Asma Sana, Mah Laka, Sidrah Hamidani, Habib Bokhari.
Predictive mathematical modeling and statistical analysis for bacterial inactivation using microwave treatment.
Pak J Public Health Jan ;3(4):27-30.
Introduction:Worst flooding catastrophe that has hit Pakistan in 2010, result in huge health and financial crisis. Rain water and poor drainage systems in flooded areas have resulted in outbreaks of various water-borne diseases like cholera, malaria, diarrhea and skin infections due to drinking water contamination. One of the common pathogens found during 2010 floods was Vibrio Cholera. Among Vibrio CholeraO1E1TOR is the major cause of cholera in Pakistan. Therefore provision to clean drinking water to decrease disease burden due to cholera is considered to be important remedy of time. Methods:This study shows purification of drinking water using microwave treatment is more advanced, rapid and efficient. The data generated pertaining to microwave heating effect on optical density of bacterial colony was used further to show the entire procedure in the form of proposed and predictive mathematical model. Predictive models are used to describe the behavior of microorganisms under different physical and chemical factors such as temperature, pH and water activity. Predictive model proposed in this study is based on the basic assumption of Original Logistic model. Taking the growth curve used in logistic model, per-capita growth constant is redefined, and re-parameterized to incorporate biologically significant parameters in the model. Statistical comparison and analysis is carried out to measure the accuracy of two most widely used mathematical models in the literature that are Gompertz Model and Logistic Model and also of the new proposed logistic type model. This analysis also helps to evaluate the new model verses the original logistic model, statistical techniques used are Hypothesis testing, Information testing and Regression Analysis. Results:Results from this statistical analysis are compiled by using SPSS (statistical package for social sciences) software. Proposed model shows better curve fitting with experimental curves. These results showed that this model could be a useful tool for predicting possible bacterial growth recovery after microwave irradiation. Conclusion:The approach used in this study will provide a useful method to get microbiologically clean drinking water by determining the dynamics of such other pathogens.
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