Research

Temporal correlation between malaria and rainfall in Sri Lanka

Olivier JT Briët^1,2 , Penelope Vounatsou² , Dissanayake M Gunawardena³ , Gawrie NL Galappaththy⁴ and Priyanie H Amerasinghe⁵

¹International Water Management Institute, PO Box 2075, Colombo, Sri Lanka

²Swiss Tropical Institute, Socinstrasse 57, PO Box CH-4002, Basel, Switzerland

³US Agency for International Development, PO Box 7856, Kampala, Uganda

⁴Anti Malaria Campaign, Head Office Colombo, Sri Lanka

⁵International Water Management Institute Sub Regional Office for South Asia, c/o ICRISAT, Patancheru, AP 502 324, Andhra Pradesh, India

author email corresponding author email

Malaria Journal 2008, 7:77doi:10.1186/1475-2875-7-77

Published:	6 May 2008

Abstract

Background

Rainfall data have potential use for malaria prediction. However, the relationship between rainfall and the number of malaria cases is indirect and complex.

Methods

The statistical relationships between monthly malaria case count data series and monthly mean rainfall series (extracted from interpolated station data) over the period 1972 – 2005 in districts in Sri Lanka was explored in four analyses: cross-correlation; cross-correlation with pre-whitening; inter-annual; and seasonal inter-annual regression.

Results

For most districts, strong positive correlations were found for malaria time series lagging zero to three months behind rainfall, and negative correlations were found for malaria time series lagging four to nine months behind rainfall. However, analysis with pre-whitening showed that most of these correlations were spurious. Only for a few districts, weak positive (at lags zero and one) or weak negative (at lags two to six) correlations were found in pre-whitened series. Inter-annual analysis showed strong negative correlations between malaria and rainfall for a group of districts in the centre-west of the country. Seasonal inter-annual analysis showed that the effect of rainfall on malaria varied according to the season and geography.

Conclusion

Seasonally varying effects of rainfall on malaria case counts may explain weak overall cross-correlations found in pre-whitened series, and should be taken into account in malaria predictive models making use of rainfall as a covariate.