Browsing by Author "Harish Nair"
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Item Burden of Respiratory Syncytial Virus RSV Infection Among Adults in Nursing and Care Homes A Systematic ReviewR Osei-Yeboah; S Amankwah; E Begier; M Adedze; E et al; Harish NairItem COVID-19 vaccine hesitancy in rural South Africa: Deepening understanding to increase uptake and access(2022-05-22) Kathleen Kahn; Audrey Pettifor; Palesa Mataboge; Nicole K Kelly; Duduzile P Mashinini; Harish Nair; Harry Campbell; Cheryl Cohen; F Xavier Gómez-Olivé; Stephen TollmanBackground: To date, COVID-19 vaccine coverage in the African region falls far too short of global goals. Increasing vaccination rates requires understanding barriers to vaccination so that effective interventions that sensitively and effectively address barriers to vaccination can be implemented. Methods: To assess COVID-19 vaccination levels and identify major barriers to vaccine uptake we conducted a population-based, cross-sectional survey among 1662 adults 18 and older from August 25 to October 29 2021 in the Agincourt Health and Socio-Demographic Surveillance System (AHDSS) area, Mpumalanga, South Africa. Results: Half of participants reported receiving a COVID-19 vaccine (50.4%) with 41.1% being fully vaccinated and 9.3% being partially vaccinated; 49.6% were unvaccinated. More women than men were vaccinated (55.5% vs 42.8%, P < 0.001), and older age groups were more likely to be vaccinated than younger age groups (P < 0.001). Among the unvaccinated, 69.0% planned to get vaccinated as soon as possible, while 14.7% reported definitely not wanting the vaccine. Major barriers to vaccination included lacking information on eligibility (12.3%) or where to get vaccinated (13.0%), concerns about side effects (12.5%), and inconvenient hours and locations for vaccination (11.0%). Confidence in the safety and efficacy of COVID-19 vaccines was higher among those vaccinated than unvaccinated (75.3% vs 51.2%, 75.8% vs 51.0%, both P < 0.001, respectively). Conclusions: Increasing vaccination in South Africa beyond current levels will require a concerted effort to address concerns around vaccine safety and increase confidence in vaccine efficacy. Clarifying eligibility and ensuring access to vaccines at times and places that are convenient to younger populations, men, and other vulnerable groups is necessary.Item COVID19related stigma within a rural South African community A mixed methods analysis(PUBLIC LIBRARY SCIENCE) P Mashinini; N Kelly; Palesa Mataboge; F Hill; Harish Nair; G Palattiyil; Kathleen Kahn; Audrey PettiforItem The disease burden of respiratory syncytial virus in older adultsHarish Nair; S KenmoeItem Incidence and outcome of SARSCoV2 reinfection in the preOmicron era A global systematic review and metaanalysisN F Ismail; A E Rahman; D Kulkarni; F Zhu; E et al; Harish NairItem Report of the WHO technical consultation on the evaluation of respiratory syncytial virus prevention cost effectiveness in low and middleincome countries April 78 2022M C Fitzpatrick; R S Laufer; R Baral; A J Driscoll; Harish Nair; E et alItem Risk factors for respiratory syncytial virusassociated acute lower respiratory infection in children under 5 years An updated systematic review and metaanalysis(ELSEVIER SCI LTD) S Deng; B Cong; M Edgoose; F De Wit; Harish Nair; Y LiItem Short-term local predictions of COVID-19 in the United Kingdom using dynamic supervised machine learning algorithms(2022-09-24) Xin Wang; Yijia Dong; William David Thompson; Harish Nair; You LiBackground: Short-term prediction of COVID-19 epidemics is crucial to decision making. We aimed to develop supervised machine-learning algorithms on multiple digital metrics including symptom search trends, population mobility, and vaccination coverage to predict local-level COVID-19 growth rates in the UK. Methods: Using dynamic supervised machine-learning algorithms based on log-linear regression, we explored optimal models for 1-week, 2-week, and 3-week ahead prediction of COVID-19 growth rate at lower tier local authority level over time. Model performance was assessed by calculating mean squared error (MSE) of prospective prediction, and naïve model and fixed-predictors model were used as reference models. We assessed real-time model performance for eight five-weeks-apart checkpoints between 1st March and 14th November 2021. We developed an online application (COVIDPredLTLA) that visualised the real-time predictions for the present week, and the next one and two weeks. Results: Here we show that the median MSEs of the optimal models for 1-week, 2-week, and 3-week ahead prediction are 0.12 (IQR: 0.08-0.22), 0.29 (0.19-0.38), and 0.37 (0.25-0.47), respectively. Compared with naïve models, the optimal models maintain increased accuracy (reducing MSE by a range of 21-35%), including May-June 2021 when the delta variant spread across the UK. Compared with the fixed-predictors model, the advantage of dynamic models is observed after several iterations of update. Conclusions: With flexible data-driven predictors selection process, our dynamic modelling framework shows promises in predicting short-term changes in COVID-19 cases. The online application (COVIDPredLTLA) could assist decision-making for control measures and planning of healthcare capacity in future epidemic growths.Item Understanding the age spectrum of respiratory syncytial virus associated hospitalisation and mortality burden based on statistical modelling methods a systematic analysis(BIOMED CENTRAL LTD) B Cong; I Dighero; T Zhang; A Chung; Harish Nair; You Li