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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

The Number of Dengue Cases in Yogyakarta Province, 2001–2010.
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pone.0152688.g001: The Number of Dengue Cases in Yogyakarta Province, 2001–2010.

Mentions: Based on Yogyakarta Province Health Profile in 2011, DHF Incidence Rate per 100,000 Population for Yogyakarta Municipality is estimated to 105, compared to the DHF Incidence Rate for Yogyakarta Province, which is estimated to 29. Yogyakarta Municipality contributes most to dengue among the five districts in the Yogyakarta Province. For the years 2001, 2002, 2005, 2006, and 2009, the number of dengue patients in the Yogyakarta Municipality contributes to around 50% of all dengue patients in the Yogyakarta Province (Fig 1). The municipality has a total area of around 1% of the total area of Yogyakarta Province, and almost all villages in Yogyakarta Municipality report dengue cases [26].


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)

The Number of Dengue Cases in Yogyakarta Province, 2001–2010.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0152688.g001: The Number of Dengue Cases in Yogyakarta Province, 2001–2010.
Mentions: Based on Yogyakarta Province Health Profile in 2011, DHF Incidence Rate per 100,000 Population for Yogyakarta Municipality is estimated to 105, compared to the DHF Incidence Rate for Yogyakarta Province, which is estimated to 29. Yogyakarta Municipality contributes most to dengue among the five districts in the Yogyakarta Province. For the years 2001, 2002, 2005, 2006, and 2009, the number of dengue patients in the Yogyakarta Municipality contributes to around 50% of all dengue patients in the Yogyakarta Province (Fig 1). The municipality has a total area of around 1% of the total area of Yogyakarta Province, and almost all villages in Yogyakarta Municipality report dengue cases [26].

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