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COVID‐19 pandemic has affected over 100 countries in a matter of weeks. People's response toward social distancing in the emerging pandemic is uncertain. In this study, we evaluated the influence of information (formal and informal) sources on situational awareness of the public for adopting health‐protective behaviors such as social distancing. For this purpose, a questionnaire‐based survey was conducted. The hypothesis proposed suggests that adoption of social distancing practices is an outcome of situational awareness which is achieved by the information sources. Results suggest that information sources, formal (P = .001) and informal (P = 0.007) were found to be significantly related to perceived understanding. Findings also indicate that social distancing is significantly influenced by situational awareness, P = .000. It can, therefore, be concluded that an increase in situational awareness in times of public health crisis using formal information sources can significantly increase the adoption of protective health behavior and in turn contain the spread of infectious diseases., Highlights Reducing mortality caused by COVID‐19 can be achieved by awareness.Situation awareness can be increased by formal information sources.Increased situational awareness lead to adoption of health protective behavior.

In recent years, there has been an increase in the use of spectroscopy as a diagnostic tool. This review aims to summarize the studies that have reported the use of ATR-FTIR spectroscopy for the ac...

Raman spectroscopy has the ability to provide highly specific disease diagnostic results based on intrinsic molecular signatures in biological samples. We recorded variation in spectral range (540–...

Travel and tourism have been hard-hit by COVID-19, and people have been forced to cancel travel plans due to governments being forced to implement travel bans to curb infection dissemination. Therefore, this study investigates the influence of situational awareness for adopting health-protective behaviours such as postponed travel plans. We used the theory of situational awareness (SAT) coupled with the health belief model (HBM) to analyze health-related behaviors. Data from a cross-sectional survey of 705 Pakistani and international adults were analyzed using Structural Equation Modelling, during the peak of the COVID-19. The results confirmed that perceived understanding of COVID-19 cause influences perceived severity, understanding of contracting COVID-19 influences perceived susceptibility, and perceived understanding of COVID-19 prevention influences both perceived barriers and perceived benefits. The results further showed that perceived understanding of COVID-19 prevention is a strong predictor of self-efficacy, which ultimately influences health-protective behaviour. In addition, the sentiment analysis revealed that as COVID-19 continues to rise, people are becoming more sentimental and avoiding travel, even during festivals. The outcome of this study demonstrates that situational awareness has an effect on the postulates of health-protective measures and plays a key role in the implementation of protective measures such as canceling travel plans to ensure protection.

UNSTRUCTURED Well-timed forecast of infectious outbreaks using time-series data can help in proper planning of public health measures. If the forecasts are generated from machine learning algorithms, they can be used to manage resources where most needed. Here we present a support vector machine (SVM) model using epidemiological data provided by Johns Hopkins University Centre for Systems Science and Engineering (JHU CCSE), world health organization (WHO), Center for Disease Control and Prevention (CDC) to predict upcoming data before official declaration by WHO. Our study conducted on the time series data available from 22nd January till 10th March 2020 reveals that COVID-19 was spreading at an alarming rate and progressing towards a pandemic. If machine learning algorithms are used to predict the dynamics of an infectious outbreak future strategies can help in better management. Besides exploratory data analysis (EDA) highlights the importance of quarantine measures taken at the onset of this endemic by China and world leadership in containing the initial COVID-19 transmission. Nevertheless, when quarantine measures were relaxed due to extreme scrutiny a sharp upsurge was seen in COVID-19 transmission. The initial insight that confirmed COVID-19 cases are increasing as these got the highest number of effects for our selected dataset from 22nd January-10th March 2020 i.e. 126,344 (64%). The recovered cases are 68289 (34%) and the death rate is around 2%. The model presented here is flexible and can include uncertainty about outbreak dynamics and can be a significant tool for combating future outbreaks.

In the present study, we evaluated attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy for the differential diagnosis hepatitis C virus (HCV) and Dengue virus (DENV) infected freeze-dried sera along with healthy freeze-dried sera. Spectral signatures were recorded from 50 freeze-dried sera samples of each sample type. To achieve improved classification accurateness between the three sample types, multivariate data classification algorithms principal component analysis (PCA) and principal component regression (PCR) were used. Outcomes show 96 % inter-class variation as depicted by PCA and a classification accuracy of 99.2 % using PCR as shown by the value of R2; therefore, we propose that ATR-FTIR coupled with chemo metric data classification algorithms has considerable potential to distinguish HCV and DENV infected freeze-dried sera samples.

