Zhidong Cao

Sample surveying to estimate the mean of a heterogeneous surface: reducing the error variance through zoning

One of the major sources of uncertainty associated with geographical data in GIS arises when they are the outcome of a sampling process. It is well known that when sampling from a spatially autocorrelated homogeneous surface, stratification reduces the error variance of the estimator of the population mean. In this study, we evaluate the efficiency of different spatial sampling strategies when the surface is not homogeneous. When the surface is first-order heterogeneous (the mean of the surface varies across the map), we examine the effects of stratifying it into first-order homogeneous zones prior to the usual stratification for a systematic or stratified random sample. We investigate the effect of this form of spatial heterogeneity on the performance of different methods for estimating the population mean and its error variance. We do so by distinguishing between the real surface to be surveyed (ℜ), the sampling frame (ℑ) including the choice of zoning, and the statistical estimators (Ψ). The study shows that zoning improves estimator efficiency when sampling a heterogeneous surface. Systematic comparison provides rules of thumb for choice of sample design, sample statistics and uncertainty estimation, based on considering different spatial heterogeneities on real surfaces.

Epidemiological analysis, detection, and comparison of space-time patterns of Beijing hand-foot-mouth disease (2008-2012)

Background Hand, foot, and mouth disease (HFMD) mostly affects the health of infants and preschool children. Many studies of HFMD in different regions have been published. However, the epidemiological characteristics and space-time patterns of individual-level HFMD cases in a major city such as Beijing are unknown. The objective of this study was to investigate epidemiological features and identify high relative risk space-time HFMD clusters at a fine spatial scale. Methods Detailed information on age, occupation, pathogen and gender was used to analyze the epidemiological features of HFMD epidemics. Data on individual-level HFMD cases were examined using Local Indicators of Spatial Association (LISA) analysis to identify the spatial autocorrelation of HFMD incidence. Spatial filtering combined with scan statistics methods were used to detect HFMD clusters. Results A total of 157,707 HFMD cases (60.25% were male, 39.75% were female) reported in Beijing from 2008 to 2012 included 1465 severe cases and 33 fatal cases. The annual average incidence rate was 164.3 per 100,000 (ranged from 104.2 in 2008 to 231.5 in 2010). Male incidence was higher than female incidence for the 0 to 14-year age group, and 93.88% were nursery children or lived at home. Areas at a higher relative risk were mainly located in the urban-rural transition zones (the percentage of the population at risk ranged from 33.89% in 2011 to 39.58% in 2012) showing High-High positive spatial association for HFMD incidence. The most likely space-time cluster was located in the mid-east part of the Fangshan district, southwest of Beijing. Conclusions The spatial-time patterns of Beijing HFMD (2008–2012) showed relatively steady. The population at risk were mainly distributed in the urban-rural transition zones. Epidemiological features of Beijing HFMD were generally consistent with the previous research. The findings generated computational insights useful for disease surveillance, risk assessment and early warning.

Heterogeneous and Stochastic Agent-Based Models for Analyzing Infectious Diseases' Super Spreaders

The detection of super-spreading events in infectious disease is crucial for public health emergency response. Here, heterogeneous and stochastic agent-based models explore the mechanism of super-spreading events.

The novel H1N1 Influenza A global airline transmission and early warning without travel containments

A novel influenza A (H1N1) has been spreading worldwide. Early studies implied that international air travels might be key cause of a severe potential pandemic without appropriate containments. In this study, early outbreaks in Mexico and some cities of United States were used to estimate the preliminary epidemic parameters by applying adjusted SEIR epidemiological model, indicating transmissibility infectivity of the virus. According to the findings, a new spatial allocation model totally based on the real-time airline data was established to assess the potential spreading of H1N1 from Mexico to the world. Our estimates find the basic reproductive number R0 of H1N1 is around 3.4, and the effective reproductive number fall sharply by effective containment strategies. The finding also implies Spain, Canada, France, Panama, Peru are the most possible country to be involved in severe endemic H1N1 spreading.

Virtual city: An individual-based digital environment for human mobility and interactive behavior

Increasing computing capability and high-resolution digital tracing of human behavior make large-scale computational models for individual-based realistic simulation available. Reconstructing a virtual computational environment is crucial for designing and implementing individual interactions in an artificial society as human beings behave in the real world. In this paper, we propose a methodology to recreate a virtual city by utilizing statistical data and geographic information. The synthetic population and physical environment are baseline components of the virtual city. Individual-based modeling is used to specify individuals’ demographic characteristics, and each individual is endowed with heterogeneous social attributes. Various physical environments are generated with geographic locations and mapped with individuals to support daily mobility, migration, and interaction. A series of algorithms are proposed to bridge the gap between macroscopic data and microscopic models, and guarantee equivalence between them. Based on the methodology, we reconstructed a virtual city of Beijing, and presented the statistical analysis of population structure, spatial distribution of physical environments, human travel characteristics, and spatial topologies of social networks. Our synthetic population can represent individual actors in the form of households and household members, and the synthetic population is statistically equivalent to a real population. The proposed methodology is efficient to recreate a synthetic virtual city and can serve as a base for computational experiments.

