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Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region.

Publication ,  Journal Article
Valle, D; Clark, JS; Zhao, K
Published in: PloS one
January 2011

A common challenge to the study of several infectious diseases consists in combining limited cross-sectional survey data, collected with a more sensitive detection method, with a more extensive (but biased) syndromic sentinel surveillance data, collected with a less sensitive method. Our article describes a novel modeling framework that overcomes this challenge, resulting in enhanced understanding of malaria in the Western Brazilian Amazon.A cohort of 486 individuals was monitored using four cross-sectional surveys, where all participants were sampled regardless of symptoms (aggressive-active case detection), resulting in 1,383 microscopy and 1,400 polymerase chain reaction tests. Data on the same individuals were also obtained from the local surveillance facility (i.e., passive and active case detection), totaling 1,694 microscopy tests. Our model accommodates these multiple pathogen and case detection methods. This model is shown to outperform logistic regression in terms of interpretability of its parameters, ability to recover the true parameter values, and predictive performance. We reveal that the main infection determinant was the extent of forest, particularly during the rainy season and in close proximity to water bodies, and participation on forest activities. We find that time residing in Acrelandia (as a proxy for past malaria exposure) decreases infection risk but surprisingly increases the likelihood of reporting symptoms once infected, possibly because non-naïve settlers are only susceptible to more virulent Plasmodium strains. We suggest that the search for asymptomatic carriers should focus on those at greater risk of being infected but lower risk of reporting symptoms once infected.The modeling framework presented here combines cross-sectional survey data and syndromic sentinel surveillance data to shed light on several aspects of malaria that are critical for public health policy. This framework can be adapted to enhance inference on infectious diseases whenever asymptomatic carriers are important and multiple datasets are available.

Duke Scholars

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Published In

PloS one

DOI

EISSN

1932-6203

ISSN

1932-6203

Publication Date

January 2011

Volume

6

Issue

11

Start / End Page

e27462

Related Subject Headings

  • Risk Factors
  • Public Health
  • Microscopy
  • Malaria
  • Humans
  • Health Surveys
  • General Science & Technology
  • Databases, Factual
  • Communicable Diseases
  • Cohort Studies
 

Citation

APA
Chicago
ICMJE
MLA
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Valle, D., Clark, J. S., & Zhao, K. (2011). Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region. PloS One, 6(11), e27462. https://doi.org/10.1371/journal.pone.0027462
Valle, Denis, James S. Clark, and Kaiguang Zhao. “Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region.PloS One 6, no. 11 (January 2011): e27462. https://doi.org/10.1371/journal.pone.0027462.
Valle, Denis, et al. “Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region.PloS One, vol. 6, no. 11, Jan. 2011, p. e27462. Epmc, doi:10.1371/journal.pone.0027462.

Published In

PloS one

DOI

EISSN

1932-6203

ISSN

1932-6203

Publication Date

January 2011

Volume

6

Issue

11

Start / End Page

e27462

Related Subject Headings

  • Risk Factors
  • Public Health
  • Microscopy
  • Malaria
  • Humans
  • Health Surveys
  • General Science & Technology
  • Databases, Factual
  • Communicable Diseases
  • Cohort Studies