Use of remote sensing and geographical information systems to identify environmental features that influence the distribution of paramphistomosis in sheep from the southern Italian Apennines

Cringoli, Giuseppe; Taddei, R.; Rinaldi, Laura; Veneziano, Vincenzo; Musella, Vincenzo; Cascone, Carmela; Sibilio, G.; Malone, J. B.

doi:10.1016/j.vetpar.2004.03.011

Cited by 43 publications

(30 citation statements)

References 13 publications

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“…Diseases with vectors such as snails, ticks, flies and mosquitoes are ideal subjects due to their dependence upon environmental variables at some point in their life cycle. Researchers studying paramphistomosis, caused by a rumen fluke in sheep that completes a portion of its lifecycle within an aquatic snail vector and encysts on vegetation which may be eaten by ruminant animals, determined that environmental variables such as remotely-sensed NDVI, land cover, elevation, slope, aspect and the presence of bodies of water on pastureland are statistically significant with regard to distribution of the disease in the southern Italian Apennines (Cringoli et al 2004).…”

Section: Ndvi ¼mentioning

confidence: 99%

Malaria incidence in Nairobi, Kenya and dekadal trends in NDVI and climatic variables

Fastring

Griffith

2009

Geocarto International

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The primary objective of this research was to determine if the remotely-sensed metric, Normalised Difference Vegetation Index (NDVI) and ground-collected dekadal climatological variables were useful predictors of future malaria outbreaks in an epidemic-prone area of Nairobi, Kenya. Data collected consisted of 36 dekadal (10-day) periods for the variables rainfall, temperature and NDVI along with yearly documented malaria admissions in 2003 for Nairobi, Kenya. Linear regression models were built for malaria cases reported in Nairobi, Kenya, as the dependent variable and various timebased groupings of temperature, rainfall and NDVI data from the dekads in both the current and the previous month as the independent variables. Data from 2003 show that malaria incidence in any given month is best predicted (R 2 ¼ 0.881, p 5 0.001) by the average NDVI for the 30 days including the final two dekads of the previous month and first dekad of the current month, and by the average rainfall for the 30 days including the three dekads of rainfall data from the prior month. Forecasting an outbreak in an epidemic zone would allow public health entities to plan for and disseminate resources to the general public such as antimalarials and insecticide impregnated bed nets. In addition, vector control measures could be implemented to slow the rate of transmission in the impacted population.

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Section: Ndvi ¼mentioning

confidence: 99%

Malaria incidence in Nairobi, Kenya and dekadal trends in NDVI and climatic variables

Fastring

Griffith

2009

Geocarto International

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“…In most of cases, adult fl ukes inhabit the rumen and/ or reticulum without damage (Rolfe et al, 1994), while immature fl ukes are found in the upper small intestine where can cause serious morbidity by protein loss, decrease of milk production and weight, oedema and eventually death (Horak, 1971;Rangel-Ruiz et al, 2003, Rolfe et al, 1994. Studies carried out in Europe (Mage et al, 2002;Cringoli et al, 2004 ;Rinaldi et al, 2005;Díaz et al, 2007;Rieu et al, 2007;Foster et al, 2008;Murphy et al, 2008, González-Warleta et al, 2013 have shown an increased prevalence in the last few years. This should probably been caused by the climate changes with warmer winters and wetter summers that positively infl uenced the life-cycle of the parasite (Gordon et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Calicophoron daubneyi in sheep and cattle of Sardinia, Italy

et al. 2016

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SummaryThis study aimed to investigate the prevalence of paramphistomosis and confi rm the species identity of rumen fl ukes from sheep and cattle of Sardinia (Italy), by molecular methods. From 2011 to 2014, 381 sheep and 59 cattle farms were selected and individual faecal samples were run on 15 sheep and 5 cattle for each farm, respectively. The prevalence at the slaughterhouse was calculated by examination of 356 sheep and 505 cattle. 13adult fl ukes collected from sheep and cattle and 5 belonging to the historical collection of Laboratory of Parasitology at the Department of Veterinary Medicine of Sassari, previously classifi ed as Paramphistomum spp., were used for PCR amplifi cation and sequencing of the ITS2+ rDNA. Previously classifi ed Paramphistomum leydeni from South America were used as controls. The EPG prevalence was 13.9 % and 55.9 % for sheep and cattle farms respectively. At slaughterhouses, paramphistomes were found in 2 % of the sheep and 10.9 % of the examined cows. Conversely to the latest reports, the sequences comparison showed that all the Sardinian rumen fl ukes belong to Calicophoron daubneyi.

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“…One among the many analytical methods employed to achieve this is through the extraction of environmental variables that are potential risk factors of a disease from data products of remotely sensed images (land cover/land use, elevation, soil survey), which in turn are modelled using logistic or other forms of regressions to estimate the strength of their association with case status. One can frequently find such studies to have extracted environmental variables from an area within one circular buffer zone (spatial extent) surrounding geocoded locations (Cringoli et al, 2004;Gibbs et al, 2006;Gouveia and Prado, 2010;Richards et al, 2010;Raghavan et al, 2011Raghavan et al, , 2012a. Two or more spatial extents have also been used, although relatively sparingly (Ghneim et al, 2007;Charoenpanyanet and Chen, 2008;Mutuku et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Spatial scale effects in environmental risk-factor modelling for diseases

et al. 2013

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Abstract. Studies attempting to identify environmental risk factors for diseases can be seen to extract candidate variables from remotely sensed datasets, using a single buffer-zone surrounding locations from where disease status are recorded. A retrospective case-control study using canine leptospirosis data was conducted to verify the effects of changing buffer-zones (spatial extents) on the risk factors derived. The case-control study included 94 case dogs predominantly selected based on positive polymerase chain reaction (PCR) test for leptospires in urine, and 185 control dogs based on negative PCR. Land cover features from National Land Cover Dataset (NLCD) and Kansas Gap Analysis Program (KS GAP) around geocoded addresses of cases/controls were extracted using multiple buffers at every 500 m up to 5,000 m, and multivariable logistic models were used to estimate the risk of different land cover variables to dogs. The types and statistical significance of risk factors identified changed with an increase in spatial extent in both datasets. Leptospirosis status in dogs was significantly associated with developed high-intensity areas in models that used variables extracted from spatial extents of 500-2000 m, developed medium-intensity areas beyond 2,000 m and up to 3,000 m, and evergreen forests beyond 3,500 m and up to 5,000 m in individual models in the NLCD. Significant associations were seen in urban areas in models that used variables extracted from spatial extents of 500-2,500 m and forest/woodland areas beyond 2,500 m and up to 5,000 m in individual models in Kansas gap analysis programme datasets. The use of ad hoc spatial extents can be misleading or wrong, and the determination of an appropriate spatial extent is critical when extracting environmental variables for studies. Potential work-arounds for this problem are discussed.

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Use of remote sensing and geographical information systems to identify environmental features that influence the distribution of paramphistomosis in sheep from the southern Italian Apennines

Cited by 43 publications

References 13 publications

Malaria incidence in Nairobi, Kenya and dekadal trends in NDVI and climatic variables

Malaria incidence in Nairobi, Kenya and dekadal trends in NDVI and climatic variables

Calicophoron daubneyi in sheep and cattle of Sardinia, Italy

Spatial scale effects in environmental risk-factor modelling for diseases

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