Volume 6, Issue 1, June 2018, Page: 25-30
Simulating the Nutritional Traits of Populations at the Small Area Levels Using Spatial Microsimulation Modelling Approach
Md. Abdul Hakim, Department of Food Technology and Nutritional Science, Mawlana Bhashani Science and Technology University, Santosh, Bangladesh
Azizur Rahman, School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, Australia
Received: Feb. 12, 2018;       Accepted: Apr. 3, 2018;       Published: May 5, 2018
DOI: 10.11648/j.cbb.20180601.13      View  1604      Downloads  131
Nutritional traits simulating at an awesome local geographic level is vital for effective nutritional promotion programs, provision of better nutritional services and population-specific nutritional planning and management. Deficient in micro-dataset readily available for attributes of individuals at small areas affects the local and national agencies on the route ahead of their smooth managing of the serious nutritional issues and related risks in the community. A solution of this ongoing challenge would be to form a method to simulate reliable small area statistics. This paper provides a dashing appraisal of the methodologies for simulating nutritional traits of populations at geographical limited areas. Findings reveal that microsimulation-based spatial models have the significant robustness over the other methods stated in this study representing a more precise means of simulatingn utrition-related traits of population at the small area levels.
Nutritional Traits, Microsimulation Modelling, SWOT Analysis, Multilevel Models, Small Area Estimates
To cite this article
Md. Abdul Hakim, Azizur Rahman, Simulating the Nutritional Traits of Populations at the Small Area Levels Using Spatial Microsimulation Modelling Approach, Computational Biology and Bioinformatics. Vol. 6, No. 1, 2018, pp. 25-30. doi: 10.11648/j.cbb.20180601.13
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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