Towards a spatial-temporal model of prevalence of nodding syndrome and epilepsy
(2019) 10th EAI International Conference on e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2018 In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST 275. p.67-77- 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 aim of the study described in this paper is to establish spatial-temporal relationships... (More)
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 aim 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 exploratory cross section survey in three districts of Northern Uganda was done. Spatial data of health centers were recorded and ArcGIS was used for display. The findings 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.
(Less)
- author
- Ongaya, Kizito ; Ssemalullu, Paul ; Oyo, Benedict ; Maiga, Gilbert and Aturinde, Augustus LU
- organization
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Emerging diseases, Geographic information system, Nodding syndrome, Spatial-temporal, Surveillance
- host publication
- AFRICOMM 2018: e-Infrastructure and e-Services for Developing Countries
- series title
- Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
- editor
- Dioum, Ibra ; Mendy, Gervais ; Ouya, Samuel and Thiaré, Ousmane
- volume
- 275
- pages
- 11 pages
- publisher
- Springer
- conference name
- 10th EAI International Conference on e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2018
- conference location
- Dakar, Senegal
- conference dates
- 2018-11-29 - 2018-11-30
- external identifiers
-
- scopus:85064131866
- ISSN
- 1867-8211
- ISBN
- 9783030160418
- DOI
- 10.1007/978-3-030-16042-5_7
- language
- English
- LU publication?
- yes
- id
- 1a6a4d55-ce7d-4e7d-827a-50137d8be062
- date added to LUP
- 2019-05-08 13:04:42
- date last changed
- 2022-04-25 23:28:21
@inproceedings{1a6a4d55-ce7d-4e7d-827a-50137d8be062, abstract = {{<p>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 aim 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 exploratory cross section survey in three districts of Northern Uganda was done. Spatial data of health centers were recorded and ArcGIS was used for display. The findings 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.</p>}}, author = {{Ongaya, Kizito and Ssemalullu, Paul and Oyo, Benedict and Maiga, Gilbert and Aturinde, Augustus}}, booktitle = {{AFRICOMM 2018: e-Infrastructure and e-Services for Developing Countries}}, editor = {{Dioum, Ibra and Mendy, Gervais and Ouya, Samuel and Thiaré, Ousmane}}, isbn = {{9783030160418}}, issn = {{1867-8211}}, keywords = {{Emerging diseases; Geographic information system; Nodding syndrome; Spatial-temporal; Surveillance}}, language = {{eng}}, pages = {{67--77}}, publisher = {{Springer}}, series = {{Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST}}, title = {{Towards a spatial-temporal model of prevalence of nodding syndrome and epilepsy}}, url = {{http://dx.doi.org/10.1007/978-3-030-16042-5_7}}, doi = {{10.1007/978-3-030-16042-5_7}}, volume = {{275}}, year = {{2019}}, }