scholarly journals 695. Regional and Longitudinal Mapping of Escherichia coli Antibiotic Susceptibility

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S250-S251
Author(s):  
Laurel Legenza ◽  
Susanne Barnett ◽  
Jim Lacy ◽  
Natalee Desotell ◽  
Andrea Eibergen ◽  
...  
Keyword(s):  
Antibiotic Susceptibility ◽  
Regional Differences ◽  
Spatial Interpolation ◽  
Public Display ◽  
E Coli ◽  
Susceptibility Data ◽  
Distance Weighted ◽  
Inverse Distance ◽  
System Point ◽  
Spatial Interpolation Methods

Abstract Background Antimicrobial resistance (AMR) is a serious threat to global health with local implications. AMR varies regionally; however, limited tools are available to aid practitioners in appropriate antibiotic selection based on statewide antimicrobial susceptibilities. The objective of this study was to map E. coli antibiotic susceptibility regionally and longitudinally in Wisconsin. Methods. Antibiograms from 2009, 2013, and 2015 were collected from health systems, hospitals, and clinics in Wisconsin, resulting in 218 antibiograms representing 201,091 Gram-negative isolates. E. coli antibiotic susceptibility percentages were weighted by number of isolates and aggregated by county per year. Results. Spatial interpolation methods (inverse distance weighted, Kriging) were tested by both county center points and facility geocode where available. Susceptibility data for clinically relevant urinary tract infection antibiotics were interpolated to create geographic visualizations of AMR in Wisconsin. Antibiotics included amoxicillin, trimethoprim/sulfamethoxazole, ciprofloxacin, nitrofurantoin, ampicillin, ampicillin/sulbactam, levofloxacin. The interpolation extends to the furthest health system point in each direction and is presented within state boundaries. Facility geocodes were masked from public display for confidentiality. City names were added for orientation. The mapping depicts regional differences, such as 2015 ampicillin susceptibilities ranging 55–64% (Figure 1). The maps provide a preliminary susceptibility prediction in areas where no AMR data were available. Average susceptibilities were compared across 2009, 2013, and 2015 to map areas with the highest rates of AMR change. Conclusion. The described mapping provides a novel visualization of AMR across Wisconsin. The maps created will be utilized in continued efforts to improve the functionality of AMR data in clinical practice to optimize antimicrobial choice. Disclosures All authors: No reported disclosures.

scholarly journals Antibiotic Susceptibility of Common Organisms Isolated from Urine Cultures of Nursing Home Residents

2020 ◽  
Vol 41 (S1) ◽  
pp. s118-s120
Author(s):  
Austin R. Penna ◽  
Taniece R. Eure Eure ◽  
Nimalie D. Stone ◽  
Grant Barney ◽  
Devra Barter ◽  
...  
Keyword(s):  
Nursing Home ◽  
Nursing Homes ◽  
Antibiotic Susceptibility ◽  
Clinical Decision Making ◽  
Service Providers ◽  
Urine Culture ◽  
Nursing Home Residents ◽  
Antibiotic Prescribing ◽  
E Coli ◽  
Susceptibility Data

Background: With the emergence of antibiotic resistant threats and the need for appropriate antibiotic use, laboratory microbiology information is important to guide clinical decision making in nursing homes, where access to such data can be limited. Susceptibility data are necessary to inform antibiotic selection and to monitor changes in resistance patterns over time. To contribute to existing data that describe antibiotic resistance among nursing home residents, we summarized antibiotic susceptibility data from organisms commonly isolated from urine cultures collected as part of the CDC multistate, Emerging Infections Program (EIP) nursing home prevalence survey. Methods: In 2017, urine culture and antibiotic susceptibility data for selected organisms were retrospectively collected from nursing home residents’ medical records by trained EIP staff. Urine culture results reported as negative (no growth) or contaminated were excluded. Susceptibility results were recorded as susceptible, non-susceptible (resistant or intermediate), or not tested. The pooled mean percentage tested and percentage non-susceptible were calculated for selected antibiotic agents and classes using available data. Susceptibility data were analyzed for organisms with ≥20 isolates. The definition for multidrug-resistance (MDR) was based on the CDC and European Centre for Disease Prevention and Control’s interim standard definitions. Data were analyzed using SAS v 9.4 software. Results: Among 161 participating nursing homes and 15,276 residents, 300 residents (2.0%) had documentation of a urine culture at the time of the survey, and 229 (76.3%) were positive. Escherichia coli, Proteus mirabilis, Klebsiella spp, and Enterococcus spp represented 73.0% of all urine isolates (N = 278). There were 215 (77.3%) isolates with reported susceptibility data (Fig. 1). Of these, data were analyzed for 187 (87.0%) (Fig. 2). All isolates tested for carbapenems were susceptible. Fluoroquinolone non-susceptibility was most prevalent among E. coli (42.9%) and P. mirabilis (55.9%). Among Klebsiella spp, the highest percentages of non-susceptibility were observed for extended-spectrum cephalosporins and folate pathway inhibitors (25.0% each). Glycopeptide non-susceptibility was 10.0% for Enterococcus spp. The percentage of isolates classified as MDR ranged from 10.1% for E. coli to 14.7% for P. mirabilis. Conclusions: Substantial levels of non-susceptibility were observed for nursing home residents’ urine isolates, with 10% to 56% reported as non-susceptible to the antibiotics assessed. Non-susceptibility was highest for fluoroquinolones, an antibiotic class commonly used in nursing homes, and ≥ 10% of selected isolates were MDR. Our findings reinforce the importance of nursing homes using susceptibility data from laboratory service providers to guide antibiotic prescribing and to monitor levels of resistance.Disclosures: NoneFunding: None

