A model comparison algorithm for increased forecast accuracy of dengue fever incidence in Singapore and the auxiliary role of total precipitation information

Abstract

Many time-series models for disease counts utilise information from environmental variables. We focus on weekly dengue fever (DF) incidence rates in Singapore and demonstrate the strong negative correlation between an appropriately time-lagged total weekly rainfall and DF incidence. A Bayesian neural network time-series model for predicting DF incidence which utilizes rainfall data is introduced. A comparison is made between this neural network model and a time-series model which does not use any covariate information. An easily implementable method for choosing between the models which optimizes future prediction accuracy is suggested as well. We note that our proposed comparison method is applicable to any competing time-series models. This algorithm is demonstrated through examples of comparisons between pairs of different time-series models.

Dengue Fever Distribution, Climate and Visitors: A Study from Badung District of Bali, Indonesia

“…This finding also agrees with the study in Kerala, India that dengue occurrence during 2010 -2014 was significantly associated with various lags of climatic factors (temperature, rainfall and humidity) and it showed spatial and temporal clustered (Valson and Soman 2017). However, the different result was reported by study in Singapore that found significant negative association between weekly percipitation and dengue incidence (Koh et al 2018). The difference might be caused by the different time unit analysis as Badung study used monthly data while Singapore study used weekly data.…”

The Impact of Dengue on Economic Growth: The Case of Southern Taiwan

“…In particular, when information on the 2015 dengue outbreak in Southern Taiwan was entered into the economic growth model, a reduction of 0.26% in the average income per capita was estimated. The model is not solely able to estimate the economic impact of dengue retrospectively; in fact, when the economic growth model is applied alongside other dengue outbreak forecast models [13,14], the forecast of economic reduction due to a future dengue outbreak can additionally be predicted. For example, imagine that a dengue outbreak forecast model predicted an outbreak in the future along with the expected number of dengue cases.…”

“…Due to dengue's significantly negative impacts, many research studies have been devoted to this issue from various angles, which include identifying the determinants of dengue's incidence [11,12], forecasting dengue's incidence [13,14], and estimating the resulting economic burden [4,15]. Publications on estimating the economic burden associated with dengue are few compared to other dengue-related research works primarily because making such estimations is not easy [16].…”

Analysis of climate factors and dengue incidence in the metropolitan region of Rio de Janeiro, Brazil

“…The best model found has as explanatory variables: minimum temperature and precipitation. Koh et al (2018) adjusted a neural network and a Poisson regression model to weekly data on dengue incidence and precipitation in Singapore. In the models presented by the authors, the incidence of dengue is explained according to the past values of the incidence rate and precipitation [49].…”