scholarly journals Current characteristics of animal rabies cases in Thailand and relevant risk factors identified by a spatial modeling approach

2021 ◽  
Vol 15 (12) ◽  
pp. e0009980
Author(s):  
Weerapong Thanapongtharm ◽  
Sarin Suwanpakdee ◽  
Arun Chumkaeo ◽  
Marius Gilbert ◽  
Anuwat Wiratsudakul
Keyword(s):  
Risk Factors ◽  
Generalized Additive Models ◽  
Spatial Models ◽  
Additive Models ◽  
Human Rabies ◽  
Spatial Risk ◽  
Risk Areas ◽  
Spatio Temporal ◽  
Current Characteristics ◽  
And Control

The situation of human rabies in Thailand has gradually declined over the past four decades. However, the number of animal rabies cases has slightly increased in the last ten years. This study thus aimed to describe the characteristics of animal rabies between 2017 and 2018 in Thailand in which the prevalence was fairly high and to quantify the association between monthly rabies occurrences and explainable variables using the generalized additive models (GAMs) to predict the spatial risk areas for rabies spread. Our results indicate that the majority of animals affected by rabies in Thailand are dogs. Most of the affected dogs were owned, free or semi-free roaming, and unvaccinated. Clusters of rabies were highly distributed in the northeast, followed by the central and the south of the country. Temporally, the number of cases gradually increased after June and reached a peak in January. Based on our spatial models, human and cattle population density as well as the spatio-temporal history of rabies occurrences, and the distances from the cases to the secondary roads and country borders are identified as the risk factors. Our predictive maps are applicable for strengthening the surveillance system in high-risk areas. Nevertheless, the identified risk factors should be rigorously considered and integrated into the strategic plans for the prevention and control of animal rabies in Thailand.

Assessment of foot-and-mouth disease risk areas in mainland China based spatial multi-criteria decision analysis

2021 ◽  
Author(s):  
Wang Haoran ◽  
Xiao Jianhua ◽  
Ouyang Maolin ◽  
Gao Hongyan ◽  
Bie Jia ◽  
...  
Keyword(s):  
Risk Factors ◽  
Decision Analysis ◽  
Disease Risk ◽  
Mainland China ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Multi Criteria Decision Analysis ◽  
Foot And Mouth ◽  
Risk Areas ◽  
And Control

Abstract Background Foot-and-mouth disease (FMD) is a highly contagious viral disease of cloven-hoofed animals. As a transboundary animal disease, the prevention and control of FMD are important. This study was based on spatial multi-criteria decision analysis (MCDA) to assess FMD risk areas in mainland China. Ten risk factors were identified for constructing risk maps by scoring, and the analytic hierarchy process (AHP) was used to calculate the criteria weights of all factors. Different risk factors had different units and attributes, and fuzzy membership was used to standardize the risk factors. The weighted linear combination (WLC) and one-at-a-time (OAT) were used to obtain risk and uncertainty maps as well as to perform sensitivity analysis. Results Four major risk areas were identified in mainland China, including western (Xinjiang and Tibet), southern (Yunnan, Guizhou, Guangxi and Guangdong), northern (Gansu, Ningxia and Inner Mongolia), and eastern (Hebei, Henan, Anhui, Jiangsu and Shandong). We found spring as the main season for FMD outbreaks. Risk areas were associated with the distance to previous outbreak points, grazing areas and cattle density. Receiver operating characteristic (ROC) analysis indicated that the risk map had good predictive power (AUC = 0.8532). Conclusions These results can be used to delineate FMD risk areas in mainland China, and provinces can adopt the targeted preventive measures and control strategies.

scholarly journals Risk Factors and Spatio-Temporal Patterns of Human Rabies Exposure in Northwestern Tigray, Ethiopia

2019 ◽  
Vol 85 (1) ◽  
Author(s):  
Gebreyohans Gebru ◽  
Gebremedhin Romha ◽  
Abrha Asefa ◽  
Haftom Hadush ◽  
Muluberhan Biedemariam
Keyword(s):  
Risk Factors ◽  
Temporal Patterns ◽  
Human Rabies ◽  
Spatio Temporal ◽  
Rabies Exposure

scholarly journals Assessment of foot-and-mouth disease risk areas in mainland China based spatial multi-criteria decision analysis

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Wang Haoran ◽  
Xiao Jianhua ◽  
Ouyang Maolin ◽  
Gao Hongyan ◽  
Bie Jia ◽  
...  
Keyword(s):  
Risk Factors ◽  
Decision Analysis ◽  
Disease Risk ◽  
Mainland China ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Multi Criteria Decision Analysis ◽  
Foot And Mouth ◽  
Risk Areas ◽  
And Control

