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Spatio-temporal mixture process estimation to detect dynamical changes in population.

ABSTRACT: Population monitoring is a challenge in many areas such as public health and ecology. We propose a method to model and monitor population distributions over space and time, in order to build an alert system for spatio-temporal data changes. Assuming that mixture models can correctly model populations, we propose a new version of the Expectation-Maximization (EM) algorithm to better estimate the number of clusters and their parameters at the same time. This algorithm is compared to existing methods on several simulated datasets. We then combine the algorithm with a temporal statistical model, allowing for the detection of dynamical changes in population distributions, and call the result a spatio-temporal mixture process (STMP). We test STMPs on synthetic data, and consider several different behaviors of the distributions, to fit this process. Finally, we validate STMPs on a real data set of positive diagnosed patients to coronavirus disease 2019. We show that our pipeline correctly models evolving real data and detects epidemic changes.

SUBMITTER: Pruilh S

PROVIDER: S-EPMC8864896 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Spatio-temporal mixture process estimation to detect dynamical changes in population.

Pruilh Solange S Jannot Anne-Sophie AS Allassonnière Stéphanie S

Artificial intelligence in medicine 20220223

Population monitoring is a challenge in many areas such as public health and ecology. We propose a method to model and monitor population distributions over space and time, in order to build an alert system for spatio-temporal data changes. Assuming that mixture models can correctly model populations, we propose a new version of the Expectation-Maximization (EM) algorithm to better estimate the number of clusters and their parameters at the same time. This algorithm is compared to existing metho ...[more]

PMID: 35346441

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Project description:BackgroundDetailed knowledge of spatial and temporal variation in the genetic population structure of hosts and parasites is required for understanding of host - parasite coevolution. As hot-spots of contemporary coevolution in natural systems are difficult to detect and long-term studies are restricted to few systems, additional population genetic data from various host - parasite systems may provide important insights into the topic. This is particularly true for parasites, as these players have been under-investigated so far due to the lower availability of suitable molecular markers. Here, we traced genetic variation (based on sequence variants in the internal transcribed spacer region, ITS) among seven geographically isolated populations of the ichthyosporean Caullerya mesnili, a common microparasite of the cladoceran Daphnia (here, the D. longispina hybrid complex). At three sites, we also studied parasite genetic variation over time (three to four sampling points) and tested for associations between parasite genotypes and host species.ResultsParasite (and host) populations were significantly structured across space, indicating limited dispersal. Moreover, the frequency of parasite genotypes varied significantly over time, suggesting rapid evolutionary change in Caullerya. However, the distribution of parasite genotypes was similar across different host species, which might in turn have important consequences for parasite epidemiology.ConclusionsThe approach proposed here can be applied to track spatial and temporal changes in the population structure of other microparasite species for which sequence variation in the ITS or other highly variable genome regions has been documented but other types of polymorphic markers are lacking. Screening of parasite sequence variants allows for reliable detection of cross-species infections and, using advanced sequencing techniques in the near future, for detailed studies of parasite evolution in natural host - parasite systems.

Dataset Information

Spatio-temporal mixture process estimation to detect dynamical changes in population.

Publications

Spatio-temporal mixture process estimation to detect dynamical changes in population.

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