Methods: We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators.
Results: The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images.
Conclusions: The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.
OBJECTIVE: The objective of this study was to develop and validate an observation checklist for assessing the hygiene and sanitation of food preparation areas in preschools.
METHODOLOGY: The study was conducted in Kota Bharu Kelantan from March 2021 to February 2022. The development of the observation checklist was conducted in four stages: (1) the construction of domains and items from the existing literature, (2) content validation by six experts (using the item-level content validity index (I-CVI) and the scale-level content validity index (S-CVI), (3) face validation by 10 experts (using the item-level face validity index (I-FVI) and the scale-level face validity index (S-FVI)), and (4) reliability analysis (using the intercorrelation coefficient (ICC)). Four assessors performed the reliability analysis at two preschools.
RESULTS: The initial draft of the checklist contained three domains and 57 items: building and facility (10 subdomains and 38 items), process control (four subdomains and 12 items), and food handlers (one subdomain and seven items). The I-CVI scores for building and facility, process control, and food handlers were 0.97, 1.00, and 1.00, respectively, indicating good relevancy of items. The S-CVI value was 1.0 for all domains, showing good relevance of the items. The I-FVI above 0.8 and S-FVI values above 0.9 for all domains imply that the participants easily understood the checklist. The ICC for each domain was 0.847 (95% CI 0.716-0.902) for the building facility and 1.0 for process control and food handler, and the ICC for the three domains combined was 0.848 (95% CI 0.772-0.904). The final validated checklist consists of three domains with 57 items.
CONCLUSION: The newly developed observation checklist is a valid and reliable tool for assessing the hygiene and sanitation of preschool food preparation areas.