International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
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Cutaneous Leishmaniasis Modeling: the case of Msila Province in Algeria

Volume 10, Issue 1, January 2015, Pages 149–154

 Cutaneous Leishmaniasis Modeling: the case of Msila Province in Algeria

H. Elhadj1, Y. Kerboua Ziari2, and S. Selmane3

1 High National School of Statistics and Applied Economics, Algiers, Algeria
2 Faculty of Physics, University of Sciences and Technology Houari Boumediene, Algiers, Algeria
3 Faculty of Mathematics, University of Sciences and Technology Houari Boumediene, Algiers, Algeria

Original language: English

Received 20 October 2014

Copyright © 2015 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Cutaneous leishmaniasis is one of the infectious diseases that affects public health and represents a real threat especially in developing countries. The disease is transmitted by the bite of certain species of sandflies and occurs predominantly in warm, humid and tropical climate.
Finding the source of cutaneous leishmaniasis and identifying factors that promote its spread could help to a good prediction of the epidemic in time. The aim of this study is the construction of a statistical model that reproduces the number of affected cases using climate factors influencing the presence of sandflies.
Given the extensive development of the Generalized Linear Models and their performance in modeling count data as well as their adaptation to the problem of overdispersed data, we present the utility and the basic foundations of Poisson and quasi-Poisson regression models. Thereafter, we build a forecasting model that could predict the number of monthly cases of the cutaneous leishmaniasis from climatic factors during the period 2008-2011 in the province of Msila which is one of the Algerian provinces heavily affected by the epidemic in question. In our case the temperature and trend factor were retained in the model. Poisson regression gave a good result after eliminating the effect of overdispersion.

Author Keywords: Cutaneous leishmaniasis, Count data, Generalized Linear Models, Poisson regression, Overdispersion.

How to Cite this Article

H. Elhadj, Y. Kerboua Ziari, and S. Selmane, “Cutaneous Leishmaniasis Modeling: the case of Msila Province in Algeria,” International Journal of Innovation and Applied Studies, vol. 10, no. 1, pp. 149–154, January 2015.