Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as shopping complexes, hospitals, banks, restaurants, educational institutes, and so forth. However, the rapid spread of this virus and its consequent detrimental impacts have brought indoor air quality into the spotlight. In contrast to outdoor air, indoor air is recycled constantly causing it to trap and build up pollutants, which may facilitate the transmission of virus. There are several monitoring solutions which are available commercially, a typical system monitors the air quality using gas and particle sensors. These sensor readings are compared against well known thresholds, subsequently generating alarms when thresholds are violated. However, these systems do not predict the quality of air for future instances, which holds paramount importance for taking timely preemptive actions, especially for COVID-19 actual and potential patients as well as people suffering from acute pulmonary disorders and other health problems. In this regard, we have proposed an indoor air quality monitoring and prediction solution based on the latest Internet of Things (IoT) sensors and machine learning capabilities, providing a platform to measure numerous indoor contaminants. For this purpose, an IoT node consisting of several sensors for 8 pollutants including NH3, CO, NO2, CH4, CO2, PM 2.5 along with the ambient temperature & air humidity is developed. For proof of concept and research purposes, the IoT node is deployed inside a research lab to acquire indoor air data. The proposed system has the capability of reporting the air conditions in real-time to a web portal and mobile app through GSM/WiFi technology and generates alerts after detecting anomalies in the air quality. In order to classify the indoor air quality, several machine learning algorithms have been applied to the recorded data, where the Neural Network (NN) model outperformed all others with an accuracy of 99.1%. For predicting the concentration of each air pollutant and thereafter predicting the overall quality of an indoor environment, Long and Short Term Memory (LSTM) model is applied. This model has shown promising results for predicting the air pollutants’ concentration as well as the overall air quality with an accuracy of 99.37%, precision of 99%, recall of 98%, and F1-score of 99%. The proposed solution offers several advantages including remote monitoring, ease of scalability, real-time status of ambient conditions, and portable hardware, and so forth.

The global challenge presented by COVID-19 is unparalleled. Shortages in healthcare staff and manpower bring the practical skills of medical students under the spotlight. However, before they can be placed on hospital frontlines, it is crucial to assess their preparedness for patient interaction. This can be achieved by comparing their behavioral dynamics to those of physicians. An online questionnaire was administered between March 20, 2020 and March 27, 2020. The preventive strategies adopted by medical students and physicians at different ages and levels of education were compared by using chi-square test where a p value of <0.05 was considered statistically significant. We report that the demonstration of preventive behaviors increased with educational attainment and age. Older age groups avoided crowded areas, wore more masks, used disinfectants and did not touch their faces as compared to the younger participants (p < 0.001). Similarly, postgraduate doctors used more masks and disinfectants as compared to graduate doctors and medical students (p < 0.001). Based on our results, the lack of preventive behavior shown by medical students has implications for policy makers. We recommend shortand long-term changes to medical programs and admissions policies to equip medical students with the personal and professional skills to better contribute to the healthcare system in the present pandemic and beyond.

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The COVID-19 pandemic is one of unmatched scale and severity. A continued state of crisis has been met with poor public adherence to preventive measures and difficulty implementing public health policy. This study aims to identify and evaluate the factors underlying such a response. Thus, it assesses the knowledge, perceived risk, and trust in the sources of information in relation to the novel coronavirus disease at the outset of the COVID-19 pandemic. An online questionnaire was completed between March 20 and 27, 2020. Knowledge, perceptions, and perceived risk (Likert scale) were assessed for 737 literate participants of a representative sample in an urban setting. We found that respondents’ risk perception for novel coronavirus disease was high. The perceived risk score for both cognitive and affective domains was raised at 2.24 ± 1.3 (eight items) and 3.01 ± 1 (seven items) respectively. Misconceptions and gaps in knowledge regarding COVID-19 were noted. Religious leadership was the least trusted (10%) while health authorities were the most trusted (35%) sources of information. Our findings suggest that there was a deficiency in knowledge and high concern about the pandemic, leading to a higher risk perception, especially in the affective domain. Thus, we recommend comprehensive education programs, planned intensive risk communication, and a concerted effort by all stakeholders to mitigate the spread of disease. The first of its kind in the region, this study will be critical to response efforts against current and future outbreaks.

Objective: To explore the perceptions of the dental faculty regarding the changes required with regards to subjects, the teaching methodology, assessment and innovative recommendations in Pakistan. Study Design: Qualitative Research Project. Setting: Riphah International University, Rawalpindi. Period: February 2019 till July 2019. Material & Methods: In which 13 dental faculty members with post graduate degrees in Medical/Dental Education in addition to the Dental Specialties were selected via a purposive sampling technique for semi-structured one to one interview.  Data was collected from 8 various institutes. The protocol for thematic data analysis was utilized. Explanatory, exploratory and interpretative approaches of content analysis were employed to screen out prominent and relevant concepts and emergent themes. Results: Participants reported that many new subjects need to be incorporated. A uniform curriculum all over the country is required. The content of subjects needs to be aligned to the needs of the community. Need analysis is to be carried out at regulatory body level for what sort of General Dentists are to be produced in the country who can later work in other parts of the world, if they desire. Conclusion: Course content needs to be is aligned with rest of the world and community. Teaching and Learning Strategies should be reevaluated with the present day needs of the curriculum. Curriculum must be patient centered and inculcate the local needs of the community.