Ghent University Academic Bibliography

Advanced

Joint Bayesian modeling of time to malaria and mosquito abundance in Ethiopia

Denekew Bitew Belay, Yehenew Getachew Kifle, Ayele Taye Goshu, Jon Michael Gran, Delenasaw Yewhalaw, Luc Duchateau UGent and Arnoldo Frigessi (2017) BMC INFECTIOUS DISEASES. 17.
abstract
Background: This paper studies the effect of mosquito abundance and malaria incidence in the last 3 weeks, and their interaction, on the hazard of time to malaria in a previously studied cohort of children in Ethiopia. Methods: We model the mosquito abundance and time to malaria data jointly in a Bayesian framework. Results: We found that the interaction of mosquito abundance and incidence plays a prominent role on malaria risk. We quantify and compare relative risks of various factors, and determine the predominant role of the interaction between incidence and mosquito abundance in describing malaria risk. Seasonal rain patterns, distance to a water source of the households, temperature and relative humidity are all significant in explaining mosquito abundance, and through this affect malaria risk. Conclusion: Analyzing jointly the time to malaria data and the mosquito abundance allows a precise comparison of factors affecting the spread of malaria. The effect of the interaction between mosquito abundances and local presence of malaria parasites has an important effect on the hazard of time to malaria, beyond abundance alone. Each additional one km away from the dam gives an average reduction of malaria relative risk of 5.7%. The importance of the interaction between abundance and incidence leads to the hypothesis that preventive intervention could advantageously target the infectious population, in addition to mosquito control, which is the typical intervention today.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
EPIDEMIOLOGY, SPLINES, BURDEN, DAMS, Mosquito abundance, Time to malaria, MCMC, Abundance and incidence, interaction, Bayesian inference
journal title
BMC INFECTIOUS DISEASES
BMC Infect. Dis.
volume
17
article number
415
pages
12 pages
Web of Science type
Article
Web of Science id
000403049800002
ISSN
1471-2334
DOI
10.1186/s12879-017-2496-4
language
English
UGent publication?
yes
classification
A1
copyright statement
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
id
8537979
handle
http://hdl.handle.net/1854/LU-8537979
date created
2017-11-19 21:45:20
date last changed
2017-11-27 09:55:03
@article{8537979,
  abstract     = {Background: This paper studies the effect of mosquito abundance and malaria incidence in the last 3 weeks, and their interaction, on the hazard of time to malaria in a previously studied cohort of children in Ethiopia. 
Methods: We model the mosquito abundance and time to malaria data jointly in a Bayesian framework. 
Results: We found that the interaction of mosquito abundance and incidence plays a prominent role on malaria risk. We quantify and compare relative risks of various factors, and determine the predominant role of the interaction between incidence and mosquito abundance in describing malaria risk. Seasonal rain patterns, distance to a water source of the households, temperature and relative humidity are all significant in explaining mosquito abundance, and through this affect malaria risk. 
Conclusion: Analyzing jointly the time to malaria data and the mosquito abundance allows a precise comparison of factors affecting the spread of malaria. The effect of the interaction between mosquito abundances and local presence of malaria parasites has an important effect on the hazard of time to malaria, beyond abundance alone. Each additional one km away from the dam gives an average reduction of malaria relative risk of 5.7\%. The importance of the interaction between abundance and incidence leads to the hypothesis that preventive intervention could advantageously target the infectious population, in addition to mosquito control, which is the typical intervention today.},
  articleno    = {415},
  author       = {Belay, Denekew Bitew and Kifle, Yehenew Getachew and Goshu, Ayele Taye and Gran, Jon Michael and Yewhalaw, Delenasaw and Duchateau, Luc and Frigessi, Arnoldo},
  issn         = {1471-2334},
  journal      = {BMC INFECTIOUS DISEASES},
  keyword      = {EPIDEMIOLOGY,SPLINES,BURDEN,DAMS,Mosquito abundance,Time to malaria,MCMC,Abundance and incidence,interaction,Bayesian inference},
  language     = {eng},
  pages        = {12},
  title        = {Joint Bayesian modeling of time to malaria and mosquito abundance in Ethiopia},
  url          = {http://dx.doi.org/10.1186/s12879-017-2496-4},
  volume       = {17},
  year         = {2017},
}

Chicago
Belay, Denekew Bitew, Yehenew Getachew Kifle, Ayele Taye Goshu, Jon Michael Gran, Delenasaw Yewhalaw, Luc Duchateau, and Arnoldo Frigessi. 2017. “Joint Bayesian Modeling of Time to Malaria and Mosquito Abundance in Ethiopia.” Bmc Infectious Diseases 17.
APA
Belay, D. B., Kifle, Y. G., Goshu, A. T., Gran, J. M., Yewhalaw, D., Duchateau, L., & Frigessi, A. (2017). Joint Bayesian modeling of time to malaria and mosquito abundance in Ethiopia. BMC INFECTIOUS DISEASES, 17.
Vancouver
1.
Belay DB, Kifle YG, Goshu AT, Gran JM, Yewhalaw D, Duchateau L, et al. Joint Bayesian modeling of time to malaria and mosquito abundance in Ethiopia. BMC INFECTIOUS DISEASES. 2017;17.
MLA
Belay, Denekew Bitew, Yehenew Getachew Kifle, Ayele Taye Goshu, et al. “Joint Bayesian Modeling of Time to Malaria and Mosquito Abundance in Ethiopia.” BMC INFECTIOUS DISEASES 17 (2017): n. pag. Print.