Regression Analysis on Scorpion Envenomation and Climate Variables in M'Sila Province, Algeria from 2001 to 2010

Schehrazad Selmane; Mohamed L'hadj

doi:https://doi.org/10.14445/22315373/IJMTT-V13P501

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 13 | Number 1 | Year 2014 | Article Id. IJMTT-V13P501 | DOI : https://doi.org/10.14445/22315373/IJMTT-V13P501

Regression Analysis on Scorpion Envenomation and Climate Variables in M'Sila Province, Algeria from 2001 to 2010

Schehrazad Selmane, Mohamed L'hadj

Citation :

Schehrazad Selmane, Mohamed L'hadj, "Regression Analysis on Scorpion Envenomation and Climate Variables in M'Sila Province, Algeria from 2001 to 2010," International Journal of Mathematics Trends and Technology (IJMTT), vol. 13, no. 1, pp. 1-9, 2014. Crossref, https://doi.org/10.14445/22315373/IJMTT-V13P501

Abstract

Scorpionism represents a serious public health problem in Algeria. More than 68% of the national population is at risk of scorpion stings. M'Sila ranks among the endemic provinces of the country and records every year a high incidence of scorpion stings.A survey on epidemiological characteristics of scorpion stings was established. Using the monthly recorded scorpion sting data for the period 2001-2010 for M'Sila province, the linkage between scorpion stings and weather conditions was demonstrated through time series analysis and regression analysis considering the number of scorpion stings as dependent variable and climatic conditions as independent variables. The temperature, precipitation and wind are the retained climate factors, and the temperature has the higher effect. The model predicted the number of scorpion stings in 2011 with a good accuracy. The model could be used by public health makers of the province to anticipate the demand for antivenoms and symptomatic drugs so that they can be distributed in advance. This raises optimism for forecasting scorpion stings provided the availability of appropriate climate information.

Keywords

Climate, Forecasting, Regression Analysis, Scorpion, Temperature.

References

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