Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition.

The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88-95%), sensitivity (86-94%), and specificity (88-95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.

Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.

School reopening: Evidence-based recommendations during COVID-19 pandemic in Indonesia

Since the first Coronavirus Disease 2019 (COVID-19) case was reported it has run amok and caused global changes. It has affected human lives in almost every aspect including education. In response to COVID-19 governments and policymakers decided to shift the educational activities into online learning and institute school closure. As of March 2020, many countries worldwide have implemented school closure including Indonesia. Large scale social distancing and stay-at-home policies have begun to negatively impact society’s physical and mental health. As people start to adapt to the first wave of the COVID-19 pandemic policymakers and the government need to consider how to reopen the schools and its system to keep students and staff safe. School reopening is an important step toward loosening the lockdown. Schools play a crucial role in preserving children’s well-being. The objective of this review was to give a recommendation to facilitate school reopening. Research articles were gathered and assessed based on the themes of the articles. Forty articles were found reflecting SARS-CoV-2 and school reopening. Findings were adapted and modified according to Indonesian situations during the SARS-CoV-2 pandemic. Indonesia is currently preparing the first steps toward school reopening. For schools to be reopened there are several health measurements that need to be considered. A good collaboration between various authorities and stakeholders is essential in school reopening so that children’s safety and disease mitigation strategies remain stable. This review presents insights and recommendations for every element involved in school safety including the government schools’ teachers’ parents and students including what each needs to do to prepare in advance for the up-coming decision to reopen schools.