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A spatial agent-based model of Anopheles vagus for malaria epidemiology: examining the impact of vector control interventions

Overview of attention for article published in Malaria Journal, October 2017
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Title
A spatial agent-based model of Anopheles vagus for malaria epidemiology: examining the impact of vector control interventions
Published in
Malaria Journal, October 2017
DOI 10.1186/s12936-017-2075-6
Pubmed ID
Authors

Md. Zahangir Alam, S. M. Niaz Arifin, Hasan Mohammad Al-Amin, Mohammad Shafiul Alam, M. Sohel Rahman

Abstract

Malaria, being a mosquito-borne infectious disease, is still one of the most devastating global health issues. The malaria vector Anopheles vagus is widely distributed in Asia and a dominant vector in Bandarban, Bangladesh. However, despite its wide distribution, no agent based model (ABM) of An. vagus has yet been developed. Additionally, its response to combined vector control interventions has not been examined. A spatial ABM, denoted as ABM[Formula: see text], was designed and implemented based on the biological attributes of An. vagus by modifying an established, existing ABM of Anopheles gambiae. Environmental factors such as temperature and rainfall were incorporated into ABM[Formula: see text] using daily weather profiles. Real-life field data of Bandarban were used to generate landscapes which were used in the simulations. ABM[Formula: see text] was verified and validated using several standard techniques and against real-life field data. Using artificial landscapes, the individual and combined efficacies of existing vector control interventions are modeled, applied, and examined. Simulated female abundance curves generated by ABM[Formula: see text] closely follow the patterns observed in the field. Due to the use of daily temperature and rainfall data, ABM[Formula: see text] was able to generate seasonal patterns for a particular area. When two interventions were applied with parameters set to mid-ranges, ITNs/LLINs with IRS produced better results compared to the other cases. Moreover, any intervention combined with ITNs/LLINs yielded better results. Not surprisingly, three interventions applied in combination generate best results compared to any two interventions applied in combination. Output of ABM[Formula: see text] showed high sensitivity to real-life field data of the environmental factors and the landscape of a particular area. Hence, it is recommended to use the model for a given area in connection to its local field data. For applying combined interventions, three interventions altogether are highly recommended whenever possible. It is also suggested that ITNs/LLINs with IRS can be applied when three interventions are not available.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 87 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 17 20%
Researcher 16 18%
Student > Ph. D. Student 10 11%
Student > Bachelor 9 10%
Student > Postgraduate 4 5%
Other 8 9%
Unknown 23 26%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 14%
Engineering 8 9%
Medicine and Dentistry 6 7%
Biochemistry, Genetics and Molecular Biology 5 6%
Computer Science 4 5%
Other 23 26%
Unknown 29 33%
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 09 November 2017.
All research outputs
#14,039,096
of 24,580,204 outputs
Outputs from Malaria Journal
#3,159
of 5,786 outputs
Outputs of similar age
#159,747
of 333,524 outputs
Outputs of similar age from Malaria Journal
#67
of 121 outputs
Altmetric has tracked 24,580,204 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,786 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 333,524 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 51% of its contemporaries.
We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.