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4 results on '"Sandra Adele"'

1. Informing the pandemic response: the role of the WHO’s COVID-19 Weekly Epidemiological Update

Author

Maria D Van Kerkhove, Mahmoud Hassan, Ibrahima Socé Fall, Olivier Le Polain de Waroux, Boris Igor Pavlin, Sandra Adele, Kazuki Shimizu, Oliver Morgan, Daniel Feikin, Lorenzo Subissi, Richard G Pebody, Masaya Kato, Opeayo Ogundiran, Ana Riviere-Cinnamond, Tshewang Dorji, Ingrid Hammermeister Nezu, Friday Idoko, Tamano Matsui, Silviu Ciobanu, Juniorcaius Ikejezie, Blanche Johanna Greene-Cramer, Tondri Noe Guinko, Samuel Mesfin, Harsh Lata, Alessandro Miglietta, Yurie Izawa, Aura Rocio Escobar Corado Waeber, Anahi Rico Chinchilla, Adedoyin Awofisayo-Okuyelu, Esther Hamblion, Melissa M Higdon, Ayse Acma, Samuel Moro, Homa Attar Cohen, Mary Anissa Sinnathamby, James Richard Otieno, Yosef Temre, Brian Ngongheh Ajong, Bernadette Basuta Mirembe, Vaishali Sodagar, Craig Schultz, Joao Muianga, Stéphane De Barros, Yeowon Jin, Shagun Khare, Marcia Poole, Nyka Alexander, Manilay Phengxay, Emilie Peron, Brett Nicholas Archer, Zyleen Alnashir Kassamali, and Abdi Mahamud

Subjects
Medicine (General), R5-920, Infectious and parasitic diseases, RC109-216
Abstract

On 31 December 2019, the Municipal Health Commission of Wuhan, China, reported a cluster of atypical pneumonia cases. On 5 January 2020, the WHO publicly released a Disease Outbreak News (DON) report, providing information about the pneumonia cases, implemented response interventions, and WHO’s risk assessment and advice on public health and social measures. Following 9 additional DON reports and 209 daily situation reports, on 17 August 2020, WHO published the first edition of the COVID-19 Weekly Epidemiological Update (WEU). On 1 September 2023, the 158th edition of the WEU was published on WHO’s website, marking its final issue. Since then, the WEU has been replaced by comprehensive global epidemiological updates on COVID-19 released every 4 weeks. During the span of its publication, the webpage that hosts the WEU and the COVID-19 Operational Updates was accessed annually over 1.4 million times on average, with visits originating from more than 100 countries. This article provides an in-depth analysis of the WEU process, from data collection to publication, focusing on the scope, technical details, main features, underlying methods, impact and limitations. We also discuss WHO’s experience in disseminating epidemiological information on the COVID-19 pandemic at the global level and provide recommendations for enhancing collaboration and information sharing to support future health emergency responses.

Published
2024
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2. WHO Global Situational Alert System: a mixed methods multistage approach to identify country-level COVID-19 alerts

Author

Richard Pebody, Maria D Van Kerkhove, Mahmoud Hassan, Thierno Balde, Olivier Le Polain de Waroux, Sandra Adele, Kazuki Shimizu, Charlotte Christiane Hammer, Lisa Owen, Villyen Motaze, Michael Höhle, Piers Mook, Masaya Kato, Kathleen Ryan, Tanja Schmidt, Marjam Esmail, Jessica Lee Abbate, Opeayo Ogundiran, Sophie Maes, Victoria D Bélorgeot, Ana Riviere-Cinnamond, Martina McMenamin, Jessica Kolmer, Irena Djordjevic, Finlay Campbell, Henry Laurenson-Schafer, Basma Mostafa Abdelgawad, Amarnath Babu, Neale Batra, Hannah Brindle, Tshewang Dorji, Ingrid Hammermeister Nezu, Lucía Hernández-García, Friday Idoko, Sarah Karmin, Zyleen A Kassamali, Tamano Matsui, Mengjuan Duan, Boris I Pavlin, Tika Sedai, Teresa Zakaria, Abdi R Mahamud, Dominic Cocciolone, Christian Gapp, Kayla King, Aimee Latta, Sandra Lindmark, Ihor Perehinets, Amy Gimma, Jason McKnight, Margaux Mesle, Maarten Van Haverbeke, Tamar Amar, Ka Yeung Cheng, Silviu Ciobanu, Lauren E MacDonald, Jeff Pires, Jukka Pukkila, Celine Roman, Laila Skrowny, Ara Tadevosyan, Catherine Smallwood, Juniorcaius Ikejezie, Blanche Johanna Greene-Cramer, Adedoyin Abiola Awofisayo-Okuyelu, Bernadette Mirembe, Tondri Noe Guinko, Homa Attar-Cohen, Stephen Leshan Koyie, Samuel Mesfin, Veronica Cristea, Nikola Sklenovska, Mostafa Mahran, Emily Meyer, Grace Brough, Harsh Lata, Alessandro Miglietta, Sydel Parikh, Eri Togami, Yurie Izawa, Aura Rocio Escobar Corado Waeber, Anahi Rico Chinchilla, Yeo Won Jin, Raquel Medialdea Carrera, Amaia Artazcoz Glaria, Hyo Jeong Kim, and Muhammed Tayyab

