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Malar J. 2015 Jun 18;14:245. doi: 10.1186/s12936-015-0758-4.

Forecasting malaria in a highly endemic country using environmental and clinical predictors.

Author information

1
Clinical and Health Informatics Group, McGill University, 1140 Pine Ave West, Montreal, QC, H3A 1A3, Canada. kate.zinszer@mail.mcgill.ca.
2
Children's Hospital Informatics Program, Boston Children's Hospital, Boston, MA, USA. kate.zinszer@mail.mcgill.ca.
3
Uganda Malaria Surveillance Project, Kampala, Uganda. kigoziruth@gmail.com.
4
Clinical and Health Informatics Group, McGill University, 1140 Pine Ave West, Montreal, QC, H3A 1A3, Canada. charlandk@yahoo.com.
5
School of Medicine, University of California San Francisco, San Francisco, CA, USA. gdorsey@medsfgh.ucsf.edu.
6
University of California Los Angeles, Los Angeles, CA, USA. tbrewer@conet.ucla.edu.
7
Children's Hospital Informatics Program, Boston Children's Hospital, Boston, MA, USA. John.Brownstein@childrens.harvard.edu.
8
College of Health Sciences, Makerere University, Kampala, Uganda. mkamya@infocom.co.ug.
9
Clinical and Health Informatics Group, McGill University, 1140 Pine Ave West, Montreal, QC, H3A 1A3, Canada. David.Buckeridge@mcgill.ca.

Abstract

BACKGROUND:

Malaria thrives in poor tropical and subtropical countries where local resources are limited. Accurate disease forecasts can provide public and clinical health services with the information needed to implement targeted approaches for malaria control that make effective use of limited resources. The objective of this study was to determine the relevance of environmental and clinical predictors of malaria across different settings in Uganda.

METHODS:

Forecasting models were based on health facility data collected by the Uganda Malaria Surveillance Project and satellite-derived rainfall, temperature, and vegetation estimates from 2006 to 2013. Facility-specific forecasting models of confirmed malaria were developed using multivariate autoregressive integrated moving average models and produced weekly forecast horizons over a 52-week forecasting period.

RESULTS:

The model with the most accurate forecasts varied by site and by forecast horizon. Clinical predictors were retained in the models with the highest predictive power for all facility sites. The average error over the 52 forecasting horizons ranged from 26 to 128% whereas the cumulative burden forecast error ranged from 2 to 22%.

CONCLUSIONS:

Clinical data, such as drug treatment, could be used to improve the accuracy of malaria predictions in endemic settings when coupled with environmental predictors. Further exploration of malaria forecasting is necessary to improve its accuracy and value in practice, including examining other environmental and intervention predictors, including insecticide-treated nets.

PMID:
26081838
PMCID:
PMC4470343
DOI:
10.1186/s12936-015-0758-4
[Indexed for MEDLINE]
Free PMC Article

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