Is My Model Up-to-date? Detecting CoViD-19 Variants by Machine Learning

Machine learning extracts models from huge quantities of data. Models trained and validated over past data can be deployed in making forecasts as well as in classifying new incoming data. The real world which generates data may change over time, making the deployed model an obsolete one. To preserve the quality of the currently deployed model, continuous machine learning is required. Our approach retrospectively evaluates in an online fashion the behaviour of the currently deployed model. A drift detector detects any performance slump, and, in case, can replace the previous model with an up-to-date one. The approach experiments on a dataset of 8642 hematochemical examinations from hospitalized patients gathered over 6 months: the outcome of the model predicts the RT-PCR test result about CoViD-19. The method reached an area under the curve (AUC) of 0.794, 6% better than offline and 5% better than standard online-binary classification techniques. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Is My Model Up-to-date? Detecting CoViD-19 Variants by Machine Learning

Machine learning extracts models from huge quantities of data. Models trained and validated over past data can be deployed in making forecasts as well as in classifying new incoming data. The real world which generates data may change over time, making the deployed model an obsolete one. To preserve the quality of the currently deployed model, continuous machine learning is required. Our approach retrospectively evaluates in an online fashion the behaviour of the currently deployed model. A drift detector detects any performance slump, and, in case, can replace the previous model with an up-to-date one. The approach experiments on a dataset of 8642 hematochemical examinations from hospitalized patients gathered over 6 months: the outcome of the model predicts the RT-PCR test result about CoViD-19. The method reached an area under the curve (AUC) of 0.794, 6% better than offline and 5% better than standard online-binary classification techniques. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Family Tensions and Information Privacy: A Barrier to Diffusion of Proximity Tracing Applications?

Technology played a central role during the pandemic for communications and services. It was also touted as a potential solution to control the spread of COVID-19 via proximity tracing applications, also known as contact tracing (CT) apps worldwide. In non-mandated settings, however, these apps did not attain popularity. Privacy concerns were highlighted as one reason. We explored how family perceptions of CT apps can affect the family's use of such apps. We surveyed parent-teen dyads twice over a 5-month period. We analyzed parent-teen perceptions of each other's intentions and use of CT apps at time 1 and 2, exploring changes over time. Parents' use intentions were influenced by their and their teens' perceptions of the benefits but not privacy concerns. Teen intentions were influenced by their own perceptions of benefits, not their parent's, and their parent's concerns for the family. Intentions always influenced usage, including intentions at time 1 influencing use at time 2, demonstrating a longitudinal effect of intentions on usage existed for parents and teens. © 2022 IEEE Computer Society. All rights reserved.

A Lean Approach for Reducing Downtimes in Healthcare: A Case Study

Lean Management is considered one of the most successful management paradigms for enhancing operational performance in the manufacturing environment. However, it has been applied throughout the years to several sectors and organisational areas, such as service, healthcare, and office departments. After the Covid-19 outbreak, increasing attention has been given to potential performance improvements in healthcare organisations by leveraging Lean. This paper intends to add further knowledge to this field by presenting a case study in a hospital. In this paper, a pilot project is presented carried out in a healthcare organisation. Lean methods were used to improve the operating room performance, particularly by reducing the operating room changeover time. The A3 template was used to drive the project and implement a new procedure using the Single Minute Exchange of Die (SMED) method. With the implementation of the new procedure, the changeover time between two different surgeries in the operating room was significantly reduced, together with a more stable and reliable process. © 2023, IFIP International Federation for Information Processing.

Viewing the changes in community annoyance due to aircraft noise over the times

More than 60 years have passed since the introduction of jet aircraft to civil aviation, and technological innovations have made aircraft much quieter. Nevertheless, people still complain that they experience severe suffering from aircraft noise. The changes in lifestyles, values concerning the sound environment, and aircraft operating conditions including the air traffic control system, over time, may have influenced the differences in annoyance responses. This paper overviews and considers the changes over time in the aircraft sound exposure level around the airport and the community annoyance caused by aircraft noise. Then it discusses the issue of recent noise complaints associated with the introduction of new air traffic management systems and flight routes as well as views the impact of coronavirus pandemic over the last two years or longer. Finally, it gives a minor consideration to how we should deal with these changes in the annoyance response. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.

Detecting Drift in Healthcare AI Models Based on Data Availability

There is an increasing interest in the use of AI in healthcare due to its potential for diagnosis or disease prediction. However, healthcare data is not static and is likely to change over time leading a non-adaptive model to poor decision-making. The need of a drift detector in the overall learning framework is therefore essential to guarantee reliable products on the market. Most drift detection algorithms consider that ground truth labels are available immediately after prediction since these methods often work by monitoring the model performance. However, especially in real-world clinical contexts, this is not always the case as collecting labels is often more time consuming as requiring experts' input. This paper investigates methodologies to address drift detection depending on which information is available during the monitoring process. We explore the topic within a regulatory standpoint, showing challenges and approaches to monitoring algorithms in healthcare with subsequent batch updates of data. This paper explores three different aspects of drift detection: drift based on performance (when labels are available), drift based on model structure (indicating causes of drift) and drift based on change in underlying data characteristics (distribution and correlation) when labels are not available. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Topic Modeling for Tracking COVID-19 Communication on Twitter

