Remote sensing, land cover changes, and vector-borne diseases: Use of high spatial resolution satellite imagery to map the risk of occurrence of cutaneous leishmaniasis in Ghardaïa, Algeria

Garni, Rafik; Tran, Annelise; Guis, Hélène; Baldet, Thierry; Benallal, Kamal Eddine; Boubidi, S. C.; Harrat, Zoubir

doi:10.1016/j.meegid.2014.09.036

Cited by 24 publications

(17 citation statements)

References 43 publications

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“…This process needs a profound reflection and a long period of time [50]; and (ii) author bias may be associated with the identification, choice, and interpretation of the disease risk factors [29,30]. In the present study, a systematic literature review of the studies dealing with the relations between LULC and malaria transmission in the Amazon was done prior to establishing the model which, in conjunction with the consensual nature of the knowledge-based model, tends to reduce investigator bias and make the NLHI pertinent.…”

Section: Discussionmentioning

confidence: 99%

“…The review by Stevens et al [29] emphasizes that knowledge-based risk mapping could provide a pertinent framework for modeling qualitative and quantitative variables in data-sparse contexts, integrating statistical methods and expert knowledge concerning vectors, reservoirs, and humans, and/or their interactions. Garni et al [30] established hazard maps (i.e., presence of both vectors and reservoirs) using expert and bibliographic knowledge and remote sensing data, and combining them with vulnerability maps for mapping the risk of occurrence of cutaneous leishmaniasis in Ghardaïa in Algeria. Vector habitat and risk maps were established using available entomological and epidemiological data.…”

Section: Introductionmentioning

confidence: 99%

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Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil

Roux

Dessay

et al. 2016

Remote Sensing

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Malaria remains one of the most common vector-borne diseases in the world and the definition of novel control strategies can benefit from the modeling of transmission processes. However, data-driven models are often difficult to build, as data are very often incomplete, heterogeneous in nature and in quality, and/or biased. In this context, a knowledge-based approach is proposed to build a robust and general landscape-based hazard index for malaria transmission that is tailored to the Amazonian region. A partial knowledge-based model of the risk of malaria transmission in the Amazonian region, based on landscape features and extracted from a systematic literature review, was used. Spatialization of the model was obtained by generating land use and land cover maps of the cross-border area between French Guiana and Brazil, followed by computing and combining landscape metrics to build a set of normalized landscape-based hazard indices. An empirical selection of the best index was performed by comparing the indices in terms of adequacy with the knowledge-based model, intelligibility and correlation with P. falciparum incidence rates. The selected index is easy to interpret and successfully represents the current knowledge about the role played by landscape patterns in malaria transmission within the study area. It was significantly associated with P. falciparum incidence rates, using the Pearson and Spearman correlation coefficients (up to 0.79 and 0.75, respectively; p-value < 0.001), and the linear regression coefficient of determination (reaching 0.63; p-values < 0.001). This study establishes a spatial knowledge-driven, landscape-based hazard malaria index using remote sensing that can be easily produced on a regular basis and might be useful for malaria prediction, surveillance, and control.Keywords: remote sensing; land use and land cover; landscape metric; knowledge-based hazard modeling; malaria; cross-border area between French Guiana and Brazil

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil

Roux

Dessay

et al. 2016

Remote Sensing

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“…Early efforts focused on mapping the global distribution of key drivers of malaria transmission [55,56]. These approaches have since been used to estimate the burden from a wide range of pathogens [57][58][59][60], vectors [61], and host reservoirs [62]. These analyses have allowed disease burden and risk to be estimated in areas with limited surveillance capabilities, expanding our understanding of the global burden of many pathogens.…”

Section: Big Datamentioning

confidence: 99%

Trends in the Mechanistic and Dynamic Modeling of Infectious Diseases

et al. 2016

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The dynamics of infectious disease epidemics are driven by interactions between individuals with differing disease status (e.g., susceptible, infected, immune). Mechanistic models that capture the dynamics of such Bdependent happenings^are a fundamental tool of infectious disease epidemiology. Recent methodological advances combined with access to new data sources and computational power have resulted in an explosion in the use of dynamic models in the analysis of emerging and established infectious diseases. Increasing use of models to inform practical public health decision making has challenged the field to develop new methods to exploit available data and appropriately characterize the uncertainty in the results. Here, we discuss recent advances and areas of active research in the mechanistic and dynamic modeling of infectious disease. We highlight how a growing emphasis on data and inference, novel forecasting methods, and increasing access to Bbig data^are changing the field of infectious disease dynamics. We showcase the application of these methods in phylodynamic research, which combines mechanistic models with rich sources of molecular data to tie genetic data to population-level disease dynamics. As dynamics and mechanistic modeling methods mature and are increasingly tied to principled statistical approaches, the historic separation between the infectious disease dynamics and Btraditional^epidemiologic methods is beginning to erode; this presents new opportunities for cross pollination between fields and novel applications.

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“…Ecological niche modelling of vectors and/or pathogens may have a strong impact on the development of control strategies against the respective vector‐borne diseases . High‐resolution spatial satellite imagery has been employed by a number of recent studies in mapping CL and identifying potential risk zones at local and global levels . The present study also identified a zone of CL transmission in Chitral, Pakistan.…”

Section: Discussionmentioning

confidence: 91%

Environmental risk modelling and potential sand fly vectors of cutaneous leishmaniasis in Chitral district: a leishmanial focal point of mount Tirich Mir, Pakistan

Zaidi

Fatima

Jan

et al. 2017

Tropical Med Int Health

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Remote sensing, land cover changes, and vector-borne diseases: Use of high spatial resolution satellite imagery to map the risk of occurrence of cutaneous leishmaniasis in Ghardaïa, Algeria

Cited by 24 publications

References 43 publications

Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil

Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil

Trends in the Mechanistic and Dynamic Modeling of Infectious Diseases

Environmental risk modelling and potential sand fly vectors of cutaneous leishmaniasis in Chitral district: a leishmanial focal point of mount Tirich Mir, Pakistan

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