Predicting Malaria in a Highly Endemic Country using Environmental and Clinical Data Sources

Authors

  • Kate Zinszer McGill University, Montreal, QC, Canada.
  • Ruth Kigozi UMSP, Kampala, Uganda.
  • Katia Charland McGill University, Montreal, QC, Canada.
  • Grant Dorsey UMSP, Kampala, Uganda; University of California San Francisco, San Francisco, CA, United States
  • Moses Kamya UMSP, Kampala, Uganda; Makerere University, Kampala, Uganda.
  • David Buckeridge McGill University, Montreal, QC, Canada.

DOI:

https://doi.org/10.5210/ojphi.v6i1.5150

Abstract

The objective of the research was to identify the most accurate models for forecasting malaria at six different sentinel sites in Uganda, using environmental and clinical data sources. We generated short-term, intermediate, and long-term forecasts of malaria prevalence at weekly intervals. The model with the most accurate forecasts varied by site and by forecasting horizon. Treatment predictors were retained in the most accurate models across all clinical sites and forecasting horizons. These results demonstrate the utility of using treatment predictors in conjunction with environmental covariates to predict malaria burden.

Author Biography

Kate Zinszer, McGill University, Montreal, QC, Canada.

Kate Zinszer is working with the Uganda Malaria Surveillance Program in developing malaria forecasting models for Uganda which include environmental and non-environmental predictors for her PhD project. Kate is situated within the Surveillance Lab at McGill University and is also involved with public health surveillance projects. She has previously worked as a communicable disease epidemiologist with the Canadian Field Epidemiology Program as well as with the Vancouver Coastal Health Authority.

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Published

2014-03-09

How to Cite

Zinszer, K., Kigozi, R., Charland, K., Dorsey, G., Kamya, M., & Buckeridge, D. (2014). Predicting Malaria in a Highly Endemic Country using Environmental and Clinical Data Sources. Online Journal of Public Health Informatics, 6(1). https://doi.org/10.5210/ojphi.v6i1.5150

Issue

Section

Oral Presentations