Enhanced Understanding of Infectious Diseases by Fusing Multiple Datasets: A Case Study on Malaria in the Western Brazilian Amazon Region

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.

Large-scale drivers of malaria and priority areas for prevention and control in the Brazilian Amazon region using a novel multi-pathogen geospatial model

“…The gross underestimation of malaria symptomatic cases is not an issue in this study because malaria drugs are only available after obtaining positive microscopy detection in a government health facility. However, asymptomatic individuals and subpatent infections are likely to be missed by the data [ 43 – 45 ], and thus a map of infection risk might be substantially different from a disease risk map. Furthermore, P. vivax and Plasmodium malariae are commonly confounded in microscopic examination of thick blood smears [ 46 – 48 ], and as a result, findings regarding P. vivax are expected to be generally accurate but may falter in locations with high P. malariae incidence.…”

Vulnerability to the health effects of climate variability in rural southwestern Uganda

“…The mosquito Anopheles gambiae is of particular interest due to its role as a primary vector of the malarial parasite Plasmodium falciparum in this region (Sinka et al 2012). This population is likely to have low historic immunity because of a history of limited prevalence of the A. gambiae vector in this highland region, with possible implications for concurrent trends in changing exposure and immunity over time (Katsuragawa et al 2010;Drakeley et al 2005;Valle et al 2011). Participant definitions of malaria differed among individuals.…”

Deforestation and Malaria on the Amazon Frontier: Larval Clustering of Anopheles darlingi (Diptera: Culicidae) Determines Focal Distribution of Malaria

“…It has also been noted that malaria risk increases with the extent of forest surrounding a subject's house, 51 that proximity to conservation areas increase malaria risk and conservation efforts to decrease deforestation in places where people are already settled might inadvertently increase the number of malaria cases. 52 Since a round-trip is necessary for breeding (larval-habitatto-human-dwelling and back again), it seems reasonable that distances over 400 m are unlikely to be spanned.…”