Subjects
Medicine (General), R5-920, Infectious and parasitic diseases, RC109-216
Abstract

Background Globally, since 1 January 2020 and as of 24 January 2023, there have been over 664 million cases of COVID-19 and over 6.7 million deaths reported to WHO. WHO developed an evidence-based alert system, assessing public health risk on a weekly basis in 237 countries, territories and areas from May 2021 to June 2022. This aimed to facilitate the early identification of situations where healthcare capacity may become overstretched.Methods The process involved a three-stage mixed methods approach. In the first stage, future deaths were predicted from the time series of reported cases and deaths to produce an initial alert level. In the second stage, this alert level was adjusted by incorporating a range of contextual indicators and accounting for the quality of information available using a Bayes classifier. In the third stage, countries with an alert level of ‘High’ or above were added to an operational watchlist and assistance was deployed as needed.Results Since June 2021, the system has supported the release of more than US$27 million from WHO emergency funding, over 450 000 rapid antigen diagnostic testing kits and over 6000 oxygen concentrators. Retrospective evaluation indicated that the first two stages were needed to maximise sensitivity, where 44% (IQR 29%–67%) of weekly watchlist alerts would not have been identified using only reported cases and deaths. The alerts were timely and valid in most cases; however, this could only be assessed on a non-representative sample of countries with hospitalisation data available.Conclusions The system provided a standardised approach to monitor the pandemic at the country level by incorporating all available data on epidemiological analytics and contextual assessments. While this system was developed for COVID-19, a similar system could be used for future outbreaks and emergencies, with necessary adjustments to parameters and indicators.

Published
2023
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3. T-cell and antibody responses to first BNT162b2 vaccine dose in previously infected and SARS-CoV-2-naive UK health-care workers: a multicentre prospective cohort study

