Network Models for Malaria: Antigens, Dynamics, and Evolution Over Space and Time

Childs, Lauren M.; Larremore, Daniel B.

doi:10.1016/b978-0-12-801238-3.11512-0

Cited by 2 publications

(2 citation statements)

References 67 publications

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“…Studies of P. falciparum epidemiology and evolution have generated insights by comparing the var repertoires between parasites through β-diversity calculations [20][21][22][23][24][25][26][27]. Theory suggests that if a human population has been exposed to particular var genes, then repertoires containing those var genes will have lower fitness than repertoires that are entirely unrecognized by local hosts, shaping the var population structure [23][24][25][28][29][30].…”

Section: P Falciparum Repertoire Overlap Problemmentioning

confidence: 99%

Bayesian estimation of community size and overlap from random subsamples

Johnson

Larremore

2022

PLoS Comput Biol

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Counting the number of species, items, or genes that are shared between two groups, sets, or communities is a simple calculation when sampling is complete. However, when only partial samples are available, quantifying the overlap between two communities becomes an estimation problem. Furthermore, to calculate normalized measures of β-diversity, such as the Jaccard and Sorenson-Dice indices, one must also estimate the total sizes of the communities being compared. Previous efforts to address these problems have assumed knowledge of total community sizes and then used Bayesian methods to produce unbiased estimates with quantified uncertainty. Here, we address communities of unknown size and show that this produces systematically better estimates—both in terms of central estimates and quantification of uncertainty in those estimates. We further show how to use species, item, or gene count data to refine estimates of community size in a Bayesian joint model of community size and overlap.

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Section: P Falciparum Repertoire Overlap Problemmentioning

confidence: 99%

Bayesian estimation of community size and overlap from random subsamples

Johnson

Larremore

2022

PLoS Comput Biol

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“…Recent studies of P. falciparum epidemiology and evolution have generated insights by comparing the var repertoires between parasites through β -diversity calculations [3, 1, 11, 4, 5, 34, 15, 12]. Indeed, since var repertoires are, themselves, under selection, theory suggests that if a human population has been exposed to particular var genes, then repertoires containing those var genes will have lower fitness than repertoires that are entirely unrecognized by local hosts, shaping the var population structure [4, 34, 7, 19, 29, 5].…”

Section: Introductionmentioning

confidence: 99%

Bayesian estimation of population size and overlap from random subsamples

Larremore

2021

Preprint

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Counting the number of species, items, or genes that are shared between two sets is a simple calculation when sampling is complete. However, when only partial samples are available, quantifying the overlap between two sets becomes an estimation problem. Furthermore, to calculate normalized measures of β-diversity, such as the Jaccard and Sorenson-Dice indices, one must also estimate the total sizes of the sets being compared. Previous efforts to address these problems have assumed knowledge of total population sizes and then used Bayesian methods to produce unbiased estimates with quantified uncertainty. Here, we address populations of unknown size and show that this produces systematically better estimates--both in terms of central estimates and quantification of uncertainty in those estimates. We further show how to use species count data to refine estimates of population size in a Bayesian joint model of populations and overlap.

show abstract

Network Models for Malaria: Antigens, Dynamics, and Evolution Over Space and Time

Cited by 2 publications

References 67 publications

Bayesian estimation of community size and overlap from random subsamples

Bayesian estimation of community size and overlap from random subsamples

Bayesian estimation of population size and overlap from random subsamples

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