Detection of COVID-19 by Cough Sound: A Method Based on DSC + BiLSTM

In recent years, the outbreak of COVID-19 has brought a new round of challenges to global health care, and daily large-scale testing has also increased the consumption of medical resources. However, studies have shown that the cough sounds of patients with COVID-19 are significantly different from other Characteristics of respiratory infectious diseases. Therefore, this paper considers the use of the patient's cough as a detection sample to give the preliminary screening results. The research was conducted on the COUGHVID dataset. The experiment is divided into two stages: (1) Preprocessing stage: use Pitch Shift and Time Stretch to perform data enhancement on audio data, and use spec Augment to perform data enhancement on mel spectrogram. (2) Model construction stage: use two layers of DSC and one layer of BILSTM to splicing to obtain a classification model. Finally, the method is compared with the baseline method using only two layers of LSTM. The results show that accuracy has increased by 1.9%, F1 has increased by 1.9%, and AUC has increased by 1.6%. © 2022 IEEE.

Optimizing Large-Scale COVID-19 Nucleic Acid Testing with a Dynamic Testing Site Deployment Strategy.

The COVID-19 epidemic has spread worldwide, infected more than 0.6 billion people, and led to about 6 million deaths. Conducting large-scale COVID-19 nucleic acid testing is an effective measure to cut off the transmission chain of the COVID-19 epidemic, but it calls for deploying numerous nucleic acid testing sites effectively. In this study, we aim to optimize the large-scale nucleic acid testing with a dynamic testing site deployment strategy, and we propose a multiperiod location-allocation model, which explicitly considers the spatial-temporal distribution of the testing population and the time-varied availability of various testing resources. Several comparison models, which implement static site deployment strategies, are also developed to show the benefits of our proposed model. The effectiveness and benefits of our model are verified with a real-world case study on the Chenghua district of Chengdu, China, which indicates that the optimal total cost of the dynamic site deployment strategy can be 15% less than that of a real plan implemented in practice and about 2% less than those of the other comparison strategies. Moreover, we conduct sensitivity analysis to obtain managerial insights and suggestions for better testing site deployment in field practices. This study highlights the importance of dynamically deploying testing sites based on the target population's spatial-temporal distribution, which can help reduce the testing cost and increase the robustness of producing feasible plans with limited medical resources.

Considering social inequalities in health in large-scale testing for COVID-19 in Montréal: a qualitative case study.

BACKGROUND: Evidence continues to demonstrate that certain marginalised populations are disproportionately affected by COVID-19. While many studies document the impacts of COVID-19 on social inequalities in health, none has examined how public health responses to the pandemic have unfolded to address these inequities in Canada. The purpose of our study was to assess how social inequalities in health were considered in the design and planning of large-scale COVID-19 testing programs in Montréal (Québec, Canada). METHODS: Part of the multicountry study HoSPiCOVID, this article reports on a qualitative case study of large-scale testing for COVID-19 in Montréal. We conducted semi-structured interviews with 19 stakeholders involved in planning large-scale testing or working with vulnerable populations during the pandemic. We developed interview guides and a codebook using existing literature on policy design and planning, and analysed data deductively and inductively using thematic analysis in NVivo. RESULTS: Our findings suggest that large-scale COVID-19 testing in Montréal did not initially consider social inequalities in health in its design and planning phases. Considering the sense of urgency brought by the pandemic, participants noted the challenges linked to the uptake of an intersectoral approach and of a unified vision of social inequalities in health. However, adaptations were gradually made to large-scale testing to improve its accessibility, acceptability, and availability. Actors from the community sector, among others, played an important role in supporting the health sector to address the needs of specific subgroups of the population. CONCLUSIONS: These findings contribute to the reflections on the lessons learned from COVID-19, highlighting that public health programs must tackle structural barriers to accessing healthcare services during health crises. This will be necessary to ensure that pandemic preparedness and response, including large-scale testing, do not further increase social inequalities in health.

SARS-CoV-2 rapid antigen tests provide benefits for epidemic control - observations from Austrian schools.

OBJECTIVE: This paper motivates and justifies the use of antigen tests for epidemic control as distinct from a diagnostic test. STUDY DESIGN AND SETTING: We discuss the relative advantages of antigen and PCR tests, summarizing evidence from both the literature as well as Austrian schools, which conducted frequent, mass rapid antigen testing during the spring of 2021. While our report on testing predates Delta, we have updated the review with recent data on viral loads in breakthrough infections and more information about testing efficacy, especially in children. RESULTS: Rapid antigen tests detect proteins at the surface of virus particles, identifying the disease during its infectious phase. In contrast, PCR tests detect viral genomes: they can thus diagnose COVID-19 before the infectious phase but also react to remnants of the virus genome, even weeks after live virus ceases to be detectable in the respiratory tract. Furthermore, the logistics for administering the tests are different. Large-scale rapid antigen testing in Austrian schools showed low false-positive rates along with an approximately 10% lower effective reproduction number in the tested cohort. CONCLUSION: Using antigen tests at least 2-3 times per week could become a powerful tool to suppress the COVID-19 pandemic.

Large-Scale Testing of Asymptomatic Healthcare Personnel for Severe Acute Respiratory Syndrome Coronavirus 2.

Large-scale, 1-time testing of >12,000 asymptomatic healthcare personnel in California, USA, during April-June 2020 showed that prevalence of severe acute respiratory syndrome coronavirus 2 was low (<1%). Testing might identify asymptomatic and presymptomatic persons, including some with high viral burden, enabling prompt implementation of measures to limit nosocomial spread.