Author

Adrienn Angyal, PhD, Stephanie Longet, PhD, Shona C Moore, PhD, Rebecca P Payne, DPhil, Adam Harding, MSc, Tom Tipton, PhD, Patpong Rongkard, MSc, Mohammad Ali, MD, Luisa M Hering, MSc, Naomi Meardon, MBChB, James Austin, PhD, Rebecca Brown, PhD, Donal Skelly, PhD, Natalie Gillson, BSc, Sue L Dobson, MSc, Andrew Cross, PhD, Gurjinder Sandhar, MSc, Jonathan A Kilby, MSc, Jessica K Tyerman, BSc, Alexander R Nicols, MSc, Jarmila S Spegarova, PhD, Hema Mehta, DPhil, Hailey Hornsby, MSc, Rachel Whitham, MSc, Christopher P Conlon, ProfPhD, Katie Jeffery, PhD, Philip Goulder, ProfDPhil, John Frater, ProfPhD, Christina Dold, PhD, Matthew Pace, PhD, Ane Ogbe, PhD, Helen Brown, BSc, M Azim Ansari, DPhil, Emily Adland, PhD, Anthony Brown, BSc, Meera Chand, FRCPath, Adrian Shields, PhD, Philippa C Matthews, PhD, Susan Hopkins, PhD, Victoria Hall, PhD, William James, ProfDPhil, Sarah L Rowland-Jones, ProfDM, Paul Klenerman, ProfPhD, Susanna Dunachie, ProfPhD, Alex Richter, ProfPhD, Christopher J A Duncan, DPhil, Eleanor Barnes, ProfPhD, Miles Carroll, ProfPhD, Lance Turtle, PhD, Thushan I de Silva, PhD, Adam Harding, Adam Watson, Adrian Shields, Adrienn Angyal, Ahmed Alhussni, Alex Richter, Alexander Nicols, Alexandra Deeks, Alice Webb-Bridges, Andrew Cross, Ane Ogbe, Anni Jämsén, Anthony Brown, Anu Chawla, Christina Dold, Christopher Duncan, Christopher Conlon, Donal Skelly, Denise O'Donnell, Eleanor Barnes, Emily Adland, Esme Weeks, Gurjinder Sandhar, Hailey Hornsby, Helen Brown, Hema Mehta, Hibatullah Abuelgasim, Huiyuan Xiao, James Austin, Jarmila Spegarova, Jennifer Holmes, Jenny Haworth, Jessica Tyerman, John Frater, Jonathan Kilby, Joseph Cutteridge, Katie Jeffery, Katy Lillie, Lance Turtle, Leigh Romaniuk, Lucy Denly, Luisa Hering, M. Azim Ansari, Matthew Pace, Meera Chand, Miles Carroll, Mohammad Ali, Mwila Kasanyinga, Naomi Meardon, Natalie Gillson, Patpong Rongkard, Paul Klenerman, Philip Goulder, Philippa Matthews, Rachel Whitham, Rebecca Brown, Rebecca Payne, Robert Wilson, Sarah Rowland-Jones, Sarah Thomas, Shona Moore, Siobhan Gardiner, Stephanie Longet, Stephanie Tucker, Sue Dobson, Susan Hopkins, Susanna Dunachie, Syed Adlou, Thushan de Silva, Tom Tipton, Victoria Hall, William James, Allan Lawrie, Nikki Smith, Helena Turton, Amira Zawia, Martin Bayley, Alex Fairman, Kate Harrington, Rosemary Kirk, Louise Marsh, Lisa Watson, Steven Wood, Benjamin Diffey, Chris Jones, Lauren Lett, Gareth Platt, Krishanthi Subramaniam, Daniel Wootton, Brendan Payne, Sophie Hambleton, Sinead Kelly, Judith Marston, Sonia Poolan, Dianne Turner, Muzlifah Haniffa, Emily Stephenson, Sandra Adele, Hossain Delowar Akhter, Senthil Chinnakannan, Catherine de Lara, Timothy Donnison, Carl-Philipp Hackstein, Lian Lee, Nicholas Lim, Tom Malone, Eloise Phillips, Narayan Ramamurthy, Nichola Robinson, Oliver Sampson, David Eyre, Beatrice Simmons, Lizzie Stafford, Alexander Mentzer, Ali Amini, Carolina Arancibia-Cárcamo, Nicholas Provine, Simon Travis, Stavros Dimitriadis, Sile Johnson, Sarah Foulkes, Jameel Khawam, Edgar Wellington, Javier Gilbert-Jaramillo, Michael Knight, Maeva Dupont, Emily Horner, James Thaventhiran, and Jeremy Chalk

