ABSTRACT
With the global outbreak of COVID-19, hundreds of pneumonias caused by cold chain products occurred worldwide, which seriously threatened the safety of people's lives and properties. To effectively prevent product quality problems caused by cold chain logistics, it is urgent to establish a cold chain logistics traceability system with interoperability of heterogeneous systems, to record, share and track the temperature, location, time, and other specific information. The traditional cold chain logistics traceability systems have many problems, such as broken cold chains, untrustworthy data, and data tampering and sharing, which hinder the coordination and interaction efficiency of cold chain logistics traceability data. This paper creatively proposes a cold chain logistics traceability system framework based on the identification and resolution system for the Industrial Internet. It establishes a general cold chain logistics traceability identification data model. The system framework and data model can effectively solve the difficulties of multi-code identification and multi-source heterogeneous system interaction, to improve the efficiency of cold chain logistics traceability, and ensure the quality of cold chain logistics products. © 2022 ACM.
ABSTRACT
The government addresses that one of the biggest problems in the country is lacking an effective contact tracing solution. The Philippines' current contact tracing systems have encountered a lot of challenges because of the lack of features that would ensure safety and awareness to users around. The study aims to propose a system framework that can be used as Contact Tracing Solution using data warehousing and edge computing would improve the tracing in small and concentrated areas such as universities and offices. The researchers gather reviews and studies on how to develop a system that would address the current problem in the contact tracing scenario in the Philippines, particularly in the education field. The researcher will be going to apply the descriptive and development design for the conduct of the study and the waterfall methodology will be the software model for the development of the proposed system. Therefore, it is better to develop a contact tracing application that will be used by universities whose main objective is to spread awareness to potentially close contacts of a COVID-19 positive case and further implement the system framework to provide a proactive solution for contact tracing in the academe. © 2022 IEEE.
ABSTRACT
Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners.
ABSTRACT
Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners. © 2023 Institute of Advanced Engineering and Science. All rights reserved.
ABSTRACT
Bangladesh suffered disruptions in the utilization of essential health and nutrition services (EHNS) during the COVID-19 pandemic. The magnitude of the pandemic has been documented, but little is known from the perspectives of health administrators. A rapid qualitative assessment of division-level capacity identified successes and bottlenecks in providing EHNS- and COVID-19-related services during the first months of the pandemic in Bangladesh. Semi-structured interviews were held with the Health and Family Planning Divisional Directors of the Ministry of Health and Family Welfare. The Primary Health Care System Framework guided the content analysis, focusing on (i) service delivery, (ii) communication and community outreach, and (iii) surveillance and service monitoring. Our findings identified low care seeking due to fears of getting infected and unawareness that EHNS were still available. Adaptations to telemedicine were highly heterogeneous between divisions, but collaboration with NGOs were fruitful in reinstating outreach activities. Guidelines were centered on COVID-19 information and less so on EHNS. The inflexibility of spending capacities at divisional and clinic levels hindered service provision. Misinformation and information voids were difficult to handle all around the country. Community health workers were useful for outreach communication. EHNS must be guaranteed during sanitary emergencies, and Bangladesh presented with both significant efforts and areas of opportunity for improvement.