The aim of this article is to predict the rainfall evolution of a sub-Saharan area in which one of the most important freshwater resources is located: Lake Guiers. Characterized by short seasonal rains of three months...The aim of this article is to predict the rainfall evolution of a sub-Saharan area in which one of the most important freshwater resources is located: Lake Guiers. Characterized by short seasonal rains of three months, it experienced a long period of drought in the 1970s. We begin by analyzing the temporal distribution of the rainfall including the variability of the data, with a view to predicting a possible return. For this reason, we present here univariate modeling results of rainfall series collected on three stations in the area. The challenge lies in the adequacy of the parameters for the monthly rainfall series, which generates more or less significant forecast errors on the learning bases because of the missing data. This later motivated their conversion to moving average series. On the other hand, the normality of the latter seems to be rejected by the D’Agostino test. Student’s and Mann-Whitney’s tests confirmed the homogeneity. The autocorlograms show the presence of autoregressive terms in the data. Dickey-Fuller and Mann-Kendall tests reveal both trend and seasonality. The stationarity tests of Dickey-Fuller, Phillips-Perron and KPSS have shown that they are non-stationary. As a result, we did an ARIMA modeling method using the Box-Jenkins [1] method with the R software, which involves estimating model parameters, tests of significance, analysis of residualss, selection according to information criteria and forecasts. The results obtained during the learning-test phase showed a quasi-similarity of the base-tests in all the series except for that of Louga.展开更多
Vector-borne diseases are highly sensitive to environment and to environmental changes. Rift Valley Fever (RFV) is a mosquito-borne zootic virus associated with severe diseases in human beings and economic consequence...Vector-borne diseases are highly sensitive to environment and to environmental changes. Rift Valley Fever (RFV) is a mosquito-borne zootic virus associated with severe diseases in human beings and economic consequences to livestock sector. Animal and human movements have a fundamental impact on RVF transmission. In recent years, there has been a growing interest in the use of mathematics and agent based models to represent and analyze the dynamic of RFV transmission. However, no previous study has taken into consideration animal herds’ mobility and precipitation factors to understand the disease spread. This limitation underlines the necessity to use computational model approach based on multi-agent system in the study of vector-borne diseases transmission and diffusion. In this paper, a multi-agent system combining conceptual model expressiveness is used to study animal herds’ mobility and the precipitation parameter impact on the Rift Valley Fever outbreak in Ferlo Barkedji in Northern Senegal. Simulation scenarios with various parameters, including rain quality, hosts, vectors, camp dispersal around ponds, etc., are unrolled. The different results we have obtained show that the evolution of the number of infected hosts and infected vectors depend on the degree of animal herds’ mobility and on precipitations. Our model provides a framework that permits predicting the spread of the disease associated with the mobility of animal herds.展开更多
文摘The aim of this article is to predict the rainfall evolution of a sub-Saharan area in which one of the most important freshwater resources is located: Lake Guiers. Characterized by short seasonal rains of three months, it experienced a long period of drought in the 1970s. We begin by analyzing the temporal distribution of the rainfall including the variability of the data, with a view to predicting a possible return. For this reason, we present here univariate modeling results of rainfall series collected on three stations in the area. The challenge lies in the adequacy of the parameters for the monthly rainfall series, which generates more or less significant forecast errors on the learning bases because of the missing data. This later motivated their conversion to moving average series. On the other hand, the normality of the latter seems to be rejected by the D’Agostino test. Student’s and Mann-Whitney’s tests confirmed the homogeneity. The autocorlograms show the presence of autoregressive terms in the data. Dickey-Fuller and Mann-Kendall tests reveal both trend and seasonality. The stationarity tests of Dickey-Fuller, Phillips-Perron and KPSS have shown that they are non-stationary. As a result, we did an ARIMA modeling method using the Box-Jenkins [1] method with the R software, which involves estimating model parameters, tests of significance, analysis of residualss, selection according to information criteria and forecasts. The results obtained during the learning-test phase showed a quasi-similarity of the base-tests in all the series except for that of Louga.
文摘Vector-borne diseases are highly sensitive to environment and to environmental changes. Rift Valley Fever (RFV) is a mosquito-borne zootic virus associated with severe diseases in human beings and economic consequences to livestock sector. Animal and human movements have a fundamental impact on RVF transmission. In recent years, there has been a growing interest in the use of mathematics and agent based models to represent and analyze the dynamic of RFV transmission. However, no previous study has taken into consideration animal herds’ mobility and precipitation factors to understand the disease spread. This limitation underlines the necessity to use computational model approach based on multi-agent system in the study of vector-borne diseases transmission and diffusion. In this paper, a multi-agent system combining conceptual model expressiveness is used to study animal herds’ mobility and the precipitation parameter impact on the Rift Valley Fever outbreak in Ferlo Barkedji in Northern Senegal. Simulation scenarios with various parameters, including rain quality, hosts, vectors, camp dispersal around ponds, etc., are unrolled. The different results we have obtained show that the evolution of the number of infected hosts and infected vectors depend on the degree of animal herds’ mobility and on precipitations. Our model provides a framework that permits predicting the spread of the disease associated with the mobility of animal herds.
