17 results on '"Agoti, Charles"'
Search Results
2. Symptom prevalence and secondary attack rate of SARS-CoV-2 in rural Kenyan households: A prospective cohort study.
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Gallagher KE, Nyiro J, Agoti CN, Maitha E, Nyagwange J, Karani A, Bottomley C, Murunga N, Githinji G, Mutunga M, Ochola-Oyier LI, Kombe I, Nyaguara A, Kagucia EW, Warimwe G, Agweyu A, Tsofa B, Bejon P, Scott JAG, and Nokes DJ
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- Humans, Incidence, Kenya epidemiology, Prospective Studies, Prevalence, SARS-CoV-2, COVID-19 diagnosis, COVID-19 epidemiology, Spike Glycoprotein, Coronavirus
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Background: We estimated the secondary attack rate of SARS-CoV-2 among household contacts of PCR-confirmed cases of COVID-19 in rural Kenya and analysed risk factors for transmission., Methods: We enrolled incident PCR-confirmed cases and their household members. At baseline, a questionnaire, a blood sample, and naso-oropharyngeal swabs were collected. Household members were followed 4, 7, 10, 14, 21 and 28 days after the date of the first PCR-positive in the household; naso-oropharyngeal swabs were collected at each visit and used to define secondary cases. Blood samples were collected every 1-2 weeks. Symptoms were collected in a daily symptom diary. We used binomial regression to estimate secondary attack rates and survival analysis to analyse risk factors for transmission., Results: A total of 119 households with at least one positive household member were enrolled between October 2020 and September 2022, comprising 503 household members; 226 remained in follow-up at day 14 (45%). A total of 43 secondary cases arose within 14 days of identification of the primary case, and 81 household members remained negative. The 7-day secondary attack rate was 4% (95% CI 1%-10%), the 14-day secondary attack rate was 28% (95% CI 17%-40%). Of 38 secondary cases with data, eight reported symptoms (21%, 95% CI 8%-34%). Antibody to SARS-CoV-2 spike protein at enrolment was not associated with risk of becoming a secondary case., Conclusion: Households in our setting experienced a lower 7-day attack rate than a recent meta-analysis indicated as the global average (23%-43% depending on variant), and infection is mostly asymptomatic in our setting., (© 2023 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)
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- 2023
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3. Serum immunoglobulin G and mucosal immunoglobulin A antibodies from prepandemic samples collected in Kilifi, Kenya, neutralize SARS-CoV-2 in vitro.
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Nyagwange J, Kutima B, Mwai K, Karanja HK, Gitonga JN, Mugo D, Sein Y, Wright D, Omuoyo DO, Nyiro JU, Tuju J, Nokes DJ, Agweyu A, Bejon P, Ochola-Oyier LI, Scott JAG, Lambe T, Nduati E, Agoti C, and Warimwe GM
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- Humans, SARS-CoV-2, Kenya epidemiology, COVID-19 Serotherapy, Immunoglobulin A, Antibodies, Viral, Immunoglobulin G, COVID-19 epidemiology
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Objectives: Many regions of Africa have experienced lower COVID-19 morbidity and mortality than Europe. Pre-existing humoral responses to endemic human coronaviruses (HCoV) may cross-protect against SARS-CoV-2. We investigated the neutralizing capacity of SARS-CoV-2 spike reactive and nonreactive immunoglobulin (Ig)G and IgA antibodies in prepandemic samples., Methods: To investigate the presence of pre-existing immunity, we performed enzyme-linked immunosorbent assay using spike antigens from reference SARS-CoV-2, HCoV HKU1, OC43, NL63, and 229E using prepandemic samples from Kilifi in coastal Kenya. In addition, we performed neutralization assays using pseudotyped reference SARS-CoV-2 to determine the functionality of the identified reactive antibodies., Results: We demonstrate the presence of HCoV serum IgG and mucosal IgA antibodies, which cross-react with the SARS-CoV-2 spike. We show pseudotyped reference SARS-CoV-2 neutralization by prepandemic serum, with a mean infective dose 50 of 1: 251, which is 10-fold less than that of the pooled convalescent sera from patients with COVID-19 but still within predicted protection levels. The prepandemic naso-oropharyngeal fluid neutralized pseudo-SARS-CoV-2 at a mean infective dose 50 of 1: 5.9 in the neutralization assay., Conclusion: Our data provide evidence for pre-existing functional humoral responses to SARS-CoV-2 in Kilifi, coastal Kenya and adds to data showing pre-existing immunity for COVID-19 from other regions., Competing Interests: Declaration of Competing Interest The authors have no competing interests to declare., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
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- 2023
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4. Global disparities in SARS-CoV-2 genomic surveillance.
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Brito AF, Semenova E, Dudas G, Hassler GW, Kalinich CC, Kraemer MUG, Ho J, Tegally H, Githinji G, Agoti CN, Matkin LE, Whittaker C, Howden BP, Sintchenko V, Zuckerman NS, Mor O, Blankenship HM, de Oliveira T, Lin RTP, Siqueira MM, Resende PC, Vasconcelos ATR, Spilki FR, Aguiar RS, Alexiev I, Ivanov IN, Philipova I, Carrington CVF, Sahadeo NSD, Branda B, Gurry C, Maurer-Stroh S, Naidoo D, von Eije KJ, Perkins MD, van Kerkhove M, Hill SC, Sabino EC, Pybus OG, Dye C, Bhatt S, Flaxman S, Suchard MA, Grubaugh ND, Baele G, and Faria NR
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- Humans, Genome, Viral genetics, Pandemics, Genomics, SARS-CoV-2 genetics, COVID-19 epidemiology
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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., (© 2022. The Author(s).)
