Transmission Dynamics and Genomic Epidemiology of Emerging Variants of SARS-CoV-2 in Bangladesh

With the progression of the global SARS-CoV-2 pandemic, the new variants have become more infectious and continue spreading at a higher rate than pre-existing ones. Thus, we conducted a study to explore the epidemiology of emerging variants of SARS-CoV-2 that circulated in Bangladesh from December 2020 to September 2021, representing the 2nd and 3rd waves. We collected new cases and deaths per million daily data with the reproduction rate. We retrieved 928 SARS-CoV-2 sequences from GISAID and performed phylogenetic tree construction and mutation analysis. Case counts were lower initially at the end of 2020, during January–February and April–May 2021, whereas the death toll reached the highest value of 1.587 per million on the first week of August and then started to decline. All the variants (α, β, δ, η) were prevalent in the capital city, Dhaka, with dispersion to large cities, such as Sylhet and Chattogram. The B.1.1.25 lineage was prevalent during December 2020, but the B.1.617.2/δ variant was later followed by the B.1.351/β variant. The phylogeny revealed that the various strains found in Bangladesh could be from numerous countries. The intra-cluster and inter-cluster communication began in Bangladesh soon after the virus arrived. The prominent amino acid substitution was D614G from December 2020 to July 2021 (93.5 to 100%). From February–April, one of the VOC’s important mutations, N501Y substitution, was also estimated at 51.8%, 76.1%, and 65.1% for the α, β and γ variants, respectively. The γ variant’s unique mutation K417T was detected only at 1.8% in February. Another frequent mutation was P681R, a salient feature of the δ variant, detected in June (88.2%) and July (100%). Furthermore, only one γ variant was detected during the entire second and third wave, whereas no η variant was observed in this period. This rapid growth in the number of variants identified across Bangladesh shows virus adaptation and a lack of strict quarantine, prompting periodic genomic surveillance to foresee the spread of new variants, if any, and to take preventive measures as soon as possible

Evolutionary Dynamics and Epidemiology of Endemic and Emerging Coronaviruses in Humans, Domestic Animals, and Wildlife

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

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats.

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development's (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security