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1. Epidemiologically Optimal Static Networks from Temporal Network Data.

Author

Holme, Petter

Subjects
EPIDEMIOLOGY, INFECTIOUS disease transmission, PROBABILITY theory, PUBLIC health, BIG data, COMPUTER network resources
Abstract

One of network epidemiology's central assumptions is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures most relevant information is a so-called exponential-threshold network. In these, each contact contributes with a weight decreasing exponentially with time, and there is an edge between a pair of vertices if the weight between them exceeds a threshold. Networks of aggregated contacts over an optimally chosen time window perform almost as good as the exponential-threshold networks. On the other hand, networks of accumulated contacts over the entire sampling time, and networks of concurrent partnerships, perform worse. We discuss these observations in the context of the temporal and topological structure of the data sets. [ABSTRACT FROM AUTHOR]

Published
2013
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2. An automated alarm system for food safety by using electronic invoices

Author

Thai Son Dinh, Wan-Tzu Chang, Yen-Po Yeh, Iebin Lian, Hong-Yi Wu, and Yu-Fen Lin

Subjects
Decision Analysis, Food Safety, Invoice, Computer science, Text Mining, Big data, computer.software_genre, 01 natural sciences, Biochemistry, Machine Learning, Food chain, Automation, Medicine and Health Sciences, Food Chains, Data Mining, Public and Occupational Health, Multidisciplinary, Database, Ecology, 04 agricultural and veterinary sciences, 040401 food science, Lipids, Random forest, Medicine, Engineering and Technology, Safety, Information Technology, Management Engineering, Research Article, Computer and Information Sciences, Neural Networks, Science, Decision tree, Research and Analysis Methods, ALARM, 0404 agricultural biotechnology, Artificial Intelligence, Support Vector Machines, Edible oil, business.industry, 010401 analytical chemistry, Decision Trees, Ecology and Environmental Sciences, Biology and Life Sciences, Food safety, 0104 chemical sciences, Electronics, business, computer, Oils, Neuroscience
Abstract

Background Invoices had been used in food product traceability, however, none have addressed the automated alarm system for food safety by utilizing electronic invoice big data. In this paper, we present an alarm system for edible oil manufacture that can prevent a food safety crisis rather than trace problematic sources post-crisis. Materials and methods Using nearly 100 million labeled e-invoices from the 2013‒2014 of 595 edible oil manufacturers provided by Ministry of Finance, we applied text-mining, statistical and machine learning techniques to “train” the system for two functions: (1) to sieve edible oil-related e-invoices of manufacturers who may also produce other merchandise and (2) to identify suspicious edible oil manufacture based on irrational transactions from the e-invoices sieved. Results The system was able to (1) accurately sieve the correct invoices with sensitivity >95% and specificity >98% via text classification and (2) identify problematic manufacturers with 100% accuracy via Random Forest machine learning method, as well as with sensitivity >70% and specificity >99% through simple decision-tree method. Conclusion E-invoice has bright future on the application of food safety. It can not only be used for product traceability, but also prevention of adverse events by flag suspicious manufacturers. Compulsory usage of e-invoice for food producing can increase the accuracy of this alarm system.

Published
2020

3. A machine learning method to monitor China’s AIDS epidemics with data from Baidu trends

Author

Yongqing Nan and Yanyan Gao

Subjects
Viral Diseases, Index (economics), Computer science, Epidemiology, Big data, lcsh:Medicine, 010501 environmental sciences, computer.software_genre, 01 natural sciences, Geographical Locations, Machine Learning, Search engine, Database and Informatics Methods, 0302 clinical medicine, Mathematical and Statistical Techniques, Animal Cells, Medicine and Health Sciences, 030212 general & internal medicine, Computer Networks, lcsh:Science, Neurons, Multidisciplinary, AIDS, Variable (computer science), Infectious Diseases, HIV epidemiology, Physical Sciences, Information Retrieval, The Internet, Cellular Types, Statistics (Mathematics), Research Article, medicine.medical_specialty, Computer and Information Sciences, China, Asia, Machine learning, Research and Analysis Methods, 03 medical and health sciences, Acquired immunodeficiency syndrome (AIDS), Artificial Intelligence, medicine, Humans, Statistical Methods, Epidemics, Artificial Neural Networks, 0105 earth and related environmental sciences, Computational Neuroscience, Internet, Acquired Immunodeficiency Syndrome, business.industry, Public health, lcsh:R, Biology and Life Sciences, Computational Biology, Cell Biology, medicine.disease, Cellular Neuroscience, People and Places, lcsh:Q, Artificial intelligence, business, computer, Mathematics, Forecasting, Neuroscience
Abstract

Background AIDS is a worrying public health issue in China and lacks timely and effective surveillance. With the diffusion and adoption of the Internet, the ‘big data’ aggregated from Internet search engines, which contain users’ information on the concern or reality of their health status, provide a new opportunity for AIDS surveillance. This paper uses search engine data to monitor and forecast AIDS in China. Methods A machine learning method, artificial neural networks (ANNs), is used to forecast AIDS incidences and deaths. Search trend data related to AIDS from the largest Chinese search engine, Baidu.com, are collected and selected as the input variables of ANNs, and officially reported actual AIDS incidences and deaths are used as the output variable. Three criteria, the mean absolute percentage error, the root mean squared percentage error, and the index of agreement, are used to test the forecasting performance of the ANN method. Results Based on the monthly time series data from January 2011 to June 2017, this article finds that, under the three criteria, the ANN method can lead to satisfactory forecasting of AIDS incidences and deaths, regardless of the change in the number of search queries. Conclusions Despite the inability to self-detect HIV/AIDS through online searching, Internet-based data should be adopted as a timely, cost-effective complement to a traditional AIDS surveillance system.

Published
2018