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Aim: “This study Analytical Hierarchy Process (AHP) Model for Malaria Control” was aimed at using analytical hierarchy process model to prioritize alternative strategies for malaria control. Place and the Duration of the Study: The study was carried out in Bauchi State, Nigeria from May, 2017 to June, 2019. Methodology: The study used primary and secondary data. The secondary data were the identified alternatives strategies for malaria control and the criteria for evaluating these strategies obtained from malaria control journals and World Health Organization report. The criteria and malaria control strategies were used as input for developing a 9-point scale used in a questionnaire to obtained responses from the Experts in scoring the pairwise comparison of the criteria and the alternatives. Analytical hierarchy process (AHP) model was used to develop the pairwise comparison matrices from the Experts opinions. Computations were carried out with the help of computer software, business performance management Singapore (BPMSG-AHP ONLINE). Results:The result of the analysis shows that the use of insecticide treated nets was ranked the best strategy for malaria control (AHP score 0.348). Based on the findings of this paper, it is recommended that the use of treated mosquito net should be given much attention in controlling malaria in Nigeria. Conclusion:We therefore conclude that in a multi -criteria decision making situation, AHP is a powerful tool to assists decision makers.

Clients (expecting mothers) wait for hours in ante-natal clinic to receive medical service – waiting before, during or after being served. This study deals with a dynamic queuing system. Results of the study evaluate the effectiveness of queuing simulation model by identifying the ante-natal clinic queuing system parameters in terms of server utilization, usage, and clients flow time. The study uses the Simmer package in R for Discrete-event Simulation of the clients' flow in the system. The study showed that the resources are highly utilized with a bottleneck at the Doctors station, with constant service time for all clients, and long waiting time in the system. By replicating the parameters or replicate the model execution, once, with different initial conditions (by adding resources) and then performing another simulation over the output, the result showed that the resources are utilized with no bottlenecks at each server station, constant activity and flow time for all clients (expecting mothers). Hence, the model has proved to be accurate and efficient. This will help the clinic to utilize the resources and reduce long flow time.