Prediction of Monthly Malaria Incidence in Uganda and its Implications for Preventive InterventionsMuwanika FR*, Atuhaire LK and Ocaya B
School of Statistics and Applied Economics, College of Business and Management Science, Makerere University, Uganda
- *Corresponding Author:
- Muwanika FR
School of Statistics and Applied Economics
College of Business and Management Science
Makerere University, PO Box 7062 Kampala, Uganda
E-mail: [email protected]
Received date: May 31, 2017, Accepted date: June 14, 2017, Published date: June 21, 2017
Citation: Muwanika FR, Atuhaire LK, Ocaya B (2017) Prediction of Monthly Malaria Incidence in Uganda and its Implications for Preventive Interventions. J Med Diagn Meth 6:248. doi: 10.4172/2168-9784.1000248
Copyright: © 2017 Muwanika FR, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background: Malaria is a major public health concern and leading cause of morbidity and mortality in Uganda. Failure to get accurate predictions of the malaria incidence in the population does not only make difficult it to effectively reduce the burden of the disease but also increases the risk of the development of resistant malaria strains which may result from use of insufficient dosages for treatment. Objective: This study aimed to develop a model that predicts new cases of malaria in a month using routine data and to use the model as a monitoring tool in the fight against malaria. Methods: This was retrospective longitudinal study design that involved a secondary analysis of data from two sources. Malaria count data was obtained from department of health information Ministry of Health while population projection data was obtained from Uganda Bureau of statistics (UBOS).The model was formulated using the theory of malaria transmission between the human and mosquito host. The model was then developed using the law of mass action in the Susceptible- Infectious-Susceptible (SIS) modelling framework. Results: The proposed model was considered good for one-month to 12 month ahead prediction accuracy in the range of 45 cases per 10000 people. The findings revealed that among the Ugandan population one infectious individual is likely to infect on average about three susceptible individuals in a month. Our finding of one infectious individual being likely to infect on average about three susceptible individuals indicates that even in the population where the Insecticide Treated Nets (ITNs) are being distributed there are many asymptomatic individuals. Conclusions: The proposed model is simple and can be used to produce reasonable predictions of malaria incidences in Uganda. The model can also detect the presence of asymptomatic infectious individuals in the general population. To further strengthen the use ITNs as a strategy for malaria prevention in the general population, it is important that authorities incorporate a malaria testing strategy to recipients to reduce on the number of asymptomatic individuals in the population. This effectively reduces the incidence originating from asymptomatic individuals infecting other members of the population.