COVID - 19 related knowledge and preventive practices early in the outbreak among health care workers in selected public health facilities of Illu aba Bor and Buno Bedelle zones, Southwest Ethiopia.

BACKGROUND: Novel-coronavirus 2019 (COVID-19) disease is currently a worldwide health risk and public health emergency concern. The virus is transmitted from an infected person to another person through close contact and droplets. Frontline health care workers are the most at risk of infection, and so a WHO interim guidance document was issued by the World Health Organization (WHO) which underscores the importance of proper sanitation and waste management practices for COVID- 19 in health-care settings. This study aimed at assessing knowledge and preventive practices towards Covid-19 among health care providers in selected health facilities of Illu Aba Bor and Buno Bedele zones, Southwest Ethiopia. METHODS: An institution-based cross-sectional study was conducted from April to May 2020 among 330 health workers in selected health facilities of Illu Aba Bor and Buno-Bedelle Zones, Southwest Ethiopia. Data were collected using a self-administered structured questionnaire. The collected data were entered into Epidata version 3.1 and exported to SPSS version 23 for analysis. Bivariate and multivariable logistic regression analysis was used to identify independent predictors of preventive practices towards Covid-19. Statistical significance was declared at a p-value of < 0.05. RESULT: The majority of respondents (93.3%) demonstrated good knowledge of COVID-19, and the mean (SD) knowledge score was 9.04 ± 1.06. Nearly two-thirds (64.2%) of the study participants had good infection prevention practices. Being male (AOR = 3.65, 95% CI: (1.96, 6.80)), education level (AOR = 1.82, 95% CI (1.02, 3.22)), profession (AOR = 3.17, 95% CI (1.08, 9.33)), service year (5-10 years) (AOR = 2.00 (1.02, 3.92)) and more than 10 years (AOR = 3.14 (1.51, 6.52)), availability of personal protective equipment (AOR = 1.96 (1.06, 3.61)) and Knowledge level (AOR = 2.61 (1.48, 4.62)) were independent predictors of COVID-19 preventive practices. CONCLUSION: The overall level of knowledge of HCWs was good. However, the practice was relatively low. Gender, educational status, profession, year of service, knowledge towards COVID-19, and availability of personal protective equipment were independent predictors of good infection prevention practices. Optimizing the infection prevention and control loop of the health facilities is recommended.

Optimal control of diffusion processes pertaining to an opioid epidemic dynamical model with random perturbations.

In this paper, we consider the problem of controlling a diffusion process pertaining to an opioid epidemic dynamical model with random perturbation so as to prevent it from leaving a given bounded open domain. In particular, we assume that the random perturbation enters only through the dynamics of the susceptible group in the compartmental model of the opioid epidemic dynamics and, as a result of this, the corresponding diffusion is degenerate, for which we further assume that the associated diffusion operator is hypoelliptic, i.e., such a hypoellipticity assumption also implies that the corresponding diffusion process has a transition probability density function with strong Feller property. Here, we minimize the asymptotic exit rate of such a controlled-diffusion process from the given bounded open domain and we derive the Hamilton-Jacobi-Bellman equation for the corresponding optimal control problem, which is closely related to a nonlinear eigenvalue problem. Finally, we also prove a verification theorem that provides a sufficient condition for optimal control.

Probabilistic mixture regression models for alignment of LC-MS data.

A novel framework of a probabilistic mixture regression model (PMRM) is presented for alignment of liquid chromatography-mass spectrometry (LC-MS) data with respect to retention time (RT) points. The expectation maximization algorithm is used to estimate the joint parameters of spline-based mixture regression models and prior transformation density models. The latter accounts for the variability in RT points and peak intensities. The applicability of PMRM for alignment of LC-MS data is demonstrated through three data sets. The performance of PMRM is compared with other alignment approaches including dynamic time warping, correlation optimized warping, and continuous profile model in terms of coefficient variation of replicate LC-MS runs and accuracy in detecting differentially abundant peptides/proteins.

A Bayesian based functional mixed-effects model for analysis of LC-MS data.

A Bayesian multilevel functional mixed-effects model with group specific random-effects is presented for analysis of liquid chromatography-mass spectrometry (LC-MS) data. The proposed framework allows alignment of LC-MS spectra with respect to both retention time (RT) and mass-to-charge ratio (m/z). Affine transformations are incorporated within the model to account for any variability along the RT and m/z dimensions. Simultaneous posterior inference of all unknown parameters is accomplished via Markov chain Monte Carlo method using the Gibbs sampling algorithm. The proposed approach is computationally tractable and allows incorporating prior knowledge in the inference process. We demonstrate the applicability of our approach for alignment of LC-MS spectra based on total ion count profiles derived from two LC-MS datasets.

Multi-class alignment of LC-MS data using probabilistic-based mixture regression models.

In this paper, a framework of probabilistic-based mixture regression models (PMRM) is presented for multi-class alignment of liquid chromatography-mass spectrometry (LC-MS) data. The proposed framework performs the alignment in both time and measurement spaces of the LC-MS spectra. The expectation maximization (EM) algorithm is used to estimate the joint parameters of spline-based mixture regression models and prior transformation densities. The latter are incorporated to account for variability in time and measurement spaces of the data. As a proof of concept, the proposed method is applied to align a single-class replicate LC-MS spectra generated from proteins of lysed E.coli cells. Its performance is compared with the dynamic time warping (DTW) and continuous profile model (CPM) approaches.