Weather-Based Prediction of Plasmodium Falciparum Malaria in Epidemic-Prone Regions of Ethiopia II. Weather-Based Prediction Systems Perform Comparably to Early Detection Systems in Identifying Times for Interventions

DSpace/Manakin Repository

Weather-Based Prediction of Plasmodium Falciparum Malaria in Epidemic-Prone Regions of Ethiopia II. Weather-Based Prediction Systems Perform Comparably to Early Detection Systems in Identifying Times for Interventions

Show simple item record

dc.contributor.author Teklehaimanot, Hailay D
dc.contributor.author Teklehaimanot, Awash
dc.contributor.author Schwartz, Joel David
dc.contributor.author Lipsitch, Marc
dc.date.accessioned 2012-01-22T01:57:04Z
dc.date.issued 2004
dc.identifier.citation Teklehaimanot, Hailay D., Joel Schwartz, Awash Teklehaimanot, and Marc Lipsitch. 2004. Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia II. Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions. Malaria Journal 3: 44. en_US
dc.identifier.issn 1475-2875 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:8000925
dc.description.abstract Background: Timely and accurate information about the onset of malaria epidemics is essential for effective control activities in epidemic-prone regions. Early warning methods that provide earlier alerts (usually by the use of weather variables) may permit control measures to interrupt transmission earlier in the epidemic, perhaps at the expense of some level of accuracy. Methods: Expected case numbers were modeled using a Poisson regression with lagged weather factors in a 4th-degree polynomial distributed lag model. For each week, the numbers of malaria cases were predicted using coefficients obtained using all years except that for which the prediction was being made. The effectiveness of alerts generated by the prediction system was compared against that of alerts based on observed cases. The usefulness of the prediction system was evaluated in cold and hot districts. Results: The system predicts the overall pattern of cases well, yet underestimates the height of the largest peaks. Relative to alerts triggered by observed cases, the alerts triggered by the predicted number of cases performed slightly worse, within 5% of the detection system. The prediction-based alerts were able to prevent 10–25% more cases at a given sensitivity in cold districts than in hot ones. Conclusions: The prediction of malaria cases using lagged weather performed well in identifying periods of increased malaria cases. Weather-derived predictions identified epidemics with reasonable accuracy and better timeliness than early detection systems; therefore, the prediction of malarial epidemics using weather is a plausible alternative to early detection systems. en_US
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.relation.isversionof doi:10.1186/1475-2875-3-44 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC535541/pdf/ en_US
dash.license LAA
dc.title Weather-Based Prediction of Plasmodium Falciparum Malaria in Epidemic-Prone Regions of Ethiopia II. Weather-Based Prediction Systems Perform Comparably to Early Detection Systems in Identifying Times for Interventions en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal Malaria Journal en_US
dash.depositing.author Schwartz, Joel David
dc.date.available 2012-01-22T01:57:04Z
dash.affiliation.other HMS^Medicine-Brigham and Women's Hospital en_US
dash.affiliation.other SPH^Exposure Epidemiology and Risk Program en_US
dash.affiliation.other SPH^Epidemiology en_US

Files in this item

Files Size Format View
535541.pdf 446.1Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

 
 

Search DASH


Advanced Search
 
 

Submitters