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Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence

Overview of attention for article published in Malaria Journal, November 2016
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1 tweeter

Citations

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Title
Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence
Published in
Malaria Journal, November 2016
DOI 10.1186/s12936-016-1602-1
Pubmed ID
Authors

Mohammad Y. Anwar, Joseph A. Lewnard, Sunil Parikh, Virginia E. Pitzer

Abstract

Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Kenya 1 <1%
Unknown 101 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 18 18%
Student > Bachelor 13 13%
Student > Ph. D. Student 12 12%
Student > Doctoral Student 9 9%
Researcher 8 8%
Other 18 18%
Unknown 24 24%
Readers by discipline Count As %
Computer Science 15 15%
Medicine and Dentistry 14 14%
Nursing and Health Professions 7 7%
Mathematics 7 7%
Agricultural and Biological Sciences 6 6%
Other 27 26%
Unknown 26 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 02 December 2016.
All research outputs
#7,555,353
of 8,713,305 outputs
Outputs from Malaria Journal
#2,771
of 3,054 outputs
Outputs of similar age
#241,932
of 298,885 outputs
Outputs of similar age from Malaria Journal
#80
of 91 outputs
Altmetric has tracked 8,713,305 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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We're also able to compare this research output to 91 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.