Danhuai Guo

Effect of closure of live poultry markets on poultry-to-person transmission of avian influenza A H7N9 virus: an ecological study.

BACKGROUND Transmission of the novel avian influenza A H7N9 virus seems to be predominantly between poultry and people. In the major Chinese cities of Shanghai, Hangzhou, Huzhou, and Nanjing--where most human cases of infection have occurred--live poultry markets (LPMs) were closed in April, 2013, soon after the initial outbreak, as a precautionary public health measure. Our objective was to quantify the effect of LPM closure in these cities on poultry-to-person transmission of avian influenza A H7N9 virus. METHODS We obtained information about every laboratory-confirmed human case of avian influenza A H7N9 virus infection reported in the four cities by June 7, 2013, from a database built by the Chinese Center for Disease Control and Prevention. We used data for age, sex, location, residence type (rural or urban area), and dates of illness onset. We obtained information about LPMs from official sources. We constructed a statistical model to explain the patterns in incidence of cases reported in each city on the basis of the assumption of a constant force of infection before LPM closure, and a different constant force of infection after closure. We fitted the model with Markov chain Monte Carlo methods. FINDINGS 85 human cases of avian influenza A H7N9 virus infection were reported in Shanghai, Hangzhou, Huzhou, and Nanjing by June 7, 2013, of which 60 were included in our main analysis. Closure of LPMs reduced the mean daily number of infections by 99% (95% credibility interval 93-100%) in Shanghai, by 99% (92-100%) in Hangzhou, by 97% (68-100%) in Huzhou, and by 97% (81-100%) in Nanjing. Because LPMs were the predominant source of exposure to avian influenza A H7N9 virus for confirmed cases in these cities, we estimated that the mean incubation period was 3·3 days (1·4-5·7). INTERPRETATION LPM closures were effective in the control of human risk of avian influenza A H7N9 virus infection in the spring of 2013. In the short term, LPM closure should be rapidly implemented in areas where the virus is identified in live poultry or people. In the long term, evidence-based discussions and deliberations about the role of market rest days and central slaughtering of all live poultry should be renewed. FUNDING Ministry of Science and Technology, China; Research Fund for the Control of Infectious Disease; Hong Kong University Grants Committee; China-US Collaborative Program on Emerging and Re-emerging Infectious Diseases; Harvard Center for Communicable Disease Dynamics; and the US National Institutes of Health.

Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia

Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.

Geographical Analysis of the Distribution and Spread of Human Rabies in China from 2005 to 2011

Background Rabies is a significant public health problem in China in that it records the second highest case incidence globally. Surveillance data on canine rabies in China is lacking and human rabies notifications can be a useful indicator of areas where animal and human rabies control could be integrated. Previous spatial epidemiological studies lacked adequate spatial resolution to inform targeted rabies control decisions. We aimed to describe the spatiotemporal distribution of human rabies and model its geographical spread to provide an evidence base to inform future integrated rabies control strategies in China. Methods We geo-referenced a total of 17,760 human rabies cases of China from 2005 to 2011. In our spatial analyses we used Gaussian kernel density analysis, average nearest neighbor distance, Spatial Temporal Density-Based Spatial Clustering of Applications with Noise and developed a model of rabies spatiotemporal spread. Findings Human rabies cases increased from 2005 to 2007 and decreased during 2008 to 2011 companying change of the spatial distribution. The ANN distance among human rabies cases increased between 2005 and 2011, and the degree of clustering of human rabies cases decreased during that period. A total 480 clusters were detected by ST-DBSCAN, 89.4% clusters initiated before 2007. Most of clusters were mainly found in South of China. The number and duration of cluster decreased significantly after 2008. Areas with the highest density of human rabies cases varied spatially each year and in some areas remained with high outbreak density for several years. Though few places have recovered from human rabies, most of affected places are still suffering from the disease. Conclusion Human rabies in mainland China is geographically clustered and its spatial extent changed during 2005 to 2011. The results provide a scientific basis for public health authorities in China to improve human rabies control and prevention program.

A New Framework for Traffic Anomaly Detection

Trajectory data is becoming more and more popular nowadays and extensive studies have been conducted on trajectory data. One important research direction about trajectory data is the anomaly detection which is to find all anomalies based on trajectory patterns in a road network. In this paper, we introduce a road segment-based anomaly detection problem, which is to detect the abnormal road segments each of which has its “real” traffic deviating from its “expected” traffic and to infer the major causes of anomalies on the road network. First, a deviation-based method is proposed to quantify the anomaly of reach road segment. Second, based on the observation that one anomaly from a road segment can trigger other anomalies from the road segments nearby, a diffusionbased method based on a heat diffusion model is proposed to infer the major causes of anomalies on the whole road network. To validate our methods, we conduct intensive experiments on a large real-world GPS dataset of about 23,000 taxis in Shenzhen, China to demonstrate the performance of our algorithms.