Abstract Background Foot-and-mouth disease (FMD) is a highly contagious viral disease of cloven-hoofed animals. As a transboundary animal disease, the prevention and control of FMD are important. This study was based on spatial multi-criteria decision analysis (MCDA) to assess FMD risk areas in mainland China. Ten risk factors were identified for constructing risk maps by scoring, and the analytic hierarchy process (AHP) was used to calculate the criteria weights of all factors. Different risk factors had different units and attributes, and fuzzy membership was used to standardize the risk factors. The weighted linear combination (WLC) and one-at-a-time (OAT) were used to obtain risk and uncertainty maps as well as to perform sensitivity analysis. Results Four major risk areas were identified in mainland China, including western (parts of Xinjiang and Tibet), southern (parts of Yunnan, Guizhou, Guangxi, Sichuan and Guangdong), northern (parts of Gansu, Ningxia and Inner Mongolia), and eastern (parts of Hebei, Henan, Anhui, Jiangsu and Shandong). Spring is the main season for FMD outbreaks. Risk areas were associated with the distance to previous outbreak points, grazing areas and cattle density. Receiver operating characteristic (ROC) analysis indicated that the risk map had good predictive power (AUC=0.8634). Conclusions These results can be used to delineate FMD risk areas in mainland China, and veterinary services can adopt the targeted preventive measures and control strategies.

scholarly journals Sardine potential habitat and environmental forcing off western Portugal

2010 ◽  
Vol 67 (8) ◽  
pp. 1553-1564 ◽  
Author(s):  
Juan P. Zwolinski ◽  
Paulo B. Oliveira ◽  
Victor Quintino ◽  
Yorgos Stratoudakis
Keyword(s):  
Chlorophyll A ◽  
Seasonal Pattern ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Seasonal Effect ◽  
Oceanic Circulation ◽  
Environmental Forcing ◽  
Potential Habitat ◽  
The North ◽  
Spatio Temporal

Abstract Zwolinski, J. P., Oliveira, P. B., Quintino, V., and Stratoudakis, Y. 2010. Sardine potential habitat and environmental forcing off western Portugal. – ICES Journal of Marine Science, 67: 1553–1564. Relationships between sardine (Sardina pilchardus) distribution and the environment off western Portugal were explored using data from seven acoustic surveys (spring and autumn of 2000, 2001, 2005, and spring 2006). Four environmental variables (salinity, temperature, chlorophyll a, and acoustic epipelagic backscatter other than fish) were related to the acoustic presence and density of sardine. Univariate quotient analysis revealed sardine preferences for waters with high chlorophyll a content, low temperature and salinity, and low acoustic epipelagic backscatter. Generalized additive models depicted significant relationships between the environment and sardine presence but not with sardine density. Maps of sardine potential habitat (SPH) built upon the presence/absence models revealed a clear seasonal effect in the across-bathymetry and alongshelf extension of SPH off western Portugal. During autumn, SPH covered a large part of the northern Portuguese continental shelf but was almost absent from the southern region, whereas in spring SPH extended farther south but was reduced to a narrow band of shallow coastal waters in the north. This seasonal pattern agrees with the spatio-temporal variation of primary production and oceanic circulation described for the western Iberian shelf.

scholarly journals Blowing in the wind? The effect of weather on the intensity and spatial distribution of crime

2019 ◽  
Author(s):  
Rannveig Hart ◽  
Willy Pedersen ◽  
Torbjørn Skardhamar
Keyword(s):  
Spatial Distribution ◽  
Minimum Temperature ◽  
Negative Binomial ◽  
Generalized Additive Models ◽  
Spatial Models ◽  
Weather Conditions ◽  
Additive Models ◽  
Extensive Literature ◽  
Weather Effects ◽  
Count Models

Despite an extensive literature on weather and crime, the magnitude of weather effects on crime and their implications for practical policing remain unclear. Similarly, the effects of weather on the location of crime have barely been explored empirically. We investigated how weather influences the intensity and spatial distribution of crime in Oslo, the capital of Norway. Geocoded locations of criminal offences were combined with data on temperature, wind, and rain. We used negative binomial count models to assess the effect of weather on the intensity of crime and generalized additive models (GAMs) to test for spatial variations. The intensity and spatial distribution of crime were not very sensitive to weather in Oslo. The largest effect was for drug crimes, for which maximum relative to minimum temperature was related to a single incident increase every six days. No effects were found for dislocation in the spatial models. In Oslo, Norway, weather conditions are of little importance for practical policing. The effects of weather on the intensity of crime are miniscule, and effects on the location of crime even smaller.

scholarly journals Comparative evaluation of multi-basin production performance and application of spatio-temporal models for unconventional oil and gas production prediction

2020 ◽  
Vol 10 (8) ◽  
pp. 3091-3110 ◽  
Author(s):  
M. E. Wigwe ◽  
E. S. Bougre ◽  
M. C. Watson ◽  
A. Giussani
Keyword(s):  
Oil And Gas ◽  
Generalized Additive Models ◽  
Gas Production ◽  
Additive Models ◽  
Machine Learning Algorithms ◽  
Production Performance ◽  
Spatial And Temporal Variability ◽  
Temporal Models ◽  
Spatio Temporal ◽  
Production Prediction

