All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing.

The conversion rate between asymptomatic infections and reported/unreported symptomatic infections is a very sensitive parameter for model variables that spread COVID-19. This is important information for follow-up use in screening, prediction, prognostics, contact tracing, and drug development for the COVID-19 pandemic. The model described here suggests that there may not be enough researchers to solve all of these problems thoroughly and effectively, and it requires careful selection of what we are doing and rapid sharing of results and models and optimizing modeling simulations with value to reduce the impact of COVID-19. Exploring simulation modeling will help decision makers make the most informed decisions. In order to fight against the "Delta" virus, the establishment of a line of defense through all-people testing (APT) is not only an effective method summarized from past experience but also one of the best means to effectively cut the chain of epidemic transmission. The effect of large-scale testing has been fully verified in the international community. We developed a practical dynamic infectious disease model-SETPG (A + I) RD + APT by considering the effects of the all-people test (APT). The model is useful for studying effects of screening measures and providing a more realistic modelling with all-people-test strategies, which require everybody in a population to be tested for infection. In prior work, a total of 370 epidemic cases were collected. We collected three kinds of known cases: the cumulative number of daily incidences, daily cumulative recovery, and daily cumulative deaths in Hong Kong and the United States between 22 January 2020 and 13 November 2020 were simulated. In two essential strategies of the integrated SETPG (A + I) RD + APT model, comparing the cumulative number of screenings in derivative experiments based on daily detection capability and tracking system application rate, we evaluated the performance of the timespan required for the basic regeneration number (R0) and real-time regeneration number (R0t) to reach 1; the optimal policy of each experiment is available, and the screening effect is evaluated by screening performance indicators. with the binary encoding screening method, the number of screenings for the target population is 8667 in HK and 1,803,400 in the U.S., including 6067 asymptomatic cases in HK and 1,262,380 in the U.S. as well as 2599 cases of mild symptoms in HK and 541,020 in the U.S.; there were also 8.25 days of screening timespan in HK and 9.25 days of screening timespan required in the U.S. and a daily detectability of 625,000 cases in HK and 6,050,000 cases in the U.S. Using precise tracking technology, number of screenings for the target population is 6060 cases in HK and 1,766,420 cases in the U.S., including 4242 asymptomatic cases in HK and 1,236,494 cases in the U.S. as well as 1818 cases of mild symptoms in HK and 529,926 cases in the U.S. Total screening timespan (TS) is 8.25~9.25 days. According to the proposed infectious dynamics model that adapts to the all-people test, all of the epidemic cases were reported for fitting, and the result seemed more reasonable, and epidemic prediction became more accurate. It adapted to densely populated metropolises for APT on prevention.

Empowering multi-class medical data classification by Group-Of-Single-Class-predictors and transfer optimization: Cases of structured dataset by machine learning and radiological images by deep learning

In the medical domain, data are often collected over time, evolving from simple to refined categories. The data and the underlying structures of the medical data as to how they have grown to today’s complexity can be decomposed into crude forms when data collection starts. For instance, the cancer dataset is labeled either benign or malignant at its simplest or perhaps the earliest form. As medical knowledge advances and/or more data become available, the dataset progresses from binary class to multi-class, having more labels of sub-categories of the disease added. In machine learning, inducing a multi-class model requires more computational power. Model optimization is enforced over the multi-class models for the highest possible accuracy, which of course, is necessary for life-and-death decision making. This model optimization task consumes an extremely long model training time. In this paper, a novel strategy called Group-of-Single-Class prediction (GOSC) coupled with majority voting and model transfer is proposed for achieving maximum accuracy by using only a fraction of the model training time. The main advantage is the ability to achieve an optimized multi-class classification model that has the highest possible accuracy near to the absolute maximum, while the training time could be saved by up to 70%. Experiments on machine learning over liver dataset classification and deep learning over COVID19 lung CT images were tested. Preliminary results suggest the feasibility of this new approach.

