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dc.contributor.authorBenedetti, G
dc.contributor.authorWhite, RA
dc.contributor.authorAkello Pasquale, H
dc.contributor.authorStassjins, J
dc.contributor.authorvan den Boogaard, W
dc.contributor.authorOwiti, P
dc.contributor.authorVan den Bergh, R
dc.date.accessioned2019-11-20T01:53:05Z
dc.date.available2019-11-20T01:53:05Z
dc.date.issued2019-09-21
dc.date.submitted2019-11-13
dc.identifier.urihttp://hdl.handle.net/10144/619518
dc.description.abstractBackground: Detecting unusual malaria events that may require an operational intervention is challenging, especially in endemic contexts with continuous transmission such as South Sudan. Médecins Sans Frontières (MSF) utilises the classic average plus standard deviation (AV+SD) method for malaria surveillance. This and other available approaches, however, rely on antecedent data, which are often missing. Objective: To investigate whether a method using linear regression (LR) over only 8 weeks of retrospective data could be an alternative to AV+SD. Design: In the absence of complete historical malaria data from South Sudan, data from weekly influenza reports from 19 Norwegian counties (2006–2015) were used as a testing data set to compare the performance of the LR and the AV+SD methods. The moving epidemic method was used as the gold standard. Subsequently, the LR method was applied in a case study on malaria occurrence in MSF facilities in South Sudan (2010–2016) to identify malaria events that required a MSF response. Results: For the Norwegian influenza data, LR and AV+SD methods did not perform differently (P  0.05). For the South Sudanese malaria data, the LR method identified historical periods when an operational response was mounted. Conclusion: The LR method seems a plausible alternative to the AV+SD method in situations where retrospective data are missing.en_US
dc.language.isoenen_US
dc.publisherInternational Union Against Tuberculosis and Lung Diseaseen_US
dc.rightsWith thanks to the International Union Against Tuberculosis and Lung Disease.en_US
dc.titleIdentifying exceptional malaria occurrences in the absence of historical data in South Sudan: a method validationen_US
dc.identifier.journalPublic Health Actionen_US
refterms.dateFOA2019-11-20T01:53:05Z


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