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Objective: Nigeria records it largest outbreak of Lassa fever in 2018, with 416 confirmed cases by 22 nd April 2018 affecting 22 states and the Federal Capital Territory (FCT). This study determines the descriptive features of Lassa fever in Bauchi, Northeastern Nigeria. Methods: A retrospective study that encompasses all the suspected and confirmed cases of Lassa fever managed in Abubakar Tafawa Balewa University Teaching Hospital (ATBUTH), Bauchi, between October 2016 and April 2018. Clinical notes of these patients were used to source information including sociodemographic features, clinical characteristics of the presenting illness, laboratory investigations results, working diagnosis, line of management, and treatment outcomes. Results: A total 127 suspected cases records were reviewed out of which 27(21%) were positive for Lassa fever Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) test. Thirteen (48%) of the confirmed cases were males and fourteen (51.9%) females giving a male to female ratio of 1:1.08. their ages ranged from 1 to 60 years with a mean age of 27.5. Most cases were seen between October and April. Fever was the most common clinical presentation, occurring in 23 cases (85%) at presentation. Other clinical features were headache 20 cases (74%), bleeding 19 cases (70%), abdominal pain 14 cases (52%), Nausea/Vomiting 12 cases (44%), Diarrhoea 11 cases (41%), cough 11 cases (41%), encephalitis 9 cases (33%), Chest pain 8 cases (30%), Hypotension 7 cases (26%), Sore throat 5 cases (19%) and Facial and or Neck swelling 3 cases (11%). Mortality among the confirmed cases was (44.4%) with all deaths occurring within 72hours of presentation and had bleeding and or encephalitis as presenting symptoms. Conclusion: Lassa fever is relatively common in Bauchi state and affects people of all age groups and sexes. Late presentation, bleeding and encephalitis were common features among mortality cases. Keywords: Lassa Fever, clinical features, mortality, Nigeria

The necessitation in the optimal power demand model, make it an impetus to present this work as a glaring path in determining the optimum power demand curve, using the available data of a substation. The data are used to find the equation and its coefficients of determination that can give the best-fit curve of the power demand of a substation, based on the available data. This will produce a model that can be validated by comparing the data and figured out the equation.