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Biology subjects

Brown, T. S.

Publications and source records attributed to Brown, T. S..

2 recordsLinked to original sources

Distinguishing gene flow between malaria parasite populations

Measuring gene flow between malaria parasite populations in different geographic locations can provide strategic information for malaria control interventions. Multiple important questions pertaining to the design of such studies remain unanswered, limiting efforts to operationalize genomic surveillance tools for routine public health use. This report evaluates numerically the ability to distinguish different levels of gene flow between malaria populations, using different amounts of real and simulated data, where data are simulated using parameters that approximate different epidemiological conditions. Specifically, using Plasmodium falciparum whole genome sequence data and sequence data simulated for a metapopulation with different migration rates and effective population sizes, we compare two estimators of gene flow, explore the number of genetic markers and number of individuals required to reliably rank highly connected locations, and describe how these thresholds change given different effective population sizes and migration rates. Our results have implications for the design and implementation of malaria genomic surveillance efforts.

genomics↗

Modeling missing cases and transmission links in networks of extensively drug-resistant tuberculosis in KwaZulu-Natal, South Africa

The transmission patterns of drug-resistant tuberculosis (TB) remain poorly understood, despite over half a million incident cases in 2017. Modeling TB transmission networks can provide insight into the nature and drivers of transmission, but incomplete and non-random sampling of TB cases can pose challenges to making inferences from epidemiologic and molecular data. We conducted a quantitative bias analysis to assess the effect of missing cases on a transmission network inferred from Mtb sequencing data on extensively drug-resistant (XDR) TB cases in South Africa. We tested scenarios in which cases were missing at random, differentially by clinical characteristics, or differentially by transmission (i.e., cases with many links were under or over-sampled). Under the assumption cases were missing at random, cases in the complete, modeled network would have had a mean of 20 or more transmission links, which is far higher than expected, in order to reproduce the observed, partial network. Instead, we found that the most likely scenario involved undersampling of high-transmitting cases, and further models provided evidence for superspreading behavior. This is, to our knowledge, the first study to define and assess the support for different mechanisms of missingness in a study of TB transmission. Our findings should caution interpretation of results of future studies of TB transmission in high-incidence settings, given the potential for biased sampling, and should motivate further research aimed at identifying the specific host, pathogen, or environmental factors contributing to superspreading.

epidemiology↗