Detection and Molecular Characterization of Canine Alphacoronavirus in Free-Roaming Dogs, Bangladesh.

Canine coronavirus (CCoV) is widespread among the dog population and causes gastrointestinal disorders, and even fatal cases. As the zoonotic transmission of viruses from animals to humans has become a worldwide concern nowadays, it is necessary to screen free-roaming dogs for their common pathogens due to their frequent interaction with humans. We conducted a cross-sectional study to detect and characterize the known and novel Corona, Filo, Flavi, and Paramyxoviruses in free-roaming dogs in Bangladesh. Between 2009-10 and 2016-17, we collected swab samples from 69 dogs from four districts of Bangladesh, tested using RT-PCR and sequenced. None of the samples were positive for Filo, Flavi, and Paramyxoviruses. Only three samples (4.3%; 95% CI: 0.9-12.2) tested positive for Canine Coronavirus (CCoV). The CCoV strains identified were branched with strains of genotype CCoV-II with distinct distances. They are closely related to CCoVs from the UK, China, and other CoVs isolated from different species, which suggests genetic recombination and interspecies transmission of CCoVs. These findings indicate that CCoV is circulating in dogs of Bangladesh. Hence, we recommend future studies on epidemiology and genetic characterization with full-genome sequencing of emerging coronaviruses in companion animals in Bangladesh.

Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh.

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.

Transmission dynamics and susceptibility patterns of SARS-CoV-2 in domestic, farmed and wild animals: Sustainable One Health surveillance for conservation and public health 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.

Escalating SARS-CoV-2 circulation in environment and tracking waste management in South Asia.

The novel coronavirus disease of 2019 (COVID-19) pandemic has caused an exceptional drift of production, utilization, and disposal of personal protective equipment (PPE) and different microplastic objects for safety against the virus. Hence, we reviewed related literature on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA detected from household, biomedical waste, and sewage to identify possible health risks and status of existing laws, regulations, and policies regarding waste disposal in South Asian (SA) countries. The SARS-CoV-2 RNA was detected in sewage and wastewater samples of Nepal, India, Pakistan, and Bangladesh. Besides, this review reiterates the enormous amounts of PPE and other single-use plastic wastes generated from healthcare facilities and households in the SA region with inappropriate disposal, landfilling, and/or incineration techniques wind-up polluting the environment. Consequently, the Delta variant (B.1.617.2) of SARS-CoV-2 has been detected in sewer treatment plant in India. Moreover, the overuse of non-biodegradable plastics during the pandemic is deteriorating plastic pollution condition and causes a substantial health risk to the terrestrial and aquatic ecosystems. We recommend making necessary adjustments, adopting measures and strategies, and enforcement of the existing biomedical waste management and sanitation-related policy in SA countries. We propose to adopt the knowledge gaps to improve COVID-19-associated waste management and legislation to prevent further environmental pollution. Besides, the citizens should follow proper disposal procedures of COVID-19 waste to control the environmental pollution.

Molecular Epidemiology of SARS-CoV-2 in Diverse Environmental Samples Globally.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has swamped the global environment greatly in the current pandemic. Wastewater-based epidemiology (WBE) effectively forecasts the surge of COVID-19 cases in humans in a particular region. To understand the genomic characteristics/footprints and diversity of SARS-CoV-2 in the environment, we analyzed 807 SARS-CoV-2 sequences from 20 countries deposited in GISAID till 22 May 2021. The highest number of sequences (n = 638) were reported in Austria, followed by the Netherlands, China, and Bangladesh. Wastewater samples were highest (40.0%) to successfully yield the virus genome followed by a 24 h composite wastewater sample (32.6%) and sewage (18.5%). Phylogenetic analysis revealed that SARS-CoV-2 environmental strains are a close congener with the strains mostly circulating in the human population from the same region. Clade GRY (32.7%), G (29.2%), GR (25.3%), O (7.2%), GH (3.4%), GV (1.4%), S (0.5%), and L (0.4%) were found in environmental samples. Various lineages were identified in environmental samples; nevertheless, the highest percentages (49.4%) of the alpha variant (B.1.1.7) were detected in Austria, Liechtenstein, Slovenia, Czech Republic, Switzerland, Germany, and Italy. Other prevalent lineages were B.1 (18.2%), B.1.1 (9.2%), and B.1.160 (3.9%). Furthermore, a significant number of amino acid substitutions were found in environmental strains where the D614G was found in 83.8% of the sequences. However, the key mutations-N501Y (44.6%), S982A (44.4%), A570D (43.3%), T716I (40.4%), and P681H (40.1%) were also recorded in spike protein. The identification of the environmental belvedere of SARS-CoV-2 and its genetic signature is crucial to detect outbreaks, forecast pandemic harshness, and prepare with the appropriate tools to control any impending pandemic. We recommend genomic environmental surveillance to trace the emerging variants and diversity of SARS-CoV-2 viruses circulating in the community. Additionally, proper disposal and treatment of wastewater, sewage, and medical wastes are important to prevent environmental contamination.

Assessment of basic reproduction number (R0), spatial and temporal epidemiological determinants, and genetic characterization of SARS-CoV-2 in Bangladesh.

Epidemiological and molecular characterization of SARS-CoV-2 is essential for identifying the source of the virus and for effective control of the spread of local strains. We estimated case fatality rate, cumulative recovery number, basic reproduction number (R0) and future incidence of COVID-19 in Bangladesh. We illustrated the spatial distribution of cases throughout the country. We performed phylogenetic and mutation analysis of SARS-CoV-2 sequences from Bangladesh. As of July 31, 2020, Bangladesh had a case fatality rate of 1.32%. The cases were initially clustered in Dhaka and its surrounding districts in March but spreads throughout the country over time. The R0 calculated as 1.173 in Exponential Growth method. For the projection, a 20% change in R0 with subsequent infection trend has been calculated. The genomic analysis of 292 Bangladeshi SARS-CoV-2 strains suggests diverse genomic clades L, O, S, G, GH, where predominant circulating clade was GR (83.9%; 245/292). The GR clades' phylogenetic analysis revealed distinct clusters (I to XIII) with intra-clade variations. The mutation analysis revealed 1634 mutations where 94.6% and 5.4% were non-synonymous and unique mutation, respectively. The Spike, Nucleocapsid, NSP2, and RdRP showed substantially high mutation but no mutation was recorded in NSP9 and NSP11 protein. In spike (S) protein, 355 predominant mutations were recorded, highest in D614G. Alternatively, I120F in NSP2 protein, R203K and G204R in nucleocapsid protein, and P323L in RdRp were more recurrent. The Bangladeshi genomes belonged to phylogenetic lineages A, B, B.1, B.1.1, B.1.1.23, B.1.1.25, B.1.113, and B.1.36, among which 50.0% sequences owned by the pangolin lineage B.1.1.25. The study illustrates the spatial distribution of SARS-CoV-2, and molecular epidemiology of Bangladeshi isolates. We recommend continuous monitoring of R0 and genomic surveillance to understand the transmission dynamics and detect new variants of SARS-CoV-2 for proper control of the current pandemic and evaluate the effectiveness of vaccination globally.

Spatiotemporal patterns and trends of community transmission of the pandemic COVID-19 in South Asia: Bangladesh as a case study.

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.

Assessment of Epidemiological Determinants of COVID-19 Pandemic Related to Social and Economic Factors Globally

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.