes, hypertension and heart disease are the most significant pre-existing factors associated with risk of poor outcome and long duration of hospital treatments, pyrexia, headache, dyspnoea, chills, fatigue, various kind of pain and dizziness are the most significant clinical predictors. The machine learning classifiers using medical history were also able to predict patients most likely to have complication-free vaccination with an accuracy score above 85%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches. Important classifiers achieving these reactions notably included allergic susceptibility and incidence of heart disease or type-2 diabetes.

The prevalence of olfactory dysfunction and its associated factors in patients with COVID-19 infection (preprint)

Objective To determine the prevalence of olfactory dysfunctions, mainly, anosmia and to identify its associated factors in patients with COVID-19 infection. Study design A hospital-based prospective observational cohort study Setting A COVID dedicated hospital, Square Hospitals Ltd., Dhaka, Bangladesh. Methods We collected patients information including laboratory-confirmed COVID-19 test results. We used Pearson Chi-square test and logistic regression model to assess the associations between demographic and clinical characteristics and olfactory outcomes. Results Out of 600 COVID-19 positive patients, 38.7% were diagnosed with olfactory dysfunction. Our analyses showed that patients age, smoking status, cough, dyspnea, sore throat, asthenia, and nausea or vomiting were significantly associated with the anosmia. We observed the risk of developing anosmia was greater in younger patients than in older patients, and this risk decreased as age increased [odds ratio (OR) range for different age groups: 1.26 to 1.08]. Smoking patients were 1.73 times more likely to experience anosmia than non-smoking patients [OR=1.73, 95% confidence interval (CI) = 1.01-2.98]. In addition, patients complained asthenia had a significantly double risk of developing the anosmia [OR = 1.96, CI = 1.23-3.06]. Conclusions Our study shows that about 39% of patients diagnosed with olfactory dysfunction. Patients age, smoking status, and asthenia are significantly positively associated with the anosmia. Since anosmia can be a significant marker for the diagnosis of COVID-19, we suggest regular screening of olfactory dysfunction in patients with early symptoms of COVID-19, particularly younger patients, smoker, and complained asthenia.

Population Exposure to Amphan-Scale Cyclones Under Future Climates (preprint)

Background: Climate change impacts are felt disproportionately in developing countries, and in particular Southern Asia experiences the most damaging hydrometeorological events in the world, with loss of life from past cyclones in the hundreds of thousands. Despite this, the Bay of Bengal cyclone basin receives far less research attention than many of the others around the world. Here, we study the historical and future impacts of Super Cyclone Amphan, which made landfall in May 2020, bringing storm surges of 2-4 meters to coastlines of India and Bangladesh. Methods: In this modelling study, we combine projections of sea level rise from the Coupled Model Intercomparison Projection, phase 6 (CMIP6), with estimates of storm surge using a dynamic storm surge model. Sampling the spectrum of possible sea level rises, we consider a low, medium and high scenario, based on projections in 2100. We then feed these into a flood inundation model to simulate storm surge-induced flooding, had Cyclone Amphan occurred in these future worlds. Finally, we consider the change in future population growth and urbanisation, thereby calculating the change in population exposure to these future flooding events. Our approach is that of the extreme event attribution community, but projecting into the future rather than interrogating the past. Findings: We find that in 2100, the local sea level rise in the Bay of Bengal during the pre-monsoon cyclone season is between 0.32-0.84 m, depending on which emissions scenario is followed. If a Cyclone Amphan-scale storm surge occurred on top of that sea level rise, the future population of both India and Bangladesh will be more exposed, with India showing >200% increased exposure to extreme (>3 m) and moderate (>1 m) flooding under a high emissions scenario, and Bangladesh showing ~60-80% increased exposure to the same scenarios. The majority of this change in both countries comes from sea level rise rather than population changes, and in Bangladesh the future population change contributes negatively to the change in exposure, as more citizens migrate further inshore. However, if we follow an emissions scenario consistent with meeting the upper Paris Agreement climate goal, we project very little change in exposure. Interpretation: There is an urgent need to reduce carbon emissions to net zero, to prevent the negative impacts of climate change. By far the majority of cyclone research has been undertaken for countries such as America and Japan, with less resilient countries such as those in South Asia, which are more sensitive to changes in climate, seeing far less attention. With Cyclone Amphan occurring at the height of the COVID-19 crisis, we highlight how the risk was compounded and recommend that future climate risk assessments explicitly account for these potential non-linearities. Funding Statement: The main funding is from the Natural Environment Research Council.Declaration of Interests: The authors declare no competing interests.

