The Structural Identifiability of a Humidity-Driven Epidemiological Model of Influenza Transmission.

Influenza epidemics cause considerable morbidity and mortality every year worldwide. Climate-driven epidemiological models are mainstream tools to understand seasonal transmission dynamics and predict future trends of influenza activity, especially in temperate regions. Testing the structural identifiability of these models is a fundamental prerequisite for the model to be applied in practice, by assessing whether the unknown model parameters can be uniquely determined from epidemic data. In this study, we applied a scaling method to analyse the structural identifiability of four types of commonly used humidity-driven epidemiological models. Specifically, we investigated whether the key epidemiological parameters (i.e., infectious period, the average duration of immunity, the average latency period, and the maximum and minimum daily basic reproductive number) can be uniquely determined simultaneously when prevalence data is observable. We found that each model is identifiable when the prevalence of infection is observable. The structural identifiability of these models will lay the foundation for testing practical identifiability in the future using synthetic prevalence data when considering observation noise. In practice, epidemiological models should be examined with caution before using them to estimate model parameters from epidemic data.

A Nomogram-Based Study: A Way Forward to Predict the Anxiety Status in Medical Staff During the COVID-19 Pandemic.

Background and Objective: Anxiety influences job burnout and health. This study aimed to establish a nomogram to predict the anxiety status of medical staff during the coronavirus disease (COVID-19) pandemic. Methods: A total of 600 medical members were randomized 7:3 and divided into training and validation sets. The data was collected using a questionnaire. Logistic regression analysis and Akaike information criterion (AIC) were applied to investigate the risk factors for anxiety. Odds ratio (OR) and 95% confidence interval (95% CI) were calculated to establish a nomogram. Results: Participation time (OR=44.28, 95% CI=13.13~149.32), rest time (OR=38.50, 95% CI=10.43~142.19), epidemic prevention area (OR=10.16, 95% CI=3.51~29.40), epidemic prevention equipment (OR=15.24, 95% CI=5.73~40.55), family support (OR=9.63, 95% CI=3.55~26.11), colleague infection (OR=6.25, 95% CI=2.18~19.11), and gender (OR=3.30, 95% CI=1.15~9.47) were the independent risk factors (P<0.05) for anxiety in medical staff. The areas under the receiver operating characteristic (ROC) curves of the training and validation sets were 0.987 and 0.946, respectively. The decision curve's net benefit shows the nomogram's clinical utility. Conclusion: The nomogram established in this study exhibited an excellent ability to predict anxiety status with sufficient discriminatory power and calibration. Our findings provide a protocol for predicting and identifying anxiety status in medical staff during the COVID-19 pandemic.

Comparison of throat swab and sputum specimens for viral nucleic acid detection in four cases of 2019-nCoV infection

To comparatively analyze the detection effect of viral nucleic acid in throat swab and sputum sample of 2019-nCoV cases. GAPDH housekeeping genes, viral ORF 1ab genes, N genes, and S genes were tested and compared by real-time RT-PCR in throat swabs and sputum specimens from 4 confirmed cases of 2019-nCoV. In the throat swabs and sputum samples of 4 cases, the human cell housekeeping gene GAPDH showed obvious and typical amplification signal curves. In the detection of viral ORF 1ab gene, N gene, and S gene, the amplification signal of sputum samples was stronger than that of throat swab, and the CT value of amplification curve was lower than that of throat swab, especially in case 1 and case 4, and the throat swab of case 4 showed negative results using commercial real-time RT-PCR kits, while the sputum specimens showed clear positive results. Therefore, in the viral nucleic acid testing of 2019-nCoV laboratory detection, the virus content in sputum specimens was higher than that in throat swab specimens, and the detection effect was better than that in throat swab specimens.

COVID-19 screening using breath-borne volatile organic compounds.

Rapid screening of COVID-19 is key to controlling the pandemic. However, current nucleic acid amplification involves lengthy procedures in addition to the discomfort of taking throat/nasal swabs. Here we describe potential breath-borne volatile organic compound (VOC) biomarkers together with machine learning that can be used for point-of-care screening of COVID-19. Using a commercial gas chromatograph-ion mobility spectrometer, higher levels of propanol were detected in the exhaled breath of COVID-19 patients (N= 74) and non-COVID-19 respiratory infections (RI) (N= 30) than those of non-COVID-19 controls (NC)/health care workers (HCW) (N= 87), and backgrounds (N= 87). In contrast, breath-borne acetone was found to be significantly lower for COVID-19 patients than other subjects. Twelve key endogenous VOC species using supervised machine learning models (support vector machines, gradient boosting machines (GBMs), and Random Forests) were shown to exhibit strong capabilities in discriminating COVID-19 from (HCW + NC) and RI with a precision ranging from 91% to 100%. GBM and Random Forests models can also discriminate RI patients from healthy subjects with a precision of 100%. In addition, the developed models using breath-borne VOCs could also detect a confirmed COVID-19 patient but with a false negative throat swab polymerase chain reaction test. It takes 10 min to allow an entire breath test to finish, including analysis of the 12 key VOC species. The developed technology provides a novel concept for non-invasive rapid point-of-care-test screening for COVID-19 in various scenarios.