Modelling infection spread using location tracking.

The precision of location tracking technology has improved greatly over the last few decades. We aim to show that by tracking the locations of individuals in a closed environment, it is now possible to record the nature and frequency of interactions between them. Further, that it is possible to use such data to predict the way in which an infection will spread throughout such a population, given parameters such as transmission and recovery rates. We accordingly present a software package that is capable of recording and then replaying location data provided by a high-precision location tracking system. The software then employs a combination of SIR modelling and the epidemiological technique of contact tracing in order to predict the spread of an ..

Parallel computation of response time densities and quantiles in large Markov and semiMarkov models

Response time quantiles reflect user-perceived quality of service more accurately than mean or average response time measures. Consequently, on-line transaction process-ing benchmarks, telecommunications Service Level Agreements and emergency ser-vices legislation all feature stringent 90th percentile response time targets. This thesis presents techniques and tools for extracting response time densities, quan-tiles and moments from large-scale models of real-life systems. This work expands the applicability, capacity and specification power of prior work, which was hitherto focused on the analysis of Markov models which only support exponential delays. Response time densities or cumulative distribution functions of interest are computed by calculating and subsequently numerically inverting their Laplace transforms. We develop techniques for the extraction of response time measures from Generalised Stochastic Petri Nets (GSPNs) and Semi-Markov Stochastic Petri Nets (SM-SPNs). The latter is our proposed modelling formalism for the high-level specification of semi-Markov models which support generally-distributed delays

Antibody testing for COVID-19: A report from the National COVID Scientific Advisory Panel [version 1; peer review: 2 approved]

The COVID-19 pandemic caused >1 million infections during January-March 2020. There is an urgent need for reliable antibody detection approaches to support diagnosis, vaccine development, safe release of individuals from quarantine, and population lock-down exit strategies. We set out to evaluate the performance of ELISA and lateral flow immunoassay (LFIA) devices. Methods: We tested plasma for COVID (severe acute respiratory syndrome coronavirus 2; SARS-CoV-2) IgM and IgG antibodies by ELISA and using nine different LFIA devices. We used a panel of plasma samples from individuals who have had confirmed COVID infection based on a PCR result (n=40), and pre-pandemic negative control samples banked in the UK prior to December-2019 (n=142). Results: ELISA detected IgM or IgG in 34/40 individuals with a confirmed history of COVID infection (sensitivity 85%, 95%CI 70-94%), vs. 0/50 pre-pandemic controls (specificity 100% [95%CI 93-100%]). IgG levels were detected in 31/31 COVID-positive individuals tested ≥10 days after symptom onset (sensitivity 100%, 95%CI 89-100%). IgG titres rose during the 3 weeks post symptom onset and began to fall by 8 weeks, but remained above the detection threshold. Point estimates for the sensitivity of LFIA devices ranged from 55-70% versus RT-PCR and 65-85% versus ELISA, with specificity 95-100% and 93-100% respectively. Within the limits of the study size, the performance of most LFIA devices was similar. Conclusions: Currently available commercial LFIA devices do not perform sufficiently well for individual patient applications. However, ELISA can be calibrated to be specific for detecting and quantifying SARS-CoV-2 IgM and IgG and is highly sensitive for IgG from 10 days following first symptoms