WMS-Based Flow Mapping Services

Flow Mapping, also known as spatial interaction data visualization, has become widely used for exploratory spatiotemporal data analysis to understand complex spatial phenomena such as human migration, commercial trading, and social networks. The unitary flow mapping architecture, in which data storing, computing and representation are deployed in a single computer, is facing the challenges being brought from increasing data scale, higher timing demand, computing complexity of visual clutter detecting and integrating with other GIS and spatio-temporal analysis tools. In this paper, a novel 3-tiers flow mapping service architecture is proposed. In this architecture, flow data integration tier provide a unified data access interface for variant data sources; flow mapping models tier provide a computing resource pool to support different flow mapping algorithms and scalable computing capability; and result visualization tier to view map interactively. In this paper, we expand the OGC Web Map Services (WMS) standard protocol to support spatio-temporal interaction data visualization and analytics, and integrate WMS-based flow mapping service with other map resources by JavaScript toolkits in browsers. This architecture is validate to be improved in performance and scalability by three typical application cases.

Land cover change during a period of extensive landscape restoration in Ningxia Hui Autonomous Region, China

Environmental change has been a topic of great interest over the last century due to its potential impact on ecosystem services that are fundamental for sustainable development and human well-being. Here, we assess and quantify the spatial and temporal variation in land cover in Ningxia Hui Autonomous Region (NHAR), China. With high-resolution (30m) imagery from Landsat 4/5-TM and 8-OLI for the entire region, land cover maps of the region were created to explore local land cover changes in a spatially explicit way. The results suggest that land cover changes observed in NHAR from 1991 to 2015 reflect the main goals of a national policy implemented there to recover degraded landscapes. Forest, herbaceous vegetation and cultivated land increased by approximately 410,200ha, 708,600ha and 164,300ha, respectively. The largest relative land cover change over the entire study period was the increase in forestland. Forest growth resulted mainly from the conversion of herbaceous vegetation (53.8%) and cultivated land (30.8%). Accurate information on the local patterns of land cover in NHAR may contribute to the future establishment of better landscape policies for ecosystem management and protection. Spatially explicit information on land cover change may also help decision makers to understand and respond appropriately to emerging environmental risks for the local population.

An algorithm for event detection based on social media data

Abstract Online social network applications such as Twitter, Weibo, have played an important role in people’s life. There exists tremendous information in the tweets. However, how to mine the tweets and get valuable information is a difficult problem. In this paper, we design the whole process for extracting data from Weibo and develop an algorithm for the foodborne disease event detection. The detected foodborne disease information are then utilized to assist the restaurant recommendation. The experiments results show the effectiveness and efficiency of our method.

Mining Common Spatial-Temporal Periodic Patterns of Animal Movement

Advanced satellite tracking technologies enable biologists to track animal movements at finer spatial and temporal scales. The resulting long-term movement data is very meaningful for understanding animal activities. Periodic pattern analysis can provide insightful approach to reveal animal activity patterns. However, individual GPS data is usually incomplete and in limited lifespan. In addition, individual periodic behaviors are inherently complicated with many uncertainties. In this paper, we address the problem of mining periodic patterns of animal movements by combining multiple individuals with similar periodicities. We formally define the problem of mining common periodicity and propose a novel periodicity measure. We introduce the information entropy in the proposed measure to detect common period. Data incompleteness, noises, and ambiguity of individual periodicity are considered in our method. Furthermore, we mine multiple common periodic patterns by grouping periodic segments w.r.t. the detected period, and provide a visualization method of common periodic patterns by designing a cyclical filled line chart. To assess effectiveness of our proposed method, we provide an experimental study using a real GPS dataset collected on 29 birds in Qinghai Lake, China.

Vehicle delay series forecast based on trajectories of GPS tracked cabs

The vehicle delay serves as effective criteria for evaluating and optimizing the level of service of intersections in traffic. Various methods based on analytical models, simulations, or sensors like GPS have been proposed for estimating vehicle delay. However, the absence of large scale fine-grained GPS data limits its wide application. Taking advantages of available coarse-grained trajectories of GPS tracked cabs, this paper proposes alternative definition of vehicle delay and its time series in pursuit of capturing long-range characteristics of traffic. The forecast performance of several vehicle delay series analysis algorithms are compared. A new Robust TBATS algorithm is proposed to predict vehicle delay series with outliers. 8-month trajectories of data in the city of Shenzhen, P.R. China are utilized for quantified comparison. The test verifies the value of vehicle delay series. The proposed Robust TBATS algorithm is capable of automatically forecast and its forecast performance outperforms other baselines significantly. Furthermore, this paper demonstrates one of potential applications of vehicle delay series forecast in route planning.

A cloud Computation Architecture for Unconventional Emergency Management

With the development of technologies and the deterioration of natural environment, unconventional emergencies outbreak more unexpectedly and diffuse more quickly and broadly. Secondary and derived disasters increase, and the impacts tend to be indirect and tremendous. Emergency management decisions are facing great challenges, and have attracted great concerns from government departments, academia and industries. In recent years, as a service-oriented computing mode, the cloud computing technology brings advantage in information sharing, resource allocating, and distributed high-performance computing, which makes it a feasible solution to unconventional emergency management, research, quick response and decision support. In this paper, we propose a cloud computation architecture for unconventional emergency management, which involves the key technologies including computation resource pooling, scalable extension of computation resource and services and user-centroid service management. The proposed architecture supports multilevel demand in computation and storage resource by providing services such as virtual machine, big data storage, web information detection and spatio-temporal data visualization. Three experimental scenarios are designed to validate the improvement of decision support capabilities and emergency response speed.