Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data

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.

Dengue viruses circulating in Indonesia: A systematic review and phylogenetic analysis of data from five decades

“…Changes in epidemiological aspects include that outbreak trend of dengue haemorrhagic fever (DHF) has become irregular, with a high interepidemic background, increasing mean age of DHF cases, increasing annual DHF incidence, and decreasing case fatality rate . Studies have been conducted to assess the plausible determinants associated with increased dengue incidence or outbreaks, including meteorological data, vector activities, and socio‐economic and environmental factors . However, missing from this literature is a review of the dynamics of DENV transmission in Indonesia by means of a chronological overview and assessment of whether changes in DENV circulation may be associated with an increase in dengue incidence.…”

Assessment of Knowledge regarding Climate Change and Health among Adolescents in Yogyakarta, Indonesia

“…Local health authorities recorded during the last ten years the number of vector-borne diseases such as dengue fever, which has increased three times during 2001–2010 [15]. They also reported leptospirosis cases in some years [16, 17].…”

Cluster-Randomized Test-Negative Design Trials: A Novel and Efficient Method to Assess the Efficacy of Community-Level Dengue Interventions

“…Parameter choices for test-negative illness incidence were selected to yield 2 distinct values of the intercluster coefficient of variation ( k ), k = 0.5 and 0.25; k was set at 0.5 for dengue incidence. The rate parameters were selected to mimic dengue case notification rates (36–38) and k values (39) from southeast Asian dengue endemic settings. Note that any covariation of dengue and test-negative case counts arises solely from common cluster population sizes.…”