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1.
Sci Rep ; 12(1): 890, 2022 01 18.
Article in English | MEDLINE | ID: covidwho-1635924

ABSTRACT

The control of the initial outbreak and spread of SARS-CoV-2/COVID-19 via the application of population-wide non-pharmaceutical mitigation measures have led to remarkable successes in dampening the pandemic globally. However, with countries beginning to ease or lift these measures fully to restart activities, concern is growing regarding the impacts that such reopening of societies could have on the subsequent transmission of the virus. While mathematical models of COVID-19 transmission have played important roles in evaluating the impacts of these measures for curbing virus transmission, a key need is for models that are able to effectively capture the effects of the spatial and social heterogeneities that drive the epidemic dynamics observed at the local community level. Iterative forecasting that uses new incoming epidemiological and social behavioral data to sequentially update locally-applicable transmission models can overcome this gap, potentially resulting in better predictions and policy actions. Here, we present the development of one such data-driven iterative modelling tool based on publicly available data and an extended SEIR model for forecasting SARS-CoV-2 at the county level in the United States. Using data from the state of Florida, we demonstrate the utility of such a system for exploring the outcomes of the social measures proposed by policy makers for containing the course of the pandemic. We provide comprehensive results showing how the locally identified models could be employed for accessing the impacts and societal tradeoffs of using specific social protective strategies. We conclude that it could have been possible to lift the more disruptive social interventions related to movement restriction/social distancing measures earlier if these were accompanied by widespread testing and contact tracing. These intensified social interventions could have potentially also brought about the control of the epidemic in low- and some medium-incidence county settings first, supporting the development and deployment of a geographically-phased approach to reopening the economy of Florida. We have made our data-driven forecasting system publicly available for policymakers and health officials to use in their own locales, so that a more efficient coordinated strategy for controlling SARS-CoV-2 region-wide can be developed and successfully implemented.


Subject(s)
COVID-19 , Contact Tracing , Models, Biological , Pandemics , Physical Distancing , SARS-CoV-2 , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , Florida/epidemiology , Forecasting , Humans
2.
MAbs ; 14(1): 2014296, 2022.
Article in English | MEDLINE | ID: covidwho-1624515

ABSTRACT

In this 13th annual installment of the annual 'Antibodies to Watch' article series, we discuss key events in commercial antibody therapeutics development that occurred in 2021 and forecast events that might occur in 2022. Regulatory review of antibody therapeutics that target the SARS-CoV-2 coronavirus proceeded at an unprecedented pace in 2021, resulting in both emergency use authorizations and full approvals for sotrovimab, regdanvimab, REGEN-COV2, as well as others, in numerous countries. As of November 1, a total of 11 antibody therapeutics had been granted first approvals in either the United States or European Union in 2021 (evinacumab, dostarlimab loncastuximab tesirine, amivantamab, aducanumab, tralokinumab, anifrolumab, bimekizumab, tisotumab vedotin, regdanvimab, REGEN-COV2). The first global approvals of seven products, however, were granted elsewhere, including Japan (pabinafusp alfa), China (disitamab vedotin, penpulimab, zimberelimab), Australia (sotrovimab, REGEN-COV2), or the Republic of Korea (regdanvimab). Globally, at least 27 novel antibody therapeutics are undergoing review by regulatory agencies. First actions by the Food and Drug Administration on the biologics license applications for faricimab, sutimlimab, tebentafusp, relatlimab, sintilimab, ublituximab and tezepelumab are expected in the first quarter of 2022. Finally, our data show that, with antibodies for COVID-19 excluded, the late-stage commercial clinical pipeline of antibody therapeutics grew by over 30% in the past year. Of those in late-stage development, marketing applications for at least 22 may occur by the end of 2022.


