PEACOCK: A Map-Based Multitype Infectious Disease Outbreak Information System

A Detection-Service-Mobile Three-Terminal Software Platform for Point-of-Care Infectious Disease Detection System

The traditional infectious disease detection process is cumbersome, and there is only a single application scenario. In recent years, with the development of the medical industry and the impact of the epidemic situation, the number of infectious disease detection instruments based on nursing point detection has been increasing. Due to this trend, many detection instruments and massive detection data urgently need to be managed. In addition, the experiment failed due to the abnormal fluorescence curve generated by a human operator or sample impurities. Finally, the geographic information system has also played an active role in spreading and preventing infectious diseases; this paper designs a “detection-service-mobile” three-terminal system to realize the control of diagnostic instruments and the comprehensive management of data. Machine learning is used to classify the enlarged curve and calculate the cycle threshold of the positive curve; combined with a geographic information system, the detection results are marked on the mobile terminal map to realize the visual display of the positive results of nucleic acid amplification detection and the early warning of infectious diseases. In the research, applying this system to portable field pathogen detection is feasible and practical.

Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices

The COVID-19 disease has once again reiterated the impact of pandemics beyond a biomedical event with potential rapid, dramatic, sweeping disruptions to the management, and conduct of everyday life. Not only the rate and pattern of contagion that threaten our sense of healthy living but also the safety measures put in place for containing the spread of the virus may require social distancing. Three different measures to counteract this pandemic situation have emerged, namely: (i) vaccination, (ii) herd immunity development, and (iii) lockdown. As the first measure is not ready at this stage and the second measure is largely considered unreasonable on the account of the gigantic number of fatalities, a vast majority of countries have practiced the third option despite having a potentially immense adverse economic impact. To mitigate such an impact, this paper proposes a data-driven dynamic clustering framework for moderating the adverse economic impact of COVID-19 flare-up. Through an intelligent fusion of healthcare and simulated mobility data, we model lockdown as a clustering problem and design a dynamic clustering algorithm for localized lockdown by taking into account the pandemic, economic and mobility aspects. We then validate the proposed algorithms by conducting extensive simulations using the Malaysian context as a case study. The findings signify the promises of dynamic clustering for lockdown coverage reduction, reduced economic loss, and military unit deployment reduction, as well as assess potential impact of uncooperative civilians on the contamination rate. The outcome of this work is anticipated to pave a way for significantly reducing the severe economic impact of the COVID-19 spreading. Moreover, the idea can be exploited for potentially the next waves of corona virus-related diseases and other upcoming viral life-threatening calamities.

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

For a long time, various epidemics, such as lower respiratory infections and diarrheal diseases, have caused serious social losses and costs. Various methods for analyzing infectious disease occurrences have been proposed for effective prevention and proactive response to reduce such losses and costs. However, the results of the occurrence analyses were limited because numerous factors affect the outbreak of infectious diseases and there are complex interactions between these factors. To alleviate this limitation, we propose a cluster-based analysis scheme of infectious disease occurrences that can discover commonalities or differences between clusters by grouping elements with similar occurrence patterns. To do this, we collect and preprocess infectious disease occurrence data according to time, region, and disease. Then, we construct a tensor for the data and apply Tucker decomposition to extract latent features in the dimensions of time, region, and disease. Based on these latent features, we conduct k-means clustering and analyze the results for each dimension. To demonstrate the effectiveness of this scheme, we conduct a case study on data from South Korea and report some of the results.