Abstract Modern data analytic techniques, statistical and machine-learning algorithms have received widespread applications for solving oil and gas problems. As we face problems of parent–child well interactions, well spacing, and depletion concerns, it becomes necessary to model the effect of geology, completion design, and well parameters on production using models that can capture both spatial and temporal variability of the covariates on the response variable. We accomplish this using a well-formulated spatio-temporal (ST) model. In this paper, we present a multi-basin study of production performance evaluation and applications of ST models for oil and gas data. We analyzed dataset from 10,077 horizontal wells from 2008 to 2019 in five unconventional formations in the USA: Bakken, Marcellus, Eagleford, Wolfcamp, and Bone Spring formations. We evaluated well production performance and performance of new completions over time. Results show increased productivity of oil and gas since 2008. Also, the Bakken wells performed better for the counties evaluated. We present two methods for fitting spatio-temporal models: fixed rank kriging and ST generalized additive models using thin plate and cubic regression splines as basis functions in the spline-based smooths. Results show a significant effect on production by the smooth term, accounting for between 60 and 95% of the variability in the six-month production. Overall, we saw a better production response to completions for the gas formations compared to oil-rich plays. The results highlight the benefits of spatio-temporal models in production prediction as it implicitly accounts for geology and technological changes with time.

scholarly journals Spatio-Temporal Modeling of Small-Scale Ultrafine Particle Variability Using Generalized Additive Models

2021 ◽  
Vol 14 (1) ◽  
pp. 313
Author(s):  
Alessandra Gaeta ◽  
Gianluca Leone ◽  
Alessandro Di Menno di Bucchianico ◽  
Mariacarmela Cusano ◽  
Raffaela Gaddi ◽  
...  
Keyword(s):  
Ultrafine Particles ◽  
Current Knowledge ◽  
Generalized Additive Models ◽  
Ultrafine Particle ◽  
Ambient Air ◽  
Urban Morphology ◽  
Additive Models ◽  
Small Scale ◽  
Spatio Temporal ◽  
The University

High-resolution measurements of ultrafine particle concentrations in ambient air are needed for the study of health human effects of long-term exposure. This work, carried out in the framework of the VIEPI project (Integrated Evaluation of Indoor Particulate Exposure), aims to extend current knowledge on small-scale spatio-temporal variability of Particle Number Concentration (PNC, considered a proxy of the ultrafine particles) at a local scale domain (1 km × 1 km). PNC measurements were made in the university district of San Lorenzo in Rome using portable condensation particle counters for 7 consecutive days at 21 sites in November 2017 and June 2018. Generalized Additive Models (GAMs) were performed in the area for winter, summer and the overall period. The log-transformed two-hour PNC averages constitute the response variable, and covariates were grouped by urban morphology, land use, traffic and meteorology. Winter PNC values were about twice the summer ones. PNC recorded in the university area were significantly lower than those observed in the external routes. GAMs showed a rather satisfactory result in order to capture the spatial variability, in accordance with those of other previous studies: variances were equal to 71.1, 79.7 and 84%, respectively, for winter, summer and the overall period.

scholarly journals Geocoding and spatio-temporal modeling of long-term PM2.5 and NO2 exposure: the Mexican Teachers' Cohort

2021 ◽  
Author(s):  
Cervantes - Martínez Karla ◽  
Riojas - Rodríguez Horacio ◽  
Díaz - Ávalos Carlos ◽  
Moreno - Macías Hortensia ◽  
López - Ridaura Ruy ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Generalized Additive Models ◽  
Epidemiological Studies ◽  
Additive Models ◽  
Temporal Modeling ◽  
Secondary Sources ◽  
Temporal Models ◽  
Out Of Sample ◽  
Spatio Temporal

Abstract Epidemiological studies on the effects of air pollution in Mexico often use the environmental concentrations of monitors closest to the home as exposure proxies, yet this approach disregards the space gradients of pollutants and assumes that individuals have no intra-city mobility. Our aim was to develop high-resolution spatial and temporal models for predicting long-term exposure to PM2.5 and NO2 in a population of ~ 16 500 participants from the Mexican Teachers’ Cohort study. We geocoded the home and work addresses of participants. Using information from secondary sources on geographic and meteorological variables as well as other pollutants, we fitted two generalized additive models to predict monthly PM2.5 and NO2 concentrations in the 2004–2019 period. The models were evaluated through 10-fold cross validation. Both showed high predictive accuracy with out-of-sample data and no overfitting (CV RMSE = 0.102 for PM2.5 and CV RMSE = 4.497 for NO2). Participants were exposed to a monthly average of 24.38 (6.78) µg/m3 of PM2.5 and 28.21 (8.00) ppb of NO2 during the study period. These models offer a solid alternative for estimating PM2.5 and NO2 exposure with high spatio-temporal resolution for epidemiological studies in the Valle de México region.

Sign in / Sign up

Export Citation Format

Share Document