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Inclusion of edaphic predictors for enhancement of models to determine distribution of soil-transmitted helminths: the case of Zimbabwe

Overview of attention for article published in Parasites & Vectors, January 2018
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  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

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Title
Inclusion of edaphic predictors for enhancement of models to determine distribution of soil-transmitted helminths: the case of Zimbabwe
Published in
Parasites & Vectors, January 2018
DOI 10.1186/s13071-017-2586-6
Pubmed ID
Authors

Nicholas Midzi, Blessing Kavhu, Portia Manangazira, Isaac Phiri, Susan L. Mutambu, Cremants Tshuma, Moses J. Chimbari, Shungu Munyati, Stanely M. Midzi, Lincon Charimari, Anatoria Ncube, Masceline J. Mutsaka-Makuvaza, White Soko, Emmanuel Madzima, Gibson Hlerema, Joel Mbedzi, Gibson Mhlanga, Mhosisi Masocha

Abstract

Reliable mapping of soil-transmitted helminth (STH) parasites requires rigorous statistical and machine learning algorithms capable of integrating the combined influence of several determinants to predict distributions. This study tested whether combining edaphic predictors with relevant environmental predictors improves model performance when predicting the distribution of STH, Ascaris lumbricoides and hookworms at a national scale in Zimbabwe. Geo-referenced parasitological data obtained from a 2010/2011 national survey indicating a confirmed presence or absence of STH among school children aged 10-15 years was used to calibrate ten species distribution models (SDMs). The performance of SDMs calibrated with a set of environmental and edaphic variables was compared to that of SDMs calibrated with environmental variables only. Model performance was evaluated using the true skill statistic and receiver operating characteristic curve. Results show a significant improvement in model performance for both A. lumbricoides and hookworms for all ten SDMs after edaphic variables were combined with environmental variables in the modelling of the geographical distribution of the two STHs at national scale. Using the top three performing models, a consensus prediction was developed to generate the first continuous maps of the potential distribution of the two STHs in Zimbabwe. The findings from this study demonstrate significant model improvement if relevant edaphic variables are included in model calibration resulting in more accurate mapping of STH. The results also provide spatially-explicit information to aid targeted control of STHs in Zimbabwe and other countries with STH burden.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 17%
Student > Bachelor 5 9%
Student > Ph. D. Student 5 9%
Other 4 8%
Researcher 4 8%
Other 6 11%
Unknown 20 38%
Readers by discipline Count As %
Nursing and Health Professions 6 11%
Medicine and Dentistry 5 9%
Agricultural and Biological Sciences 4 8%
Sports and Recreations 3 6%
Immunology and Microbiology 2 4%
Other 10 19%
Unknown 23 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 January 2018.
All research outputs
#12,746,763
of 23,016,919 outputs
Outputs from Parasites & Vectors
#2,056
of 5,506 outputs
Outputs of similar age
#199,127
of 441,339 outputs
Outputs of similar age from Parasites & Vectors
#63
of 145 outputs
Altmetric has tracked 23,016,919 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,506 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 441,339 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.