Learning from serosurveillance for SARS-CoV-2 to inform pandemic preparedness and response.
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ABSTRACT:
SUBMITTER: Public Health Collaborators on Serosurveillance for Pandemic Preparedness and Response PHSeroPPR
PROVIDER: S-EPMC10219629 | biostudies-literature | 2023 May
REPOSITORIES: biostudies-literature
Publications
Learning from serosurveillance for SARS-CoV-2 to inform pandemic preparedness and response.
Lancet (London, England) 20230526 10399
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Project description:BackgroundPrimary care manages a significant proportion of healthcare in the United Kingdom and should be a key part of the SARS-CoV-2 pandemic response.AimTo assess preparedness for the SARS-CoV-2 pandemic by understanding GPs' perception of their ability to manage current and future service demand, set-up of triage processes, and training in Covid-19 infection prevention and control procedures.Design and settingCross-sectional survey of practicing GPs in the United Kingdom, with 2 rounds of data collection early in the pandemic.MethodsOnline survey, scripted and hosted by medeConnect Healthcare, comprising 6 closed prompts on 7-point Likert scales, and an optional free-text component. Quantitative data were analysed using descriptive statistics. Free-text data were analysed thematically.ResultsOne thousand two GPs completed each round; 51 GPs completed free-text responses in March, and 64 in April. Quantitative data showed greatest confidence in triage of Covid-19 patients, and GPs were more confident managing current than future Covid-19 demand. GPs' responses were more optimistic and aligned in April than March. Free-text data highlighted that GPs were concerned about lack of appropriate personal protective equipment and personal risk of Covid-19 infection in March, and unmet needs of non-Covid-19 patients in April. In both rounds, GPs expressed feeling overlooked by government and public health bodies.ConclusionGuidance to support general practice clinicians to manage future waves of Covid-19 or other health emergencies must be tailored to general practice from the outset, to support clinicians to manage competing health demands, and mitigate impacts on primary care providers' wellbeing.
| S-EPMC8828273 | biostudies-literature
Project description:Recent reports from the World Health Organization regarding Influenza A cases of zoonotic origin in humans (H1v and H9N2) and publications describing emergence swine Influenza A cases in humans together with "G4" Eurasian avian-like H1N1 Influenza A virus have drawn global attention to Influenza A pandemic threat. Additionally, the current COVID-19 epidemic has stressed the importance of surveillance and preparedness to prevent potential outbreaks. One feature of the QIAstat-Dx Respiratory SARS-CoV-2 panel is the double target approach for Influenza A detection of seasonal strains affecting humans using a generic Influenza A assay plus the three specific human subtype assays. This work explores the potential use of this double target approach in the QIAstat-Dx Respiratory SARS-Co-V-2 Panel as a tool to detect zoonotic Influenza A strains. A set of recently recorded H9 and H1 spillover strains and the G4 EA Influenza A strains as example of recent zoonotic Flu A strains were subjected to detection prediction with QIAstat-Dx Respiratory SARS-CoV-2 Panel using commercial synthetic dsDNA sequences. In addition, a large set of available commercial human and non-human influenza A strains were also tested using QIAstat-Dx Respiratory SARS-CoV-2 Panel for a better understanding of detection and discrimination of Influenza A strains. Results show that QIAstat-Dx Respiratory SARS-CoV-2 Panel generic Influenza A assay detects all the recently recorded H9, H5 and H1 zoonotic spillover strains and all the G4 EA Influenza A strains. Additionally, these strains yielded negative results for the three-human seasonal IAV (H1, H3 and H1N1 pandemic) assays. Additional non-human strains corroborated those results of Flu A detection with no subtype discrimination, whereas human Influenza strains were positively discriminated. These results indicate that QIAstat-Dx Respiratory SARS-CoV-2 Panel could be a useful tool to diagnose zoonotic Influenza A strains and differentiate them from the seasonal strains commonly affecting humans.
| S-EPMC9936110 | biostudies-literature
Project description:Infection with the SARS-CoV-2 virus has rapidly become a global pandemic for which we were not prepared. Several clinical trials using previously approved drugs and drug combinations are urgently under way to improve the current situation. A vaccine option has only recently become available, but worldwide distribution is still a challenge. It is imperative that, for future viral pandemic preparedness, we have a rapid screening technology for drug discovery and repurposing. The primary purpose of this research project was to evaluate the DeepNEU stem-cell based platform by creating and validating computer simulations of artificial lung cells infected with SARS-CoV-2 to enable the rapid identification of antiviral therapeutic targets and drug repurposing. The data generated from this project indicate that (a) human alveolar type lung cells can be simulated by DeepNEU (v5.0), (b) these simulated cells can then be infected with simulated SARS-CoV-2 virus, (c) the unsupervised learning system performed well in all simulations based on available published wet lab data, and (d) the platform identified potentially effective anti-SARS-CoV2 combinations of known drugs for urgent clinical study. The data also suggest that DeepNEU can identify potential therapeutic targets for expedited vaccine development. We conclude that based on published data plus current DeepNEU results, continued development of the DeepNEU platform will improve our preparedness for and response to future viral outbreaks. This can be achieved through rapid identification of potential therapeutic options for clinical testing as soon as the viral genome has been confirmed.
