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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

Cutaneous leishmaniasis cases in the study region, 1994–2008.(A) Cases presenting to the Corte de Pedra health post, aggregated by month; (B) Autocorrelation function computed from the square root-transformed case series during the training period; (C) Partial autocorrelation function computed from the square root-transformed case series during the training period. For (B) and (C): the dotted line indicates the 95% significance cut-off.
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pntd-0003283-g001: Cutaneous leishmaniasis cases in the study region, 1994–2008.(A) Cases presenting to the Corte de Pedra health post, aggregated by month; (B) Autocorrelation function computed from the square root-transformed case series during the training period; (C) Partial autocorrelation function computed from the square root-transformed case series during the training period. For (B) and (C): the dotted line indicates the 95% significance cut-off.

Mentions: We normalized the time series of monthly CL cases by taking the square root. We identified an autoregressive integrated moving average (ARIMA) or seasonal ARIMA (SARIMA) specification for a model describing temporal dependence in the transformed case series. Formal descriptions of the ARIMA and SARIMA frameworks, and procedures for model identification, are presented elsewhere [37], [38]. We determined an appropriate order for non-seasonal and seasonal autoregressive, integrated, and moving-average parameters in the model according to three factors: (1) we identified significant lags in the autocorrelation and partial autocorrelation functions computed from the time series (Figure 1); (2) we ensured residuals from the models did not retain significant temporal autocorrelation based on the Ljung-Box test [39] and inspection of the autocorrelation and partial autocorrelation functions computed from the residuals; and (3) we investigated potential overfitting relative to simpler order specifications according to the Akaike and Bayesian Information Criteria (AIC and BIC) [40], [41].


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)

Cutaneous leishmaniasis cases in the study region, 1994–2008.(A) Cases presenting to the Corte de Pedra health post, aggregated by month; (B) Autocorrelation function computed from the square root-transformed case series during the training period; (C) Partial autocorrelation function computed from the square root-transformed case series during the training period. For (B) and (C): the dotted line indicates the 95% significance cut-off.
© Copyright Policy
Related In: Results  -  Collection

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

pntd-0003283-g001: Cutaneous leishmaniasis cases in the study region, 1994–2008.(A) Cases presenting to the Corte de Pedra health post, aggregated by month; (B) Autocorrelation function computed from the square root-transformed case series during the training period; (C) Partial autocorrelation function computed from the square root-transformed case series during the training period. For (B) and (C): the dotted line indicates the 95% significance cut-off.
Mentions: We normalized the time series of monthly CL cases by taking the square root. We identified an autoregressive integrated moving average (ARIMA) or seasonal ARIMA (SARIMA) specification for a model describing temporal dependence in the transformed case series. Formal descriptions of the ARIMA and SARIMA frameworks, and procedures for model identification, are presented elsewhere [37], [38]. We determined an appropriate order for non-seasonal and seasonal autoregressive, integrated, and moving-average parameters in the model according to three factors: (1) we identified significant lags in the autocorrelation and partial autocorrelation functions computed from the time series (Figure 1); (2) we ensured residuals from the models did not retain significant temporal autocorrelation based on the Ljung-Box test [39] and inspection of the autocorrelation and partial autocorrelation functions computed from the residuals; and (3) we investigated potential overfitting relative to simpler order specifications according to the Akaike and Bayesian Information Criteria (AIC and BIC) [40], [41].

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