Data Analytics of climatic factor influence on the impact of malaria incidence

Babagana Modu, A. Taufiq Asyhari, Yonghong Peng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

2 Citations (Scopus)

Abstract

Predicting association between the malaria risk and its climatic predictors provides individuals and public health officials with prior knowledge for effective prevention and control measures. This paper presents an integrated analysis of a total of 2,148 confirmed cases of malaria incidence for Aboh Mbaise General Hospital, together with the satellite meteorological data downloaded from National Centre for Environmental Prediction (NCEP). By pre-whitening the climatic data sets and analysing their cross-correlation with the malaria incidence, we find that temperature and precipitation have negligible lagged effects on the malaria occurrence in the study area. A further analysis reveals that relative humidity shows significant association (P-value < 0:05) with the malaria incidence. However, regression model with autoregressive error structure AR(1) is then used to establish the relationship between the malaria incidence and relative humidity time series. The findings look to confirm the significant contribution of relative humidity to the malaria incidence in the study area due to its high humidity characteristics (about 74% average relative humidity) occurring mostly during the wet season.

Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781509042401
DOIs
Publication statusPublished - 9 Feb 2017
Externally publishedYes
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece
Duration: 6 Dec 20169 Dec 2016

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
CountryGreece
CityAthens
Period6/12/169/12/16

Keywords

  • Diseases
  • Humidity
  • Time series analysis
  • Temperature distribution
  • Correlation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Control and Optimization
  • Artificial Intelligence

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  • Cite this

    Modu, B., Asyhari, A. T., & Peng, Y. (2017). Data Analytics of climatic factor influence on the impact of malaria incidence. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 [7849891] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2016.7849891