Visceral Leishmaniasis is a very dangerous form of leishmaniasis and, shorn of appropriate diagnosis and handling, it leads to death and physical disability. Depicting the spatiotemporal pattern of disease is important for disease regulator and deterrence strategies. Spatiotemporal modeling has distended broad veneration in recent years. Spatial and spatiotemporal disease modeling is extensively used for the analysis of registry data and usually articulated in a hierarchical Bayesian framework. In this study, we have developed the hierarchical spatiotemporal Bayesian modeling of the infected rate of Visceral leishmaniasis in Human (VLH). We applied the Stochastics Partial Differential Equation (SPDE) approach for a spatiotemporal hierarchical model for Visceral leishmaniasis in human (VLH) that involves a GF and a state process is associated with an autoregressive order one temporal dynamics and the spatially correlated error term, along with the effect of land shield, metrological, demographic, socio-demographic and geographical covariates in an endemic area of Amhara regional state, Ethiopia. The model encompasses a Gaussian Field (GF), affected by an error term, and a state process described by a first-order autoregressive dynamic model and spatially correlated innovations. A hierarchical model including spatially and temporally correlated errors was fit to the infected rate of Visceral leishmaniasis in human (VLH) weekly data from January 2015 to December 2017 using the R package R-INLA, which allows for Bayesian modeling using the stochastic partial differential equation (SPDE) approach. We found that the mean weekly temperature had a significant positive association with infected rate of VLH. Moreover, net migration rate, clean water coverage, average number of households, population density per square kilometer, average number of persons per household unit, education coverage, health facility coverage, mortality rate, and sex ratio had a significant association with the infected rate of visceral leishmaniasis (VLH) in the region. In this study, we investigated the dynamic spatiotemporal modeling of Visceral leishmaniasis in Human (VLH) through a stochastic partial differential equation approach (SPDE) using integrated nested Laplace approximation (INLA). Our study had confirmed both metrological, demographic, sociodemographic and geographic covariates had a significant association with the infected rate of visceral leishmaniasis (VLH) in the region.

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The aim of this study was to model and identify determinants of monthly domestic price volatility of sugar in Ethiopia over the study period from December 2001 to December 2011 GC. The volatility in the domestic price of Sugar has been found to vary over months suggesting the use of GARCH family approach. Thus, family of special characteristics of time series models, namely ARCH, GARCH, TGARCH and EGARCH models with ARIMA mean equations were fitted to the data. The best fitting model among each family of models was selected based on how well the model captures the variation in the data and the optimal lag specification accessed via AIC and SBIC. Comparisons of the symmetric and asymmetric model were carried out based on the significance of asymmetric term in TGARCH and EGARCH models. The analysis showed that: statistically significance asymmetric term and least forecast error from the model established that EGARCH model with Student-t distributional assumptions for residual were superior to the GARCH and TGARCH models. Therefore, ARIMA (0,0,2)-EGARCH(1,3) with Student-t were chosen to be the best fitting models for monthly domestic price volatility of Sugar. Moreover, it was found that from candidate explanatory variables, import price for sugar, fuel oil price, exchange rate (dollar-birr), general inflation, inflation for non food items, inflation for food items, past shock, and volatility on monthly domestic price had statistically significant effect on the current month domestic price volatility on sugar.

Background Agriculture is a critical source of food and income, making it a key component of initiatives aimed at reducing poverty and ensuring food security across the globe. It is the backbone of Ethiopia's economy, contributing significantly to the country's financial development. The sector earns 88.8 percent of trade profit and contributes 36.7 percent of GDP. The purpose of this paper was to identify the homogeneous and heterogeneous effects of agricultural inputs on crop productivity of the three-grain crop types in Ethiopia. Method The central statistical agency (CSA) provided the data for this study, which covered the entire country from 1990 to 2012 Ethiopian Calendar (E.C). Crop productivity, which is assessed in kilograms per hectare for cereal, pulse, and oil crops, was utilized as the response variable. For three-grain crop types from 1990 to 2012 E.C, the study used the pooled mean group estimate method, which allows for long-run homogeneity effects across cross-sections as well as short-run heterogeneity. Results In the long run, the study found that a one percent increase in fertilizer consumption resulted in a 2.686 percent increase in grain crop productivity in Ethiopia, while a one percent increase in improved seed per hectare and land size, resulted in a 48.31 percent and 10.58 percent increase in grain crop productivity per crop category respectively. Short-run productivity for grain crops increased by 30.29 percent as the amount of improved seed value at one period lag value of commercial farm holders is increased by one percent. In the same way, when the arable land at the first difference is increased by one percent then the productivity of grain crops increased by 40.61 percent. Conclusion The findings of this research showed that in the long run, fertilizer consumption, amount of improved seed use, and arable land area size had homogeneous significant contributions, while in the short run, agricultural inputs like the use of pesticides and improved seed use at first lagged value had heterogeneous significant contributions to grain crop productivity improvement across all cross-sectional units.

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