ABSTRACT
BackgroundBangladesh is going through an unprecedented crisis since the onset of the COVID-19 pandemic. Throughout the COVID-19 pandemic, the reproduction number of COVID-19 swarmed in the scientific community and public media due to its simplicity in explaining an infectious disease dynamic. This paper aims to estimate the effective reproduction number (Rt) for COVID-19 over time in Bangladesh and its districts using reported cases. MethodsAdapted methods derived from Bettencourt and Ribeiro (2008), which is a sequential Bayesian approach using the compartmental Susceptible-Infectious-Recovered (SIR) model, have been used to estimate Rt. ResultsAs of July 21, the mean Rt is 1.32(0.98-1.70, 90% HDI), with a median of 1.16(0.99-1.34 90% HDI). The initial Rt of Bangladesh was 3, whereas the Rt on the day of imposing nation-wide lockdown was 1.47, at the end of lockdown phase 1 was 1.06, at the end of lockdown phase 2 was 1.33. Each phase of nation-wide lockdown has contributed to the decline of effective reproduction number (Rt) for Bangladesh by 28.44%, and 26.70%, respectively, implying moderate effectiveness of the epidemic response strategies. Interpretation and ConclusionThe mean Rt fell by 13.55% from May 31 to July 21, 2020, despite easing of lockdown in Bangladesh. The Rt continued to fall below the threshold value one steadily from the beginning of July and sustained around 1. The mean Rt fell by 13.55% from May 31 to July 21, 2020, despite easing of lockdown in Bangladesh. As of July 21, the current estimate of Rt is 1.07(0.92-1.15: 90% HDI), meaning that an infected individual is spreading the virus to an average of one other, with 0.07 added chance of infecting a second individual. This whole research recommends two things- broader testing and careful calibration of measures to keep Rt a long way below the crucial threshold one. HighlightsO_LIAs of July 21, the mean Rt and growth factor is 1.32 and 1.02, respectively. C_LIO_LIEach phase of nation-wide lockdown has contributed to the decline of effective reproduction number (Rt) for Bangladesh by 28.44%, and 26.70%, respectively, implying moderate effectiveness of the epidemic response strategies. C_LIO_LIThe Rt of Bangladesh was below 1 for only 20 days, which was observed during May 24- 25, June 19-21, from June 30 to July 6, July 9-12, and July 16-19,2020. C_LIO_LIThe initial Rt of Bangladesh was 3, whereas the Rt on the day of imposing nation-wide lockdown was 1.47, at the end of lockdown phase-1 was 1.06, at the end of lockdown phase-2 was 1.33. C_LIO_LIThe mean Rt fell by 13.55% from May 31 to July 21, 2020, despite easing of lockdown in Bangladesh. C_LIO_LIThe Rt continued to fall below the threshold value one steadily from the beginning of July and sustained around one. C_LIO_LIWe suspect that a low testing rate may influence the constant decline of Rt below threshold value 1 in the course of July. C_LIO_LIThe mean Rt fell by 13.55% from May 31 to July 21, 2020, despite easing of lockdown in Bangladesh. C_LIO_LIAs of July 21, the current estimate of Rt is 1.07(0.92-1.15: 90% HDI), meaning that an infected individual is spreading the virus to an average of one other, with 0.07 added chance of infecting a second individual. C_LI
ABSTRACT
On December 31, 2019, the World Health Organization (WHO) was informed that atypical pneumonia-like cases have emerged in Wuhan City, Hubei province, China. WHO identified it as a novel coronavirus and declared a global pandemic on March 11th, 2020. At the time of writing this, the COVID-19 claimed more than 440 thousand lives worldwide and led to the global economy and social life into an abyss edge in the living memory. As of now, the confirmed cases in Bangladesh have surpassed 100 thousand and more than 1343 deaths putting startling concern on the policymakers and health professionals; thus, prediction models are necessary to forecast a possible number of cases in the future. To shed light on it, in this paper, we presented data-driven estimation methods, the Long Short-Term Memory (LSTM) networks, and Logistic Curve methods to predict the possible number of COVID-19 cases in Bangladesh for the upcoming months. The results using Logistic Curve suggests that Bangladesh has passed the inflection point on around 28-30 May 2020, a plausible end date to be on the 2nd of January 2021 and it is expected that the total number of infected people to be between 187 thousand to 193 thousand with the assumption that stringent policies are in place. The logistic curve also suggested that Bangladesh would