Technological Challenges in Emergency Response [Guest editors' introduction]

The guest editors discuss some recent advances in using intelligent systems for emergency management, as well as remaining technological challenges.

A Framework for Generating Geospatial Social Computing Environments

Computational social science plays an important role in emergency management from a quantitative perspective. Reconstructing an individual-based social computing environment is crucial for both accurate computational experiments and determining optimal decisions. Here, the authors propose a formalization method to define basic componential models in the artificial society and the inner logic in these models. A detailed generation process is presented, and multisource statistical data, social interactive behavior, and multilayer social networks are integrated together. As an evaluation of the proposed framework, a virtual city of Beijing is reconstructed. Each citizen is endowed with demographic attributes, including age, gender, social role, correlated geographic locations, and multiple social relations. The generated synthetic population is statistically equivalent to the real population. Transmission experiments of influenza are performed in the reconstructed computational environment, and individual daily interacting behavior is tracked and analyzed. The results indicate that the framework can provide an effective methodology to reconstruct the computing environment in high resolution by using low-resolution statistical data, leading to better prediction and management of emergencies.

Using multi-source web data for epidemic surveillance: A case study of the 2009 Influenza A (H1N1) pandemic in Beijing

Timely and effective surveillance is critical for the prevention and control of epidemics. However, due to technical challenges and shortage of human resources, comprehensive and timely data collection required for effective surveillance, especially collection of data about sudden epidemic outbreaks, is still very difficult. In this paper, we propose the use of multi-source web data for epidemic surveillance. We use the 2009 Influenza A (H1N1) pandemic in Beijing as a case study to demonstrate the utility of our proposed approach. Experiments using data from the Beijing Center for Disease Control and Prevention (CDC) and several search engines show encouraging results. This case study also has direct practical values in the real setting.

A geospatial analysis on the potential value of news comments in infectious disease surveillance

With the development of Internet, widely kind of web data have been applied in influenza surveillance and epidemic early warning. However there were less works focusing on the estimation of geospatial distribution of influenza. In order to evaluate the potential power of news comments for geospatial distribution estimation, we choose the H1N1 pandemic in the mainland of China in 2009 as case. After collecting 75878 comments of H1N1 related news from www.sina.com(a famous news site in the mainland of China), we compared the geospatial distribution of comments against surveillance data. The result shows that the comments data share a similar geospatial distribution with the epidemic data(a correlation of 0.848 p<0.01), especially with a larger data volume(a correlation of 0.902 p<0.01). It suggests that extracting geospatial distribution from comments data for estimation could be an important supplementary method when the surveillance data are incomplete and unreliable.

Comorbidity Analysis According to Sex and Age in Hypertension Patients in China

Background: Hypertension, an important risk factor for the health of human being, is often accompanied by various comorbidities. However, the incidence patterns of those comorbidities have not been widely studied. Aim: Applying big-data techniques on a large collection of electronic medical records, we investigated sex-specific and age-specific detection rates of some important comorbidities of hypertension, and sketched their relationships to reveal the risk for hypertension patients. Methods: We collected a total of 6,371,963 hypertension-related medical records from 106 hospitals in 72 cities throughout China. Those records were reported to a National Center for Disease Control in China between 2011 and 2013. Based on the comprehensive and geographically distributed data set, we identified the top 20 comorbidities of hypertension, and disclosed the sex-specific and age-specific patterns of those comorbidities. A comorbidities network was constructed based on the frequency of co-occurrence relationships among those comorbidities. Results: The top four comorbidities of hypertension were coronary heart disease, diabetes, hyperlipemia, and arteriosclerosis, whose detection rates were 21.71% (21.49% for men vs 21.95% for women), 16.00% (16.24% vs 15.74%), 13.81% (13.86% vs 13.76%), and 12.66% (12.25% vs 13.08%), respectively. The age-specific detection rates of comorbidities showed five unique patterns and also indicated that nephropathy, uremia, and anemia were significant risks for patients under 39 years of age. On the other hand, coronary heart disease, diabetes, arteriosclerosis, hyperlipemia, and cerebral infarction were more likely to occur in older patients. The comorbidity network that we constructed indicated that the top 20 comorbidities of hypertension had strong co-occurrence correlations. Conclusions: Hypertension patients can be aware of their risks of comorbidities based on our sex-specific results, age-specific patterns, and the comorbidity network. Our findings provide useful insights into the comorbidity prevention, risk assessment, and early warning for hypertension patients.