Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa.

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Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa.

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2018 nî lūn-bûn

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2018年の論文

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年學術文章

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2018年學術文章

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2018年學術文章

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Evaluating Efficacy of Landsat ...... Vhembe District, South Africa.

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Evaluating Efficacy of Landsat ...... Vhembe District, South Africa.

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Evaluating Efficacy of Landsat ...... Vhembe District, South Africa.

@en

Evaluating Efficacy of Landsat ...... Vhembe District, South Africa.

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Evaluating Efficacy of Landsat ...... Vhembe District, South Africa.

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Evaluating Efficacy of Landsat ...... Vhembe District, South Africa.

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P1476

Evaluating Efficacy of Landsat ...... Vhembe District, South Africa.

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P2093

Clement Adjorlolo

http://www.w3.org/2001/XMLSchema#string

Jane M Olwoch

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P2860

The use of remotely sensed environmental data in the study of malaria

Evaluating local vegetation cover as a risk factor for malaria transmission: a new analytical approach using ImageJ

Towards the elimination of malaria in South Africa: a review of surveillance data in Mutale Municipality, Limpopo Province, 2005 to 2010

A climate-based distribution model of malaria transmission in sub-Saharan Africa.

Driving forces for changes in geographical distribution of Ixodes ricinus ticks in Europe

Long-run relative importance of temperature as the main driver to malaria transmission in Limpopo Province, South Africa: a simple econometric approach.

Ranking malaria risk factors to guide malaria control efforts in African highlands.

Assessing the relationship between environmental factors and malaria vector breeding sites in Swaziland using multi-scale remotely sensed data.

Predicting potential ranges of primary malaria vectors and malaria in northern South America based on projected changes in climate, land cover and human population.

Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data.

P2888

s10393-017-1307-0

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scholarly article

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10.1007/S10393-017-1307-0

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P50

Oupa Malahlela

P577

2018-01-12T00:00:00Z

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P698

29330677

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