2021
DOI: 10.1016/s2542-5196(20)30292-8
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Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study

Abstract: Background Temperature and rainfall patterns are known to influence seasonal patterns of dengue transmission. However, the effect of severe drought and extremely wet conditions on the timing and intensity of dengue epidemics is poorly understood. In this study, we aimed to quantify the non-linear and delayed effects of extreme hydrometeorological hazards on dengue risk by level of urbanisation in Brazil using a spatiotemporal model. MethodsWe combined distributed lag non-linear models with a spatiotemporal Bay… Show more

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Cited by 104 publications
(127 citation statements)
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“…We included a distributed lag non-linear model formulation using natural cubic spline (NS) in R packages dlnm and splines to quantify the non-linear relationship between each climate factor (var df) with changing dengue risk over different lag periods 0-3 months (lag df; appendix pp 4-7). 20,21 The difference between cases reported in the previous year and mean long-term annual average (over years 2014-19) (annual anomaly [c,a(t) -1] ) was introduced to account for inter-annual immunity changes, where a(t) = 2014,…,2019. We included structured random effects to account for spatial, seasonal, and extra-seasonal variations in unknown and unmeasurable factors (such as differences in health care, vector control, and human mobility).…”
Section: Discussionmentioning
confidence: 99%
“…We included a distributed lag non-linear model formulation using natural cubic spline (NS) in R packages dlnm and splines to quantify the non-linear relationship between each climate factor (var df) with changing dengue risk over different lag periods 0-3 months (lag df; appendix pp 4-7). 20,21 The difference between cases reported in the previous year and mean long-term annual average (over years 2014-19) (annual anomaly [c,a(t) -1] ) was introduced to account for inter-annual immunity changes, where a(t) = 2014,…,2019. We included structured random effects to account for spatial, seasonal, and extra-seasonal variations in unknown and unmeasurable factors (such as differences in health care, vector control, and human mobility).…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, the effects of meteorological conditions are often non-linear 28,29 . In general, biological processes have an optimal range rather than a linear relationship with temperature 30 .…”
Section: Challenges Of Climate Change Models and Infectionsmentioning
confidence: 99%
“…These conditions arise when rapid urbanisation occurs without adequate improvements to infrastructure, such as access to piped water and refuse collection [ 16 , 17 ]. There is evidence that areas lacking reliable access to piped water are more susceptible to dengue outbreaks, particularly in highly urbanised areas following drought [ 18 ]. Prior studies have found that extremely wet conditions also increased the risk of dengue outbreaks, thought to be linked to the creation of larval habitat in the short term [ 18 , 19 ].…”
Section: Introductionmentioning
confidence: 99%
“…There is evidence that areas lacking reliable access to piped water are more susceptible to dengue outbreaks, particularly in highly urbanised areas following drought [ 18 ]. Prior studies have found that extremely wet conditions also increased the risk of dengue outbreaks, thought to be linked to the creation of larval habitat in the short term [ 18 , 19 ]. Suitable temperature conditions are required for the mosquitoes to breed and transmit the virus.…”
Section: Introductionmentioning
confidence: 99%