Development of a prediction model for 30-day COVID-19 hospitalization and death in a national cohort of Veterans Health Administration patients - March 2022 - April 2023. (preprint)

ObjectiveDevelop models to predict 30-day COVID-19 hospitalization and death in the Omicron era for clinical and research applications. Material and MethodsWe used comprehensive electronic health records from a national cohort of patients in the Veterans Health Administration (VHA) who tested positive for SARS-CoV-2 between March 1, 2022, and March 31, 2023. Full models incorporated 84 predictors, including demographics, comorbidities, and receipt of COVID-19 vaccinations and anti-SARS-CoV-2 treatments. Parsimonious models included 19 predictors. We created models for 30-day hospitalization or death, 30-day hospitalization, and 30-day all-cause mortality. We used the Super Learner ensemble machine learning algorithm to fit prediction models. Model performance was assessed with the area under the receiver operating characteristic curve (AUC), Brier scores, and calibration intercepts and slopes in a 20% holdout dataset. ResultsModels were trained and tested on 198,174 patients, of whom 8% were hospitalized or died within 30 days of testing positive. AUCs for the full models ranged from 0.80 (hospitalization) to 0.91 (death). Brier scores were close to 0, with the lowest error in the mortality model (Brier score: 0.01). All three models were well calibrated with calibration intercepts <0.23 and slopes <1.05. Parsimonious models performed comparably to full models. DiscussionThese models may be used for risk stratification to inform COVID-19 treatment and to identify high-risk patients for inclusion in clinical trials. ConclusionsWe developed prediction models that accurately estimate COVID-19 hospitalization and mortality risk following emergence of the Omicron variant and in the setting of COVID-19 vaccinations and antiviral treatments.

Clinical and Pulmonary CT Characteristics of Patients Infected With the SARS-CoV-2 Omicron Variant Compared With Those of Patients Infected With the Alpha Viral Strain

Background Omicron has become the dominant variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) globally. We aimed to compare the clinical and pulmonary computed tomography (CT) characteristics of the patients infected with SARS-CoV-2 Omicron with those of patients infected with the Alpha viral strain. Methods Clinical profiles and pulmonary CT images of 420 patients diagnosed with coronavirus disease-2019 (COVID-19) at Ningbo First Hospital between January 2020 and April 2022 were collected. Demographic characteristics, symptoms, and imaging manifestations of patients infected with the SARS-CoV-2 Omicron variant were compared with those of patients infected with the Alpha strain. Results A total of 38 patients were diagnosed to be infected with the Alpha strain of SARS-CoV-2, whereas 382 patients were thought to be infected with the Omicron variant. Compared with patients infected with the Alpha strain, those infected with the Omicron variant were younger, and a higher proportion of men were infected (P < 0.001). Notably, 93 (24.3%) of the patients infected with Omicron were asymptomatic, whereas only two (5.3%) of the patients infected with the Alpha strain were asymptomatic. Fever (65.8%), cough (63.2%), shortness of breath (21.1%), and diarrhea (21.1%) were more common in patients infected with the SARS-CoV-2 Alpha strain, while runny nose (24.1%), sore throat (31.9%), body aches (13.6%), and headache (12.3%) were more common in patients with the Omicron variant. Compared with 33 (86.84%) of 38 patients infected with the Alpha strain, who had viral pneumonia on pulmonary CT images, only 5 (1.3%) of 382 patients infected with the Omicron variant had mild foci. In addition, the distribution of opacities in the five patients was unilateral and centrilobular, whereas most patients infected with the Alpha strain had bilateral involvement and multiple lesions in the peripheral zones of the lung. Conclusion The SARS-CoV-2 Alpha strain mainly affects the lungs, while Omicron is confined to the upper respiratory tract in patients with COVID-19.

A Comparative Study of Positive and Negative Electronic Word-of-Mouth on the SERVQUAL Scale under the COVID-19 epidemic -Taking a Regional Teaching Hospital in Taiwan as an Example (preprint)

Background In recent years, studies have disclosed that electronic WOM (eWOM) more directly reflects consumers' post-purchase psychological perception, and directly affects repurchase behavior, which is valued by institutions in various fields. Within the scope of service characteristic evaluation, medical service is the most invisible and difficult to evaluate service attribute. They are service organizations that must have high trust attributes. Therefore, the eWOM review will significantly influence people's decision-making process for choosing a healthcare provider. The purpose of this research is to combine eWOM reviews with the SERVQUAL scale in a comparative study of positive and negative eWOM reviews of a certain regional teaching hospital in Taiwan.Methods This research obtained data of eWOM reviews publicly available on Google maps from a Regional Teaching hospital in Taiwan in the past 10 years (from June 24, 2011, to December 31, 2021) by using website scraping technology. The semantic content analysis method was used in this study to classify WOM reviews according to the revised PZB SERVQUAL scale.Results Statistical analysis is then conducted. During the COVID-19 pandemic, the positive reviews have shown a downward trend. Among the five determiners of SERVQUAL of PZB, positive WOM reviews performed best in “Assurance”, with a positive review rate of 60.00%, followed by 42.11% of “Reliability”. In negative WOM reviews, “Assurance” performed the worst, with a positive rate of 72.34%, followed by “Responsiveness” at 28.37% and “Reliability” at 26.95%.Conclusion Since the onset of the COVID-19 in 2020, negative eWOM has increased significantly and exceeded the numbers of positive eWOM. Regardless of the positive and negative reviews, what patients care most about is “Assurance” of the professional attitude and skills of the medical staff, which needs to be strengthened most urgently. In addition, good “Reliability” will help to build up positive eWOM. However, the "Responsiveness" of poor service waiting time can easily lead to the spread of negative eWOM. This study suggests that the hospital management should focus on these few service-oriented qualities.

