Search

• The Pangolin classification clustered the South Kivu sequences into seven lineages. • The Delta variant is responsible for COVID-19 outbreaks during the third wave. • D614G is the most frequent mutation observed, followed by L4715L. • There are multiple introductions of SARS-CoV-2 and subsequent circulating variants. We used whole-genome sequencing of SARS-CoV-2 to identify variants circulating in the Democratic Republic of the Congo and obtain molecular information useful for diagnosis, improving treatment, and general pandemic control strategies. A total of 74 SARS-CoV-2 isolates were sequenced using Oxford Nanopore platforms. Generated reads were processed to obtain consensus genome sequences. Sequences with more than 80% genome coverage were used for variant calling, phylogenetic analysis, and classification using Pangolin lineage annotation nomenclature. Phylogenetic analysis based on Pangolin classification clustered South Kivu sequences into seven lineages (A.23.1, B.1.1.6, B.1.214, B.1.617.2, B.1.351, C.16, and P.1). The Delta (B.1.617.2) variant was the most dominant and responsible for outbreaks during the third wave. Based on the Wuhan reference genome, 289 distinct mutations were detected, including 141 missenses, 123 synonymous, and 25 insertions/deletions when our isolates were mapped to the Wuhan reference strain. Most of these point mutations were located within the coding sequences of the SARS-CoV-2 genome that includes spike, ORF1ab, ORF3, and nucleocapsid protein genes. The most common mutation was D614G (1841A>G) observed in 61 sequences, followed by L4715L (14143 C>T) found in 60 sequences. Our findings highlight multiple introductions of SARS-CoV-2 into South Kivu through different sources and subsequent circulation of variants in the province. These results emphasize the importance of timely monitoring of genetic variation and its effect on disease severity. This work set a foundation for the use of genomic surveillance as a tool for future global pandemic management and control. [ABSTRACT FROM AUTHOR]

[Display omitted] • The mean latent/removed periods of the COVID-19 epidemic in China were 5.40/2.13 days. • There were 1.72 latent/infected persons per 10,000 Wuhan travelers up to January 20th. • Space-time R 0 estimates indicated an initial value between 2 and 3.5 in most cities. • There were 196 cities that had an estimated R 0 < 1 on February 29th. • A clear descent in temporal R 0 started one week after the outbreak and lasted for two weeks. The novel coronavirus (COVID-19) epidemic is reaching its final phase in China. The epidemic data are available for a complete assessment of epidemiological parameters in all regions and time periods. This study aims to present a spatiotemporal epidemic model based on spatially stratified heterogeneity (SSH) to simulate the epidemic spread. A susceptible-exposed/latent-infected-removed (SEIR) model was constructed for each SSH-identified stratum (each administrative city) to estimate the spatiotemporal epidemiological parameters of the outbreak. We estimated that the mean latent and removed periods were 5.40 and 2.13 days, respectively. There was an average of 1.72 latent or infected persons per 10,000 Wuhan travelers to other locations until January 20th, 2020. The space-time basic reproduction number (R 0) estimates indicate an initial value between 2 and 3.5 in most cities on this date. The mean period for R 0 estimates to decrease to 80%, and 50% of initial values in cities were an average of 14.73 and 19.62 days, respectively. Our model estimates the complete spatiotemporal epidemiological characteristics of the outbreak in a space-time domain. These findings will help enhance a comprehensive understanding of the outbreak and inform the strategies of prevention and control in other countries worldwide. [ABSTRACT FROM AUTHOR]

CORONA virus disease -19 (COVID-19) is a disease caused by SARS-CoV-2. The outbreak started from Wuhan, China in Dec'2019 and became a pandemic. Human to human transmission occurs through droplet, fomites, aerosol and contaminated surfaces.This study shows that use of portable air purifier in patient's room can actually reduce the viral load in the room air that might minimize the risk of acquiring COVID through air-borne transmission by heath care professional taking care of the COVID patients. The study was carried out in room/cabins of special ward in LN Hospital, New Delhi where recently detected (within 48 Hr of RT-PCR +Ve results) COVID-19 patient with cough were admitted for treatment.The COVID-19 patients were asked to cough on the petri dish fixed with a polyvinylidene fluoride (PVDF) membrane filter (80 mm in diameter and 3 um pore size) keeping it within 2 feet distances from his/her nose for 15 mins. Then the membrane was then removed and added to falcon tube (15ML) containing 3 ML of viral transport medium (VTM). The tube were sent to PCR lab in an ice box. Air sampler was run for 1Hr keeping it at 3 Meter distance from recently diagnosed COVID-19 patient. RT-PCR done from particulate matters collected in VTM from the air sampler.Air sampler was run similarly and RT-PCR was done from PM collected in the filter of air sampler. The primary air suction was done at two different air flow 16.7 and 27 litre per minute (LPM), after which both RDRP and E gene were positive, after the use of purifier both the genes were still positive but with increased Ct values. With continous use of the air purifier the viral load of COVID-19 present in the air of ICU with paitents is primarily reduced but not completely eliminated. [ABSTRACT FROM AUTHOR]