Subject(s)
Antibodies, Monoclonal , Antibodies, Monoclonal/immunology , Antibodies, Monoclonal/therapeutic use , Antibodies, Viral/immunology , Antibodies, Viral/therapeutic use , Antibody Specificity , Antigens, Viral/immunology , Asia , Australia , COVID-19/immunology , COVID-19/prevention & control , COVID-19/therapy , Clinical Trials as Topic , Compassionate Use Trials , Drug Approval , European Union , Forecasting , Humans , SARS-CoV-2/immunology , United States , United States Food and Drug Administration
5.
AAPS J ; 24(1): 19, 2022 01 04.
Article in English | MEDLINE | ID: covidwho-1605878

ABSTRACT

Over the past decade, artificial intelligence (AI) and machine learning (ML) have become the breakthrough technology most anticipated to have a transformative effect on pharmaceutical research and development (R&D). This is partially driven by revolutionary advances in computational technology and the parallel dissipation of previous constraints to the collection/processing of large volumes of data. Meanwhile, the cost of bringing new drugs to market and to patients has become prohibitively expensive. Recognizing these headwinds, AI/ML techniques are appealing to the pharmaceutical industry due to their automated nature, predictive capabilities, and the consequent expected increase in efficiency. ML approaches have been used in drug discovery over the past 15-20 years with increasing sophistication. The most recent aspect of drug development where positive disruption from AI/ML is starting to occur, is in clinical trial design, conduct, and analysis. The COVID-19 pandemic may further accelerate utilization of AI/ML in clinical trials due to an increased reliance on digital technology in clinical trial conduct. As we move towards a world where there is a growing integration of AI/ML into R&D, it is critical to get past the related buzz-words and noise. It is equally important to recognize that the scientific method is not obsolete when making inferences about data. Doing so will help in separating hope from hype and lead to informed decision-making on the optimal use of AI/ML in drug development. This manuscript aims to demystify key concepts, present use-cases and finally offer insights and a balanced view on the optimal use of AI/ML methods in R&D.


Subject(s)
Artificial Intelligence , Clinical Trials as Topic , Computational Biology , Drug Development , Machine Learning , Pharmaceutical Research , Research Design , Animals , Artificial Intelligence/trends , Computational Biology/trends , Diffusion of Innovation , Drug Development/trends , Forecasting , Humans , Machine Learning/trends , Pharmaceutical Research/trends , Research Design/trends
6.
Sci Rep ; 11(1): 24171, 2021 12 17.
Article in English | MEDLINE | ID: covidwho-1593554

ABSTRACT

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.


Subject(s)
Algorithms , COVID-19/transmission , Cell Phone/statistics & numerical data , Hospitalization/statistics & numerical data , Models, Theoretical , Patient Admission/statistics & numerical data , COVID-19/epidemiology , COVID-19/virology , Disease Transmission, Infectious/statistics & numerical data , Forecasting/methods , Geography , Hospitalization/trends , Humans , Pandemics/prevention & control , Patient Admission/trends , Retrospective Studies , SARS-CoV-2/physiology , Sweden/epidemiology , Travel/statistics & numerical data
7.
Science ; 374(6575): 1546, 2021 Dec 24.
Article in English | MEDLINE | ID: covidwho-1592448
9.
Nat Microbiol ; 7(1): 97-107, 2022 01.
Article in English | MEDLINE | ID: covidwho-1596437

ABSTRACT

Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.


Subject(s)
COVID-19/epidemiology , Models, Statistical , SARS-CoV-2/isolation & purification , Basic Reproduction Number , Bias , COVID-19/diagnosis , COVID-19/transmission , COVID-19 Testing/statistics & numerical data , Forecasting , Humans , Prevalence , Reproducibility of Results , SARS-CoV-2/genetics , Spatio-Temporal Analysis , United Kingdom/epidemiology
10.
J Contin Educ Nurs ; 53(1): 21-29, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1595973

ABSTRACT

BACKGROUND: Midwifery across the world is facing changes and uncertainties. By recognizing plausible future options, a contemporary and strategic scope of midwifery practice and education can be established. The city of Antwerp, Belgium, was the indicative case for this study. Key drivers were identified to serve as input for scenarios. METHOD: Structuration theory and intuitive logics scenario planning methods were used to structure contextual midwifery scenarios. RESULTS: Six certain and six uncertain variables were identified. A two-dimensional framework showed these factors: (a) maternity care services and organization and (b) the society of child-bearing women and their families. Three scenarios described the plausible future of midwifery: (a) midwife-led care monitoring maternal health needs, (b) midwife-led holistic care, and (c) midwife/general practitioner-led integrated maternity care. CONCLUSION: All of the scenarios show the direction of change with a strategic focus, the importance of midwifery authenticity, and digital adaptability in maternity services. Also, the coronavirus disease 2019 (COVID-19) pandemic cannot be ignored in future midwifery. [J Contin Educ Nurs. 2022;53(1):21-29.].


