GHEP-ISFG collaborative simulated exercise for DVI/MPI: Lessons learned about large-scale profile database comparisons

Vullo, Carlos; Romero, Magdalena; Catelli, Laura; Šakić, Mustafa; Saragoni, Victor G.; Pleguezuelos, María Jose Jimenez; Romanini, Carola; Porto, Maria João; Prieto, Jorge Puente; Castro, Alicia Bofarull; Hernández, Alexis; Farfán, M.J.; Prieto, Victoria; Álvarez, David; Penacino, Gustavo; Zabalza, Santiago; Bolaños, Alejandro Hernández; Manterola, Irati Miguel; Prieto, Lourdes; Parsons, Thomas J.

doi:10.1016/j.fsigen.2015.11.004

“…Similarly, to the regular approach used in genetic analysis, the total number of missing/deceased persons related to an event has to be estimated as accurately as possible to build the prior odds value, mandatory within the Bayesian framework on which the genetic statistics are based, in order to calculate the posterior odds or probability of identity. The prior odds adopted in a Bayesian approach should reflect the number of individuals in the event to properly define levels of identification certainty in the form of posterior probability [ 10 , 11 ].…”

Section: The Search Processmentioning

confidence: 99%

The search process: Integrating the investigation and identification of missing and unidentified persons

Puerto¹,

Abboud

²

,

Baraybar

³

et al. 2021

Forensic Science International: Synergy

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The effective search for the missing and identification of persons, alive or dead, are core components in the prevention and in resolving the issue of Missing Persons. Despite the growing literature on this topic, there is still a lack of publications describing the Search as a process that includes different phases inherently composed of forensic investigative and identification principles for both living and deceased missing persons. This paper is the result of discussions between the Forensic Unit of the International Committee of the Red Cross (ICRC) and members of its external Forensic Advisory Board. It aims to present the Search process as an overarching concept that includes the investigation and identification phases of the missing in any state (dead or alive), in any scenario (with or without bodies), with an integrated, multidisciplinary, and multiagency approach for implementation by all actors involved in the investigation and identification phases of missing persons.

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“…The largest LR points to the most likely match for , and a successful identification is declared if this largest LR exceeds a prescribed threshold. Also, if priors are specified the LRs can be converted to posterior probabilities 10 .…”

Section: Introductionmentioning

confidence: 99%

Joint DNA-based disaster victim identification

Vigeland

¹

,

Egeland

²

2021

Sci Rep

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We address computational and statistical aspects of DNA-based identification of victims in the aftermath of disasters. Current methods and software for such identification typically consider each victim individually, leading to suboptimal power of identification and potential inconsistencies in the statistical summary of the evidence. We resolve these problems by performing joint identification of all victims, using the complete genetic data set. Individual identification probabilities, conditional on all available information, are derived from the joint solution in the form of posterior pairing probabilities. A closed formula is obtained for the a priori number of possible joint solutions to a given DVI problem. This number increases quickly with the number of victims and missing persons, posing computational challenges for brute force approaches. We address this complexity with a preparatory sequential step aiming to reduce the search space. The examples show that realistic cases are handled efficiently. User-friendly implementations of all methods are provided in the R package dvir, freely available on all platforms.

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“…The largest LR points to the most likely match for V, and a successful identification is declared if this largest LR exceeds a prescribed threshold. Also, if priors are specified the LRs can be converted to posterior probabilities 10 .…”

Section: Introductionmentioning

confidence: 99%

“…The question of how the statistical evidence should be reported in identification cases is difficult and lacks general consensus. Although there is a tradition of specifying priors and reporting posterior probabilities in addition to likelihood ratios 10 , our view is that specifying priors should be left to the decision makers. This is supported by ISFG recommendations 6 , whose point 11 includes:…”

mentioning

confidence: 99%

Joint DNA-based Disaster Victim Identification

Vigeland

¹

,

Egeland

²

2021

Preprint

1

0

View full text Add to dashboard Cite

We address computational and statistical aspects of DNA-based identification of victims in the aftermath of disasters. Current methods and software for such identification typically consider each victim individually, leading to suboptimal power of identification and potential inconsistencies in the statistical summary of the evidence. We resolve these problems by performing joint identification of all victims, using the complete genetic data set. Individual identification probabilities, conditional on all available information, are derived from the joint solution in the form of posterior pairing probabilities. A closed formula is obtained for the a priori number of possible joint solutions to a given DVI problem. This number increases quickly with the number of victims and missing persons, posing computational challenges for brute force approaches. We address this complexity with a preparatory sequential step aiming to reduce the search space. The examples show that realistic cases are handled efficiently. User-friendly implementations of all methods are provided in the R package dvir, freely available on all platforms.

show abstract

GHEP-ISFG collaborative simulated exercise for DVI/MPI: Lessons learned about large-scale profile database comparisons

Cited by 20 publications

References 23 publications

The search process: Integrating the investigation and identification of missing and unidentified persons

The search process: Integrating the investigation and identification of missing and unidentified persons

Joint DNA-based disaster victim identification

Joint DNA-based Disaster Victim Identification

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