Limits...
Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data.

Ramadona AL, Lazuardi L, Hii YL, Holmner Å, Kusnanto H, Rocklöv J - PLoS ONE (2016)

Bottom Line: Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods.The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead.However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health and Clinical Medicine, Epidemiology and Global Health, Umeå University, Umeå, Sweden.

ABSTRACT
Research is needed to create early warnings of dengue outbreaks to inform stakeholders and control the disease. This analysis composes of a comparative set of prediction models including only meteorological variables; only lag variables of disease surveillance; as well as combinations of meteorological and lag disease surveillance variables. Generalized linear regression models were used to fit relationships between the predictor variables and the dengue surveillance data as outcome variable on the basis of data from 2001 to 2010. Data from 2011 to 2013 were used for external validation purposed of prediction accuracy of the model. Model fit were evaluated based on prediction performance in terms of detecting epidemics, and for number of predicted cases according to RMSE and SRMSE, as well as AIC. An optimal combination of meteorology and autoregressive lag terms of dengue counts in the past were identified best in predicting dengue incidence and the occurrence of dengue epidemics. Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods. A combination of surveillance and meteorological data including lag patterns up to a few years in the past showed most predictive of dengue incidence and occurrence in Yogyakarta, Indonesia. The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead. Prior studies support the fact that past meteorology and surveillance data can be predictive of dengue. However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.

Show MeSH

Related in: MedlinePlus

Monthly Observed and Predicted Dengue Cases from 2001–2010.Black line represents observed dengue cases and red line represents predicted cases. The vertical axis shows dengue cases and the horizontal axis denotes time in month from January 2001 to December 2010.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4816319&req=5

pone.0152688.g005: Monthly Observed and Predicted Dengue Cases from 2001–2010.Black line represents observed dengue cases and red line represents predicted cases. The vertical axis shows dengue cases and the horizontal axis denotes time in month from January 2001 to December 2010.

Mentions: The simplest model (Model A) using the months of the year showed poorest performance when compared with another model. Meanwhile the model using the optimal combination of meteorological factors (B) only (temperature at lag 3, rainfall at lag 2 and rainfall at lag 3) showed a relatively good fit and reasonable predictive ability. The model included a dengue count time series at lag 2 (C) showed, however, poor predictions, but the optimal autoregressive model including dengue at lag 2 and lag 24 (D) showed a rather good predictive performance that was comparable to the meteorological based model (Fig 5). The predictive ability as evaluated by RMSE and SMRSE, as well as the values of AIC for the models A-E consistently shows that model (E) combining model (B) and (D) is the best-predictive model (Table 1). The final model (E) included combinations of the meteorology and the autoregressive lag terms of dengue counts in the past according to:log(D0,t)∼α+∑l=33ns(TMPlt,df=3)+∑l=23ns(PRElt,df=3)+∑l=22ns(Dlt,df=3)+∑l=2424ns(Dlt,df=3)(4)


Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data.

Ramadona AL, Lazuardi L, Hii YL, Holmner Å, Kusnanto H, Rocklöv J - PLoS ONE (2016)

Monthly Observed and Predicted Dengue Cases from 2001–2010.Black line represents observed dengue cases and red line represents predicted cases. The vertical axis shows dengue cases and the horizontal axis denotes time in month from January 2001 to December 2010.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4816319&req=5

pone.0152688.g005: Monthly Observed and Predicted Dengue Cases from 2001–2010.Black line represents observed dengue cases and red line represents predicted cases. The vertical axis shows dengue cases and the horizontal axis denotes time in month from January 2001 to December 2010.
Mentions: The simplest model (Model A) using the months of the year showed poorest performance when compared with another model. Meanwhile the model using the optimal combination of meteorological factors (B) only (temperature at lag 3, rainfall at lag 2 and rainfall at lag 3) showed a relatively good fit and reasonable predictive ability. The model included a dengue count time series at lag 2 (C) showed, however, poor predictions, but the optimal autoregressive model including dengue at lag 2 and lag 24 (D) showed a rather good predictive performance that was comparable to the meteorological based model (Fig 5). The predictive ability as evaluated by RMSE and SMRSE, as well as the values of AIC for the models A-E consistently shows that model (E) combining model (B) and (D) is the best-predictive model (Table 1). The final model (E) included combinations of the meteorology and the autoregressive lag terms of dengue counts in the past according to:log(D0,t)∼α+∑l=33ns(TMPlt,df=3)+∑l=23ns(PRElt,df=3)+∑l=22ns(Dlt,df=3)+∑l=2424ns(Dlt,df=3)(4)

Bottom Line: Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods.The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead.However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health and Clinical Medicine, Epidemiology and Global Health, Umeå University, Umeå, Sweden.

ABSTRACT
Research is needed to create early warnings of dengue outbreaks to inform stakeholders and control the disease. This analysis composes of a comparative set of prediction models including only meteorological variables; only lag variables of disease surveillance; as well as combinations of meteorological and lag disease surveillance variables. Generalized linear regression models were used to fit relationships between the predictor variables and the dengue surveillance data as outcome variable on the basis of data from 2001 to 2010. Data from 2011 to 2013 were used for external validation purposed of prediction accuracy of the model. Model fit were evaluated based on prediction performance in terms of detecting epidemics, and for number of predicted cases according to RMSE and SRMSE, as well as AIC. An optimal combination of meteorology and autoregressive lag terms of dengue counts in the past were identified best in predicting dengue incidence and the occurrence of dengue epidemics. Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods. A combination of surveillance and meteorological data including lag patterns up to a few years in the past showed most predictive of dengue incidence and occurrence in Yogyakarta, Indonesia. The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead. Prior studies support the fact that past meteorology and surveillance data can be predictive of dengue. However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.

Show MeSH
Related in: MedlinePlus