scholarly journals Spatial Interpolation of Historical Seasonal Rainfall Indices over Peninsular Malaysia

2018 ◽  
Vol 34 ◽  
pp. 02048
Author(s):  
Zulkarnain Hassan ◽  
Ahmad Haidir ◽  
Farah Naemah Mohd Saad ◽  
Afizah Ayob ◽  
Mustaqqim Abdul Rahim ◽  
...  
Keyword(s):  
Spatial Interpolation ◽  
Rainfall Intensity ◽  
Southwest Monsoon ◽  
Peninsular Malaysia ◽  
Seasonal Rainfall ◽  
Seasonal Patterns ◽  
Northeast Monsoon ◽  
Rainfall Characteristics ◽  
Distance Weighted ◽  
Inverse Distance

The inconsistency in inter-seasonal rainfall due to climate change will cause a different pattern in the rainfall characteristics and distribution. Peninsular Malaysia is not an exception for this inconsistency, in which it is resulting extreme events such as flood and water scarcity. This study evaluates the seasonal patterns in rainfall indices such as total amount of rainfall, the frequency of wet days, rainfall intensity, extreme frequency, and extreme intensity in Peninsular Malaysia. 40 years (1975-2015) data records have been interpolated using Inverse Distance Weighted method. The results show that the formation of rainfall characteristics are significance during the Northeast monsoon (NEM), as compared to Southwest monsoon (SWM). Also, there is a high rainfall intensity and frequency related to extreme over eastern coasts of Peninsula during the NEM season.

scholarly journals Spatial interpolation methods for estimating monthly rainfall distribution in Thailand

2021 ◽  
Author(s):  
Nawinda Chutsagulprom ◽  
Kuntalee Chaisee ◽  
Ben Wongsaijai ◽  
Papangkorn Inkeaw ◽  
Chalump Oonariya
Keyword(s):  
Spatial Interpolation ◽  
Cross Validation ◽  
Monthly Rainfall ◽  
Rainfall Distribution ◽  
Mathematical Concepts ◽  
Interpolation Methods ◽  
Distance Weighting ◽  
Inverse Distance ◽  
Fold Cross Validation ◽  
Spatial Interpolation Methods

Abstract Spatial interpolation methods usually differ in their underlying mathematical concepts, each with inherent advantages and drawbacks depending on the properties of data. This paper, therefore, aims to compare and evaluate the performances of well-established interpolation techniques for estimating monthly rainfall data in Thailand. The selected methods include the inverse distance-based method, multiple linear regression (MLR), artificial neural networks (ANN), and ordinary kriging (OK). The technique of searching nearest stations is additionally imposed for some aforementioned schemes. The k -fold cross-validation method is exploited to assess the efficiency of each method, then the metric scores, RMSE, and MAE are used for comparisons. The results suggest the ANN might be the least favorite as it underperforms in many folds. While the OK method provides the most accurate prediction, the inverse distance weighting (IDW), particularly inverse exponential weighting (IEW), and MLR are considerably comparative. Overall, IEW is plausible for monthly rainfall estimation of Thailand because it is less computationally expensive than the OK and its flexible computation.