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- 2022
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5. Sero-surveillance for IgG to SARS-CoV-2 at antenatal care clinics in three Kenyan referral hospitals: Repeated cross-sectional surveys 2020-21.
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Lucinde RK, Mugo D, Bottomley C, Karani A, Gardiner E, Aziza R, Gitonga JN, Karanja H, Nyagwange J, Tuju J, Wanjiku P, Nzomo E, Kamuri E, Thuranira K, Agunda S, Nyutu G, Etyang AO, Adetifa IMO, Kagucia E, Uyoga S, Otiende M, Otieno E, Ndwiga L, Agoti CN, Aman RA, Mwangangi M, Amoth P, Kasera K, Nyaguara A, Ng'ang'a W, Ochola LB, Namdala E, Gaunya O, Okuku R, Barasa E, Bejon P, Tsofa B, Ochola-Oyier LI, Warimwe GM, Agweyu A, Scott JAG, and Gallagher KE
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- Antibodies, Viral, Cross-Sectional Studies, Female, Hospitals, Humans, Immunoglobulin G, Kenya epidemiology, Pregnancy, Prenatal Care, Referral and Consultation, SARS-CoV-2, Seroepidemiologic Studies, Spike Glycoprotein, Coronavirus, COVID-19 epidemiology, Pregnancy Complications, Infectious
- Abstract
Introduction: The high proportion of SARS-CoV-2 infections that have remained undetected presents a challenge to tracking the progress of the pandemic and estimating the extent of population immunity., Methods: We used residual blood samples from women attending antenatal care services at three hospitals in Kenya between August 2020 and October 2021and a validated IgG ELISA for SARS-Cov-2 spike protein and adjusted the results for assay sensitivity and specificity. We fitted a two-component mixture model as an alternative to the threshold analysis to estimate of the proportion of individuals with past SARS-CoV-2 infection., Results: We estimated seroprevalence in 2,981 women; 706 in Nairobi, 567 in Busia and 1,708 in Kilifi. By October 2021, 13% of participants were vaccinated (at least one dose) in Nairobi, 2% in Busia. Adjusted seroprevalence rose in all sites; from 50% (95%CI 42-58) in August 2020, to 85% (95%CI 78-92) in October 2021 in Nairobi; from 31% (95%CI 25-37) in May 2021 to 71% (95%CI 64-77) in October 2021 in Busia; and from 1% (95% CI 0-3) in September 2020 to 63% (95% CI 56-69) in October 2021 in Kilifi. Mixture modelling, suggests adjusted cross-sectional prevalence estimates are underestimates; seroprevalence in October 2021 could be 74% in Busia and 72% in Kilifi., Conclusions: There has been substantial, unobserved transmission of SARS-CoV-2 in Nairobi, Busia and Kilifi Counties. Due to the length of time since the beginning of the pandemic, repeated cross-sectional surveys are now difficult to interpret without the use of models to account for antibody waning., Competing Interests: This project was funded by a commercial source, the Wellcome Trust (grants 220991/Z/20/Z and 203077/Z/16/Z). This does not alter our adherence to PLOS ONE policies on sharing data and materials. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors declare no other competing interests.
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- 2022
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6. The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance.
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Tegally H, San JE, Cotten M, Moir M, Tegomoh B, Mboowa G, Martin DP, Baxter C, Lambisia AW, Diallo A, Amoako DG, Diagne MM, Sisay A, Zekri AN, Gueye AS, Sangare AK, Ouedraogo AS, Sow A, Musa AO, Sesay AK, Abias AG, Elzagheid AI, Lagare A, Kemi AS, Abar AE, Johnson AA, Fowotade A, Oluwapelumi AO, Amuri AA, Juru A, Kandeil A, Mostafa A, Rebai A, Sayed A, Kazeem A, Balde A, Christoffels A, Trotter AJ, Campbell A, Keita AK, Kone A, Bouzid A, Souissi A, Agweyu A, Naguib A, Gutierrez AV, Nkeshimana A, Page AJ, Yadouleton A, Vinze A, Happi AN, Chouikha A, Iranzadeh A, Maharaj A, Batchi-Bouyou AL, Ismail A, Sylverken AA, Goba A, Femi A, Sijuwola AE, Marycelin B, Salako BL, Oderinde BS, Bolajoko B, Diarra B, Herring BL, Tsofa B, Lekana-Douki B, Mvula B, Njanpop-Lafourcade BM, Marondera BT, Khaireh BA, Kouriba B, Adu B, Pool B, McInnis B, Brook C, Williamson