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

Denis Valle,Kaiguang Zhao,James S Clark

doi:10.1371/journal.pone.0027462

Abstract

BackgroundA 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.Methodology/Principal FindingsA 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.Conclusions/SignificanceThe 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.

Highlights

Extensive syndromic sentinel surveillance data are often routinely collected by public health agencies
This dataset contained a total of 1383 microscopy and 1400 Polymerase Chain Reaction (PCR) malaria tests
The proposed model uses information from both datasets to improve the estimates of infection and disease prevalence at our research site, which is extrapolated to a larger area

Summary

Introduction

Extensive syndromic sentinel surveillance data are often routinely collected by public health agencies. Researchers collect data to study infectious disease risk factors and asymptomatic pathogen carriers, but using cross-sectional surveys and more expensive and sensitive diagnostic methods. These data, are often geographically and temporally limited and are not as abundant as sentinel surveillance data. We describe here a novel statistical model that coherently combines these disparate datasets, allowing for enhanced inference on infectious diseases. 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

Methods

Results

Conclusion