| S-EPMC7537153 | biostudies-literature
Project description:The recent outbreaks of influenza A/H5N1 and 'swine influenza' A/H1N1 have caused global concern over the potential for a new influenza pandemic. Although it is impossible to predict when the next pandemic will occur, appropriate planning is still needed to maximize efficient use of resources and to minimize loss of life and productivity. Many tools now exist to assist countries in evaluating their plans but there is little to aid in writing of the plans. This study discusses the process of drafting a pandemic influenza preparedness plan for developing countries that conforms to the International Health Regulations of 2005 and recommendations of the World Health Organization. Stakeholders from many sectors should be involved in drafting a comprehensive pandemic influenza plan that addresses all levels of preparedness.
| S-EPMC4634685 | biostudies-other
Project description:Serosurveillance provides a unique opportunity to quantify the proportion of the population that has been exposed to pathogens. Here, we developed and piloted Serosurveillance for Continuous, ActionabLe Epidemiologic Intelligence of Transmission (SCALE-IT), a platform through which we systematically tested remnant samples from routine blood draws in two major hospital networks in San Francisco for SARS-CoV-2 antibodies during the early months of the pandemic. Importantly, SCALE-IT allows for algorithmic sample selection and rich data on covariates by leveraging electronic medical record data. We estimated overall seroprevalence at 4.2%, corresponding to a case ascertainment rate of only 4.9%, and identified important heterogeneities by neighborhood, homelessness status, and race/ethnicity. Neighborhood seroprevalence estimates from SCALE-IT were comparable to local community-based surveys, while providing results encompassing the entire city that have been previously unavailable. Leveraging this hybrid serosurveillance approach has strong potential for application beyond this local context and for diseases other than SARS-CoV-2.
| S-EPMC7872360 | biostudies-literature
Project description:Throughout the SARS-CoV-2 pandemic, limited diagnostic capacities prevented sentinel testing, demonstrating the need for novel testing infrastructures. Here, we describe the setup of a cost-effective platform that can be employed in a high-throughput manner, which allows surveillance testing as an acute pandemic control and preparedness tool, exemplified by SARS-CoV-2 diagnostics in an academic environment. The strategy involves self-sampling based on gargling saline, pseudonymized sample handling, automated RNA extraction, and viral RNA detection using a semiquantitative multiplexed colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay with an analytical sensitivity comparable with RT-qPCR. We provide standard operating procedures and an integrated software solution for all workflows, including sample logistics, analysis by colorimetry or sequencing, and communication of results. We evaluated factors affecting the viral load and the stability of gargling samples as well as the diagnostic sensitivity of the RT-LAMP assay. In parallel, we estimated the economic costs of setting up and running the test station. We performed > 35,000 tests, with an average turnover time of < 6 h from sample arrival to result announcement. Altogether, our work provides a blueprint for fast, sensitive, scalable, cost- and labor-efficient RT-LAMP diagnostics, which is independent of potentially limiting clinical diagnostics supply chains.
| S-EPMC10157315 | biostudies-literature
Project description:ObjectivesSerological studies have been critical in tracking the evolution of the COVID-19 pandemic. Data on anti-SARS-CoV-2 antibodies persistence remain sparse, especially from infected individuals with few to no symptoms. The objective of the study was to quantify the sensitivity for detecting historic SARS-CoV-2 infections as a function of time since infection for three commercially available SARS-CoV-2 immunoassays and to explore the implications of decaying immunoassay sensitivity in estimating seroprevalence.MethodsWe followed a cohort of mostly mild/asymptomatic SARS-CoV-2-infected individuals (n = 354) at least 8 months after their presumed infection date and tested their serum for anti-SARS-CoV-2 antibodies with three commercially available assays: Roche-N, Roche-RBD and EuroImmun-S1. We developed a latent class statistical model to infer the specificity and time-varying sensitivity of each assay and show through simulations how inappropriately accounting for test performance can lead to biased serosurvey estimates.ResultsAntibodies were detected at follow-up in 74-100% of participants, depending on immunoassays. Both Roche assays maintain high sensitivity, with the EuroImmun assay missing 40% of infections after 9 months. Simulations reveal that without appropriate adjustment for time-varying assay sensitivity, seroprevalence surveys may underestimate infection rates.DiscussionAntibodies persist for at least 8 months after infection in a cohort of mildly infected individuals with detection depending on assay choice. Appropriate assay performance adjustment is important for the interpretation of serological studies in the case of diminishing sensitivity after infection.
