Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data

• Published in: PloS one Vol. 11; no. 3; p. e0152688
• Main Authors: Ramadona, Aditya Lia, Lazuardi, Lutfan, Hii, Yien Ling, Holmner, Åsa, Kusnanto, Hari, Rocklöv, Joacim
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 31.03.2016; Public Library of Science (PLoS)
• Subjects: Aedes aegypti; Analysis; Atmospheric models; Climate; Climate change; Clinical medicine; Control; Dengue; Dengue - diagnosis; Dengue - epidemiology; Dengue - transmission; Dengue fever; Dengue Virus - isolation & purification; Disease control; Disease Outbreaks; Earth Sciences; Environmental studies; Epidemics; Epidemiology; Estimates; Humans; Humidity; Illnesses; Immunity; Incidence; Indonesia - epidemiology; Infections; Linear Models; Medicine; Medicine and Health Sciences; Meteorological Concepts; Meteorological data; Meteorology; Model accuracy; Models, Biological; Models, Statistical; Mosquitoes; Outbreaks; Physical Sciences; Prediction models; Public health; Rain; Regression analysis; Regression models; Research and Analysis Methods; Risk factors; Sentinel surveillance; Studies; Surveillance; Temperature; Vector-borne diseases; Viral diseases; Viruses; Indonesia; Jakarta Indonesia; Sweden; Singapore; Thailand; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0152688

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.