Spatial Distribution and Risk Factors of Influenza in Jiangsu Province, China, based on Geographical Information System

ghGeospatial Health, Volume 8, Number 2, May 2014, Pages 429-435

By Jia-Cheng Zhang, Wen-Dong Liu, Qi Liang, Jian-Li Hu, Jessie Norris, Ying Wu, Chang-Jun Bao, Fen-Yang Tang, Peng Huang, Yang Zhao, Rong-Bin Yu, Ming-Hao Zhou, Hong-Bing Shen, Feng Chen, and Zhi-Hang Peng

“Influenza poses a constant, heavy burden on society. Recent research has focused on ecological factors associated with influenza incidence and has also studied influenza with respect to its geographic spread at different scales. This research explores the temporal and spatial parameters of influenza and identifies factors influencing its transmission.

Spatial clusters of annual incidence of influenza (hotspots) in Jiangsu province, P.R. China, for the years 2004 (a), 2 006 (b), 2009 (c) and 2011 (d).

Spatial clusters of annual incidence of influenza (hotspots) in Jiangsu province, P.R. China, for the years 2004 (a), 2006 (b), 2009 (c) and 2011 (d).

“A spatial autocorrelation analysis, a spatial-temporal cluster analysis and a spatial regression analysis of influenza rates, carried out in Jiangsu province from 2004 to 2011, found that influenza rates to be spatially dependent in 2004, 2005, 2006 and 2008. South-western districts consistently revealed hotspots of high-incidence influenza. The regression analysis indicates that railways, rivers and lakes are important predictive environmental variables for influenza risk. A better understanding of the epidemic pattern and ecological factors associated with pandemic influenza should benefit public health officials with respect to prevention and controlling measures during future epidemics. ”

A Spatial and Temporal Analysis of Japanese Encephalitis in Mainland China, 1963–1975

PLOS_ONEPLOS ONE, Published Online June 09, 2014

By Xiaolong Li, Xiaoyan Gao, Zhoupeng Ren, Yuxi Cao, Jinfeng Wang, Guodong Liang

“More than a million Japanese encephalitis (JE) cases occurred in mainland China from the 1960s to 1970s without vaccine interventions. The aim of this study is to analyze the spatial and temporal pattern of JE cases reported in mainland China from 1965 to 1973 in the absence of JE vaccination, and to discuss the impacts of climatic and geographical factors on JE during that period. Thus, the data of reported JE cases at provincial level and monthly precipitation and monthly mean temperature from 1963 to 1975 in mainland China were collected. Local Indicators of Spatial Association analysis was performed to identify spatial clusters at the province level. During that period, The epidemic peaked in 1966 and 1971 and the JE incidence reached up to 20.58/100000 and 20.92/100000, respectively. The endemic regions can be divided into three classes including high, medium, and low prevalence regions.

Japanese encephalitis in mainland China during 1963–1975. (A) China experienced a natural JE epidemic period with no vaccine interventions from 1963 to 1975. The bar graph of JE incidence in mainland China from 1951 to 2008 is cited from reference 10. (B) During the JE epidemic in 1971, JE incidence in 11 provinces distributed in the coastal areas of eastern China (red color) was higher than the national average (20.92/100,000). (C) LISA cluster map for JE incidence during 1963–1975 shows the center of cluster in color. High-High indicates a significant (P<0.05) spatial cluster of high JE incidence values; Low-Low represents a spatial cluster of low JE incidence values.

Japanese encephalitis in mainland China during 1963–1975. (A) China experienced a natural JE epidemic period with no vaccine interventions from 1963 to 1975. The bar graph of JE incidence in mainland China from 1951 to 2008 is cited from reference 10. (B) During the JE epidemic in 1971, JE incidence in 11 provinces distributed in the coastal areas of eastern China (red color) was higher than the national average (20.92/100,000). (C) LISA cluster map for JE incidence during 1963–1975 shows the center of cluster in color. High-High indicates a significant (P<0.05) spatial cluster of high JE incidence values; Low-Low represents a spatial cluster of low JE incidence values.

“Through spatial cluster analysis, JE epidemic hot spots were identified; most were located in the Yangtze River Plain which lies in the southeast of China. In addition, JE incidence was shown to vary among eight geomorphic units in China. Also, the JE incidence in the Loess Plateau and the North China Plain was showed to increase with the rise of temperature. Likewise, JE incidence in the Loess Plateau and the Yangtze River Plain was observed a same trend with the increase of rainfall. In conclusion, the JE cases clustered geographically during the epidemic period. Besides, the JE incidence was markedly higher on the plains than plateaus. These results may provide an insight into the epidemiological characteristics of JE in the absence of vaccine interventions and assist health authorities, both in China and potentially in Europe and Americas, in JE prevention and control strategies.”