C, Nduwimana C, Anscombe C, Pratt CB, Scheepers C, Akoua-Koffi CG, Agoti CN, Mapanguy CM, Loucoubar C, Onwuamah CK, Ihekweazu C, Malaka CN, Peyrefitte C, Grace C, Omoruyi CE, Rafaï CD, Morang'a CM, Erameh C, Lule DB, Bridges DJ, Mukadi-Bamuleka D, Park D, Rasmussen DA, Baker D, Nokes DJ, Ssemwanga D, Tshiabuila D, Amuzu DSY, Goedhals D, Grant DS, Omuoyo DO, Maruapula D, Wanjohi DW, Foster-Nyarko E, Lusamaki EK, Simulundu E, Ong'era EM, Ngabana EN, Abworo EO, Otieno E, Shumba E, Barasa E, Ahmed EB, Ahmed EA, Lokilo E, Mukantwari E, Philomena E, Belarbi E, Simon-Loriere E, Anoh EA, Manuel E, Leendertz F, Taweh FM, Wasfi F, Abdelmoula F, Takawira FT, Derrar F, Ajogbasile FV, Treurnicht F, Onikepe F, Ntoumi F, Muyembe FM, Ragomzingba FEZ, Dratibi FA, Iyanu FA, Mbunsu GK, Thilliez G, Kay GL, Akpede GO, van Zyl GU, Awandare GA, Kpeli GS, Schubert G, Maphalala GP, Ranaivoson HC, Omunakwe HE, Onywera H, Abe H, Karray H, Nansumba H, Triki H, Kadjo HAA, Elgahzaly H, Gumbo H, Mathieu H, Kavunga-Membo H, Smeti I, Olawoye IB, Adetifa IMO, Odia I, Ben Boubaker IB, Muhammad IA, Ssewanyana I, Wurie I, Konstantinus IS, Halatoko JWA, Ayei J, Sonoo J, Makangara JC, Tamfum JM, Heraud JM, Shaffer JG, Giandhari J, Musyoki J, Nkurunziza J, Uwanibe JN, Bhiman JN, Yasuda J, Morais J, Kiconco J, Sandi JD, Huddleston J, Odoom JK, Morobe JM, Gyapong JO, Kayiwa JT, Okolie JC, Xavier JS, Gyamfi J, Wamala JF, Bonney JHK, Nyandwi J, Everatt J, Nakaseegu J, Ngoi JM, Namulondo J, Oguzie JU, Andeko JC, Lutwama JJ, Mogga JJH, O'Grady J, Siddle KJ, Victoir K, Adeyemi KT, Tumedi KA, Carvalho KS, Mohammed KS, Dellagi K, Musonda KG, Duedu KO, Fki-Berrajah L, Singh L, Kepler LM, Biscornet L, de Oliveira Martins L, Chabuka L, Olubayo L, Ojok LD, Deng LL, Ochola-Oyier LI, Tyers L, Mine M, Ramuth M, Mastouri M, ElHefnawi M, Mbanne M, Matsheka MI, Kebabonye M, Diop M, Momoh M, Lima Mendonça MDL, Venter M, Paye MF, Faye M, Nyaga MM, Mareka M, Damaris MM, Mburu MW, Mpina MG, Owusu M, Wiley MR, Tatfeng MY, Ayekaba MO, Abouelhoda M, Beloufa MA, Seadawy MG, Khalifa MK, Matobo MM, Kane M, Salou M, Mbulawa MB, Mwenda M, Allam M, Phan MVT, Abid N, Rujeni N, Abuzaid N, Ismael N, Elguindy N, Top NM, Dia N, Mabunda N, Hsiao NY, Silochi NB, Francisco NM, Saasa N, Bbosa N, Murunga N, Gumede N, Wolter N, Sitharam N, Ndodo N, Ajayi NA, Tordo N, Mbhele N, Razanajatovo NH, Iguosadolo N, Mba N, Kingsley OC, Sylvanus O, Femi O, Adewumi OM, Testimony O, Ogunsanya OA, Fakayode O, Ogah OE, Oludayo OE, Faye O, Smith-Lawrence P, Ondoa P, Combe P, Nabisubi P, Semanda P, Oluniyi PE, Arnaldo P, Quashie PK, Okokhere PO, Bejon P, Dussart P, Bester PA, Mbala PK, Kaleebu P, Abechi P, El-Shesheny R, Joseph R, Aziz RK, Essomba RG, Ayivor-Djanie R, Njouom R, Phillips RO, Gorman R, Kingsley RA, Neto Rodrigues RMDESA, Audu RA, Carr RAA, Gargouri S, Masmoudi S, Bootsma S, Sankhe S, Mohamed SI, Femi S, Mhalla S, Hosch S, Kassim SK, Metha S, Trabelsi S, Agwa SH, Mwangi SW, Doumbia S, Makiala-Mandanda S, Aryeetey S, Ahmed SS, Ahmed SM, Elhamoumi S, Moyo S, Lutucuta S, Gaseitsiwe S, Jalloh S, Andriamandimby SF, Oguntope S, Grayo S, Lekana-Douki S, Prosolek S, Ouangraoua S, van Wyk S, Schaffner SF, Kanyerezi S, Ahuka-Mundeke S, Rudder S, Pillay S, Nabadda S, Behillil S, Budiaki SL, van der Werf S, Mashe T, Mohale T, Le-Viet T, Velavan TP, Schindler T, Maponga TG, Bedford T, Anyaneji UJ, Chinedu U, Ramphal U, George UE, Enouf V, Nene V, Gorova V, Roshdy WH, Karim WA, Ampofo WK, Preiser W, Choga WT, Ahmed YA, Ramphal Y, Bediako Y, Naidoo Y, Butera Y, de Laurent ZR, Ouma AEO, von Gottberg A, Githinji G, Moeti M, Tomori O, Sabeti PC, Sall AA, Oyola SO, Tebeje YK, Tessema SK, de Oliveira T, Happi C, Lessells R, Nkengasong J, and Wilkinson E
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- Africa epidemiology, Genomics, Humans, COVID-19 epidemiology, COVID-19 virology, Epidemiological Monitoring, Pandemics, SARS-CoV-2 genetics
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Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern-particularly Alpha, Beta, Delta, and Omicron-on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century.