In this study, we analyze the trends of COVID-19 related communication in Croatian language on Twitter. First, we prepare a dataset of 147,028 tweets about COVID-19 posted during the first three waves of the pandemic, and then perform an analysis in three steps. In the first step, we train the LDA model and calculate the coherence values of the topics. We identify seven topics and report the ten most frequent words for each topic. In the second step, we analyze the proportion of tweets in each topic and report how these trends change over time. In the third step, we study spreading properties for each topic. The results show that all seven topics are evenly distributed across the three pandemic waves. The topic “vaccination” stands out with the change in percentage from 14.6% tweets in the first wave to 25.7% in the third wave. The obtained results contribute to a better understanding of pandemic communication in social media in Croatia. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Feedback SAIR Model for the Spread of Infectious Disease with Application to COVID-19 Pandemic

The COVID-19 global pandemic has highlighted the importance of identifying effective ways to control the spread of an infectious disease in a population. A solid understanding of the dynamics and the underlying mechanisms that govern this spread is an important step toward such a goal. Susceptible-Asymptomatic-Infected-Recovered (SAIR) models and their variants have played an important role in providing such insight. However, these models have limited explanatory and predictive power due to policy and behavior changes over time. In this paper we introduce a feedback version of the SAIR model by introducing feedback in the disease transmission rate. We apply this model to publicly available COVID-19 infection data. We show this model better captures the dynamics of the disease spread and has much better explanatory and predictive power. Our analysis suggests that public health policies based on daily infection numbers can be more effective than policies based on estimations of infection levels. © 2022 American Automatic Control Council.

An unsupervised framework for tracing textual sources of moral change

Morality plays an important role in social wellbeing, but people's moral perception is not stable and changes over time. Recent advances in natural language processing have shown that text is an effective medium for informing moral change, but no attempt has been made to quantify the origins of these changes. We present a novel unsupervised framework for tracing textual sources of moral change toward entities through time. We characterize moral change with probabilistic topical distributions and infer the source text that exerts prominent influence on the moral time course. We evaluate our framework on a diverse set of data ranging from social media to news articles. We show that our framework not only captures fine-grained human moral judgments, but also identifies coherent source topics of moral change triggered by historical events. We apply our methodology to analyze the news in the COVID-19 pandemic and demonstrate its utility in identifying sources of moral change in high-impact and real-time social events. © 2021 Association for Computational Linguistics.

Vaccinating a population is a changing programming problem

How best to apply vaccines to a population is an open problem. It is trivial to derive intuitive strategies, but until tested, their efficacy i s n ot k nown. T his p roblem i s particularly challenging when considering the dynamics of social contact networks and their changes over time. A system for automatically discovering tested vaccination strategies with evolutionary computation has been improved upon to include additional graph metrics and to generate vaccination strategies for dynamic graphs, something that is expected of real social networks within communities. The system’s ability to generate effective strategies was demonstrated along with a comparison of the strategies developed when fit t o a s tatic g raph v ersus a d ynamic g raph. I t w as observed that the additional computational resources required to generate strategies on a dynamic graph may not be necessary as strategies developed for static graphs performed similarly well;however, the authors are careful to acknowledge that results may differ significantly w hen a djusting t he s ystems m any parameters. © IEEE 2021.

What we Talk about when we Talk about Earth on Earth Day?

April 22, 2021, marked the 51st anniversary of Earth Day. With the growing imperativeness of environmental protection and sustainability, we want to study people's collective attention and conversations on this themed day. What are the top-of-mind discourses and central topics about the earth? How do people feel about them, hopeful or pessimistic? How do they change over time, especially after the COVID-19 pandemic? To answer these, we extracted and quantified top frequent features, co-occurring hashtags, sentiment words, and latent sub-topics from about 300K tweets posted on the Earth Day of 2009, 2013, 2017, and 2021. The results demonstrated the longitudinal dynamics of people's rhetoric and focus regarding protecting the earth - from resources conservation to climate changes, as well as the plummeted optimism toward environmental topics after the pandemic. The findings of our paper can help decision-makers to better assess the "voices of the people"and inform evidence-based decision-making. © 2021 ACM.

Analysis of Ammonia in Kali Lamong River Estuary Surabaya during Pandemic Covid-19

Water quality monitoring is an important instrument in the management of freshwater resources because they offer essential information about the physical, chemical, and biological water resources status, determining patterns and changes over time, and identifying emerging water quality issues especially in a specific situation. This study investigates the ammonia concentration in Kali Lamong river estuaries Surabaya to comprehensive the level of pollution that occurs during pandemic Covid-19. This research was conducted in the river downstream of Kali Lamong in the dry season. Sampling has occurred in 3 stations. Each station has 3 sampling sites that were ¼ of the left side, ½ from the side of the left, and ¼ of the right side. The measurement ammonia in water was measured by SNI 06-6989.30-2005 method. The laboratory result depicted the highest of ammonia concentration (0.765 mg/L) at B1 site. The ammonia concentration in water was <0.02 to 0.13 mg/L in another site. The water sampling result was classified based on PP number 22 of 2021 implementation of protection and management of environment in sixth appendix about national water quality standard with third-class purpose. © 2021 Institute of Physics Publishing. All rights reserved.