Research Article
Towards a Spatial-Temporal Model of Prevalence of Nodding Syndrome and Epilepsy
@INPROCEEDINGS{10.1007/978-3-030-16042-5_7, author={Kizito Ongaya and Paul Ssemalullu and Benedict Oyo and Gilbert Maiga and Augustus Aturinde}, title={Towards a Spatial-Temporal Model of Prevalence of Nodding Syndrome and Epilepsy}, proceedings={e-Infrastructure and e-Services for Developing Countries. 10th EAI International Conference, AFRICOMM 2018, Dakar, Senegal, November 29-30, 2019, Proceedings}, proceedings_a={AFRICOMM}, year={2019}, month={3}, keywords={Nodding syndrome Emerging diseases Surveillance Spatial-temporal Geographic information system}, doi={10.1007/978-3-030-16042-5_7} }- Kizito Ongaya
Paul Ssemalullu
Benedict Oyo
Gilbert Maiga
Augustus Aturinde
Year: 2019
Towards a Spatial-Temporal Model of Prevalence of Nodding Syndrome and Epilepsy
AFRICOMM
Springer
DOI: 10.1007/978-3-030-16042-5_7
Abstract
Nodding syndrome is an emerging disease which have unknown transmission patterns and no properly established mechanisms for diagnosis leading to numerous hypothetical postulations. It has affected thousands of children in Uganda with debilitating effect and serious economic consequences. Spatial-temporal analysis may provide a quick mechanism to establish comparative understanding of the various hypotheses ascribed to nodding syndrome and any other emerging diseases with similar clinical manifestation. There is considerable suspicion that “nodding syndrome is a form of epilepsy”, a hypothesis that has hardly been investigated in literature. The of the study described in this paper is to establish spatial-temporal relationships between ailments diagnosed as nodding syndrome and ailments diagnosed as epilepsy. An survey in three districts of Northern Uganda was done. Spatial data of health centers were recorded and ArcGIS was used for display. The show significant spatial-temporal correlation of diagnosis reporting of nodding syndrome to epilepsy. The regression statistics overall, epilepsy significantly (p < 0.05) ex-plains about 58% of Nodding syndrome variability. The F-statistic shows a very highly significant value (p = 8.20481E-13; p < 0.05), meaning that the output of the regression is not by chance.
