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Major pandemics and seasonal epidemics that have ravaged the world in the past and even at present, are mostly caused by RNA viruses. This has necessitated the need for continuous research to identify important natural products, with antiviral potentials, which can be harnessed for use in the prevention and treatment of viral infections. This study therefore, evaluated the antiviral property of Curcuma longa on two important RNA viruses of public health importance, namely polio and measles viruses. Extraction of active ingredients from turmeric rhizomes was done with the use of Analar grade methanol and concentrated using rotary evaporator. Polio and measles viruses were isolated from their respective vaccines using Reed-Muench method. Infective doses of the viruses and toxicity profile of extract were determined. Confluent Vero cells were inoculated with the viruses at different dilutions of the extract, incubated and observed for 7 days. Methanol extract of Curcuma longa inhibited polio virus at the maximum non-toxic concentration (MNTC) of 0.031μg μL-1 and inhibitory concentration (IC50) of 0.067 μg μL-1 with selectivity index of 2.16. Inhibition by the extract was observed prior to infection with the viruses. Phytochemical analysis of the extract showed presence of terpenes, saponins, alkaloids, flavonoids, tannins, cardiac glycosides and phenol as the bioactive phytochemicals. This study has shown that curcuma longa has potent inhibitory activity, hence can be harnessed in the development of an effective antiviral agent against polio and measles viruses.

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This paper examines the modelling and forecasting Murder crimes using Auto-Regressive Integrated Moving Average models (ARIMA). Twenty-nine years data obtained from Nigeria Information Resource Center were used to make predictions. Among the most effective approaches for analyzing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). The augmented Dickey-Fuller test for unit root was applied to the data set to investigate for Stationarity, the data set was found to be non-stationary hence transformed using first-order differencing to make them Stationary. The Stationarities were confirmed with time series plots. Statistical analysis was performed using GRETL software package from which, ARIMA (0, 1, 0) was found to be the best and adequate model for Murder crimes. Forecasted values suggest that Murder would slightly be on the increase.