Login / Signup

Spatio-Temporal Bayesian Models for Malaria Risk Using Survey and Health Facility Routine Data in Rwanda.

Muhammed SemakulaFrançois NiragireChristel Faes
Published in: International journal of environmental research and public health (2023)
The findings of this analysis suggest that using DHS combined with routine health services data for active malaria surveillance may provide provide more precise estimates of the malaria burden, which can be used toward malaria elimination targets. We compared findings from geostatistical modeling of malaria prevalence among under-five-year old children using DHS 2019-2020 and findings from malaria relative risk spatio-temporal modeling using both DHS survey 2019-2020 and health facility routine data. The strength of routinely collected data at small scales and high-quality data from the survey contributed to a better understanding of the malaria relative risk at the subnational level in Rwanda.
Keyphrases
  • plasmodium falciparum
  • electronic health record
  • public health
  • big data
  • healthcare
  • clinical practice
  • cross sectional
  • mental health
  • climate change
  • machine learning
  • risk assessment
  • social media
  • human health