| S-EPMC8261139 | biostudies-literature
Project description:BackgroundCoronavirus disease 2019 (COVID-19) has again highlighted the crucial role of healthcare workers in case management, disease surveillance, policy development, and healthcare education and training. The ongoing pandemic demonstrates the importance of having an emergency response plan that accounts for the safety of frontline healthcare workers, including those working in critical care settings.ObjectivesThe aim of the study was to explore Australian critical care nurses' knowledge, preparedness, and experiences of managing patients diagnosed with severe acute respiratory syndrome coronavirus 2 infection (SARS-CoV-2) and COVID-19.MethodsAn exploratory cross-sectional study of Australian critical care nurses was conducted between June and September 2020. An anonymised online survey was sent to Australian College of Critical Care Nurses' members to collect information about their knowledge, preparedness, and experiences during the COVID-19 pandemic. Descriptive statistics were used to summarise and report data.ResultsA total of 157 critical care nurses participated, with 138 fully complete surveys analysed. Most respondents reported 'good' to 'very good' level of knowledge about COVID-19 and obtained up-to-date COVID-19 information from international and local sources. Regarding managing patients with COVID-19, 82.3% felt sufficiently prepared at the time of data collection, and 93.4% had received specific education, training, or instruction. Most participants were involved in assessing (89.3%) and treating (92.4%) patients with COVID-19. Varying levels of concerns about SARS-CoV-2 infection were expressed by respondents, and 55.7% thought the pandemic had increased their workload. The most frequent concerns expressed by participants were a lack of appropriate personal protective equipment (PPE) and fear of PPE shortage.ConclusionsWhile most nurses expressed sufficient preparedness for managing COVID-19 patients, specific education had been undertaken and experiential learning was evident. Fears of insufficient or lack of appropriate PPE made the response more difficult for nurses and the community. Preparedness and responsiveness are critical to successful management of the COVID-19 pandemic and future outbreaks of emerging infectious diseases.
| S-EPMC8114809 | biostudies-literature
Project description:IntroductionThe SARS-CoV-2 pandemic has devastated populations, posing unprecedented challenges for healthcare services, staff and service-users. In the UK, rapid reconfiguration of maternity healthcare service provision changed the landscape of antenatal, intrapartum and postnatal care. This study aimed to explore the experiences of maternity services staff who provided maternity care during the SARS-CoV-2 pandemic to inform future improvements in care.Material and methodsA qualitative interview service evaluation was undertaken at a single maternity service in an NHS Trust, South London. Respondents (n = 29) were recruited using a critical case purposeful sample of maternity services staff. Interviews were conducted using video-conferencing software, and were transcribed and analyzed using Grounded Theory Analysis appropriate for cross-disciplinary health research. The focus of analysis was on staff experiences of delivering maternity services and care during the SARS-CoV-2 pandemic.ResultsA theory of "Precarity and Preparedness" was developed, comprising three main emergent themes: "Endemic precarity: A health system under pressure"; "A top-down approach to managing the health system shock"; and "From un(der)-prepared to future flourishing".ConclusionsMaternity services in the UK were under significant strain and were inherently precarious. This was exacerbated by the SARS-CoV-2 pandemic, which saw further disruption to service provision, fragmentation of care and pre-existing staff shortages. Positive changes are required to improve staff retention and team cohesion, and ensure patient-centered care remains at the heart of maternity care.
| S-EPMC9538337 | biostudies-literature
Project description:Risk-cost-benefit analysis requires the enumeration of decision alternatives, their associated outcomes, and the quantification of uncertainty. Public and private decision-making surrounding the COVID-19 pandemic must contend with uncertainty about the probability of infection during activities involving groups of people, in order to decide whether that activity is worth undertaking. We propose a model of SARS-CoV-2 infection probability that can produce estimates of relative risk of infection for diverse activities, so long as those activities meet a list of assumptions, including that they do not last longer than one day (e.g., sporting events, flights, concerts), and that the probability of infection among possible routes of infection (i.e., droplet, aerosol, fomite, and direct contact) are independent. We show how the model can be used to inform decisions facing governments and industry, such as opening stadiums or flying on airplanes; in particular, it allows for estimating the ranking of the constituent components of activities (e.g., going through a turnstile, sitting in one's seat) by their relative risk of infection, even when the probability of infection is unknown or uncertain. We prove that the model is a good approximation of a more refined model in which we assume infections come from a series of independent risks. A linearity assumption governing several potentially modifiable risks factors-such as duration of the activity, density of participants, and infectiousness of the attendees-makes interpreting and using the model straightforward, and we argue that it does so without significantly diminishing the reliability of the model.
| S-EPMC7842882 | biostudies-literature