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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.

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Related in: MedlinePlus

Relationship between Autoregressive Lags and Dengue Counts.Upper panel shows (a) relative risks of dengue cases as functions of dengue surveillance at 2-month lag times. Lower panel shows (b) the relation between case intensity and dengue risk categories at all lag months; and (c) the risk in each future month following an increase of 5 dengue cases in a specific month.
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pone.0152688.g004: Relationship between Autoregressive Lags and Dengue Counts.Upper panel shows (a) relative risks of dengue cases as functions of dengue surveillance at 2-month lag times. Lower panel shows (b) the relation between case intensity and dengue risk categories at all lag months; and (c) the risk in each future month following an increase of 5 dengue cases in a specific month.

Mentions: The highest correlations between dengue incidence and lagged dengue incidence, with lag terms within a few months and when only lags longer or equal to 2 were included, were found at lag 2 (r, 0.47). Dengue cases at lag 2 months back show associations to increase dengue transmission with lower values, but to decreasing when the dengue cases counts is more than 150 (Fig 4A). Meanwhile, for lag within longer-term months, the non-linear distributed lag models showed a peak around lag 24 (Fig 4B) suggesting a negative feedback cyclic pattern with lower relative risks of transmission up to two years following a large outbreak in around lag 24. An increase of the dengue cases counts in a specific month will be increasing the dengue risk in each following month with a peak at approximately in the next 24 months (Fig 4C). Based on these, the optimal variables for the prediction models included dengue count at lag 2 and at lag 24.


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)

Relationship between Autoregressive Lags and Dengue Counts.Upper panel shows (a) relative risks of dengue cases as functions of dengue surveillance at 2-month lag times. Lower panel shows (b) the relation between case intensity and dengue risk categories at all lag months; and (c) the risk in each future month following an increase of 5 dengue cases in a specific month.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0152688.g004: Relationship between Autoregressive Lags and Dengue Counts.Upper panel shows (a) relative risks of dengue cases as functions of dengue surveillance at 2-month lag times. Lower panel shows (b) the relation between case intensity and dengue risk categories at all lag months; and (c) the risk in each future month following an increase of 5 dengue cases in a specific month.
Mentions: The highest correlations between dengue incidence and lagged dengue incidence, with lag terms within a few months and when only lags longer or equal to 2 were included, were found at lag 2 (r, 0.47). Dengue cases at lag 2 months back show associations to increase dengue transmission with lower values, but to decreasing when the dengue cases counts is more than 150 (Fig 4A). Meanwhile, for lag within longer-term months, the non-linear distributed lag models showed a peak around lag 24 (Fig 4B) suggesting a negative feedback cyclic pattern with lower relative risks of transmission up to two years following a large outbreak in around lag 24. An increase of the dengue cases counts in a specific month will be increasing the dengue risk in each following month with a peak at approximately in the next 24 months (Fig 4C). Based on these, the optimal variables for the prediction models included dengue count at lag 2 and at lag 24.

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