reach peak COVID-19 cases at the end of August with more than 185 thousand total confirmed cases, and around 6000 thousand daily new cases may observe. Our findings recommend that the containment strategies should immediately implement to reduce transmission and epidemic rate of COVID-19 in upcoming days. HighlightsO_LIAccording to the Logistic curve fitting analysis, the inflection point of the COVID-19 pandemic has recently passed, which was approximately between May 28, 2020, to May 30, 2020. C_LIO_LIIt is estimated that the total number of confirmed cases will be around 187-193 thousand at the end of the epidemic. We expect that the actual number will most likely to in between these two values, under the assumption that the current transmission is stable and improved stringent policies will be in place to contain the spread of COVID-19. C_LIO_LIThe estimated total death toll will be around 3600-4000 at the end of the epidemic. C_LIO_LIThe epidemic of COVID-19 in Bangladesh will be mostly under control by the 2nd of January 2021 if stringent measures are taken immediately. C_LI
ABSTRACT
BackgroundA new pathogenic disease named COVID-19 became a global threat, first reported in Wuhan, China, in December 2019. The number of affected cases growing exponentially and now, more than 210 countries confirmed the cases. ObjectiveThis meta-analysis aims to evaluate risk factors, the prevalence of comorbidity, and clinical characteristics in COVID-19 death patients compared to survival patients that can be used as a reference for further research and clinical decisions. MethodsPubMed, Science Direct, SAGE were searched to collect data about demographic, clinical characteristics, and comorbidities of confirmed COVID-19 patients from January 1, 2020, to May 17, 2020. Meta-analysis was performed with the use of Review Manager 5.3 ResultsEighty-five studies were included in Meta-analysis, including a total number of 67,299 patients with SARS-CoV-2 infection. Males are severely affected or died than females (OR = 2.26, p < 0.00001; OR = 3.59, p < 0.00001) are severely affected, or died by COVID-19 and cases with age [≥]50 are at higher risk of death than age <50 years (OR=334.23). Presence of any comorbidity or comorbidities like hypertension, cardiovascular disease, diabetes, cerebrovascular disease, respiratory disease, kidney disease, liver disease, malignancy significantly increased the risk of death compared to survival (OR = 3.46, 3.16, 4.67, 2.45, 5.84, 2.68, 5.62, 2.81,2.16). Among the clinical characteristics such as fever, cough, myalgia, diarrhea, abdominal pain, dyspnea, fatigue, sputum production, chest tightness headache and nausea or vomiting, only fatigue (OR = 1.31, 95%) and dyspnea increased the death significantly (OR= 1.31, 4.57). The rate of death of COVID-19 cases is 0.03-times lower than the rate of survival (OR = 0.03). ConclusionOur result indicates that male patients are affected severely or died, the rate of death is more in the age [≥]50 group, and the rate of death is affected by comorbidities and clinical symptoms.
ABSTRACT
Objective: To verify possible associations between polymorphisms of glutathione S-transferase Mu (GSTM1), glutathione S-transferase θ (GSTT1) and glutathione S-transferase Pi (GSTP1) genes and susceptibility to lung cancer. Methods: A total of 106 lung cancer patients and 116 controls were enrolled in a case-control study. The GSTM1 and GSTT1 were analyzed using PCR while GSTP1 was analyzed using PCR-restriction fragment length polymorphism. Risk of lung cancer was estimated as odds ratio at 95% confidence interval using unconditional logistic regression models adjusting for age, sex, and tobacco use. Results: GSTM1 null and GSTT1 null genotypes did not show a significant risk for developing lung cancer. A significantly elevated lung cancer risk was associated with GSTP1 heterozygous, mutant and combined heterozygous+mutant variants of rs1695. When classified by tobacco consumption status, no association with risk of lung cancer was found in case of tobacco smokers and nonsmokers carrying null and present genotypes of GSTM1 and GSTT1. There is a three-fold (approximately) increase in the risk of lung cancer in case of both heterozygous (AG) and heterozygous+mutant homozygous (AG+GG) genotypes whereas there is an eightfold increase in risk of lung cancer in cases of GG with respect to AA genotype in smokers. Conclusions: Carrying the GSTM1 and GSTT1 null genotype is not a risk factor for lung cancer and GSTP1Ile105Val is associated with elevated risk of lung cancer.
