Sequential Monte Carlo for on-line estimation of the heat loss coefficient

The calibration of building energy models based on in-situ sensor information is generally performed after the measurement period, using all data in a single batch. Alternatively, on-line parameter estimation proposes updating a model every time a new data point is available: this allows observing a direct relation between external events and the identifiability of parameters. The present study uses the Sequential Monte Carlo method to train a RC model, and thus estimate a Heat Loss Coefficient, and other parameters, sequentially. Results show the direct impact of solicitations (solar irradiance and indoor heat input) on this estimation, in real time. The method is validated by comparing its results with the Metropolis-Hastings algorithm for off-line estimation

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types