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- 2022
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7. Epidemiological impact and cost-effectiveness analysis of COVID-19 vaccination in Kenya.
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Orangi S, Ojal J, Brand SP, Orlendo C, Kairu A, Aziza R, Ogero M, Agweyu A, Warimwe GM, Uyoga S, Otieno E, Ochola-Oyier LI, Agoti CN, Kasera K, Amoth P, Mwangangi M, Aman R, Ng'ang'a W, Adetifa IM, Scott JAG, Bejon P, Keeling MJ, Flasche S, Nokes DJ, and Barasa E
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- Cost-Benefit Analysis, Humans, Kenya epidemiology, SARS-CoV-2, Young Adult, COVID-19 prevention & control, COVID-19 Vaccines
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Background: A few studies have assessed the epidemiological impact and the cost-effectiveness of COVID-19 vaccines in settings where most of the population had been exposed to SARS-CoV-2 infection., Methods: We conducted a cost-effectiveness analysis of COVID-19 vaccine in Kenya from a societal perspective over a 1.5-year time frame. An age-structured transmission model assumed at least 80% of the population to have prior natural immunity when an immune escape variant was introduced. We examine the effect of slow (18 months) or rapid (6 months) vaccine roll-out with vaccine coverage of 30%, 50% or 70% of the adult (>18 years) population prioritising roll-out in those over 50-years (80% uptake in all scenarios). Cost data were obtained from primary analyses. We assumed vaccine procurement at US$7 per dose and vaccine delivery costs of US$3.90-US$6.11 per dose. The cost-effectiveness threshold was US$919.11., Findings: Slow roll-out at 30% coverage largely targets those over 50 years and resulted in 54% fewer deaths (8132 (7914-8373)) than no vaccination and was cost saving (incremental cost-effectiveness ratio, ICER=US$-1343 (US$-1345 to US$-1341) per disability-adjusted life-year, DALY averted). Increasing coverage to 50% and 70%, further reduced deaths by 12% (810 (757-872) and 5% (282 (251-317) but was not cost-effective, using Kenya's cost-effectiveness threshold (US$919.11). Rapid roll-out with 30% coverage averted 63% more deaths and was more cost-saving (ICER=US$-1607 (US$-1609 to US$-1604) per DALY averted) compared with slow roll-out at the same coverage level, but 50% and 70% coverage scenarios were not cost-effective., Interpretation: With prior exposure partially protecting much of the Kenyan population, vaccination of young adults may no longer be cost-effective., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
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- 2022
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8. Genomic Epidemiology of SARS-CoV-2 in Seychelles, 2020-2021.
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Morobe JM, Pool B, Marie L, Didon D, Lambisia AW, Makori T, Mohammed KS, de Laurent ZR, Ndwiga L, Mburu MW, Moraa E, Murunga N, Musyoki J, Mwacharo J, Nyamako L, Riako D, Ephnatus P, Gambo F, Naimani J, Namulondo J, Tembo SZ, Ogendi E, Balde T, Dratibi FA, Yahaya AA, Gumede N, Achilla RA, Borus PK, Wanjohi DW, Tessema SK, Mwangangi J, Bejon P, Nokes DJ, Ochola-Oyier LI, Githinji G, Biscornet L, and Agoti CN
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- Genomics, Humans, Seychelles epidemiology, COVID-19 epidemiology, SARS-CoV-2 genetics
- Abstract
Seychelles, an archipelago of 155 islands in the Indian Ocean, had confirmed 24,788 cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the 31st of December 2021. The first SARS-CoV-2 cases in Seychelles were reported on the 14th of March 2020, but cases remained low until January 2021, when a surge was observed. Here, we investigated the potential drivers of the surge by genomic analysis of 1056 SARS-CoV-2 positive samples collected in Seychelles between 14 March 2020 and 31 December 2021. The Seychelles genomes were classified into 32 Pango lineages, 1042 of which fell within four variants of concern, i.e., Alpha, Beta, Delta and Omicron. Sporadic cases of SARS-CoV-2 detected in Seychelles in 2020 were mainly of lineage B.1 (lineage predominantly observed in Europe) but this lineage was rapidly replaced by Beta variant starting January 2021, and which was also subsequently replaced by the Delta variant in May 2021 that dominated till November 2021 when Omicron cases were identified. Using the ancestral state reconstruction approach, we estimated that at least 78 independent SARS-CoV-2 introduction events occurred in Seychelles during the study period. The majority of viral introductions into Seychelles occurred in 2021, despite substantial COVID-19 restrictions in place during this period. We conclude that the surge of SARS-CoV-2 cases in Seychelles in January 2021 was primarily due to the introduction of more transmissible SARS-CoV-2 variants into the islands.
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- 2022
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9. Transmission networks of SARS-CoV-2 in Coastal Kenya during the first two waves: A retrospective genomic study.
