Using internet search data to predict new HIV diagnoses in China: a modelling study.

ObjectivesInternet data are important sources of abundant information regarding HIV epidemics and risk factors. A number of case studies found an association between internet searches and outbreaks of infectious diseases, including HIV. In this research, we examined the feasibility of using search query data to predict the number of new HIV diagnoses in China.DesignWe identified a set of search queries that are associated with new HIV diagnoses in China. We developed statistical models (negative binomial generalised linear model and its Bayesian variants) to estimate the number of new HIV diagnoses by using data of search queries (Baidu) and official statistics (for the entire country and for Guangdong province) for 7 years (2010 to 2016).ResultsSearch query data were positively associated with the number of new HIV diagnoses in China and in Guangdong province. Experiments demonstrated that incorporating search query data could improve the prediction performance in nowcasting and forecasting tasks.ConclusionsBaidu data can be used to predict the number of new HIV diagnoses in China up to the province level. This study demonstrates the feasibility of using search query data to predict new HIV diagnoses. Results could potentially facilitate timely evidence-based decision making and complement conventional programmes for HIV prevention

A demand-driven approach for a multi-agent system in Supply Chain Management

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit. © 2010 Springer-Verlag Berlin Heidelberg

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

National Multi-Modal Travel Forecasts. Literature Review: Aggregate Models

This paper reviews the current state-of-the-art in the production of National Multi-Modal Travel Forecasts. The review concentrates on the UK travel market and the various attempts to produce a set of accurate, coherent and credible forecasts. The paper starts by a brief introduction to the topic area. The second section gives a description of the background to the process and the problems involved in producing forecasts. Much of the material and terminology in the section, which covers modelling methodologies, is from Ortúzar and Willumsen (1994). The paper then goes on to review the forecasting methodology used by the Department of Transport (DoT) to produce the periodic National Road Traffic Forecasts (NRTF), which are the most significant set of travel forecasts in the UK. A brief explanation of the methodology will be given. The next section contains details of how other individuals and organisations have used, commented on or attempted to enhance the DoT methodology and forecasts. It will be noted that the DoT forecasts are only concerned with road traffic forecasts, with other modes (rail, air and sea) only impacting on these forecasts when there is a transfer to or from the road transport sector. So the following sections explore the attempts to produce explicit travel and transportation forecasts for these other modes. The final section gathers together a set of issues which are raised by this review and might be considered by the project

National Multi-Modal Travel Forecasts. Literature Review: Aggregate Models

This paper reviews the current state-of-the-art in the production of National Multi-Modal Travel Forecasts. The review concentrates on the UK travel market and the various attempts to produce a set of accurate, coherent and credible forecasts. The paper starts by a brief introduction to the topic area. The second section gives a description of the background to the process and the problems involved in producing forecasts. Much of the material and terminology in the section, which covers modelling methodologies, is from Ortúzar and Willumsen (1994). The paper then goes on to review the forecasting methodology used by the Department of Transport (DoT) to produce the periodic National Road Traffic Forecasts (NRTF), which are the most significant set of travel forecasts in the UK. A brief explanation of the methodology will be given. The next section contains details of how other individuals and organisations have used, commented on or attempted to enhance the DoT methodology and forecasts. It will be noted that the DoT forecasts are only concerned with road traffic forecasts, with other modes (rail, air and sea) only impacting on these forecasts when there is a transfer to or from the road transport sector. So the following sections explore the attempts to produce explicit travel and transportation forecasts for these other modes. The final section gathers together a set of issues which are raised by this review and might be considered by the project

Mining Reaction and Diffusion Dynamics in Social Activities

Large quantifies of online user activity data, such as weekly web search volumes, which co-evolve with the mutual influence of several queries and locations, serve as an important social sensor. It is an important task to accurately forecast the future activity by discovering latent interactions from such data, i.e., the ecosystems between each query and the flow of influences between each area. However, this is a difficult problem in terms of data quantity and complex patterns covering the dynamics. To tackle the problem, we propose FluxCube, which is an effective mining method that forecasts large collections of co-evolving online user activity and provides good interpretability. Our model is the expansion of a combination of two mathematical models: a reaction-diffusion system provides a framework for modeling the flow of influences between local area groups and an ecological system models the latent interactions between each query. Also, by leveraging the concept of physics-informed neural networks, FluxCube achieves high interpretability obtained from the parameters and high forecasting performance, together. Extensive experiments on real datasets showed that FluxCube outperforms comparable models in terms of the forecasting accuracy, and each component in FluxCube contributes to the enhanced performance. We then show some case studies that FluxCube can extract useful latent interactions between queries and area groups.Comment: Accepted by CIKM 202

Risk Management in the Arctic Offshore: Wicked Problems Require New Paradigms

Recent project-management literature and high-profile disasters—the financial crisis, the BP Deepwater Horizon oil spill, and the Fukushima nuclear accident—illustrate the flaws of traditional risk models for complex projects. This research examines how various groups with interests in the Arctic offshore define risks. The findings link the wicked problem framework and the emerging paradigm of Project Management of the Second Order (PM-2). Wicked problems are problems that are unstructured, complex, irregular, interactive, adaptive, and novel. The authors synthesize literature on the topic to offer strategies for navigating wicked problems, provide new variables to deconstruct traditional risk models, and integrate objective and subjective schools of risk analysis