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• W4236084337 startingPage "117693430600200" @default.
• W4236084337 abstract "Using the structured serial coalescent with Bayesian MCMC and serial samples, we estimate population size when some demes are not sampled or are hidden, ie ghost demes. It is found that even with the presence of a ghost deme, accurate inference was possible if the parameters are estimated with the true model. However with an incorrect model, estimates were biased and can be positively misleading. We extend these results to the case where there are sequences from the ghost at the last time sample. This case can arise in HIV patients, when some tissue samples and viral sequences only become available after death. When some sequences from the ghost deme are available at the last sampling time, estimation bias is reduced and accurate estimation of parameters associated with the ghost deme is possible despite sampling bias. Migration rates for this case are also shown to be good estimates when migration values are low." @default.
• W4236084337 created "2022-05-12" @default.
• W4236084337 creator A5015045944 @default.
• W4236084337 creator A5023810472 @default.
• W4236084337 date "2006-01-01" @default.
• W4236084337 modified "2023-09-25" @default.
• W4236084337 title "Estimating Population Parameters using the Structured Serial Coalescent with Bayesian MCMC Inference when some Demes are Hidden" @default.
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• W4236084337 doi "https://doi.org/10.1177/117693430600200026" @default.
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