Literature DB >> 24383820

Temporal relationship between environmental factors and the occurrence of dengue fever.

Marco Aurelio Horta1, Robson Bruniera, Fabricio Ker, Cristina Catita, Aldo Pacheco Ferreira.   

Abstract

To determine the time-lag effect of meteorological factors on the relative risk (RR) of dengue incidence in Coronel Fabriciano city, Brazil, we applied a distributed lag nonlinear model, a modeling framework that can simultaneously represent nonlinear exposure-response dependencies and delayed effects, to establish the association between dengue incidence and weather predictors. The weekly number of notified dengue cases during the period 2004-2010 was used for analysis. When considering the rainfall, the highest RR (1.2) was observed for lag 10. Observing the cumulative effect of the precipitation, the RR for 12th and 13th week was RR = 4. The highest risk, 1.25, was observed at 25 °C, denoting that the risk of dengue transmission increases with temperature. Climate-based models that take into account the time lag between rainfall, temperature, and dengue can be useful in dengue control programs to be applied in tropical countries.

Entities:  

Keywords:  dengue fever; lag nonlinear model (DLNM); precipitation; temperature

Mesh:

Year:  2014        PMID: 24383820     DOI: 10.1080/09603123.2013.865713

Source DB:  PubMed          Journal:  Int J Environ Health Res        ISSN: 0960-3123            Impact factor:   3.411


  6 in total

1.  Risk assessment of dengue fever in Zhongshan, China: a time-series regression tree analysis.

Authors:  K-K Liu; T Wang; X-D Huang; G-L Wang; Y Xia; Y-T Zhang; Q-L Jing; J-W Huang; X-X Liu; J-H Lu; W-B Hu
Journal:  Epidemiol Infect       Date:  2016-11-22       Impact factor: 4.434

2.  Randomness of Dengue Outbreaks on the Equator.

Authors:  Yirong Chen; Alex R Cook; Alisa X L Lim
Journal:  Emerg Infect Dis       Date:  2015-09       Impact factor: 6.883

3.  Meteorological Factors for Dengue Fever Control and Prevention in South China.

Authors:  Haogao Gu; Ross Ka-Kit Leung; Qinlong Jing; Wangjian Zhang; Zhicong Yang; Jiahai Lu; Yuantao Hao; Dingmei Zhang
Journal:  Int J Environ Res Public Health       Date:  2016-08-31       Impact factor: 3.390

4.  Dengue in Araraquara, state of São Paulo: epidemiology, climate and Aedes aegypti infestation.

Authors:  Aline Chimello Ferreira; Francisco Chiaravalloti Neto; Adriano Mondini
Journal:  Rev Saude Publica       Date:  2018-02-26       Impact factor: 2.106

5.  Meteorological factors affecting dengue incidence in Davao, Philippines.

Authors:  Jesavel A Iguchi; Xerxes T Seposo; Yasushi Honda
Journal:  BMC Public Health       Date:  2018-05-15       Impact factor: 3.295

6.  Prediction of dengue outbreaks in Mexico based on entomological, meteorological and demographic data.

Authors:  Gilberto Sánchez-González; Renaud Condé; Raúl Noguez Moreno; P C López Vázquez
Journal:  PLoS One       Date:  2018-08-06       Impact factor: 3.240

  6 in total

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