Statistical Models to Investigate the Declining Seasonal Malaria Patterns 1990--2014 in an Area of Expanding Vector Control Along the Kenyan Coast

• Activity Number: 647
• Type: Contributed
• Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Stella Karuri* and Abdisalan Noor and Philip Bejon and Robert Snow
• Companies: KEMRI-Wellcome Trust Research Programme and KEMRI-Wellcome Trust Research Programme and University of Oxford and KEMRI-Wellcome Trust Research Programme
• Keywords: time series ; temporal association ; seasonal variation ; anomalies ; rainfall patterns ; vector autoregressive models

Abstract

Malaria is a vector-borne disease, that despite recent scaled efforts to control in Africa, continues to pose a major threat to child survival. The disease is caused by the parasite species Plasmodium and requires mosquitoes and humans for transmission. Rainfall is a major factor in seasonal and secular patterns of malaria transmission along the East African coast. Here we apply several statistical models to look at the temporal association between monthly pediatric malaria hospital admissions, rainfall and Indian Ocean Sea Surface temperatures. Trend and seasonal adjusted marginal and multivariate time-series models are applied to a unique data set to examine the role of climate seasonality and long-term anomalies in predicting malaria hospital admission rates. We also examine the robustness of these predictions with increasing vector control.