Practices and perception of healthcare workers towards infection control measures during the COVID-19 pandemic: a cross-sectional online survey from Nigeria.

INTRODUCTION: This study investigated the practices and perceptions of Health care workers (HCWs) in Nigeria towards infection control practices during the COVID-19 pandemic. METHODOLOGY: This cross-sectional study was conducted among HCWs in Nigeria healthcare facilities using a 25-item validated online questionnaire. The hyperlink of the questionnaire was shared with the various professional associations/societies and hospitals in June 2020. RESULTS: A total of 426 HCWs completed the questionnaire with pharmacists (28.8%), nurses/midwives (22.7%) and medical doctors (20.1%) being the highest respondents. Less than 50% of the HCWs had previous training on COVID-19 and how to use personal protective equipment (PPE). Only one in five HCWs had access to adequate PPE during the COVID-19 pandemic. Overall, the HCWs had good infection control practices with better practices observed among those who attended training on COVID-19 infection and those trained on how to use PPE. Lack of funds to purchase PPEs (55.3%), lack of access to PPE (52.5%) and lack of training on how to use PPE (44.0%) were the most common barriers to adherence to infection control guidelines. CONCLUSIONS: HCWs in Nigeria have limited access to adequate PPE and lack adequate support from health authorities. Attendance of training on the use of PPE and COVID-19 infection were associated with access to adequate PPE and better infection control practices. Training of HCWs, provision of adequate PPE, and support are recommended to improve compliance with infection control guidelines.

Speech as a Biomarker for COVID-19 Detection Using Machine Learning.

The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as "asymptomatic" COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the "recall" metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.

Carbon Emissions and Firm Performance: Evidence from Financial and Non-Financial Firms from Selected Emerging Economies

This paper examines the effects of carbon emissions on the accounting and market-based performance of financial and non-financial firms in emerging economies. Data for 104 financial and 328 non-financial firms constituting 2591 observations operating in 22 emerging economies were collected from the Datastream database for the period 2011–2020. We applied OLS and 2SLS regression techniques to analyze the data. Results show that financial firms emit less carbon than their non-financial counterparts. The results further show that carbon emissions reduce firms’ return on equity, Tobin’s Q, Z-score, and credit rating. Our findings remain robust in different estimation techniques and alternative proxies of performance. Our results have some important policy implications for emerging economies.

Food Security Sustainability: A Synthesis of the Current Concepts and Empirical Approaches for Meeting SDGs

Currently, food security is becoming a fundamental problem in the global macroeconomic dynamics for policymakers and governments in developing countries. Globally, food security offers challenges both from achieving Sustainable Development Goals (SDGs) targets and the welfare perspective of many poor households. As a result, this study is guided by Neo Malthusian and Access theories to investigate Food Security Sustainability: a Synthesis of the Current Concepts and Empirical Approaches for Meeting SDGs in Nigeria using ARDL and ECM techniques. The ARDL revealed that agricultural value-added and GDP positively affect food security for commercial agrarian investments in Nigeria. However, internal displacement, population growth, food inflation, and exchange rate volatility negatively affect sustainable food security in Nigeria. The model’s coefficient of ECMt−1 also shows negative (−0.0130 approximately) and statistically significant (0.0000) at 1%. Thus, the speed of adjustment requires 1.3% annually for the long-run equilibrium convergence to be restored. The study concludes that the SDGs targets for poverty and hunger reduction, mainly for food security sustainability alongside small producers by the year 2030, can be rarely achieved because the convergence to equilibrium is more than nine years. An active value-addition strategy for sustainable food security and the provision of humanitarian interventions are recommended.