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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

One month-ahead forecasts.(A) Null model; (B) Best-fitting model according to BIC; (C) Averaged model according to BIC. Black lines plot the square root-transformed cases; orange lines plot model fit to data during the training period; red lines plot model forecasts, with the grey area representing the 95% confidence region.
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pntd-0003283-g003: One month-ahead forecasts.(A) Null model; (B) Best-fitting model according to BIC; (C) Averaged model according to BIC. Black lines plot the square root-transformed cases; orange lines plot model fit to data during the training period; red lines plot model forecasts, with the grey area representing the 95% confidence region.

Mentions: We compared out-of-fit prediction accuracy for the model with the best-fit models and the averaged models, which accounted for meteorological covariates (Table 2). The best-fit and averaged models reduced the MSE relative to the model at all prediction lengths. Improvements in MSE relative to the model were greatest at the three-month horizon and smallest at the one-month horizon for all models considered. The best-fitting model by BIC produced one, two, and three month-ahead forecasts with 10.6%, 12.8%, and 15.7% lower MSE than the model, respectively (Figure 3, Figure S1, Figure S2). This model provided the most accurate forecasts at all prediction lengths; two month-ahead forecasts were the most accurate in terms of minimizing MSE. The averaged model constructed according to BIC offered smaller marginal reductions in MSE than the averaged model constructed according to AIC/AICc weights for all but the one month-ahead predictive horizon. Marginal reductions in prediction MSE were poorest from the best-fitting model selected according to AIC/AICc weights.


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)

One month-ahead forecasts.(A) Null model; (B) Best-fitting model according to BIC; (C) Averaged model according to BIC. Black lines plot the square root-transformed cases; orange lines plot model fit to data during the training period; red lines plot model forecasts, with the grey area representing the 95% confidence region.
© Copyright Policy
Related In: Results  -  Collection

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

pntd-0003283-g003: One month-ahead forecasts.(A) Null model; (B) Best-fitting model according to BIC; (C) Averaged model according to BIC. Black lines plot the square root-transformed cases; orange lines plot model fit to data during the training period; red lines plot model forecasts, with the grey area representing the 95% confidence region.
Mentions: We compared out-of-fit prediction accuracy for the model with the best-fit models and the averaged models, which accounted for meteorological covariates (Table 2). The best-fit and averaged models reduced the MSE relative to the model at all prediction lengths. Improvements in MSE relative to the model were greatest at the three-month horizon and smallest at the one-month horizon for all models considered. The best-fitting model by BIC produced one, two, and three month-ahead forecasts with 10.6%, 12.8%, and 15.7% lower MSE than the model, respectively (Figure 3, Figure S1, Figure S2). This model provided the most accurate forecasts at all prediction lengths; two month-ahead forecasts were the most accurate in terms of minimizing MSE. The averaged model constructed according to BIC offered smaller marginal reductions in MSE than the averaged model constructed according to AIC/AICc weights for all but the one month-ahead predictive horizon. Marginal reductions in prediction MSE were poorest from the best-fitting model selected according to AIC/AICc weights.

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