Analyzing Spatial Clustering and the Spatiotemporal Nature and Trends of HIV/AIDS Prevalence using GIS: The Case of Malawi, 1994-2010

bmcidBMC Infectious Diseases 2014, 14:285, Published online 23 May 2014

By Leo C Zulu, Ezekiel Kalipeni, and Eliza Johannes

Background
Although local spatiotemporal analysis can improve understanding of geographic variation of the HIV epidemic, its drivers, and the search for targeted interventions, it is limited in sub-Saharan Africa. Despite recent declines, Malawi’s estimated 10.0% HIV prevalence (2011) remained among the highest globally. Using data on pregnant women in Malawi, this study 1) examines spatiotemporal trends in HIV prevalence 1994-2010, and 2) for 2010, identifies and maps the spatial variation/clustering of factors associated with HIV prevalence at district level.

Methods
Inverse distance weighting was used within ArcGIS Geographic Information Systems (GIS) software to generate continuous surfaces of HIV prevalence from point data (1994, 1996, 1999, 2001, 2003, 2005, 2007, and 2010) obtained from surveillance antenatal clinics. From the surfaces prevalence estimates were extracted at district level and the results mapped nationally. Spatial dependency (autocorrelation) and clustering of HIV prevalence were also analyzed. Correlation and multiple regression analyses were used to identify factors associated with HIV prevalence for 2010 and their spatial variation/clustering mapped and compared to HIV clustering.

malawi

Results
Analysis revealed wide spatial variation in HIV prevalence at regional, urban/rural, district and sub-district levels. However, prevalence was spatially leveling out within and across ‘sub-epidemics’ while declining significantly after 1999. Prevalence exhibited statistically significant spatial dependence nationally following initial (1995-1999) localized, patchy low/high patterns as the epidemic spread rapidly. Locally, HIV “hotspots” clustered among eleven southern districts/cities while a “coldspot” captured configurations of six central region districts. Preliminary multiple regression of 2010 HIV prevalence produced a model with four significant explanatory factors (adjusted R2 = 0.688): mean distance to main roads, mean travel time to nearest transport, percentage that had taken an HIV test ever, and percentage attaining a senior primary education. Spatial clustering linked some factors to particular subsets of high HIV-prevalence districts.

Conclusions
Spatial analysis enhanced understanding of local spatiotemporal variation in HIV prevalence, possible underlying factors, and potential for differentiated spatial targeting of interventions. Findings suggest that intervention strategies should also emphasize improved access to health/HIV services, basic education, and syphilis management, particularly in rural hotspot districts, as further research is done on drivers at finer scale.”

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Spatial and Temporal Analysis of Air Pollution Index and its Timescale-dependent Relationship with Meteorological Factors in Guangzhou, China, 2001–2011

Environmental PollutionEnvironmental Pollution, Volume 190, July 2014, Pages 75–81

By Li Lia, Jun Qianb, Chun-Quan Oua, Ying-Xue Zhoua, Cui Guoa, and Yuming Guoc

“Highlights

  • Air pollution is still serious in Guangzhou, China.
  • Air Pollution Index was associated with a variety of meteorological parameters.
  • The temporal relationships were timescale-dependent.
  • The findings should be taken into account in air quality forecasts and pollution control.

MATLAB Handle Graphics“There is an increasing interest in spatial and temporal variation of air pollution and its association with weather conditions. We presented the spatial and temporal variation of Air Pollution Index (API) and examined the associations between API and meteorological factors during 2001–2011 in Guangzhou, China. A Seasonal-Trend Decomposition Procedure Based on Loess (STL) was used to decompose API. Wavelet analyses were performed to examine the relationships between API and several meteorological factors. Air quality has improved since 2005. APIs were highly correlated among five monitoring stations, and there were substantial temporal variations. Timescale-dependent relationships were found between API and a variety of meteorological factors. Temperature, relative humidity, precipitation and wind speed were negatively correlated with API, while diurnal temperature range and atmospheric pressure were positively correlated with API in the annual cycle. Our findings should be taken into account when determining air quality forecasts and pollution control measures.”

Spatio-temporal Analysis of Abundances of Three Malaria Vector Species in Southern Benin using Zero-truncated Models

pnvParasites & Vectors 2014, 7:103 , Published Online 12 March 2014

By Nicolas Moiroux, Armel Djènontin, Abdul S Bio-Bangana, Fabrice Chandre, Vincent Corbel, and Hélène Guis

“Background

A better understanding of the ecology and spatial-temporal distribution of malaria vectors is essential to design more effective and sustainable strategies for malaria control and elimination. In a previous study, we analyzed presence-absence data of An. funestus, An. coluzzii, and An. gambiae s.s. in an area of southern Benin with high coverage of vector control measures. Here, we further extend the work by analysing the positive values of the dataset to assess the determinants of the abundance of these three vectors and to produce predictive maps of vector abundance.

Methods

Positive counts of the three vectors were assessed using negative-binomial zero-truncated (NBZT) mixed-effect models according to vector control measures and environmental covariates derived from field and remote sensing data. After 8-fold cross-validation of the models, predictive maps of abundance of the sympatric An. funestus, An. coluzzii, and An. gambiae s.s. were produced.