Hot water extract of Arthrospira maxima (AHWE) has broad-spectrum antiviral activity against RNA virus including coronavirus SARS-CoV2, and the antivirus spray application (preprint)

The emergence and re-emergence of RNA virus outbreaks highlight the urgent need for the development of broad-spectrum antiviral agents. Arthrospira maxima has be used as a food source for a long time, and the protein or polysaccharide fractions were evidenced to have antiviral activity, therefore we examined the antiviral efficacy of hot water extract from Arthrospira maxima (AHWE), on Enterovirus 71 (EV71), Influenza virus, Herpes simplex virus (HSV), Respiratory syncytial virus (RSV), Ebola virus, and Coronavirus for antiviral spray application. In this study, we demonstrated that the AHWE shown 90 to 100% inhibition rate on the plaque formation of EV71, HSV-1, HSV-2, influenza virus, RSV, 229E and SARS-COV2 at virus attachment stage, and the long-lasting protection study also found while the AHWE was pre-exposed to the open air for more than 4 hours in plaque reduction assay. In addition, AHWE also had inhibitory effect on Ebola virus replication at 500 ug/ml. Finally, AHWE also shown no toxicity and skin sensitivity that imply it could be safe for future clinical use if approved by FDA. In conclusion, this study suggests that AHWE could be developed as a potential broad-spectrum antivirus spray product and therapeutic agent.

A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19 (preprint)

Epidemic models play a key role in understanding and responding to the emerging COVID-19 pandemic. Widely used compartmental models are static and are of limited use to evaluate intervention strategies with the emerging pandemic. Applying the technology of data assimilation, we propose a Bayesian updating approach for estimating epidemiological parameters using observable information for the purpose of assessing the impacts of different intervention strategies. We adopt a concise renewal model and propose new parameters by disentangling the reduction of instantaneous reproduction number Rt into mitigation and suppression factors for quantifying intervention impacts at a finer granularity. Then we developed a data assimilation framework for estimating these parameters including constructing an observation function and developing a Bayesian updating scheme. A statistical analysis framework is then built to quantify the impact of intervention strategies by monitoring the evolution of these estimated parameters. By Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in these countries and the resurgence risk in the USA.

Using EpiRank for Analyzing Emerging Coronavirus Epidemic Patterns (preprint)

Huang et al. (2019) used their EpiRank algorithm, which emphasizes forward-and-backward commuter flow between homes and workplaces, to analyze the distribution patterns of two infectious diseases in Taiwan: the 2009-H1N1 influenza virus and the widespread emergence of the 2000-2008 type 71 enterovirus (EV). As this article was being prepared, the spreading mechanism of the novel coronavirus disease now designated as COVID-19 had yet to be identified, but according to the American Centers for Disease Control, its spreading mechanism and patterns are likely more similar to influenza than to other coronaviruses such as Severe Acute Respiratory Syndrome (SARS-CoV-1) or Middle East Respiratory Syndrome (MERS-CoV). To consider potential COVID-19 spatial patterns, we applied EpiRank to the 2003 SARS outbreak in north Taiwan for comparison with H1N1 and EV. SARS was found to be less contagious than H1N1 or EV, but with a significantly higher fatality rate. The characteristics of these diseases determined their specific spatial spreading patterns, as reflected in the different effects of forward and backward commuting movement. Our motivation is to highlight these differences and to illustrate EpiRank spatial patterns for the 2003 SARS outbreak for comparison with EpiRank-determined distributions for the H1N1 and EV outbreaks. Our results indicate that the daytime parameter (i.e., forward movement effect) range was slightly higher (0.5-0.55) for the SARS outbreak than for either the influenza (0.4-0.5) or EV (0.3-0.5) outbreaks, suggesting that the forward-and-backward movements of individuals between residential and core urban areas with concentrated populations were equally important regarding the spread of SARS. While COVID-19 might resemble either SARS or H1N1 in terms of spatial spreading, its daytime parameter is likely somewhere in-between, with backward movement being dominant (similar to H1N1) or with forward and backward movement being equally important (similar to SARS). Building on Huang et al. (2019) paper, we present an estimated risk distribution pattern for the Taipei Metropolitan Area for a daytime parameter of 0.55.