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Agoti CN, Ochola-Oyier LI, Dellicour S, Mohammed KS, Lambisia AW, de Laurent ZR, Morobe JM, Mburu MW, Omuoyo DO, Ongera EM, Ndwiga L, Maitha E, Kitole B, Suleiman T, Mwakinangu M, Nyambu JK, Otieno J, Salim B, Musyoki J, Murunga N, Otieno E, Kiiru JN, Kasera K, Amoth P, Mwangangi M, Aman R, Kinyanjui S, Warimwe G, Phan M, Agweyu A, Cotten M, Barasa E, Tsofa B, Nokes DJ, Bejon P, and Githinji G
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- Genomics, Humans, Kenya epidemiology, Phylogeny, Retrospective Studies, COVID-19 epidemiology, SARS-CoV-2 genetics
- Abstract
Background: Detailed understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) regional transmission networks within sub-Saharan Africa is key for guiding local public health interventions against the pandemic., Methods: Here, we analysed 1139 SARS-CoV-2 genomes from positive samples collected between March 2020 and February 2021 across six counties of Coastal Kenya (Mombasa, Kilifi, Taita Taveta, Kwale, Tana River, and Lamu) to infer virus introductions and local transmission patterns during the first two waves of infections. Virus importations were inferred using ancestral state reconstruction, and virus dispersal between counties was estimated using discrete phylogeographic analysis., Results: During Wave 1, 23 distinct Pango lineages were detected across the six counties, while during Wave 2, 29 lineages were detected; 9 of which occurred in both waves and 4 seemed to be Kenya specific (B.1.530, B.1.549, B.1.596.1, and N.8). Most of the sequenced infections belonged to lineage B.1 (n = 723, 63%), which predominated in both Wave 1 (73%, followed by lineages N.8 [6%] and B.1.1 [6%]) and Wave 2 (56%, followed by lineages B.1.549 [21%] and B.1.530 [5%]). Over the study period, we estimated 280 SARS-CoV-2 virus importations into Coastal Kenya. Mombasa City, a vital tourist and commercial centre for the region, was a major route for virus imports, most of which occurred during Wave 1, when many Coronavirus Disease 2019 (COVID-19) government restrictions were still in force. In Wave 2, inter-county transmission predominated, resulting in the emergence of local transmission chains and diversity., Conclusions: Our analysis supports moving COVID-19 control strategies in the region from a focus on international travel to strategies that will reduce local transmission., Funding: This work was funded by The Wellcome (grant numbers: 220985, 203077/Z/16/Z, 220977/Z/20/Z, and 222574/Z/21/Z) and the National Institute for Health and Care Research (NIHR), project references: 17/63/and 16/136/33 using UK Aid from the UK government to support global health research, The UK Foreign, Commonwealth and Development Office. The views expressed in this publication are those of the author(s) and not necessarily those of the funding agencies., Competing Interests: CA, LO, SD, KM, AL, Zd, JM, MM, DO, EO, LN, EM, BK, TS, MM, JN, JO, BS, JM, NM, EO, JK, KK, PA, MM, RA, SK, GW, MP, AA, MC, EB, BT, DN, PB, GG No competing interests declared, (© 2022, Agoti et al.)
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- 2022
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10. Seroprevalence of Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 Among Healthcare Workers in Kenya.
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Etyang AO, Lucinde R, Karanja H, Kalu C, Mugo D, Nyagwange J, Gitonga J, Tuju J, Wanjiku P, Karani A, Mutua S, Maroko H, Nzomo E, Maitha E, Kamuri E, Kaugiria T, Weru J, Ochola LB, Kilimo N, Charo S, Emukule N, Moracha W, Mukabi D, Okuku R, Ogutu M, Angujo B, Otiende M, Bottomley C, Otieno E, Ndwiga L, Nyaguara A, Voller S, Agoti CN, Nokes DJ, Ochola-Oyier LI, Aman R, Amoth P, Mwangangi M, Kasera K, Ng'ang'a W, Adetifa IMO, Wangeci Kagucia E, Gallagher K, Uyoga S, Tsofa B, Barasa E, Bejon P, Scott JAG, Agweyu A, and Warimwe GM
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- Antibodies, Viral, Bayes Theorem, Health Personnel, Humans, Kenya epidemiology, Seroepidemiologic Studies, Spike Glycoprotein, Coronavirus, COVID-19, SARS-CoV-2
- Abstract
Background: Few studies have assessed the seroprevalence of antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among healthcare workers (HCWs) in Africa. We report findings from a survey among HCWs in 3 counties in Kenya., Methods: We recruited 684 HCWs from Kilifi (rural), Busia (rural), and Nairobi (urban) counties. The serosurvey was conducted between 30 July and 4 December 2020. We tested for immunoglobulin G antibodies to SARS-CoV-2 spike protein, using enzyme-linked immunosorbent assay. Assay sensitivity and specificity were 92.7 (95% CI, 87.9-96.1) and 99.0% (95% CI, 98.1-99.5), respectively. We adjusted prevalence estimates, using bayesian modeling to account for assay performance., Results: The crude overall seroprevalence was 19.7% (135 of 684). After adjustment for assay performance, seroprevalence was 20.8% (95% credible interval, 17.5%-24.4%). Seroprevalence varied significantly (P < .001) by site: 43.8% (95% credible interval, 35.8%-52.2%) in Nairobi, 12.6% (8.8%-17.1%) in Busia and 11.5% (7.2%-17.6%) in Kilifi. In a multivariable model controlling for age, sex, and site, professional cadre was not associated with differences in seroprevalence., Conclusion: These initial data demonstrate a high seroprevalence of antibodies to SARS-CoV-2 among HCWs in Kenya. There was significant variation in seroprevalence by region, but not by cadre., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America.)
