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Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

Lewnard JA, Jirmanus L, Júnior NN, Machado PR, Glesby MJ, Ko AI, Carvalho EM, Schriefer A, Weinberger DM - PLoS Negl Trop Dis (2014)

Bottom Line: Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a model accounting only for temporal autocorrelation.These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather.Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.

ABSTRACT

Introduction: Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions.

Methodology/principal findings: We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a model accounting only for temporal autocorrelation.

Significance: These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.

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Related in: MedlinePlus

Meteorological and climatic predictors, 1994–2008.Panels for each variable include (right) the interpolated time series for meteorological and climate conditions in the study region, and (left) the cross-correlation with the square root-transformed case series during the training period, in which the dotted line indicates the 95% significance cut-off. The X-axis gives the time separating the meteorological observation from the month of case notification; negative X values indicate lags (weather precedes cases), while positive values indicate leads.
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pntd-0003283-g002: Meteorological and climatic predictors, 1994–2008.Panels for each variable include (right) the interpolated time series for meteorological and climate conditions in the study region, and (left) the cross-correlation with the square root-transformed case series during the training period, in which the dotted line indicates the 95% significance cut-off. The X-axis gives the time separating the meteorological observation from the month of case notification; negative X values indicate lags (weather precedes cases), while positive values indicate leads.

Mentions: We identified significant cross-correlations between the case series and all predictors except temperature (Figure 2). The three-month lag at which relative humidity and CL cases were significantly correlated provided the maximum forecast horizon. We identified significant, negative-valued cross correlations linking pre-whitened CL cases to relative humidity and rainfall frequency at lags between three and five months (Table 1). We identified significant, positive cross-correlations with MEI (22-month lag) and total rainfall (10- and 21-month lags, respectively).


Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

Lewnard JA, Jirmanus L, Júnior NN, Machado PR, Glesby MJ, Ko AI, Carvalho EM, Schriefer A, Weinberger DM - PLoS Negl Trop Dis (2014)

Meteorological and climatic predictors, 1994–2008.Panels for each variable include (right) the interpolated time series for meteorological and climate conditions in the study region, and (left) the cross-correlation with the square root-transformed case series during the training period, in which the dotted line indicates the 95% significance cut-off. The X-axis gives the time separating the meteorological observation from the month of case notification; negative X values indicate lags (weather precedes cases), while positive values indicate leads.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214672&req=5

pntd-0003283-g002: Meteorological and climatic predictors, 1994–2008.Panels for each variable include (right) the interpolated time series for meteorological and climate conditions in the study region, and (left) the cross-correlation with the square root-transformed case series during the training period, in which the dotted line indicates the 95% significance cut-off. The X-axis gives the time separating the meteorological observation from the month of case notification; negative X values indicate lags (weather precedes cases), while positive values indicate leads.
Mentions: We identified significant cross-correlations between the case series and all predictors except temperature (Figure 2). The three-month lag at which relative humidity and CL cases were significantly correlated provided the maximum forecast horizon. We identified significant, negative-valued cross correlations linking pre-whitened CL cases to relative humidity and rainfall frequency at lags between three and five months (Table 1). We identified significant, positive cross-correlations with MEI (22-month lag) and total rainfall (10- and 21-month lags, respectively).

Bottom Line: Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a model accounting only for temporal autocorrelation.These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather.Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.

ABSTRACT

Introduction: Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions.

Methodology/principal findings: We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a model accounting only for temporal autocorrelation.

Significance: These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.

Show MeSH
Related in: MedlinePlus