Temporal correlation between malaria and rainfall in Sri Lanka

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 f...

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Autores principales: Briet, Olivier J.T., Vounatsou, Penelope, Gunawardena, Dissanayake M., Galappaththy, Gawrie N.L., Amerasinghe, Priyanie H.
Formato: Journal Article
Lenguaje:Inglés
Publicado: 2008
Materias:
Acceso en línea:https://hdl.handle.net/10568/40697
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author Briet, Olivier J.T.
Vounatsou, Penelope
Gunawardena, Dissanayake M.
Galappaththy, Gawrie N.L.
Amerasinghe, Priyanie H.
author_browse Amerasinghe, Priyanie H.
Briet, Olivier J.T.
Galappaththy, Gawrie N.L.
Gunawardena, Dissanayake M.
Vounatsou, Penelope
author_facet Briet, Olivier J.T.
Vounatsou, Penelope
Gunawardena, Dissanayake M.
Galappaththy, Gawrie N.L.
Amerasinghe, Priyanie H.
author_sort Briet, Olivier J.T.
collection Repository of Agricultural Research Outputs (CGSpace)
description 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.
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spelling CGSpace406972023-06-13T04:41:45Z Temporal correlation between malaria and rainfall in Sri Lanka Briet, Olivier J.T. Vounatsou, Penelope Gunawardena, Dissanayake M. Galappaththy, Gawrie N.L. Amerasinghe, Priyanie H. malaria waterborne diseases rain time series models analysis 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. 2008 2014-06-13T14:48:13Z 2014-06-13T14:48:13Z Journal Article https://hdl.handle.net/10568/40697 en Limited Access Briet, Olivier J. T.; Vounatsou, Penelope; Gunawardena, Dissanayake M.; Galappaththy, Gawrie N. L.; Amerasinghe, Priyanie H. 2008. Temporal correlation between malaria and rainfall in Sri Lanka. Malaria Journal, 7(77): 14p.
spellingShingle malaria
waterborne diseases
rain
time series
models
analysis
Briet, Olivier J.T.
Vounatsou, Penelope
Gunawardena, Dissanayake M.
Galappaththy, Gawrie N.L.
Amerasinghe, Priyanie H.
Temporal correlation between malaria and rainfall in Sri Lanka
title Temporal correlation between malaria and rainfall in Sri Lanka
title_full Temporal correlation between malaria and rainfall in Sri Lanka
title_fullStr Temporal correlation between malaria and rainfall in Sri Lanka
title_full_unstemmed Temporal correlation between malaria and rainfall in Sri Lanka
title_short Temporal correlation between malaria and rainfall in Sri Lanka
title_sort temporal correlation between malaria and rainfall in sri lanka
topic malaria
waterborne diseases
rain
time series
models
analysis
url https://hdl.handle.net/10568/40697
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