Subjects
Medicine (General), R5-920, Microbiology, QR1-502
Abstract

Summary: Background: Previous infection with SARS-CoV-2 affects the immune response to the first dose of the SARS-CoV-2 vaccine. We aimed to compare SARS-CoV-2-specific T-cell and antibody responses in health-care workers with and without previous SARS-CoV-2 infection following a single dose of the BNT162b2 (tozinameran; Pfizer–BioNTech) mRNA vaccine. Methods: We sampled health-care workers enrolled in the PITCH study across four hospital sites in the UK (Oxford, Liverpool, Newcastle, and Sheffield). All health-care workers aged 18 years or older consenting to participate in this prospective cohort study were included, with no exclusion criteria applied. Blood samples were collected where possible before vaccination and 28 (±7) days following one or two doses (given 3–4 weeks apart) of the BNT162b2 vaccine. Previous infection was determined by a documented SARS-CoV-2-positive RT-PCR result or the presence of positive anti-SARS-CoV-2 nucleocapsid antibodies. We measured spike-specific IgG antibodies and quantified T-cell responses by interferon-γ enzyme-linked immunospot assay in all participants where samples were available at the time of analysis, comparing SARS-CoV-2-naive individuals to those with previous infection. Findings: Between Dec 9, 2020, and Feb 9, 2021, 119 SARS-CoV-2-naive and 145 previously infected health-care workers received one dose, and 25 SARS-CoV-2-naive health-care workers received two doses, of the BNT162b2 vaccine. In previously infected health-care workers, the median time from previous infection to vaccination was 268 days (IQR 232–285). At 28 days (IQR 27–33) after a single dose, the spike-specific T-cell response measured in fresh peripheral blood mononuclear cells (PBMCs) was higher in previously infected (n=76) than in infection-naive (n=45) health-care workers (median 284 [IQR 150–461] vs 55 [IQR 24–132] spot-forming units [SFUs] per 106 PBMCs; p

Published
2022
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4. Modelling the COVID-19 pandemic in context: an international participatory approach

Author

Ali Mirzazadeh, Hamid Sharifi, Proochista Ariana, Sunil Pokharel, Siyu Chen, Ricardo Aguas, Sudhir Venkatesan, Lisa White, Nathaniel Hupert, Rima Shretta, Wirichada Pan-Ngum, Olivier Celhay, Ainura Moldokmatova, Fatima Arifi, Keyrellous Adib, Mohammad Nadir Sahak, Caroline Franco, Renato Coutinho, Penny Hancock, Roberto A. Kraenkel, Sompob Saralamba, Nantasit Luangasanatip, Sheetal Prakash Silal, Jared Norman, Rachel Hounsell, Sai Tun, Yu Nandar Aung, A Bakare Emmanuel, Biniam Getachew, Sandra Adele, Semeeh A. Omoleke, Rashid U Zaman, Nicholas Letchford, Daniel M. Parker, Dipti Lata, Shwe Sin Kyaw, Inke N D Lubis, Ivana Alona, John Robert C. Medina, Chris Erwin G. Mercado, Sana Eybpoosh, Ibrahim Mamadu, Manar Marzouk, Nicole Feune de Colombi, Lorena Suárez-Idueta, Francisco Obando, Luzia Freitas, Michael G. Klein, David Scales, Dooronbekova Aizhan, Chynar Zhumalieva, Aida Estebesova, Aibek Mukambetov, Shamil Ibragimov, Aisuluu Kubatova, Phetsavanh Chanthavialy, Amel H. Salim, KC Sarin, Priyanka Shrestha, Sayed Ataullah Saeedzai, Jenny Hsieh, Mick Soukavong, Yuki Yunanda, Handoyo Harsono, Mahnaz Hossain Fariba, Viviana Mabombo, Nicole Advani, Nusrat Jabin, Reshania Naidoo, Parinda Wattanasri, Amen-Patrick Nwosu, and Sopuruchukwu Obiesie

Subjects
Medicine (General), R5-920, Infectious and parasitic diseases, RC109-216
Abstract

The SARS-CoV-2 pandemic has had an unprecedented impact on multiple levels of society. Not only has the pandemic completely overwhelmed some health systems but it has also changed how scientific evidence is shared and increased the pace at which such evidence is published and consumed, by scientists, policymakers and the wider public. More significantly, the pandemic has created tremendous challenges for decision-makers, who have had to implement highly disruptive containment measures with very little empirical scientific evidence to support their decision-making process. Given this lack of data, predictive mathematical models have played an increasingly prominent role. In high-income countries, there is a long-standing history of established research groups advising policymakers, whereas a general lack of translational capacity has meant that mathematical models frequently remain inaccessible to policymakers in low-income and middle-income countries. Here, we describe a participatory approach to modelling that aims to circumvent this gap. Our approach involved the creation of an international group of infectious disease modellers and other public health experts, which culminated in the establishment of the COVID-19 Modelling (CoMo) Consortium. Here, we describe how the consortium was formed, the way it functions, the mathematical model used and, crucially, the high degree of engagement fostered between CoMo Consortium members and their respective local policymakers and ministries of health.

Published
2020
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