Introduction of the SARS-CoV-2 Beta variant from Comoros into the Marseille geographical area.

BACKGROUND: We describe the epidemiology of the first cases diagnosed in our institute of infections with the SARS-CoV-2 Beta variant and how this variant was imported to Marseille. METHODS: The Beta variant was identified based on analyses of sequences of viral genomes or of a spike gene fragment obtained by next-generation sequencing using Illumina technology, or by a real-time reverse-transcription-PCR (qPCR) specific of the Beta variant. RESULTS: The first patient diagnosed as infected with the SARS-CoV-2 Beta variant was sampled on January 15, 2021. Twenty-nine patients were diagnosed in January 2021 (two weeks). Fifteen (52%) patients were of Comorian nationality. Eight (28%) had travelled abroad, including six who had returned from Comoros. Phylogeny based on SARS-CoV-2 genomes from 11 of these patients and their best BLAST hits from the GISAID database showed that seven patients, including the four returning from Comoros, were clustered with 27 other genomes from GISAID that included the six first Beta variant genomes described in Comoros in January 2021. CONCLUSIONS: Our analyses highlight that, as for the case of other SARS-CoV-2 variants that have been diagnosed in Marseille, the Beta variant was imported to Marseille through travel from abroad. It had limited spread in our geographical area.

Detection of SARS-CoV-2 variant 501Y.V2 in Comoros Islands in January 2021.

Background. Genomic data is key in understanding the spread and evolution of SARS-CoV-2 pandemic and informing the design and evaluation of interventions. However, SARS-CoV-2 genomic data remains scarce across Africa, with no reports yet from the Indian Ocean islands. Methods. We genome sequenced six SARS-CoV-2 positive samples from the first major infection wave in the Union of Comoros in January 2021 and undertook detailed phylogenetic analysis. Results. All the recovered six genomes classified within the 501Y.V2 variant of concern (also known as lineage B.1.351) and appeared to be from 2 sub-clusters with the most recent common ancestor dated 30 th Oct-2020 (95% Credibility Interval: 06 th Sep-2020 to 10 th Dec-2020). Comparison of the Comoros genomes with those of 501Y.V2 variant of concern from other countries deposited into the GISAID database revealed their close association with viruses identified in France and Mayotte (part of the Comoros archipelago and a France, Overseas Department). Conclusions. The recovered genomes, albeit few, confirmed local transmission following probably multiple introductions of the SARS-CoV-2 501Y.V2 variant of concern during the Comoros's first major COVID-19 wave. These findings demonstrate the importance of genomic surveillance and have implications for ongoing control strategies on the islands.

A SARS-CoV-2 Surveillance System in Sub-Saharan Africa: Modeling Study for Persistence and Transmission to Inform Policy.

BACKGROUND: Since the novel coronavirus emerged in late 2019, the scientific and public health community around the world have sought to better understand, surveil, treat, and prevent the disease, COVID-19. In sub-Saharan Africa (SSA), many countries responded aggressively and decisively with lockdown measures and border closures. Such actions may have helped prevent large outbreaks throughout much of the region, though there is substantial variation in caseloads and mortality between nations. Additionally, the health system infrastructure remains a concern throughout much of SSA, and the lockdown measures threaten to increase poverty and food insecurity for the subcontinent's poorest residents. The lack of sufficient testing, asymptomatic infections, and poor reporting practices in many countries limit our understanding of the virus's impact, creating a need for better and more accurate surveillance metrics that account for underreporting and data contamination. OBJECTIVE: The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with new and decomposable surveillance metrics of COVID-19 that overcome data limitations and contamination inherent in public health surveillance systems. In addition to prevalence of observed daily and cumulative testing, testing positivity rates, morbidity, and mortality, we derived COVID-19 transmission in terms of speed, acceleration or deceleration, change in acceleration or deceleration (jerk), and 7-day transmission rate persistence, which explains where and how rapidly COVID-19 is transmitting and quantifies shifts in the rate of acceleration or deceleration to inform policies to mitigate and prevent COVID-19 and food insecurity in SSA. METHODS: We extracted 60 days of COVID-19 data from public health registries and employed an empirical difference equation to measure daily case numbers in 47 sub-Saharan countries as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: Kenya, Ghana, Nigeria, Ethiopia, and South Africa have the most observed cases of COVID-19, and the Seychelles, Eritrea, Mauritius, Comoros, and Burundi have the fewest. In contrast, the speed, acceleration, jerk, and 7-day persistence indicate rates of COVID-19 transmissions differ from observed cases. In September 2020, Cape Verde, Namibia, Eswatini, and South Africa had the highest speed of COVID-19 transmissions at 13.1, 7.1, 3.6, and 3 infections per 100,0000, respectively; Zimbabwe had an acceleration rate of transmission, while Zambia had the largest rate of deceleration this week compared to last week, referred to as a jerk. Finally, the 7-day persistence rate indicates the number of cases on September 15, 2020, which are a function of new infections from September 8, 2020, decreased in South Africa from 216.7 to 173.2 and Ethiopia from 136.7 to 106.3 per 100,000. The statistical approach was validated based on the regression results; they determined recent changes in the pattern of infection, and during the weeks of September 1-8 and September 9-15, there were substantial country differences in the evolution of the SSA pandemic. This change represents a decrease in the transmission model R value for that week and is consistent with a de-escalation in the pandemic for the sub-Saharan African continent in general. CONCLUSIONS: Standard surveillance metrics such as daily observed new COVID-19 cases or deaths are necessary but insufficient to mitigate and prevent COVID-19 transmission. Public health leaders also need to know where COVID-19 transmission rates are accelerating or decelerating, whether those rates increase or decrease over short time frames because the pandemic can quickly escalate, and how many cases today are a function of new infections 7 days ago. Even though SSA is home to some of the poorest countries in the world, development and population size are not necessarily predictive of COVID-19 transmission, meaning higher income countries like the United States can learn from African countries on how best to implement mitigation and prevention efforts. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21955.