Subject(s)
COVID-19 , Maternal Health Services , Midwifery , Female , Forecasting , Humans , Pregnancy , SARS-CoV-2
13.
Medicine (Baltimore) ; 100(50): e28134, 2021 Dec 17.
Article in English | MEDLINE | ID: covidwho-1583960

ABSTRACT

BACKGROUND: The COVID-19 pandemic caused >0.228 billion infected cases as of September 18, 2021, implying an exponential growth for infection worldwide. Many mathematical models have been proposed to predict the future cumulative number of infected cases (CNICs). Nevertheless, none compared their prediction accuracies in models. In this work, we compared mathematical models recently published in scholarly journals and designed online dashboards that present actual information about COVID-19. METHODS: All CNICs were downloaded from GitHub. Comparison of model R2 was made in 3 models based on quadratic equation (QE), modified QE (OE-m), and item response theory (IRT) using paired-t test and analysis of variance (ANOVA). The Kano diagram was applied to display the association and the difference in model R2 on a dashboard. RESULTS: We observed that the correlation coefficient was 0.48 (t = 9.87, n = 265) between QE and IRT models based on R2 when modeling CNICs in a short run (dated from January 1 to February 16, 2021). A significant difference in R2 was found (P < .001, F = 53.32) in mean R2 of 0.98, 0.92, and 0.84 for IRT, OE-mm, and QE, respectively. The IRT-based COVID-19 model is superior to the counterparts of QE-m and QE in model R2 particularly in a longer period of infected days (i.e., in the entire year in 2020). CONCLUSION: An online dashboard was demonstrated to display the association and difference in prediction accuracy among predictive models. The IRT mathematical model was recommended to make projections about the evolution of CNICs for each county/region in future applications, not just limited to the COVID-19 epidemic.


Subject(s)
COVID-19 , Models, Theoretical , COVID-19/epidemiology , Forecasting , Humans , Pandemics , SARS-CoV-2
14.
Future Microbiol ; 17: 1-3, 2022 01.
Article in English | MEDLINE | ID: covidwho-1581483
15.
Int J Environ Res Public Health ; 18(24)2021 12 18.
Article in English | MEDLINE | ID: covidwho-1580725

ABSTRACT

Australia spends more than $20 billion annually on medicines, delivering significant health benefits for the population. However, inappropriate prescribing and medicine use also result in harm to individuals and populations, and waste of precious health resources. Medication data linked with other routine collections enable evidence generation in pharmacoepidemiology; the science of quantifying the use, effectiveness and safety of medicines in real-world clinical practice. This review details the history of medicines policy and data access in Australia, the strengths of existing data sources, and the infrastructure and governance enabling and impeding evidence generation in the field. Currently, substantial gaps persist with respect to cohesive, contemporary linked data sources supporting quality use of medicines, effectiveness and safety research; exemplified by Australia's limited capacity to contribute to the global effort in real-world studies of vaccine and disease-modifying treatments for COVID-19. We propose a roadmap to bolster the discipline, and population health more broadly, underpinned by a distinct capability governing and streamlining access to linked data assets for accredited researchers. Robust real-world evidence generation requires current data roadblocks to be remedied as a matter of urgency to deliver efficient and equitable health care and improve the health and well-being of all Australians.


Subject(s)
COVID-19 , Australia , Forecasting , Humans , Pharmacoepidemiology , SARS-CoV-2
16.
Lancet Planet Health ; 5(11): e827-e839, 2021 11.
Article in English | MEDLINE | ID: covidwho-1574137

ABSTRACT

COVID-19 is disrupting and transforming the world. We argue that transformations catalysed by this pandemic should be used to improve human and planetary health and wellbeing. This paradigm shift requires decision makers and policy makers to go beyond building back better, by nesting the economic domain of sustainable development within social and environmental domains. Drawing on the engage, assess, align, accelerate, and account (E4As) approach to implementing the 2030 Agenda for Sustainable Development, we explore the implications of this kind of radical transformative change, focusing particularly on the role of the health sector. We conclude that a recovery and transition from the COVID-19 pandemic that delivers the future humanity wants and needs requires more than a technical understanding of the transformation at hand. It also requires commitment and courage from leaders and policy makers to challenge dominant constructs and to work towards a truly thriving, equitable, and sustainable future to create a world where economic development is not an end goal itself, but a means to secure the health and wellbeing of people and the planet.


