The COVID-19 pandemic has manifested more than a health crisis and has severely impacted on social, economic, and development crises in the world. The relationship of COVID-19 with countries’ economic and other demographic statuses is an important criterion with which to assess the impact of this current outbreak. Based on available data from the online platform, we tested the hypotheses of a country’s economic status, population density, the median age of the population, and urbanization pattern influence on the test, attack, case fatality, and recovery rates of COVID-19. We performed correlation and multivariate multinomial regression analysis with relative risk ratio (RRR) to test the hypotheses. The correlation analysis showed that population density and test rate had a significantly negative association (r = −0.2384, p = 0.00). In contrast, the median age had a significant positive correlation with recovery rate (r = 0.4654, p = 0.00) and case fatality rate (r = 0.2847, p = 0.00). The urban population rate had a positive significant correlation with recovery rate (r = 0.1610, p = 0.04). Lower-middle-income countries had a negative significant correlation with case fatality rate (r= −0.3310, p = 0.04). The multivariate multinomial logistic regression analysis revealed that low-income countries are more likely to have an increased risk of case fatality rate (RRR = 0.986, 95% Confidence Interval; CI = 0.97−1.00, p < 0.05) and recovery rate (RRR = 0.967, 95% CI = 0.95–0.98, p = 0.00). The lower-income countries are more likely to have a higher risk in case of attack rate (RRR = 0.981, 95% CI = 0.97–0.99, p = 0.00) and recovery rate (RRR = 0.971, 95% CI = 0.96–0.98, p = 0.00). Similarly, upper middle-income countries are more likely to have higher risk in case of attack rate (RRR = 0.988, 95% CI = 0.98–1.0, p = 0.01) and recovery rate (RRR = 0.978, 95% CI = 0.97–0.99, p = 0.00). The low- and lower-middle-income countries should invest more in health care services and implement adequate COVID-19 preventive measures to reduce the risk burden. We recommend a participatory, whole-of-government and whole-of-society approach for responding to the socio-economic challenges of COVID-19 and ensuring more resilient and robust health systems to safeguard against preventable deaths and poverty by improving public health outcomes.
South Asian (SA) countries have been fighting with the pandemic novel coronavirus disease 2019 (COVID-19) since January 2020. Earlier, the country-specific descriptive study has been done. Nevertheless, as transboundary infection, the border sharing, shared cultural and behaviour practice, effects on the temporal and spatial distribution of COVID-19 in SA is still unveiled. Therefore, this study has been revealed the spatial hotspot along with descriptive output on different parameters of COVID-19 infection. We extracted data from the WHO and the worldometer database from the onset of the outbreak up to 15 May 2020. Europe has the highest case fatality rate (CFR; 9.22%), whereas Oceania has the highest (91.15%) recovery rate from COVID-19. Among SA countries, India has the highest number of cases (85,790), followed by Pakistan (38,799) and Bangladesh (20,065). However, the number of tests conducted was minimum in this region in comparison with other areas. The highest CFR was recorded in India (3.21%) among SA countries, whereas Nepal and Bhutan had no death record due to COVID-19 so far. The recovery rate varies from 4.75% in the Maldives to 51.02% in Sri Lanka. In Bangladesh, community transmission has been recorded, and the highest number of cases were detected in Dhaka, followed by Narayanganj and Chattogram. Dhaka and its surrounding districts, Faridpur and Madaripur district of Bangladesh, is in the hotspot on the spatiotemporal tendency. But no cold spot was pointed out in Bangladesh. Three hotspots and three cold spots at different confidence levels were detected in India. Findings from this study suggested the “test, trace, and isolation” approach for earlier detection of infection to prevent further community transmission of COVID-19.
Aim:The study was aimed to determine the reference values of most commonly used hematological and biochemical parameters of indigenous sheep, reared under semi-intensive backyard farms in Dhaka and Chittagong district, Bangladesh.Materials and Methods:A total of 41 blood samples were collected from indigenous sheep (Ovis aries) from June to December 2016 from Dhaka and Chittagong Districts of Bangladesh. Hematological and serum biochemical parameters such as hemoglobin (Hb), packed cell volume (PCV), erythrocyte sedimentation rate (ESR), total erythrocyte count (TEC), total leukocyte count (TLC), neutrophil, eosinophil, basophil, monocyte, lymphocyte, urea, triglyceride, cholesterol, glucose, albumin, total protein (TP), alanine aminotransferase (ALT), and aspartate transaminase (AST) were determined by biochemical analyzer. 90% reference intervals were calculated for all parameters.Results:The hematologica and serum biochemical profiles of indigenous sheep showed wide range and variation. The results were categorized according to sex and age of the sheep for comparison. Young sheep had significantly higher PCV, eosinophil, triglyceride, and TP level than that of adult (p<0.05), whereas the urea and albumin level was higher in adult than that of juvenile (p<0.05). Most of the values of the parameters are close to each other for both male and female except TEC, urea, cholesterol, triglyceride, glucose, and AST. However, a significant difference was found only for albumin and basophil level between male and female sheep.Conclusion:Hematological and biochemical parameters in Bangladeshi indigenous sheep showed a wide range and variation implicating future study for the prophylaxis of ovine diseases.
The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world including Bangladesh. We conducted this study to assess how COVID-19 cases clustered across districts in Bangladesh and whether the pattern and duration of clusters changed following the country's containment strategy using Geographic information system (GIS) software. We calculated the epidemiological measures including incidence, case fatality rate (CFR) and spatiotemporal pattern of COVID-19. We used inverse distance weighting (IDW), Geographically weighted regression (GWR), Moran's I and Getis-Ord Gi* statistics for prediction, spatial autocorrelation and hotspot identification. We used retrospective space-time scan statistic to analyse clusters of COVID-19 cases. COVID-19 has a CFR of 1.4%. Over 50% of cases were reported among young adults (21-40 years age). The incidence varies from 0.03 -0.95 at the end of March to 15.59-308.62 per 100,000, at the end of July. Global Moran's Index indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran's I analysis stated a distinct High-High (HH) clustering of COVID-19 cases among Dhaka, Gazipur and Narayanganj districts.Twelve statistically significant high rated clusters were identified by space-time scan statistics using a discrete Poisson model. IDW predicted the cases at the undetermined area, and GWR showed a strong relationship between population density and case frequency, which was further established with Moran's I (0.734; p ≤ 0.01). Dhaka and its surrounding six districts were identified as the significant hotspot whereas Chattogram was an extended infected area, indicating the gradual spread of the virus to peripheral districts. This study provides novel insights into the geostatistical analysis of COVID-19 clusters and hotspots that might assist the policy planner to predict the spatiotemporal transmission dynamics and formulate imperative control strategies of SARS-CoV-2 in Bangladesh. The geospatial modeling tools can be used to prevent and control future epidemics and pandemics.
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