Virtual care for cyber physical systems (VH_CPS): NODE-RED, community of practice and thick data analytics ecosystem

Virtual care is changing from the mere Face-to-Face Services, which includes telephone visits in addition to audio and video visits to include several non Face-to-Face Services, including E-visits, community of practice consultations and remote Patient Monitoring (RPM). Virtual care has the capacity to transform how people interact and collaborate with each other using the cyber physical systems’ services. However, the use of cyber physical systems (CPS) in healthcare is still in its infancy and there exist many challenges to be solved. The future of virtual care based on CPS will be require the availability of an ecosystem that leverages range of technologies to enable care to shift away from the legacy clinical setting when appropriate. In order to provide such open loop type of connectivity and interfacing, this article presents a solution VH_CPS ecosystem that push beyond the telehealth visit to create an ecosystem that integrates the care team collaboration with other essential virtual services such as remote monitoring and diagnostics as well as chronic care management services. Moreover, this article incorporate at VH_CPS ecosystem a component that enables the care team to assist in driving more in depth analytics based on qualitative techniques borrowed from the paradigm of thick data analytics. The Siamese Neural Network used in this paper is an example where care team members like a radiologist can feed few labelled CT-Scans to let the decision making component of the VH_CPS to learn the diagnosis of COVID cases. Our VH_CPS ecosystem uses a scalable Node-RED framework with the care team as well as to the remote patient monitoring devices. Our ecosystem will enable more patients to access the care they need, keep more patients in a low-risk care setting, and contribute to better outcomes at lower costs.

Envisioning Insight-Driven Learning Based on Thick Data Analytics With Focus on Healthcare.

Detecting and analyzing patient insights from social media enables healthcare givers to better understand what patients want and also to identify their pain points. Healthcare institutions cannot neglect the need to monitor and analyze popular social media outlets such as Twitter and Facebook. To have a study success, a healthcare giver needs to be able to engage with their patients and adapt to their preferences effectively. However, data-driven decision-making is no longer enough, as the best-in-class organizations struggle to realize tangible benefits from their data-driven analytics investments. Relying on simplistic textual analytics that use big data technologies to learn consumer/patient insights is no longer sufficient as most of these analytics utilize sort of bag-of-words counting algorithms. The majority of projects utilizing big data analytics have failed due to the obsession with metrics at the expense of capturing the customer's perspective data, as well as the failure in turning consumer insights into actions. Most of the consumer insights can be captured with qualitative research methods that work with small, even statistically insignificant, sample sizes. Employing qualitative analytics provide some kind of actionable intelligence which acquires understanding to broad questions about the consumer needs in tandem with analytical power. Generating insight, on one hand, requires sound techniques to measure consumers' engagement more precisely and offers depth analytics to the consumer data story. On the other hand, turning relevant insights into actions requires incorporating actionable intelligence across the business by verify hypotheses based on qualitative findings by using web analytics to see if these axioms apply to a large number of customers. The first component of our visionary approach is dedicated to identifying the relationships between constituents of the healthcare pain points as echoed by the social media conversation in terms of sociographic network where the elements composing these conversations are described as nodes and their interactions as links. In this part, conversation groups of nodes that are heavily connected will be identified representing what we call conversation communities. By identifying these conversation communities several consumer hidden insights can be inferred from using techniques such as visualizing conversation graphs relevant to given pain point, conversation learning from question answering, conversations summaries, conversation timelines, conversation anomalies and other conversation pattern learning techniques. These techniques will identify and learn the patient insights without forgetting from the context of conversation communities, are tagged as "thick data analytics". Additionally machine learning methods can be used as assistive techniques to learn from the identified thick data and build models around identified thick data. With the use of transfer learning we also can fine tune these models with the arrival of new conversations. The author is currently experimenting with these seven insights driven learning methods described in this paper with massive geo-located Twitter data to infer the quality of care related to the current COVID-19 outbreak.