SERODIAGNOSIS OF SALMONELLA INFECTION: USING A LOGISTIC REGRESSION MODEL

ABSTRACT Salmonella infection remains a major global health problem and worsened by lack of appropriate diagnostic tools to aid early detection and teatment, particularly in low-income nations. Salmonella typhi is the most common causative agent of typhoid fever and the prevalence of this illness has been on the increase specifically in areas of poor personal hygiene and sanitation. This study was carried out to further improve the diagnosis of salmonella infection, through a mathematical regression model. An analysis was performed using the logistic regression approach and the predictability of the model was done by extracting fifteen (15) typhoid observations from the obtained samples; for the model to predict their status. The model was able to accurately predict 66.7% of the observations. This study showed an increased prevalence in typhoid fever including a significant correlation between typhoid fever and other parameters. The global burden of this illness can be minimized by proper vaccination, and prompt but appropriate diagnosis and treatment.Further studies and test-meaasures also needs to be carried out to improve diagnosis and treatment regimen.