Confounding effects of climatic factors temporally contribute to the prevalence of malaria. In this study, we explore a new framework for assessment and identification of hidden ecological factors to the incidence of malaria. A statistical technique, partial least squares path modeling and exploratory factor analysis, is employed to identify hidden ecological factors. Three hidden factors are identified: Factor I is related to minimum temperature and relative humidity, Factor II is related to maximum temperature and solar radiation and Factor III is related to precipitation and wind speed, respectively. Factor I is identified as the most influential hidden ecological factor of malaria incidence in the study area, as evaluated by communality and Dillon-Goldstein’s indices.
|Number of pages||2|
|Publication status||Published - 9 Jun 2017|
|Event||IS4SI 2017 Summit Digitalisation for a Sustainable Society - Gothenburg, Sweden|
Duration: 12 Jun 2017 → 16 Jun 2019
Bibliographical noteThis article belongs to the Proceedings of the IS4SI 2017 Summit DIGITALISATION FOR A SUSTAINABLE SOCIETY)
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
- malaria incidence
- climatic factors
- structural equation modeling
- partial least square model and hidden factors