Dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis in Biskra Province in Algeria

BACKGROUND AND OBJECTIVES: The present study aimed to examine the dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis (CL) in Biskra province, the largest focus of CL in Algeria, recording every year the highest incidence of CL in the country. The goal was to fi...

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Main Author: Schehrazad Selmane
Format: Article
Language:English
Published: King Faisal Specialist Hospital and Research Centre 2015-11-01
Series:Annals of Saudi Medicine
Online Access:https://www.annsaudimed.net/doi/full/10.5144/0256-4947.2015.445
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spelling doaj-084dd3f137e949e3ba17d2b381b8e4112020-11-24T20:43:02ZengKing Faisal Specialist Hospital and Research CentreAnnals of Saudi Medicine0256-49470975-44662015-11-0135644544910.5144/0256-4947.2015.445asm-6-445Dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis in Biskra Province in AlgeriaSchehrazad Selmane0From the University of Science and Technology Houari Boumedienne, Faculty of Mathematics, Algiers, AlgeriaBACKGROUND AND OBJECTIVES: The present study aimed to examine the dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis (CL) in Biskra province, the largest focus of CL in Algeria, recording every year the highest incidence of CL in the country. The goal was to find the relationship between climate factors and CL incidence and identify the best model to estimate the variability among future CL cases. METHODS: We carried out a time series analysis based on the Box-Jenkins method to fit an autoregressive moving average (ARMA) model incorporating climate factors to the monthly recorded CL cases in Biskra province from 2000 to 2014. RESULTS: An ARMA (3,3) model incorporating temperature at a lag of 5 months and relative humidity was appropriate for forecasting the monthly data for CL between 2000 and 2009 in Biskra province. Temperature had a higher effect followed by relative humidity. The model was used for predicting monthly CL cases from January 2010 to December 2014; the predictions matched the recorded data. CONCLUSIONS: ARMA models produce reliable models for prediction of CL cases provided that climate variables are available. The models could assist public health services in preparing for the future. This is an optimistic finding for forecasting CL by means of a surveillance system using climate information.https://www.annsaudimed.net/doi/full/10.5144/0256-4947.2015.445
collection DOAJ
language English
format Article
sources DOAJ
author Schehrazad Selmane
spellingShingle Schehrazad Selmane
Dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis in Biskra Province in Algeria
Annals of Saudi Medicine
author_facet Schehrazad Selmane
author_sort Schehrazad Selmane
title Dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis in Biskra Province in Algeria
title_short Dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis in Biskra Province in Algeria
title_full Dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis in Biskra Province in Algeria
title_fullStr Dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis in Biskra Province in Algeria
title_full_unstemmed Dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis in Biskra Province in Algeria
title_sort dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis in biskra province in algeria
publisher King Faisal Specialist Hospital and Research Centre
series Annals of Saudi Medicine
issn 0256-4947
0975-4466
publishDate 2015-11-01
description BACKGROUND AND OBJECTIVES: The present study aimed to examine the dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis (CL) in Biskra province, the largest focus of CL in Algeria, recording every year the highest incidence of CL in the country. The goal was to find the relationship between climate factors and CL incidence and identify the best model to estimate the variability among future CL cases. METHODS: We carried out a time series analysis based on the Box-Jenkins method to fit an autoregressive moving average (ARMA) model incorporating climate factors to the monthly recorded CL cases in Biskra province from 2000 to 2014. RESULTS: An ARMA (3,3) model incorporating temperature at a lag of 5 months and relative humidity was appropriate for forecasting the monthly data for CL between 2000 and 2009 in Biskra province. Temperature had a higher effect followed by relative humidity. The model was used for predicting monthly CL cases from January 2010 to December 2014; the predictions matched the recorded data. CONCLUSIONS: ARMA models produce reliable models for prediction of CL cases provided that climate variables are available. The models could assist public health services in preparing for the future. This is an optimistic finding for forecasting CL by means of a surveillance system using climate information.
url https://www.annsaudimed.net/doi/full/10.5144/0256-4947.2015.445
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