Subject(s)
COVID-19 , Global Health , Pandemics , COVID-19/epidemiology , Forecasting , Global Health/trends , Humans , Sustainable Development
17.
Proc Natl Acad Sci U S A ; 118(51)2021 12 21.
Article in English | MEDLINE | ID: covidwho-1569346

ABSTRACT

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.


Subject(s)
COVID-19/epidemiology , Health Status Indicators , Models, Statistical , Epidemiologic Methods , Forecasting , Humans , Internet/statistics & numerical data , Surveys and Questionnaires , United States/epidemiology
18.
Soins ; 66(861): 32-35, 2021 Dec.
Article in French | MEDLINE | ID: covidwho-1569063

ABSTRACT

Resuscitation units, and the care practices they implement, require specific procedures and technologies, as well as particular and distinct knowledge, skills and human qualities within the care setting. Already facing tensions related to the challenges and vital issues of their mission, these resuscitation units have been destabilised by the influx of patients and the unprecedented complexity of the Covid-19 pandemic, which has forced them to rethink their organisation to a large extent and to envisage the future differently.


Subject(s)
COVID-19 , Pandemics , Forecasting , Humans , Organizations , SARS-CoV-2
19.
Biomed Pharmacother ; 145: 112385, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1565522

ABSTRACT

Chemically modified mRNA represents a unique, efficient, and straightforward approach to produce a class of biopharmaceutical agents. It has been already approved as a vaccination-based method for targeting SARS-CoV-2 virus. The COVID-19 pandemic has highlighted the prospect of synthetic modified mRNA to efficiently and safely combat various diseases. Recently, various optimization advances have been adopted to overcome the limitations associated with conventional gene therapeutics leading to wide-ranging applications in different disease conditions. This review sheds light on emerging directions of chemically modified mRNAs to prevent and treat widespread chronic diseases, including metabolic disorders, cancer vaccination and immunotherapy, musculoskeletal disorders, respiratory conditions, cardiovascular diseases, and liver diseases.


Subject(s)
COVID-19/prevention & control , Chronic Disease/prevention & control , Chronic Disease/therapy , Genetic Therapy/methods , Immunotherapy/methods , Pandemics/prevention & control , RNA, Messenger/chemistry , SARS-CoV-2/immunology , Vaccines, Synthetic , Biological Availability , Drug Carriers , Forecasting , Gene Transfer Techniques , Genetic Vectors/administration & dosage , Genetic Vectors/therapeutic use , Humans , Immunotherapy, Active , RNA Stability , RNA, Messenger/administration & dosage , RNA, Messenger/immunology , RNA, Messenger/therapeutic use , SARS-CoV-2/genetics , Vaccines, Synthetic/administration & dosage , Vaccines, Synthetic/immunology , /immunology
20.
Stud Health Technol Inform ; 285: 112-117, 2021 Oct 27.
Article in English | MEDLINE | ID: covidwho-1566635

ABSTRACT

Today pneumonia is one of the main problems of all countries around the world. This disease can lead to early disability, serious complications, and severe cases of high probabilities of lethal outcomes. A big part of cases of pneumonia are complications of COVID-19 disease. This type of pneumonia differs from ordinary pneumonia in symptoms, clinical course, and severity of complications. For optimal treatment of disease, humans need to study specific features of providing 19 pneumonia in comparison with well-studied ordinary pneumonia. In this article, the authors propose a new approach to identifying these specific features. This method is based on creating dynamic disease models for COVID and non-COVID pneumonia based on Bayesian Network design and Hidden Markov Model architecture and their comparison. We build models using real hospital data. We created a model for automatically identifying the type of pneumonia (COVID-19 or ordinary pneumonia) without special COVID tests. And we created dynamic models for simulation future development of both types of pneumonia. All created models showed high quality. Therefore, they can be used as part of decision support systems for medical specialists who work with pneumonia patients.


Subject(s)
COVID-19 , Pneumonia , Bayes Theorem , COVID-19/diagnosis , Forecasting , Humans , Pneumonia/diagnosis
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