Eco-environmental, living conditions and farming issues linked to zoonotic cutaneous leishmaniasis transmission in Central Tunisia: a population based survey

Bellali, Hedia; Alaya, Nissaf Ben; Ahmadi, Zaher El; Ennigrou, S.; Chahed, Mohamed Kouni

doi:10.5455/166654/ijtmph.

Cited by 7 publications

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“…ZCL remains primarily a disease responsible for considerable morbidity and disfigurement [ 2 ]. The disease is endemic, essentially in the rural areas of southern and central regions of Tunisia where infrastructure is limited and sanitation is inadequate [ 13 – 15 ]. In these regions, the climate is favorable to the development of sand fly species, and consequently to the transmission of the disease; so the population is exposed while managing farm activities [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling zoonotic cutaneous leishmaniasis incidence in central Tunisia from 2009-2015: Forecasting models using climate variables as predictors

et al. 2017

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Transmission of zoonotic cutaneous leishmaniasis (ZCL) depends on the presence, density and distribution of Leishmania major rodent reservoir and the development of these rodents is known to have a significant dependence on environmental and climate factors. ZCL in Tunisia is one of the most common forms of leishmaniasis. The aim of this paper was to build a regression model of ZCL cases to identify the relationship between ZCL occurrence and possible risk factors, and to develop a predicting model for ZCL's control and prevention purposes. Monthly reported ZCL cases, environmental and bioclimatic data were collected over 6 years (2009–2015). Three rural areas in the governorate of Sidi Bouzid were selected as the study area. Cross-correlation analysis was used to identify the relevant lagged effects of possible risk factors, associated with ZCL cases. Non-parametric modeling techniques known as generalized additive model (GAM) and generalized additive mixed models (GAMM) were applied in this work. These techniques have the ability to approximate the relationship between the predictors (inputs) and the response variable (output), and express the relationship mathematically. The goodness-of-fit of the constructed model was determined by Generalized cross-validation (GCV) score and residual test. There were a total of 1019 notified ZCL cases from July 2009 to June 2015. The results showed seasonal distribution of reported ZCL cases from August to January. The model highlighted that rodent density, average temperature, cumulative rainfall and average relative humidity, with different time lags, all play role in sustaining and increasing the ZCL incidence. The GAMM model could be applied to predict the occurrence of ZCL in central Tunisia and could help for the establishment of an early warning system to control and prevent ZCL in central Tunisia.

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Section: Introductionmentioning

confidence: 99%

Modeling zoonotic cutaneous leishmaniasis incidence in central Tunisia from 2009-2015: Forecasting models using climate variables as predictors

et al. 2017

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“…The emergence of ZCL coincides with the extension of land farming and water forage and exploitation in this area since the 1970s and the settlement of human population around irrigated fields [15] at the edges of the salt pan "Garaat Njila". Previous studies showed high endemicity in districts which are close to this salt pan with agricultural activities as mainly occupation such as Hichria, Bir Bader and Zefzef [16]. The most common halolphytic plant in the salt pan "Garaat Njila" is chenopods which constitute the food of rodent reservoirs.…”

Section: A Rt I C L E I N F O Abstractmentioning

confidence: 99%

Effect of temperature, rainfall and relative density of rodent reservoir hosts on zoonotic cutaneous leishmaniasis incidence in Central Tunisia

2017

Asian Pac J Trop Dis

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“…Infected female sandflies transmit the disease to humans living in rural areas, where pollution and unawareness of people are highly present. The life cycle of the ZCL is highly related to bioclimatic and environment change [11]. In Tunisia, the disease first emerged in 1982 in the region of Sidi Bouzid, central Tunisia [12].…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance Analysis for Generalized Additive and Generalized Linear Modeling in Epidemiology

Talmoudi¹,

Bellali²,

Nissaf³

et al. 2017

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Abstract-Most environmental-epidemiological researches emphasize modeling as the causal link of different events (e.g., hospital admission, death, disease emergency). There has been a particular concern in the use of the Generalized Linear Models (GLMs) in the field of epidemiology. However, recent studies in this field highlighted the use of non-parametric techniques, especially the Generalized Additive Models (GAMs). The aim of this work is to compare performance of both methods in the field of epidemiology. Comparison is done in terms of sharpening the relation between the predictors and the response variable as well as in predicting outbreaks. The most suitable method is then adopted to elucidate the impact of bioclimatic factors on the emergence of the zoonotic cutaneous leishmaniasis (ZCL) disease in Central Tunisia. Monthly epidemiologic and bioclimatic data from July 2009 to June 2016 are used in this study. Akaike information criterion, R-squared and F-statistic are used to compare model performance, while the root mean square error is used for checking predictive accuracy for both models. Our results show the potential of GAM model to provide a better assessment of the nonlinear relations and to give a high predictive accuracy compared to GLMs. The results also stress the inaccurate estimation of risk factors when linear trends are used to model nonlinear structured data.

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Eco-environmental, living conditions and farming issues linked to zoonotic cutaneous leishmaniasis transmission in Central Tunisia: a population based survey

Cited by 7 publications

References 0 publications

Modeling zoonotic cutaneous leishmaniasis incidence in central Tunisia from 2009-2015: Forecasting models using climate variables as predictors

Modeling zoonotic cutaneous leishmaniasis incidence in central Tunisia from 2009-2015: Forecasting models using climate variables as predictors

Effect of temperature, rainfall and relative density of rodent reservoir hosts on zoonotic cutaneous leishmaniasis incidence in Central Tunisia

Comparative Performance Analysis for Generalized Additive and Generalized Linear Modeling in Epidemiology

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