Spatiotemporal mapping of the leprosy granuloma landscape

Author(s):  
Marco P. La Manna ◽  
Francesco Dieli ◽  
Nadia Caccamo
2020 ◽  
Vol 8 ◽  
Author(s):  
Joshua Colm Price ◽  
Raquel Mesquita-Ribeiro ◽  
Federico Dajas-Bailador ◽  
Melissa Louise Mather

2017 ◽  
Vol 47 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Olívia Bueno da COSTA ◽  
Eraldo Aparecido Trondoli MATRICARDI ◽  
Marcos Antonio PEDLOWSKI ◽  
Mark Alan COCHRANE ◽  
Luiz Cláudio FERNANDES

ABSTRACT Although soybean production has been increasing in the state of Rondônia in the last decade, soybean planted area has been estimated indirectly using secondary datasets, which has limited understanding of its spatiotemporal distribution patterns. This study aimed to map and analyze spatial patterns of soybean expansion in Rondônia. We developed a classification technique based on Spectral Mixture Analysis (SMA) derived from Landsat imagery and Decision Tree Classification to detect and map soybean plantations in 2000, 2005, 2010, and 2014. The soybean classification map showed 93% global accuracy, 23% omission and 0% of commission errors for soybean crop fields. The greatest increases of soybean cropped area in the state of Rondônia were observed between 2000-2005 and 2005-2010 time-periods (33,239 ha and 59,628 ha, respectively), mostly located in Southern Rondônia. The expansion of soybean areas to Northern Rondônia (25,627 ha) has mostly occurred in the 2010-2014 time period. We estimate that 95.4% of all newly created soybean plantations, detected by 2014, were established on lands deforested nine or more years earlier. We concluded that the incursion of soybean plantations on lands deforested for other land uses (e.g. ranching) is contributing to their displacement (pastures) from older colonization zones toward more remote frontier areas of the Amazon, exacerbating new deforestation there.


PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243622
Author(s):  
David S. Campo ◽  
Joseph W. Gussler ◽  
Amanda Sue ◽  
Pavel Skums ◽  
Yury Khudyakov

Persons who inject drugs (PWID) are at increased risk for overdose death (ODD), infections with HIV, hepatitis B (HBV) and hepatitis C virus (HCV), and noninfectious health conditions. Spatiotemporal identification of PWID communities is essential for developing efficient and cost-effective public health interventions for reducing morbidity and mortality associated with injection-drug use (IDU). Reported ODDs are a strong indicator of the extent of IDU in different geographic regions. However, ODD quantification can take time, with delays in ODD reporting occurring due to a range of factors including death investigation and drug testing. This delayed ODD reporting may affect efficient early interventions for infectious diseases. We present a novel model, Dynamic Overdose Vulnerability Estimator (DOVE), for assessment and spatiotemporal mapping of ODDs in different U.S. jurisdictions. Using Google® Web-search volumes (i.e., the fraction of all searches that include certain words), we identified a strong association between the reported ODD rates and drug-related search terms for 2004–2017. A machine learning model (Extremely Random Forest) was developed to produce yearly ODD estimates at state and county levels, as well as monthly estimates at state level. Regarding the total number of ODDs per year, DOVE’s error was only 3.52% (Median Absolute Error, MAE) in the United States for 2005–2017. DOVE estimated 66,463 ODDs out of the reported 70,237 (94.48%) during 2017. For that year, the MAE of the individual ODD rates was 4.43%, 7.34%, and 12.75% among yearly estimates for states, yearly estimates for counties, and monthly estimates for states, respectively. These results indicate suitability of the DOVE ODD estimates for dynamic IDU assessment in most states, which may alert for possible increased morbidity and mortality associated with IDU. ODD estimates produced by DOVE offer an opportunity for a spatiotemporal ODD mapping. Timely identification of potential mortality trends among PWID might assist in developing efficient ODD prevention and HBV, HCV, and HIV infection elimination programs by targeting public health interventions to the most vulnerable PWID communities.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Brian B. Zhou ◽  
Paul C. Jerger ◽  
Kan-Heng Lee ◽  
Masaya Fukami ◽  
Fauzia Mujid ◽  
...  

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