Global disparities in SARS-CoV-2 genomic surveillance

Genomic sequencing is essential to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments, vaccines, and guide public health responses. To investigate the global SARS-CoV-2 genomic surveillance, we used sequences shared via GISAID to estimate the impact of sequencing intensity and turnaround times on variant detection in 189 countries. In the first two years of the pandemic, 78% of high-income countries sequenced >0.5% of their COVID-19 cases, while 42% of low- and middle-income countries reached that mark. Around 25% of the genomes from high income countries were submitted within 21 days, a pattern observed in 5% of the genomes from low- and middle-income countries. We found that sequencing around 0.5% of the cases, with a turnaround time <21 days, could provide a benchmark for SARS-CoV-2 genomic surveillance. Socioeconomic inequalities undermine the global pandemic preparedness, and efforts must be made to support low- and middle-income countries improve their local sequencing capacity

Global disparities in SARS-CoV-2 genomic surveillance

Genomic sequencing is essential to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments, vaccines, and guide public health responses. To investigate the global SARS-CoV-2 genomic surveillance, we used sequences shared via GISAID to estimate the impact of sequencing intensity and turnaround times on variant detection in 189 countries. In the first two years of the pandemic, 78% of high-income countries sequenced >0.5% of their COVID-19 cases, while 42% of low- and middle-income countries reached that mark. Around 25% of the genomes from high income countries were submitted within 21 days, a pattern observed in 5% of the genomes from low- and middle-income countries. We found that sequencing around 0.5% of the cases, with a turnaround time <21 days, could provide a benchmark for SARS-CoV-2 genomic surveillance. Socioeconomic inequalities undermine the global pandemic preparedness, and efforts must be made to support low- and middle-income countries improve their local sequencing capacity

Virus genomes reveal factors that spread and sustained the Ebola epidemic.

The 2013-2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic 'gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics

Evolutionary dynamics of 2009 pandemic influenza A virus subtype H1N1 in South Africa during 2009–2010

BACKGROUND: 2009 Pandemic Influenza A(H1N1) was first detected in June 2009 in South Africa and later resulted in extensive transmission throughout Africa. Established routine surveillance programmes and collaboration between private and public sector laboratories allowed for comprehensive molecular epidemiological and antigenic investigation of the first and second waves of 2009 Pandemic Influenza A(H1N1) in South Africa. METHODS: We screened 9792 and 6915 specimens from patients with Influenza like illness (ILI) or severe acute respiratory infection (SARI) symptoms from surveillance programmes in hospitalised or outpatients in South Africa in 2009 and 2010, respectively for Influenza by RTPCR. Influenza-positive specimens were subjected to genetic and antigenic characterisation. Bayesian and Maximum likelihood analyses of the Hemaglutinin (HA)genes of 96 2009 Pandemic Influenza A(H1N1) strains were used for molecular epidemiological investigations. Hemaglutination inhibition assays and sequencing of the PB2 and neuraminidase genes were used to investigate pathogenicity and resistance mutations. RESULTS: The 2009 Pandemic Influenza A(H1N1) epidemic occurred as a second epidemic peak following seasonal H3N2 cases in 2009 and in 2010. Progressive drift away from the A/California/7/2009 vaccine strain was observed at both the nucleotide and amino acid level with 2010 strains clustering separate to 2009 strains. A few unique clusters of amino acid changes in severe cases were identified, but most strains were antigenically similar to the vaccine strain and no resistance or known pathogenicity mutations were detected. CONCLUSION: Despite limited drift observed over the 2 seasons in South Africa, circulating 2009 Pandemic Influenza A(H1N1) strains remained antigenically similar to strains identified in other Northern and Southern hemisphere countries from 2010 and 2011.Presented in part: Africa Influenza Scientific Symposium, NCID, Johannesburg, South Africa, 7–11 December 2009; Options for the Control of Influenza, Hong Kong, SAR, 3–7 September 2010.Centers for Disease Control and Prevention, Atlanta, Georgia, USA.http://www.journals.uchicago.edu/toc/jid/currenthb2013ay201

Influenza epidemiology and vaccine effectiveness among patients with influenza-like illness, viral watch sentinel sites, South Africa, 2005-2009.

There is limited data on the epidemiology of influenza and few published estimates of influenza vaccine effectiveness (VE) from Africa. In April 2009, a new influenza virus strain infecting humans was identified and rapidly spread globally. We compared the characteristics of patients ill with influenza A(H1N1)pdm09 virus to those ill with seasonal influenza and estimated influenza vaccine effectiveness during five influenza seasons (2005-2009) in South Africa.Epidemiological data and throat and/or nasal swabs were collected from patients with influenza-like illness (ILI) at sentinel sites. Samples were tested for seasonal influenza viruses using culture, haemagglutination inhibition tests and/or polymerase chain reaction (PCR) and for influenza A(H1N1)pdm09 by real-time PCR. For the vaccine effectiveness (VE) analysis we considered patients testing positive for influenza A and/or B as cases and those testing negative for influenza as controls. Age-adjusted VE was calculated as 1-odds ratio for influenza in vaccinated and non-vaccinated individuals.From 2005 through 2009 we identified 3,717 influenza case-patients. The median age was significantly lower among patients infected with influenza A(H1N1)pdm09 virus than those with seasonal influenza, 17 and 27 years respectively (p<0.001). The vaccine coverage during the influenza season ranged from 3.4% in 2009 to 5.1% in 2006 and was higher in the ≥50 years (range 6.9% in 2008 to 13.2% in 2006) than in the <50 years age group (range 2.2% in 2007 to 3.7% in 2006). The age-adjusted VE estimates for seasonal influenza were 48.6% (4.9%, 73.2%); -14.2% (-9.7%, 34.8%); 12.0% (-70.4%, 55.4%); 67.4% (12.4%, 90.3%) and 29.6% (-21.5%, 60.1%) from 2005 to 2009 respectively. For the A(H1N1)pdm09 season, the efficacy of seasonal vaccine was -6.4% (-93.5%, 43.3%).Influenza vaccine demonstrated a significant protective effect in two of the five years evaluated. Low vaccine coverage may have reduced power to estimate vaccine effectiveness

Interim report on pandemic H1N1 influenza virus infections in South Africa, April to October 2009 : epidemiology and factors associated with fatal cases

We provide an interim report on pandemic H1N1 influenza activity in South Africa, with a focus on the epidemiology and factors associated with deaths. Following the importation of the virus on 14 July 2009, and the epidemic peak during the week starting 3 August, the incidence in South Africa has declined. A total of 12,331 cases and 91 deaths have been laboratory-confirmed as of 12 October 2009. Age distribution and risk groups were similar to those observed elsewhere. The median age of patients who died (33.5 years) was significantly higher than that of the non-fatal cases (15.0 years p<0.01). The most common underlying conditions among fatal cases were infection with human immunodeficiency virus (17/32 tested) and pregnancy (25/45 women of reproductive age). Active tuberculosis coinfection was present in seven of 72 fatal cases. These findings should be taken into consideration when planning vaccination strategies for 2010

Comparison of vaccine composition to circulating viruses, South Africa, 2005–2009.

<p><b>Predominating circulating strains in bold.</b></p><p><b>Predominating B strains indicated.</b></p