ABSTRACT
In recent days, a combination of finite mixture model (FMM) and hidden Markov model (HMM) is becoming popular for partitioning heterogeneous temporal data into homogeneous groups (clusters) with homogeneous time points (regimes). The regression mixtures commonly considered in this approach can also accommodate for covariates present in data. The classical fixed covariate approach, however, may not always serve as a reasonable assumption as it is incapable of accounting for the contribution of covariates in cluster formation. This paper introduces a novel approach for detecting clusters and regimes in time series data in the presence of random covariates. The computational challenges related to the proposed model has been discussed, and several simulation studies are performed. An application to United States COVID-19 data yields meaningful clusters and regimes.
ABSTRACT
Many infectious pathogens can be transmitted by highly mobile species, like bats that can act as reservoir hosts for viruses such as henipaviruses, lyssaviruses and coronaviruses. In this study, we investigated the seroepidemiology of protein antigens to Severe acute respiratory syndrome virus (SARS-CoV-1) and Middle eastern respiratory syndrome virus (MERS-CoV) in Grey-headed flying foxes (Pteropus poliocephalus) in Adelaide, Australia sampled between September 2015 and February 2018. A total of 301 serum samples were collected and evaluated using a multiplex Luminex binding assay, and median fluorescence intensity thresholds were determined using finite-mixture modelling. We found evidence of antibodies reactive to SARS-CoV-1 or a related antigen with 42.5% (CI: 34.3%-51.2%) seroprevalence but insufficient evidence of reactivity to MERS-CoV antigen. This study provides evidence that the Grey-headed flying foxes sampled in Adelaide have been exposed to a SARS-like coronavirus.