scholarly journals The Utilization of Remote Sensing Imagery and Inverse Distance Weighted Scheme to Simulate the White Oil Effects on Soil Geotechnical Properties

2021 ◽  
pp. 2824-2833
Author(s):  
L. A. Jawad ◽  
H. W. Abdulwadud ◽  
Z. A. Hameed
Keyword(s):  
Spatial Interpolation ◽  
Liquid Limit ◽  
Soil Samples ◽  
Strength Parameter ◽  
Undisturbed Soil ◽  
Inverse Distance Weighted ◽  
Area Of Interest ◽  
Clayey Silt ◽  
Distance Weighted ◽  
Inverse Distance

     This research aims to utilize a complementarity of field excavations and laboratory works with spatial analyses techniques for a highly accurate modeling of soil geotechniques properties (i.e. having lower root mean square error value for the spatial interpolation). This was conducted, for a specified area of interest, firstly by adopting spatially sufficient and  well distributed samples (cores). Then, in the second step, a simulation is performed for the variations in properties when soil is contaminated with commonly used industrial material, which is white oil in our case. Cohesive (disturbed and undisturbed) soil samples were obtained from three various locations inside Baghdad University campus in AL-Jadiriya section of Baghdad, Iraq. The unified soil categorization system (USCS) was adopted and soil was categorized  as clayey silt of low plasticity (CL). The cores were contaminated in a synthetically manner using two specified values of white oil (5 and 10 % of its dry weight). Then, the samples were left for three days to certify homogeneity. The results of laboratory tests were enhanced by spatial interpolation mapping, using Inverse Distance Weighted scheme for normal soil samples and those with synthetic pollution. The liquid limit rates were raised slightly as contamination rates raised, while particle size was reduced; in contrary, shear strength parameter values were decreased.

scholarly journals Comparison of spatial interpolation methods for estimation of weekly rainfall in West Bengal, India

MAUSAM ◽  
2021 ◽  
Vol 68 (1) ◽  
pp. 41-50
Author(s):  
MADHURIMA DAS ◽  
ARNAB HAZRA ◽  
ADITI SARKAR ◽  
SABYASACHI BHATTACHARYA ◽  
PABITRA BANIK
Keyword(s):  
Spatial Interpolation ◽  
West Bengal ◽  
Agricultural Practices ◽  
Rainfall Data ◽  
Absolute Deviation ◽  
Interpolation Technique ◽  
Distance Weighted ◽  
Interpolation Techniques ◽  
Discrete Values ◽  
Inverse Distance

Rainfall is one of the most eloquently researched contemporary meteorological phenomena affecting the agricultural practices dramatically, particularly along the humid, sub-tropics, where agriculture is predominantly rainfed. It is a key parameter of agricultural production in West Bengal due to lack irrigation facilities in most of the areas. Thus, it is very important to have detailed information of rainfall distribution pattern of West Bengal. In practice rainfall data is collected only at few discrete stations scattered all over the whole state. However, rainfall is a spatially continuous phenomenon rather than discrete. Thus it becomes essential to apply a robust spatial interpolation technique to transform the discrete values into a continuous spatial pattern. In the present study, three spatial interpolation techniques namely Kriging, Inverse Distance Weighted (IDW) and SPLINE, are used for a comparative analysis to identify the most efficient interpolation technique. Weekly average rainfall data available between 1901 and 1985 for 19 standard meteorological weeks (SMW), Week 22 to Week 40 are used for the analysis. The errors of the three interpolation techniques are analyzed and the best method is chosen based on the minimum mean absolute deviation (MAD) and the minimum mean squared deviation (MSD) criteria. The IDW method is found to be the best spatial interpolation technique.

Comparison of Quantitative Precipitation Estimation Using Spatial Interpolation methods in Bukhan River Basin

2020 ◽  
Author(s):  
Sanghoo Yoon ◽  
Junseok Kim ◽  
Taeyong Kwon
Keyword(s):  
Spatial Interpolation ◽  
Mean Squared Error ◽  
Additive Model ◽  
Absolute Error ◽  
Interpolation Methods ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Weight Method ◽  
Inverse Distance ◽  
Spatial Interpolation Methods

