Jinnat Ferdous

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.

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: ) 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.

Diverse coronavirus (CoV) strains can infect both humans and animals and produce various diseases. CoVs have caused three epidemics and pandemics in the last two decades, and caused a severe impact on public health and the global economy. Therefore, it is of utmost importance to understand the emergence and evolution of endemic and emerging CoV diversity in humans and animals. For diverse bird species, the Infectious Bronchitis Virus is a significant one, whereas feline enteric and canine coronavirus, recombined to produce feline infectious peritonitis virus, infects wild cats. Bovine and canine CoVs have ancestral relationships, while porcine CoVs, especially SADS-CoV, can cross species barriers. Bats are considered as the natural host of diverse strains of alpha and beta coronaviruses. Though MERS-CoV is significant for both camels and humans, humans are nonetheless affected more severely. MERS-CoV cases have been reported mainly in the Arabic peninsula since 2012. To date, seven CoV strains have infected humans, all descended from animals. The severe acute respiratory syndrome coronaviruses (SARS-CoV and SARS-CoV-2) are presumed to be originated in Rhinolopoid bats that severely infect humans with spillover to multiple domestic and wild animals. Emerging alpha and delta variants of SARS-CoV-2 were detected in pets and wild animals. Still, the intermediate hosts and all susceptible animal species remain unknown. SARS-CoV-2 might not be the last CoV to cross the species barrier. Hence, we recommend developing a universal CoV vaccine for humans so that any future outbreak can be prevented effectively. Furthermore, a One Health approach coronavirus surveillance should be implemented at human-animal interfaces to detect novel coronaviruses before emerging to humans and to prevent future epidemics and pandemics.

The exact origin of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and source of introduction into humans has not been established yet, though it might be originated from animals. Therefore, we conducted a study to understand the putative reservoirs, transmission dynamics, and susceptibility patterns of SARS-CoV-2 in animals. Rhinolophus bats are presumed to be natural progenitors of SARS-CoV-2-related viruses. Initially, pangolin was thought to be the source of spillover to humans, but they might be infected by human or other animal species. So, the virus spillover pathways to humans remain unknown. Human-to-animal transmission has been testified in pet, farmed, zoo and free-ranging wild animals. Infected animals can transmit the virus to other animals in natural settings like mink-to-mink and mink-to-cat transmission. Animal-to-human transmission is not a persistent pathway, while mink-to-human transmission continues to be illuminated. Multiple companions and captive wild animals were infected by an emerging alpha variant of concern (B.1.1.7 lineage) whereas Asiatic lions were infected by delta variant, (B.1.617.2). To date, multiple animal species - cat, ferrets, non-human primates, hamsters and bats - showed high susceptibility to SARS-CoV-2 in the experimental condition, while swine, poultry, cattle showed no susceptibility. The founding of SARS-CoV-2 in wild animal reservoirs can confront the control of the virus in humans and might carry a risk to the welfare and conservation of wildlife as well. We suggest vaccinating pets and captive animals to stop spillovers and spillback events. We recommend sustainable One Health surveillance at the animal-human-environmental interface to detect and prevent future epidemics and pandemics by Disease X.

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 behavioral 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. We detected Dhaka and its surrounding six districts, namely Gazipur, Narsingdi, Narayanganj, Munshiganj, Manikganj, and Shariatpur, as the 99% confidence-based hotspot where Faridpur and Madaripur district as the 95% confidence-based spatial hotspots of COVID-19 in Bangladesh. However, we did not find any cold spots in Bangladesh. We identified three hotspots and three cold spots at different confidence levels 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.