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- 2022
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11. COVID-19 transmission dynamics underlying epidemic waves in Kenya.
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Brand SPC, Ojal J, Aziza R, Were V, Okiro EA, Kombe IK, Mburu C, Ogero M, Agweyu A, Warimwe GM, Nyagwange J, Karanja H, Gitonga JN, Mugo D, Uyoga S, Adetifa IMO, Scott JAG, Otieno E, Murunga N, Otiende M, Ochola-Oyier LI, Agoti CN, Githinji G, Kasera K, Amoth P, Mwangangi M, Aman R, Ng'ang'a W, Tsofa B, Bejon P, Keeling MJ, Nokes DJ, and Barasa E
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- COVID-19 virology, COVID-19 Nucleic Acid Testing, Communicable Disease Control, Epidemics, Humans, Incidence, Kenya epidemiology, Models, Biological, Seroepidemiologic Studies, Social Class, Socioeconomic Factors, COVID-19 epidemiology, COVID-19 transmission
- Abstract
Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or infection spreads to susceptible subpopulations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model, we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of higher-transmissibility variants. Reopening schools led to a minor increase in transmission between the second and third waves. Socioeconomic and urban–rural population structure are critical determinants of viral transmission in Kenya.
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- 2021
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12. A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa.
- Author
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Wilkinson E, Giovanetti M, Tegally H, San JE, Lessells R, Cuadros D, Martin DP, Rasmussen DA, Zekri AN, Sangare AK, Ouedraogo AS, Sesay AK, Priscilla A, Kemi AS, Olubusuyi AM, Oluwapelumi AOO, Hammami A, Amuri AA, Sayed A, Ouma AEO, Elargoubi A, Ajayi NA, Victoria AF, Kazeem A, George A, Trotter AJ, Yahaya AA, Keita AK, Diallo A, Kone A, Souissi A, Chtourou A, Gutierrez AV, Page AJ, Vinze A, Iranzadeh A, Lambisia A, Ismail A, Rosemary A, Sylverken A, Femi A, Ibrahimi A, Marycelin B, Oderinde BS, Bolajoko B, Dhaala B, Herring BL, Njanpop-Lafourcade BM, Kleinhans B, McInnis B, Tegomoh B, Brook C, Pratt CB, Scheepers C, Akoua-Koffi CG, Agoti CN, Peyrefitte C, Daubenberger C, Morang'a CM, Nokes DJ, Amoako DG, Bugembe DL, Park D, Baker D, Doolabh D, Ssemwanga D, Tshiabuila D, Bassirou D, Amuzu DSY, Goedhals D, Omuoyo DO, Maruapula D, Foster-Nyarko E, Lusamaki EK, Simulundu E, Ong'era EM, Ngabana EN, Shumba E, El Fahime E, Lokilo E, Mukantwari E, Philomena E, Belarbi E, Simon-Loriere E, Anoh EA, Leendertz F, Ajili F, Enoch FO, Wasfi F, Abdelmoula F, Mosha FS, Takawira FT, Derrar F, Bouzid F, Onikepe F, Adeola F, Muyembe FM, Tanser F, Dratibi FA, Mbunsu GK, Thilliez G, Kay GL, Githinji G, van Zyl G, Awandare GA, Schubert G, Maphalala GP, Ranaivoson HC, Lemriss H, Anise H, Abe H, Karray HH, Nansumba H, Elgahzaly HA, Gumbo H, Smeti I, Ayed IB, Odia I, Ben Boubaker IB, Gaaloul I, Gazy I, Mudau I, Ssewanyana I, Konstantinus I, Lekana-Douk JB, Makangara JC, Tamfum JM, Heraud JM, Shaffer JG, Giandhari J, Li J, Yasuda J, Mends JQ, Kiconco J, Morobe JM, Gyapong JO, Okolie JC, Kayiwa JT, Edwards JA, Gyamfi J, Farah J, Nakaseegu J, Ngoi JM, Namulondo J, Andeko JC, Lutwama JJ, O'Grady J, Siddle K, Adeyemi KT, Tumedi KA, Said KM, Hae-Young K, Duedu KO, Belyamani L, Fki-Berrajah L, Singh L, Martins LO, Tyers L, Ramuth M, Mastouri M, Aouni M, El Hefnawi M, Matsheka MI, Kebabonye M, Diop M, Turki M, Paye M, Nyaga MM, Mareka M, Damaris MM, Mburu MW, Mpina M, Nwando M, Owusu M, Wiley MR, Youtchou MT, Ayekaba MO, Abouelhoda M, Seadawy MG, Khalifa MK, Sekhele M, Ouadghiri M, Diagne MM, Mwenda M, Allam M, Phan MVT, Abid N, Touil N, Rujeni N, Kharrat N, Ismael N, Dia N, Mabunda N, Hsiao NY, Silochi NB, Nsenga N, Gumede N, Mulder N, Ndodo N, Razanajatovo NH, Iguosadolo N, Judith O, Kingsley OC, Sylvanus O, Peter O, Femi O, Idowu O, Testimony O, Chukwuma OE, Ogah OE, Onwuamah CK, Cyril O, Faye O, Tomori O, Ondoa P, Combe P, Semanda P, Oluniyi PE, Arnaldo P, Quashie PK, Dussart P, Bester PA, Mbala PK, Ayivor-Djanie R, Njouom R, Phillips RO, Gorman R, Kingsley RA, Carr RAA, El Kabbaj S, Gargouri S, Masmoudi S, Sankhe S, Lawal SB, Kassim S, Trabelsi S, Metha S, Kammoun S, Lemriss S, Agwa SHA, Calvignac-Spencer S, Schaffner SF, Doumbia S, Mandanda SM, Aryeetey S, Ahmed SS, Elhamoumi S, Andriamandimby S, Tope S, Lekana-Douki S, Prosolek S, Ouangraoua S, Mundeke SA, Rudder S, Panji S, Pillay S, Engelbrecht S, Nabadda S, Behillil S, Budiaki SL, van der Werf S, Mashe T, Aanniz T, Mohale T, Le-Viet T, Schindler T, Anyaneji UJ, Chinedu U, Ramphal U, Jessica U, George U, Fonseca V, Enouf V, Gorova V, Roshdy WH, Ampofo WK, Preiser W, Choga WT, Bediako Y, Naidoo Y, Butera Y, de Laurent ZR, Sall AA, Rebai A, von Gottberg A, Kouriba B, Williamson C, Bridges DJ, Chikwe I, Bhiman JN, Mine M, Cotten M, Moyo S, Gaseitsiwe S, Saasa N, Sabeti PC, Kaleebu P, Tebeje YK, Tessema SK, Happi C, Nkengasong J, and de Oliveira T
- Subjects