Evaluation of the diagnostic potential of freeze dried sera in comparison to thin dry film analysis for recording ATR-FTIR spectra. For this purpose, we compared our novel sample preparation technique i.e. freeze dried with conventional technique i.e. thin dry film sera. Using both methods ATR-FTIR spectra were recorded from Salmonella Typhi infected and healthy control human sera samples. When PCA was applied PC1 scores showed more inter-class variation among infected and healthy controls when freeze dried sample was used (90 %) as compared to thin dry film method (46 %). Potential of ATR-FTIR for discrimination of bio-molecules between two classes of samples is enhanced when freeze dried sera instead of thin dry film method is used.

Hepatitis C virus (HCV) infection is one of the leading causes of morbidity and mortality worldwide. Mortality linked with HCV infection can be lowered with effective and prompt diagnosis in early stages of infection. In this study potential of Raman spectroscopy to differentiate between healthy and HCV infected serum samples was investigated. Clear differences were observed in the Raman spectra of HCV infected and healthy sera samples. Using the analysis of variance (ANOVA) and t-test (p 0.001) on Raman spectra of diseased and healthy samples, we observed eleven unique Raman bands at 676, 825, 853, 936, 1029, 1105, 1155, 1305, 1620, 1654 and 1757 cm

In this study, we have evaluated the potential of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to differentially diagnose bimolecular markers in Salmonella typhi and dengue infected freeze-dried sera samples along with healthy freeze-dried sera samples. Spectral signatures were recorded from 18 S. typhi infected, 23 dengue infected and 28 healthy freeze-dried sera samples. In order to achieve enhanced classification accuracy between the three sample classes, multivariate data classification algorithms principal component analysis (PCA) and linear discriminant analysis (LDA) were used. Outcomes of the PCA scatter plot showed 95% inter-class variation. A classification accuracy of 100% was achieved using LDA; therefore, we propose that ATR-FTIR coupled with chemo metric data classification algorithms holds substantial potential for bimolecular differentiation between S. typhi and dengue infected freeze-dried sera samples.

Background Educational institutes around the globe are facing challenges of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Online learning is being carried out to avoid face to face contact in emergency scenarios such as coronavirus infectious disease 2019 (COVID-19) pandemic. Students need to adapt to new roles of learning through information technology to succeed in academics amid COVID-19. Objective However, access and use of online learning resources and its link with satisfaction of students amid COVID-19 are critical to explore. Therefore, in this paper, we aimed to assess and compare the access & use of online learning of Bruneians and Pakistanis amid enforced lockdown using a five-items satisfaction scale underlying existing literature. Method For this, a cross-sectional study was done in the first half of June 2020 after the pandemic situation among 320 students’ across Pakistan and Brunei with a pre-defined questionnaire. Data were analyzed with statistical software package for social sciences (SPSS) 2.0. Results The finding showed that there is a relationship between students’ satisfaction and access & use of online learning. Outcomes of the survey suggest that Bruneian are more satisfied (50%) with the use of online learning amid lockdown as compared to Pakistanis (35.9%). Living in the Urban area as compared to a rural area is also a major factor contributing to satisfaction with the access and use of online learning for both Bruneian and Pakistanis. Moreover, previous experience with the use of online learning is observed prevalent among Bruneians (P = .000), while among friends and family is using online learning (P = .000) were encouraging factors contributed to satisfaction with the use of online learning among Pakistanis amid COVID-19. Correlation results suggest that access and use factors of online learning amid COVID-19 were positively associated with satisfaction among both populations amid COVID-19 pandemic. However, Bruneian is more satisfied with internet access (r = 0.437, P Conclusion The study suggested that it is crucial for the government and other policymakers worldwide to address access and use of online learning resources of their populace amid pandemic.

Evaluation of diagnostic capability of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to classify differentially expressed bio-molecular markers in S. typhi infected and healthy freeze-dried sera samples. Using 25 typhoid infected and 25 healthy freeze-dried sera samples, ATR-FTIR spectra were recorded. For evaluating the sample discrimination amid selected wave number regions of FTIR spectra; chemometric data classification algorithms i.e. principal component analysis (PCA), partial least square regression (PLSR) and hierarchical cluster analysis (HCA) were employed. PCA scatter plot showed 90% variation between the S. typhi infected and healthy freeze-dried sera samples. Infection signs were successfully predicted using PLSR model with R2 = 0.9757. HCA dendogram yielded sensitivity and specificity 100% and 96% respectively. ATR-FTIR spectroscopy when coupled with chemometric analysis holds significant potential for biomolecular decimation between S. typhi infected and healthy; freeze-dried sera samples.