The effects of comorbidities on the change of taste and smell in COVID-19 patients.

Background: Sudden chemosensory changes were considered an early predictor of COVID-19. Here, the effects of comorbidities on changes in taste and smell in COVID-19 patients were investigated based on a worldwide study. Methods: Data analyzed here were collected from the Global Consortium for Chemosensory Research (GCCR) core questionnaire, including questions regarding preexisting disease conditions. Overall, the final sample of 12,438 participants who were diagnosed with COVID-19 included patients with preexisting conditions. Mixed linear regression models were used to test our hypothesis, and the p-value of interaction was examined. Results: A total of 61,067 participants completed the GCCR questionnaire, including 16,016 participants had preexisting diseases. The multivariate regression analysis showed that individuals with high blood pressure, lung disease, or sinus problems, or neurological diseases exhibited worse self-reported smell loss (p < .05), but no apparent significant differences in the smell or taste recovery. COVID-19 patients with seasonal allergy/hay fever lost their olfactory ability more than patients who did not have it (with 11.90 [9.67, 14.13] vs. without 6.97 [6.04, 7.91], p < .0001). The taste ability, smell loss and taste loss after COVID-19 recovery also decreased in the COVID-19 patients with seasonal allergy/hay fever (p < .001). Preexisting condition of diabetes did not worsen to chemosensory disorder but also had no obvious impact on the chemosensory recovery after acute infection. Preexisting diseases also affected the type of smell change in the COVID-19 patients with seasonal allergy/hay fever or sinus problems (p < .05). Conclusions: COVID-19 patients with high blood pressure, lung disease, or sinus problems, or neurological diseases exhibited worse self-reported smell loss, but no differences in the smell or taste recovery. COVID-19 patients with seasonal allergy/hay fever had greater loss of smell and taste, poorer smell and taste recovery. Level of Evidence: 4.

Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss.

Aim: To explore the role of smell and taste changes in preventing and controlling the COVID-19 pandemic, we aimed to build a forecast model for trends in COVID-19 prediction based on Google Trends data for smell and taste loss. Methods: Data on confirmed COVID-19 cases from 6 January 2020 to 26 December 2021 were collected from the World Health Organization (WHO) website. The keywords "loss of smell" and "loss of taste" were used to search the Google Trends platform. We constructed a transfer function model for multivariate time-series analysis and to forecast confirmed cases. Results: From 6 January 2020 to 28 November 2021, a total of 99 weeks of data were analyzed. When the delay period was set from 1 to 3 weeks, the input sequence (Google Trends of loss of smell and taste data) and response sequence (number of new confirmed COVID-19 cases per week) were significantly correlated (P < 0.01). The transfer function model showed that worldwide and in India, the absolute error of the model in predicting the number of newly diagnosed COVID-19 cases in the following 3 weeks ranged from 0.08 to 3.10 (maximum value 100; the same below). In the United States, the absolute error of forecasts for the following 3 weeks ranged from 9.19 to 16.99, and the forecast effect was relatively accurate. For global data, the results showed that when the last point of the response sequence was at the midpoint of the uptrend or downtrend (25 July 2021; 21 November 2021; 23 May 2021; and 12 September 2021), the absolute error of the model forecast value for the following 4 weeks ranged from 0.15 to 5.77. When the last point of the response sequence was at the extreme point (2 May 2021; 29 August 2021; 20 June 2021; and 17 October 2021), the model could accurately forecast the trend in the number of confirmed cases after the extreme points. Our developed model could successfully predict the development trends of COVID-19. Conclusion: Google Trends for loss of smell and taste could be used to accurately forecast the development trend of COVID-19 cases 1-3 weeks in advance.

Targeted delivery of inhalable drug particles in a patient-specific tracheobronchial tree with moderate COVID-19: A numerical study.

The coronavirus disease 2019 (COVID-19) pandemic has led to severe social and economic disruption worldwide. Although currently no consent has been reached on a specific therapy that can treat COVID-19 effectively, several inhalation therapy strategies have been proposed to inhibit SARS-CoV-2 infection. These strategies include inhalations of antiviral drugs, anti-inflammatory drugs, and vaccines. To investigate how to enhance the therapeutic effect by increasing the delivery efficiency (DE) of the inhaled aerosolized drug particles, a patient-specific tracheobronchial (TB) tree from the trachea up to generation 6 (G6) with moderate COVID-19 symptoms was selected as a testbed for the in silico trials of targeted drug delivery to the lung regions with pneumonia alba, i.e., the severely affected lung segments (SALS). The 3D TB tree geometry was reconstructed from spiral computed tomography (CT) scanned images. The airflow field and particle trajectories were solved using a computational fluid dynamics (CFD) based Euler-Lagrange model at an inhalation flow rate of 15 L/min. Particle release maps, which record the deposition locations of the released particles, were obtained at the inlet according to the particle trajectories. Simulation results show that particles with different diameters have similar release maps for targeted delivery to SALS. Point-source aerosol release (PSAR) method can significantly enhance the DE into the SALS. A C++ program has been developed to optimize the location of the PSAR tube. The optimized simulations indicate that the PSAR approach can at least increase the DE of the SALS by a factor of 3.2× higher than conventional random-release drug-aerosol inhalation. The presence of the PSAR tube only leads to a 7.12% change in DE of the SALS. This enables the fast design of a patient-specific treatment for reginal lung diseases.