- Africa epidemiology, COVID-19 transmission, COVID-19 virology, Genetic Variation, Humans, SARS-CoV-2 isolation & purification, COVID-19 epidemiology, Epidemiological Monitoring, Genomics, Pandemics, SARS-CoV-2 genetics
- Abstract
The progression of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in Africa has so far been heterogeneous, and the full impact is not yet well understood. In this study, we describe the genomic epidemiology using a dataset of 8746 genomes from 33 African countries and two overseas territories. We show that the epidemics in most countries were initiated by importations predominantly from Europe, which diminished after the early introduction of international travel restrictions. As the pandemic progressed, ongoing transmission in many countries and increasing mobility led to the emergence and spread within the continent of many variants of concern and interest, such as B.1.351, B.1.525, A.23.1, and C.1.1. Although distorted by low sampling numbers and blind spots, the findings highlight that Africa must not be left behind in the global pandemic response, otherwise it could become a source for new variants.
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- 2021
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13. Tracking the introduction and spread of SARS-CoV-2 in coastal Kenya.
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Githinji G, de Laurent ZR, Mohammed KS, Omuoyo DO, Macharia PM, Morobe JM, Otieno E, Kinyanjui SM, Agweyu A, Maitha E, Kitole B, Suleiman T, Mwakinangu M, Nyambu J, Otieno J, Salim B, Kasera K, Kiiru J, Aman R, Barasa E, Warimwe G, Bejon P, Tsofa B, Ochola-Oyier LI, Nokes DJ, and Agoti CN
- Subjects
- Adolescent, Adult, Aged, Aged, 80 and over, COVID-19 diagnosis, COVID-19 transmission, Child, Child, Preschool, Female, Genetic Variation, Humans, Infant, Kenya epidemiology, Male, Middle Aged, Pandemics, Phylogeny, Public Health, SARS-CoV-2 classification, SARS-CoV-2 isolation & purification, Sequence Analysis, Tanzania, Travel, Young Adult, COVID-19 epidemiology, COVID-19 virology, Genome, Viral, SARS-CoV-2 genetics
- Abstract
Genomic surveillance of SARS-CoV-2 is important for understanding both the evolution and the patterns of local and global transmission. Here, we generated 311 SARS-CoV-2 genomes from samples collected in coastal Kenya between 17
th March and 31st July 2020. We estimated multiple independent SARS-CoV-2 introductions into the region were primarily of European origin, although introductions could have come through neighbouring countries. Lineage B.1 accounted for 74% of sequenced cases. Lineages A, B and B.4 were detected in screened individuals at the Kenya-Tanzania border or returning travellers. Though multiple lineages were introduced into coastal Kenya following the initial confirmed case, none showed extensive local expansion other than lineage B.1. International points of entry were important conduits of SARS-CoV-2 importations into coastal Kenya and early public health responses prevented established transmission of some lineages. Undetected introductions through points of entry including imports from elsewhere in the country gave rise to the local epidemic at the Kenyan coast., (© 2021. The Author(s).)- Published
- 2021
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14. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Kenyan blood donors.
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Uyoga S, Adetifa IMO, Karanja HK, Nyagwange J, Tuju J, Wanjiku P, Aman R, Mwangangi M, Amoth P, Kasera K, Ng'ang'a W, Rombo C, Yegon C, Kithi K, Odhiambo E, Rotich T, Orgut I, Kihara S, Otiende M, Bottomley C, Mupe ZN, Kagucia EW, Gallagher KE, Etyang A, Voller S, Gitonga JN, Mugo D, Agoti CN, Otieno E, Ndwiga L, Lambe T, Wright D, Barasa E, Tsofa B, Bejon P, Ochola-Oyier LI, Agweyu A, Scott JAG, and Warimwe GM
- Subjects
- Adolescent, Adult, Aged, Communicable Disease Control, Humans, Kenya epidemiology, Middle Aged, SARS-CoV-2 physiology, Seroepidemiologic Studies, Young Adult, Antibodies, Viral blood, Blood Donors, COVID-19 epidemiology, Immunoglobulin G blood
- Abstract
The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Africa is poorly described. The first case of SARS-CoV-2 in Kenya was reported on 12 March 2020, and an overwhelming number of cases and deaths were expected, but by 31 July 2020, there were only 20,636 cases and 341 deaths. However, the extent of SARS-CoV-2 exposure in the community remains unknown. We determined the prevalence of anti-SARS-CoV-2 immunoglobulin G among blood donors in Kenya in April-June 2020. Crude seroprevalence was 5.6% (174 of 3098). Population-weighted, test-performance-adjusted national seroprevalence was 4.3% (95% confidence interval, 2.9 to 5.8%) and was highest in urban counties Mombasa (8.0%), Nairobi (7.3%), and Kisumu (5.5%). SARS-CoV-2 exposure is more extensive than indicated by case-based surveillance, and these results will help guide the pandemic response in Kenya and across Africa., (Copyright © 2021, American Association for the Advancement of Science.)