<p>Quantitative precipitation estimation is needed to reduce damages from weather disasters such as torrential rain. This study is dealt with estimates of the quantitative precipitation using multiple spatial interpolation methods and compares the results. Inverse distance weight method and k-nearest neighborhood algorithm were considered as a deterministic approach and the general additive model and kriging methods were used as a stochastic approach. To evaluate the prediction performance, leave-one-out cross-validation was performed with the root mean squared error (RMSE), mean absolute error (MAE), bias, and correlation coefficient. The research data were rain gauged and radar data in the Bukhan river, which were collected from May 2018 to August 2019. The results showed that the inverse distance weight method reflected the spatial rainfall characteristics well. However, caution is needed because the best models vary depending on the pattern of rainfall in the sense of RMSE.</p><p>*This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD(No. 2018-Tech-20)</p>

scholarly journals Drought Index Mapping at Different Spatial Units

2008 ◽  
Vol 9 (6) ◽  
pp. 1523-1534 ◽  
Author(s):  
Jinyoung Rhee ◽  
Gregory J. Carbone ◽  
James Hussey
Keyword(s):  
Spatial Interpolation ◽  
Drought Index ◽  
Cross Validation ◽  
Interpolation Method ◽  
Inverse Distance Weighted ◽  
Frequency Distributions ◽  
Distance Weighted ◽  
Cross Validation Error ◽  
Index Mapping ◽  
Inverse Distance

Abstract This paper investigates the influence of spatial interpolation and aggregation of data to depict drought at different spatial units relevant to and often required for drought management. Four different methods for drought index mapping were explored, and comparisons were made between two spatial operation methods (simple unweighted average versus spatial interpolation plus aggregation) and two calculation procedures (whether spatial operations are performed before or after the calculations of drought index values). Deterministic interpolation methods including Thiessen polygons, inverse distance weighted, and thin-plate splines as well as a stochastic and geostatistical interpolation method of ordinary kriging were compared for the two methods that use interpolation. The inverse distance weighted method was chosen based on the cross-validation error. After obtaining drought index values for different spatial units using each method in turn, differences in the empirical binned frequency distributions were tested between the methods and spatial units. The two methods using interpolation and aggregation introduced fewer errors in cross validation than the two simple unweighted average methods. Whereas the method performing spatial interpolation and aggregation before calculating drought index values generally provided consistent drought information between various spatial units, the method performing spatial interpolation and aggregation after calculating drought index values reduced errors related to the calculations of precipitation data.

scholarly journals A nonlinearities inverse distance weighting spatial interpolation approach applied to the surface electromyography signal

2022 ◽  
Vol 12 (2) ◽  
pp. 1530
Author(s):  
Ayad Assad Ibrahim ◽  
Ikhlas Mahmoud Farhan ◽  
Mohammed Ehasn Safi
Keyword(s):  
Surface Electromyography ◽  
Spatial Interpolation ◽  
Goodness Of Fit ◽  
Electrode Array ◽  
Motor Units ◽  
Biomedical Signal Processing ◽  
Semg Signal ◽  
Distance Weighted ◽  
A New Technique ◽  
Inverse Distance

Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode grid from different types of subjects, sex, age, and type of muscles have been studied when muscles are under regular tension activity. A goodness of fit measure (R2) is used to evaluate the proposed technique. The interpolated signals are compared with the actual signals; the Goodness of fit measure's value is almost 99%, with a processing time of 100msec. The resulting technique is shown to be of high accuracy and matching of spatial interpolated signals to actual signals compared with IDW and Kriging techniques.

scholarly journals Comparison of Inverse Distance Weighted and Natural Neighbor Interpolation Method at Air Temperature Data in Malang Region

CAUCHY ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. 48 ◽  
Author(s):  
Jaka Pratama Musashi ◽  
Henny Pramoedyo ◽  
Rahma Fitriani
Keyword(s):  
Air Temperature ◽  
Spatial Data ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Inverse Distance Weighted ◽  
Natural Neighbor ◽  
Comparison Results ◽  
Data Record ◽  
Distance Weighted ◽  
Inverse Distance

The purpose of this study was to compare the results of Inverse Distance Weighted (IDW) and Natural Neighbor interpolation methods for spatial data of air temperature in the Malang Region.  Interpolation is one way to determine a point of events from several points around the known value.  Spatial interpolation can be used to estimate an area that does not have a data record using the value of its known surroundings.  38 points observation air temperature of Malang Region in 2016 is used as a sample point to interpolate the surrounding air temperature.  Obtained optimum parameter power value is 2 for IDW interpolation method.  The RMSE comparison results show that IDW method is better to be used than the Natural Neighbor Interpolation method with the RMSE values of 1,2292 for the IDW method and 1,6173 for the NN method.

Sign in / Sign up

Export Citation Format

Share Document