Improved prediction of new COVID-19 cases using a simple vector autoregressive model: Evidence from seven New York State counties

With the rapid spread of COVID-19, there is an urgent need for a framework to accurately predict COVID-19 transmission. Recent epidemiological studies have found that a prominent feature of COVID-19 is its ability to be transmitted before symptoms occur, which is generally not the case for seasonal influenza and SARS. Several COVID-19 predictive epidemiological models have been proposed; however, they share a common drawback-they are unable to capture the unique asymptomatic nature of COVID-19 transmission. Here, we propose vector autoregression (VAR) as an epidemiological county-level prediction model that captures this unique aspect of COVID-19 transmission by introducing newly infected cases in other counties as lagged explanatory variables. Using the number of new COVID-19 cases in seven New York State counties, we predicted new COVID-19 cases in the counties over the next 4 weeks. We then compared our prediction results with those of 11 other state-of-the-art prediction models proposed by leading research institutes and academic groups. The results showed that VAR prediction is superior to other epidemiological prediction models in terms of the root mean square error of prediction. Thus, we strongly recommend the simple VAR model as a framework to accurately predict COVID-19 transmission.

Application of Modified Lowa Model Evidence Based Practice on Trauma Care Nurses Regarding Head Injury

Background: Head injuries are a regular occurrence in emergency departments around the world, with more than 2 million annual visits in North American EDs and more than 400 000 in the United Kingdom alone. Despite the fact that the mechanism of injury is consistent,, Head injuries are a regular occurrence in emergency departments around the world, with over 2 million visits in North American EDs and over 400 000 in the European Union alone. Regardless of how consistent the injury mechanism is. Objectives: Holds data what nurses already know about the modified LOWA model. 2. Develop and test a protocol using a IOWA model that was adjusted. 3. Assess the updated LOWA model's effectiveness 4.To connect the knowledge score to demographic data. Research Approach: Interventional approach Research design: - One group pre test and post test. Setting of the study: - The study will be conducted in AVBRH Hospital. Sample: - Staff Nurse Sample Size is 50Sampling Technique is Purposive sampling. Setting of the study is The study will be conducted in AVBRH Sample: - Staff Nurse Sampling Technique: - convenient sampling Data Collection: - Field data Will be collected by the use of standardised questionnaires with three key sections: Section A (Standard standards), Section B (Socio-demographics and work history of staff) used the modified LOWA model and check list). Expected Results: Oriented it toward the application of the LOWA model. Those characteristics are what evidence-based practise on trauma care nurses concerning head injury entails, but they may be able to address the issues that Traumatic Brain Injury Nursing faces. Adopting this paradigm into traumatic brain injury nursing units is worth a shot.With the assistance of a specific case, this article will discuss the clinical application of the Lowa Model in traumatic brain injury nursing care. Conclusion: In the light of the study findings, this study shows that, the implementation of LOWA Model evidence based practice has a positive effect on nurse’s knowledge and practices regarding trauma care nurses regarding head injury. There was a significant improvement in the nurses ‘knowledge and practice regarding LOWA Model evidence-based practice implementation compared with that before it. There was positive significant correlation between nurses’ knowledge and their practice before and after program. Nurses’ knowledge and practice about LOWA Model improved after application of this program.

Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK

We apply Bayesian inference methods to a suite of distinct compartmental models of generalised SEIR type, in which diagnosis and quarantine are included via extra compartments. We investigate the evidence for a change in lethality of COVID-19 in late autumn 2020 in the UK, using age-structured, weekly national aggregate data for cases and mortalities. Models that allow a (step-like or graded) change in infection fatality rate (IFR) have consistently higher model evidence than those without. Moreover, they all infer a close to two-fold increase in IFR. This value lies well above most previously available estimates. However, the same models consistently infer that, most probably, the increase in IFR preceded the time window during which variant B.1.1.7 (alpha) became the dominant strain in the UK. Therefore, according to our models, the caseload and mortality data do not offer unequivocal evidence for higher lethality of a new variant. We compare these results for the UK with similar models for Germany and France, which also show increases in inferred IFR during the same period, despite the even later arrival of new variants in those countries. We argue that while the new variant(s) may be one contributing cause of a large increase in IFR in the UK in autumn 2020, other factors, such as seasonality, or pressure on health services, are likely to also have contributed.