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- 2021
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15. Temporal changes in the positivity rate of common enteric viruses among paediatric admissions in coastal Kenya, during the COVID-19 pandemic, 2019–2022
- Author
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Lambisia, Arnold W., Murunga, Nickson, Mutunga, Martin, Cheruiyot, Robinson, Maina, Grace, Makori, Timothy O., Nokes, D. James, and Agoti, Charles N.
- Published
- 2024
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16. Phylogenomic analysis uncovers a 9-year variation of Uganda influenza type-A strains from the WHO-recommended vaccines and other Africa strains.
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Nabakooza, Grace, Owuor, D. Collins, de Laurent, Zaydah R., Galiwango, Ronald, Owor, Nicholas, Kayiwa, John T., Jjingo, Daudi, Agoti, Charles N., Nokes, D. James, Kateete, David P., Kitayimbwa, John M., Frost, Simon D. W., and Lutwama, Julius J.
- Subjects
COVID-19 ,EXTRACELLULAR matrix proteins ,VACCINES ,INFLUENZA viruses ,GENETIC variation ,NUCLEOTIDE sequencing - Abstract
Genetic characterisation of circulating influenza viruses directs annual vaccine strain selection and mitigation of infection spread. We used next-generation sequencing to locally generate whole genomes from 116 A(H1N1)pdm09 and 118 A(H3N2) positive patient swabs collected across Uganda between 2010 and 2018. We recovered sequences from 92% (215/234) of the swabs, 90% (193/215) of which were whole genomes. The newly-generated sequences were genetically and phylogenetically compared to the WHO-recommended vaccines and other Africa strains sampled since 1994. Uganda strain hemagglutinin (n = 206), neuraminidase (n = 207), and matrix protein (MP, n = 213) sequences had 95.23–99.65%, 95.31–99.79%, and 95.46–100% amino acid similarity to the 2010–2020 season vaccines, respectively, with several mutated hemagglutinin antigenic, receptor binding, and N-linked glycosylation sites. Uganda influenza type-A virus strains sequenced before 2016 clustered uniquely while later strains mixed with other Africa and global strains. We are the first to report novel A(H1N1)pdm09 subclades 6B.1A.3, 6B.1A.5(a,b), and 6B.1A.6 (± T120A) that circulated in Eastern, Western, and Southern Africa in 2017–2019. Africa forms part of the global influenza ecology with high viral genetic diversity, progressive antigenic drift, and local transmissions. For a continent with inadequate health resources and where social distancing is unsustainable, vaccination is the best option. Hence, African stakeholders should prioritise routine genome sequencing and analysis to direct vaccine selection and virus control. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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17. Global disparities in SARS-CoV-2 genomic surveillance
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Brito, Anderson F, Semenova, Elizaveta, Dudas, Gytis, Hassler, Gabriel W, Kalinich, Chaney C, Kraemer, Moritz U G, Ho, Joses, Tegally, Houriiyah, Githinji, George, Agoti, Charles N, Matkin, Lucy E, Whittaker, Charles, Howden, Benjamin P, Sintchenko, Vitali, Zuckerman, Neta S, Mor, Orna, Blankenship, Heather M, de Oliveira, Tulio, Lin, Raymond T P, Siqueira, Marilda Mendonça, Resende, Paola Cristina, Vasconcelos, Ana Tereza R, Spilki, Fernando R, Aguiar, Renato Santana, Alexiev, Ivailo, Ivanov, Ivan N, Philipova, Ivva, Carrington, Christine V F, Sahadeo, Nikita S D, Branda, Ben, Gurry, Céline, Maurer-Stroh, Sebastian, Naidoo, Dhamari, von Eije, Karin J, Perkins, Mark D, van Kerkhove, Maria, Hill, Sarah C, Sabino, Ester C, Pybus, Oliver G, Dye, Christopher, Bhatt, Samir, Flaxman, Seth, Suchard, Marc A, Grubaugh, Nathan D, Baele, Guy, and Faria, Nuno R
- Subjects
medicine.medical_specialty ,Multidisciplinary ,Coronavirus disease 2019 (COVID-19) ,SARS-CoV-2 ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Public health ,viruses ,COVID-19 ,General Physics and Astronomy ,Genome, Viral ,Genomics ,General Chemistry ,Biology ,sequencing ,Genome ,DNA sequencing ,Virus ,General Biochemistry, Genetics and Molecular Biology ,Temporal heterogeneity ,Environmental health ,Pandemic ,medicine ,Humans ,Pandemics - Abstract
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
- Published
- 2021
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