Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

IntroductionCutaneous 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 findingsWe 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 null model accounting only for temporal autocorrelation.SignificanceThese 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

Epidemiological and clinical changes in American tegumentary leishmaniasis in an area of Leishmania (Viannia) braziliensis transmission over a 20-year period

The Health Post of Corte de Pedra is located in a region endemic for American tegumentary leishmaniasis (ATL) in the Brazilian state of Bahia, and it treats 500-1,300 patients annually. To describe temporal changes in the epidemiology of ATL, we reviewed a random sample of 10% of patient charts (N = 1,209) from 1988 to 2008. There was a twofold increase in the number of cases over the 20-year period, with fluctuations in 10-year cycles. Patients were most frequently male, between the ages of 10 and 30 years, and engaged in agricultural labor; 4.3% of patients had mucosal disease, and 2.4% of patients had disseminated disease. Over the study period, the number of disseminated cases increased threefold, the proportion of cases in younger patients and agricultural workers decreased, and the proportion of patients residing in coastal areas increased. ATL is on the rise in Bahia, with a 10-year periodicity and evolving changes in epidemiology and manifestations of disease

Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

IntroductionCutaneous 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 findingsWe 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 null model accounting only for temporal autocorrelation.SignificanceThese 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

Covariate lag selections and model parameter estimates.

<p>The values of the cross correlation function (CCF) between the pre-whitened series are presented alongside parameter estimates in the best-fitting and averaged models according to each information criterion. Significance at the 95% confidence level is indicated with <b>bold</b> text.</p><p>Covariate lag selections and model parameter estimates.</p

Meteorological and climatic predictors, 1994–2008.

<p>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.</p

One month-ahead forecasts.

<p>(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.</p

Cutaneous leishmaniasis cases in the study region, 1994–2008.

<p>(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.</p

Measures of prediction error.

<p>Mean squared error (MSE) in predictions is presented for each model at each forecast horizon (1, 2, and 3 months ahead). Percent change in MSE relative to the null model (∂MSE₀) is presented to measure improvement in prediction accuracy. Improvements greater than 5% relative to the null model are indicated with <b>bold</b> text.</p><p>Measures of prediction error.</p