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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

Association between Meteorological Variables and Dengue over Lag 0–3.Solid lines represent relative risks of dengue cases and dotted lines depict the upper and lower limits of 95% confidence intervals.
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pone.0152688.g003: Association between Meteorological Variables and Dengue over Lag 0–3.Solid lines represent relative risks of dengue cases and dotted lines depict the upper and lower limits of 95% confidence intervals.

Mentions: Temperatures showed low association with dengue cases in lag 0, 1 and 2, but in lag 3 the relative risk increase linearly when temperature increase. Rainfall in lag 0 associate to a slightly increasing dengue transmission with lower levels, but indicate a strong drop when the monthly rainfall is more than 300 mm. Lag 1, 2 and 3 of rainfall indicate a linear increase of the relative risk with more rainfall. But also here, the increasing relative risk of dengue transmission vanishes when the rainfall is very high. Adjusted relative humidity did not show a consistent pattern, but indicated linear increase in lag 2 and 3, and linear decrease in lag 1 with increasing humidity levels (Fig 3).


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)

Association between Meteorological Variables and Dengue over Lag 0–3.Solid lines represent relative risks of dengue cases and dotted lines depict the upper and lower limits of 95% confidence intervals.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0152688.g003: Association between Meteorological Variables and Dengue over Lag 0–3.Solid lines represent relative risks of dengue cases and dotted lines depict the upper and lower limits of 95% confidence intervals.
Mentions: Temperatures showed low association with dengue cases in lag 0, 1 and 2, but in lag 3 the relative risk increase linearly when temperature increase. Rainfall in lag 0 associate to a slightly increasing dengue transmission with lower levels, but indicate a strong drop when the monthly rainfall is more than 300 mm. Lag 1, 2 and 3 of rainfall indicate a linear increase of the relative risk with more rainfall. But also here, the increasing relative risk of dengue transmission vanishes when the rainfall is very high. Adjusted relative humidity did not show a consistent pattern, but indicated linear increase in lag 2 and 3, and linear decrease in lag 1 with increasing humidity levels (Fig 3).

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