Germscape

The figure above shows confirmed cases of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) (N = 55) reported as of June 7, 2013, to the World Health Organization and history of travel from the Arabian Peninsula or neighboring countries within 14 days of illness onset, during 2012-2013. All reported cases of MERS-CoV were directly or indirectly linked to one of four countries: Saudi Arabia, Qatar, Jordan, and the United Arab Emirates.

Printed in the Morbidity and Mortality Weekly Report, June 14, 2013 / 62(23);480-483

Published in the South China Morning Post:

“The UN Food and Agricuture Organisation has just published a report on the promotion of entomophagy - the consumption of insects as food. It is a practice that has been around for millenia. Artistotle having waxed lyrical on cicada many a centuries ago.”

nycopendata:

As part of the one-year anniversary of the NYC Open Data Law – Local Law 11 of 2012 – we’re profiling users of NYC Open Data.

Check out this detailed visualization of NYC’s urban tree canopy by Tom Swanson from ESRI. The map is based on the NYC Parks Department’s high resolution land cover data set, which charts grass and shrubs, bare earth, water, buildings, roads and other paved surfaces. 

Urban trees provide environmental, social, and human-health benefits, but these benefits are hard to measure. Using NYC OpenData, the NYC GIS basemap and object-based image analysis, NYC Parks, the University of Vermont Spatial Analysis Lab, and partners developed a comprehensive land-cover map that includes the tree canopy down to the scale of individual trees.

To create the map, the team relied on high-resolution LIDAR to present the height and texture of land cover as well as multispectral imagery and contextual analysis to distinguish trees according to their physical and spectral properties. Converting high-resolution LIDAR and imagery into usable information took significant processing time and labor, but was an effective method for fine-scale canopy mapping in NYC’s complex urban environment. An article on this innovative use of object-based image analysis was published in the Journal of Applied Remote Sensing in 2012 - abstract available at http://dx.doi.org/10.1117/1.JRS.6.063567.

View NYC’s land cover data set on NYC OpenData

View the high-resolution land cover map

The figure above shows a map of confirmed cases and deaths from avian influenza A(H7N9) in China during February 19-April 29, 2013. China has reported 126 confirmed cases of H7N9 infection, of which 24 (19%) have resulted in death. Cases have been confirmed in eight contiguous provinces in eastern China (Anhui, Fujian, Henan, Hunan, Jiangsu, Jiangxi, Shandong, and Zhejiang), two municipalities (Shanghai and Beijing), and Taiwan.

Published here in the Morbidity and Mortality Weekly Report by the Centers for Disease Control and Prevention. 

Venn diagram describing different subtypes of influenza and the hosts they infect.

Created by Information is Beautiful

Influenza empirical data and occurrence maps for influenza virus subtypes H3N2 and H5N1. A) Observed cases of subtypes H3N2 and H5N1 in People’s Republic of China, according to outbreaks reported to the Chinese Ministry of Agriculture. B) Spatial model of the probability of subtype H3N2 at the prefecture scale predicted by using logistic regression. C) Risk for subtype H5N1 according to the outbreak dataset. See , for the corresponding map for the surveillance dataset.

Published in Fuller TL, Gilbert M, Martin V, Cappelle J, Hosseini P, Njabo KY, et al. Predicting hotspots for influenza virus reassortment. Emerg Infect Dis [Internet]. 2013 April. dx.doi.org/10.3201/eid1904.120903

Potential influenza reassortment areas in People’s Republic of China determined by using the influenza virus subtype H5N1 outbreak dataset. A) Density of swine. B) Spatial model of the risk for subtype H3N2 and H5N1 co-occurrence according to the outbreak dataset. C) Areas with a probability of subtype H5N1 and H3N2 co-occurrence >50% and above average swine density. D) Areas with a probability of subtype H5N1 and H3N2 co-occurrence >50% and above average human population density. See , for corresponding maps based on the subtype H5N1 surveillance dataset.

Published in Fuller TL, Gilbert M, Martin V, Cappelle J, Hosseini P, Njabo KY, et al. Predicting hotspots for influenza virus reassortment. Emerg Infect Dis [Internet]. 2013 April. dx.doi.org/10.3201/eid1904.120903

From Placer Mosquito & Vector Control District

Spatial clusters (hotspots) of typhoid in DMA during 2005–2009.

Published in Dewan AM, Corner R, Hashizume M, Ongee ET (2013) Typhoid Fever and Its Association with Environmental Factors in the Dhaka Metropolitan Area of Bangladesh: A Spatial and Time-Series Approach. PLoS Negl Trop Dis 7(1): e1998