Results

Cross-validation of the NBZT models showed a satisfactory predictive accuracy. Almost all changes in abundance between two surveys in the same village were well predicted by the models but abundances for An. gambiae s.s. were slightly underestimated. During the dry season, predictive maps showed that abundance greater than 1 bite per person per night were observed only for An. funestus and An. coluzzii. During the rainy season, we observed both increase and decrease in abundance of An. funestus, which are dependent on the ecological setting. Abundances of both An. coluzzii and An. gambiae s.s. increased during the rainy season but not in the same areas.

mal

Conclusions

Our models helped characterize the ecological preferences of three major African malaria vectors. This works highlighted the importance to study independently the binomial and the zero-truncated count processes when evaluating vector control strategies. The study of the bio-ecology of malaria vector species in time and space is critical for the implementation of timely and efficient vector control strategies.”

Spatio-temporal Analysis on Enterovirus Cases through Integrated Surveillance in Taiwan

BMC Public HealthBMC Public Health, 2014 (14:11), Published 08 January 2014

By Ta-Chien Chan, Jing-Shiang Hwang, Rung-Hung Chen, Chwan-Chuen King, and Po-Huang Chiang

fig5

Spatio-temporal clusters of mild and severe EV cases from July 1999 to December 2008. Top: Severe EV cases aged from 0 to 14; Bottom: Mild EV cases from all ages.

Background
Severe epidemics of enterovirus have occurred frequently in Malaysia, Singapore, Taiwan, Cambodia, and China, involving cases of pulmonary edema, hemorrhage and encephalitis, and an effective vaccine has not been available. The specific aim of this study was to understand the epidemiological characteristics of mild and severe enterovirus cases through integrated surveillance data.

Methods
All enterovirus cases in Taiwan over almost ten years from three main databases, including national notifiable diseases surveillance, sentinel physician surveillance and laboratory surveillance programs from July 1, 1999 to December 31, 2008 were analyzed. The Pearson’s correlation coefficient was applied for measuring the consistency of the trends in the cases between different surveillance systems. Cross correlation analysis in a time series model was applied for examining the capability to predict severe enterovirus infections. Poisson temporal, spatial and space-time scan statistics were used for identifying the most likely clusters of severe enterovirus outbreaks. The directional distribution method with two standard deviations of ellipse was applied to measure the size and the movement of the epidemic.

Results
The secular trend showed that the number of severe EV cases peaked in 2008, and the number of mild EV cases was significantly correlated with that of severe ones occurring in the same week [r = 0.553, p < 0.01]. These severe EV cases showed significantly higher association with the weekly positive isolation rates of EV-71 than the mild cases [severe: 0.498, p < 0.01 vs. mild: 0.278, p < 0.01]. In a time series model, the increase of mild EV cases was the significant predictor for the occurrence of severe EV cases. The directional distribution showed that both the mild and severe EV cases spread extensively during the peak. Before the detected spatio-temporal clusters in June 2008, the mild cases had begun to rise since May 2008, and the outbreak spread from south to north.

Conclusions
Local public health professionals can monitor the temporal and spatial trends plus spatio-temporal clusters and isolation rate of EV-71 in mild and severe EV cases in a community when virus transmission is high, to provide early warning signals and to prevent subsequent severe epidemics.

Spatial and Temporal Analysis of Human Infection with Avian Influenza A(H7N9) Virus in China, 2013

Eurosurveillance, Volume 18, Issue 47, 21 November 2013

By W. Liu, K. Yang, X. Qi, K. Xu, H. Ji, J. Ai, A. Ge, Y. Wu, Y. Li, Q Dai, Q. Liang, C. Bao, R. Bergquist, F. Tang, and Y. Zhu

“Descriptive and geographic information system methods were used to depict the spatial and temporal characteristics of the outbreak of human infection with a novel avian influenza A(H7N9) virus in mainland China, the peak of which appeared between 28 March and 18 April 2013. As of 31 May 2013, there was a total of 131 reported human infections in China, with a cumulative mortality of 29% (38/131). The outbreak affected 10 provinces, with 106 of the cases being concentrated in the eastern coastal provinces of Zhejiang, Shanghai and Jiangsu.

ZHU_fig3

“Statistically significant spatial clustering of cumulative human cases was identified by the Cuzick–Edwards’ k-nearest neighbour method. Three spatio-temporal clusters of cases were detected by space–time scan analysis. The principal cluster covered 18 counties in Zhejiang during 3 to 18 April (relative risk (RR): 26.39;p<0.0001), while two secondary clusters in March and April covered 21 counties along the provincial boundary between Shanghai and Jiangsu (RR: 6.35;p<0.0001) and two counties in Jiangsu (RR: 72.48;p=0.0025). The peak of the outbreak was in the eastern coastal provinces of Zhejiang, Shanghai and Jiangsu that was characterised by statistically significant spatio-temporal aggregation, with a particularly high incidence in March and April 2013.”