scholarly journals Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models

2013 ◽  
Vol 8 (1) ◽  
pp. 97 ◽  
Author(s):  
Ronaldo G. C. Scholte ◽  
Nadine Schur ◽  
Maria E. Bavia ◽  
Edgar M. Carvalho ◽  
Frédérique Chammartin ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Risk Mapping ◽  
Helminth Infections ◽  
Geostatistical Models

scholarly journals Heat risk mapping through spatial analysis of remotely-sensed data and socioeconomic vulnerability in Hermosillo, México

Urban Climate ◽  
2020 ◽  
Vol 31 ◽  
pp. 100576
Author(s):  
Javier Navarro-Estupiñan ◽  
Agustín Robles-Morua ◽  
Rolando Díaz-Caravantes ◽  
Enrique R. Vivoni
Keyword(s):  
Spatial Analysis ◽  
Remotely Sensed ◽  
Risk Mapping ◽  
Remotely Sensed Data ◽  
Socioeconomic Vulnerability ◽  
Heat Risk

scholarly journals Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

2020 ◽  
Vol 5 (10) ◽  
pp. e002919 ◽  
Author(s):  
Julius Nyerere Odhiambo ◽  
Chester Kalinda ◽  
Peter M Macharia ◽  
Robert W Snow ◽  
Benn Sartorius
Keyword(s):  
Spatial Clustering ◽  
Malaria Incidence ◽  
Data Extraction ◽  
Malaria Risk ◽  
Risk Mapping ◽  
Data Availability ◽  
Sub Saharan Africa ◽  
Clustering Methods ◽  
Manual Search ◽  
Geostatistical Models

BackgroundApproaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).MethodsA systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.ResultsOne hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.ConclusionsOur review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.

Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models

Acta Tropica ◽  
2014 ◽  
Vol 132 ◽  
pp. 57-63 ◽  
Author(s):  
Ronaldo G.C. Scholte ◽  
Laura Gosoniu ◽  
John B. Malone ◽  
Frédérique Chammartin ◽  
Jürg Utzinger ◽  
...  
Keyword(s):  
Risk Mapping ◽  
Geostatistical Models ◽  
Predictive Risk ◽  
Predictive Risk Mapping

scholarly journals Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa

Parasitology ◽  
2009 ◽  
Vol 136 (13) ◽  
pp. 1683-1693 ◽  
Author(s):  
C. Simoonga ◽  
J. Utzinger ◽  
S. Brooker ◽  
P. Vounatsou ◽  
C. C. Appleton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Intermediate Host ◽  
Disease Risk ◽  
Risk Mapping ◽  
Transmission Dynamics ◽  
Geographical Information ◽  
Quality Data ◽  
Parasitic Infections ◽  
Host Snail

SUMMARYBeginning in 1970, the potential of remote sensing (RS) techniques, coupled with geographical information systems (GIS), to improve our understanding of the epidemiology and control of schistosomiasis in Africa, has steadily grown. In our current review, working definitions of RS, GIS and spatial analysis are given, and applications made to date with RS and GIS for the epidemiology and ecology of schistosomiasis in Africa are summarised. Progress has been made in mapping the prevalence of infection in humans and the distribution of intermediate host snails. More recently, Bayesian geostatistical modelling approaches have been utilized for predicting the prevalence and intensity of infection at different scales. However, a number of challenges remain; hence new research is needed to overcome these limitations. First, greater spatial and temporal resolution seems important to improve risk mapping and understanding of transmission dynamics at the local scale. Second, more realistic risk profiling can be achieved by taking into account information on people's socio-economic status; furthermore, future efforts should incorporate data on domestic access to clean water and adequate sanitation, as well as behavioural and educational issues. Third, high-quality data on intermediate host snail distribution should facilitate validation of infection risk maps and modelling transmission dynamics. Finally, more emphasis should be placed on risk mapping and prediction of multiple species parasitic infections in an effort to integrate disease risk mapping and to enhance the cost-effectiveness of their control.

Bulk tank milk prevalence and production losses, spatial analysis, and predictive risk mapping of Ostertagia ostertagi infections in Mexican cattle herds

2018 ◽  
Vol 117 (5) ◽  
pp. 1613-1620 ◽  
Author(s):  
Abel Villa-Mancera ◽  
César Pastelín-Rojas ◽  
Jaime Olivares-Pérez ◽  
Alejandro Córdova-Izquierdo ◽  
Alejandro Reynoso-Palomar
Keyword(s):  
Spatial Analysis ◽  
Risk Mapping ◽  
Bulk Tank Milk ◽  
Ostertagia Ostertagi ◽  
Bulk Tank ◽  
Cattle Herds ◽  
Predictive Risk ◽  
Predictive Risk Mapping ◽  
Production Losses
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