Statistical Modeling Reveals the Effect of Absolute Humidity on Dengue in Singapore

Xu, Haiyan; Fu, Xiuju; Lee, Lionel Kim Hock; Ma, Stefan; Goh, Kee Tai; Jiancheng, Wong; Habibullah, Mohamed; Lee, Gary Kee Khoon; Lim, Tian Kuay; Tambyah, Paul Anantharajah; Lim, Chin Leong; Ng, Lee Ching

doi:10.1371/journal.pntd.0002805

Cited by 101 publications

(99 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, the national population n t and an AR(2) term are also incorporated into the GAM model. These two terms are commonly considered in dengue prediction models . In summary, the following GAM is assumed

\log (Y_{t}) = f_{0} + f_{1} (X_{t}) + g_{1} (Y_{t - 1}) + g_{2} (Y_{t - 2}) + \log (n_{t}) + ϵ_{t},

where ϵ t 's are independent and identically distributed Gaussian errors.…”

Section: Phase I Modelingmentioning

confidence: 99%

Early dengue outbreak detection modeling based on dengue incidences in Singapore during 2012 to 2017

Chen

et al. 2020

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

Dengue has been as an endemic with year‐round presence in Singapore. In the recent years 2013, 2014, and 2016, there were several severe dengue outbreaks, posing serious threat to the public health. To proactively control and mitigate the disease spread, early warnings of dengue outbreaks, at which there are rapid and large‐scale spread of dengue incidences, are extremely helpful. In this study, a two‐step framework is proposed to predict dengue outbreaks and it is evaluated based on the dengue incidences in Singapore during 2012 to 2017. First, a generalized additive model (GAM) is trained based on the weekly dengue incidence data during 2006 to 2011. The proposed GAM is a one‐week‐ahead forecasting model, and it inherently accounts for the possible correlation among the historical incidence data, making the residuals approximately normally distributed. Then, an exponentially weighted moving average (EWMA) control chart is proposed to sequentially monitor the weekly residuals during 2012 to 2017. Our investigation shows that the proposed two‐step framework is able to give persistent signals at the early stage of the outbreaks in 2013, 2014, and 2016, which provides early alerts of outbreaks and wins time for the early interventions and the preparation of necessary public health resources. In addition, extensive simulations show that the proposed method is comparable to other potential outbreak detection methods and it is robust to the underlying data‐generating mechanisms.

show abstract

\log (Y_{t}) = f_{0} + f_{1} (X_{t}) + g_{1} (Y_{t - 1}) + g_{2} (Y_{t - 2}) + \log (n_{t}) + ϵ_{t},

where ϵ t 's are independent and identically distributed Gaussian errors.…”

Section: Phase I Modelingmentioning

confidence: 99%

Early dengue outbreak detection modeling based on dengue incidences in Singapore during 2012 to 2017

Chen

et al. 2020

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors chose the appropriate df according to the Partial Autocorrelation Function (PACF), and the calculation method was as reported previously [20]. The maximum permitted lag was 35 days, which was determined by the survival time of eosinophils and a smaller value of the quasi-Akaike information criterion (QAIC) [21]. Relative risk (RR) values with their respective 95% confidence intervals (95% CI) were used to show the effect of each air pollutant with a 10 μg/m 3 increase in concentration.…”

Section: Rh -Relative Humidity;mentioning

confidence: 99%

The lagged effect of air pollution on human eosinophils: a distributed lag non-linear model

Ding

Xie

Su³

et al. 2020

Int J Occup Med Environ Health

View full text Add to dashboard Cite

Objectives: The aim of this study was to determine the lag between exposure to air pollutants and changes in human eosinophil counts. Material and Methods: This was a retrospective study employing 246 425 physical examination records dated December 2013 -December 2016 from Chengdu, China. The authors determined the prevalence of individuals with eosinophil counts above the normal reference range each day. A distributed lag non-linear model was used to evaluate the lagged effect of each air pollutant on eosinophil counts. The lagged effects of each air pollutant were counted and presented with smoothing splines. Results: The effects of air pollutants such as particulate matter (PM 2.5 , aerodynamic diameters <2.5 μm; PM 10 , aerodynamic diameters <10 μm), nitrogen dioxide (NO 2 ) and ozone (O 3 ) were evaluated. In women, the effects of PM 2.5 (RR = 1.154, 95% CI: 1.061-1.255) and PM 10 (RR = 1.309, 95% CI: 1.130-1.517) reached the maximum values on lag day 0. In men, there was no significant effect of PM 2.5 , but significant effects of PM 10 were found for lag days 20-28. The effects of NO 2 and O 3 on eosinophils were not statistically significant for either gender. Conclusions: The air pollutants of PM 10 have a significant effect on human eosinophils for both women and men, but with different temporal patterns, with women showing a lag of 0-5 days and men showing a lag of 20-28 days. In addition, PM 2.5 was significant for women with a lag of 0-3 days but it was not significant for men. Int J Occup Med Environ Health. 2020;33(3):299-310

show abstract

“…In Singapore, Xu and colleagues found that absolute humidity was a better predictor for modeling dengue incidence than other weather variables [56]. Mean temperature also correlated with incidence but absolute humidity had a more stable impact on dengue incidence [56]. Many recently published studies have focused on finding locally relevant data to provide early warnings [52,[57][58][59].…”

Section: Surveillance and Warning Systemsmentioning

confidence: 99%

Dengue: Update on Epidemiology

Wilson

Chen

2014

Curr Infect Dis Rep

View full text Add to dashboard Cite

The epidemiology of dengue fever has undergone major shifts in recent decades. The global distribution has expanded to include more geographic areas. The intensity of transmission and the severity of infections have increased in areas where infection was already endemic. Multiple studies provide a clearer picture of the epidemiology and allow mapping of its distribution and change over time. Despite major efforts to control transmission, competent vectors now infest most tropical and subtropical regions; Aedes albopictus, also a competent vector, is able to survive in temperate areas, placing parts of Europe and North America at risk for local transmission. Many research teams in dengue-endemic areas are working to identify key local weather, vector, and other variables that would allow prediction of a likely epidemic early enough to permit interventions to avert it or blunt its impact.

show abstract

Statistical Modeling Reveals the Effect of Absolute Humidity on Dengue in Singapore

Cited by 101 publications

References 36 publications

Early dengue outbreak detection modeling based on dengue incidences in Singapore during 2012 to 2017

Early dengue outbreak detection modeling based on dengue incidences in Singapore during 2012 to 2017

The lagged effect of air pollution on human eosinophils: a distributed lag non-linear model

Dengue: Update on Epidemiology

Contact Info

Product

Resources

About