Estimation of novel coronavirus (covid-19) reproduction number and case fatality rate: a systematic review and meta-analysis (preprint)

Understanding the transmission dynamics and the severity of the novel coronavirus disease 2019 (COVID-19) informs public health interventions, surveillance, and planning. Two important parameters, the basic reproduction number (R0) and case fatality rate (CFR) of COVID-19, help in this understanding process. The objective of this study was to estimate the R0 and CFR of COVID-19 and assess whether the parameters vary in different regions of the world. We carried out a systematic review to retrieve the published estimates of the R0 and the CFR in articles from international databases between 1st January and 31st August 2020. Random-effect models and Forest plots were implemented to evaluate the mean effect size of the R0 and the CFR. Furthermore, the R0 and CFR of the studies were quantified based on geographic location, the tests/thousand population, and the median population age of the countries where studies were conducted. The I2 statistic and the Cochran's Q test were applied to assess statistical heterogeneity among the selected studies. Forty-five studies involving R0 and thirty-four studies involving CFR were included. The pooled estimation of the R0 was 2.69 (95% CI: 2.40, 2.98), and that of the CFR was 2.67 (2.25, 3.13). The CFR in different regions of the world varied significantly, from 2.51 (2.12, 2.95) in Asia to 7.11 (6.38, 7.91) in Africa. We observed higher mean CFR values for the countries with lower tests (3.15 vs. 2.16) and greater median population age (3.13 vs. 2.27). However, the R0 did not vary significantly in different regions of the world. An R0 of 2.69 and CFR of 2.67 indicate the severity of the COVID-19. Although R0 and CFR may vary over time, space, and demographics, we recommend considering these figures in control and prevention measures.

Prevalence and correlation of symptoms and comorbidities in COVID-19 patients: A systematic review and meta-analysis (preprint)

Background: The COVID-19 affected millions of people, and the patients present a constellation of symptoms and comorbidities. We aimed to chronicle the prevalence and correlations of symptoms and comorbidities, and associated covariates among the patients. Methods: We performed a systematic review and meta-analysis [PROSPERO registration: CRD42020182677]. Databases [PubMed, SCOPUS, EMBASE, WHO, Semantic Scholar, and COVID-19 Primer] were searched for clinical studies published in English from January 1 to April 20, 2020. The pooled prevalence of symptoms and comorbidities were identified using the random effect model, and sub-groups analysis of patients age and locations were investigated. A multivariable factor analysis was also performed to show the correlation among symptoms, comorbidities and age of the COVID-19 patients. Findings: Twenty-nine articles [China (24); Outside of China (5)], with 4,884 COVID-19 patients were included in this systematic review. The meta-analysis investigated 33 symptoms, where fever [84%], cough/dry cough [61%], and fatigue/weakness [42%] were found frequent. Out of 43 comorbidities investigated, acute respiratory distress syndrome (ARDS) [61%] was a common condition, followed by hypertension [23%] and diabetes [12%]. According to the patients age, the prevalence of symptoms like fatigue/weakness, dyspnea/shortness of breath, and anorexia were highly prevalent in older adults [[≥]50 years] than younger adults [<50 years]. Diabetes, hypertension, coronary heart disease, and COPD/lung disease were more prevalent comorbidities in older adults than younger adults. The patients from outside of China had significantly higher prevalence [p<0.005] of diarrhea, fatigue, nausea, sore throat, and dyspnea, and the prevalent comorbidities in that region were diabetes, hypertension, coronary heart disease, and ARDS. The multivariable factor analysis showed positive association between a group of symptoms and comorbidities, and with the patients age. Interpretation: Epitomizing the correlation of symptoms of COVID-19 with comorbidities and patients age would help clinicians effectively manage the patients.