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Register a new userStatistical Modeling for Practical Pooled Testing During the COVID-19 Pandemic
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Added by
Claire Donnat
Publication date
2021
fields
Mathematical Statistics
and research's language is
English
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Pooled testing offers an efficient solution to the unprecedented testing demands of the COVID-19 pandemic, although with potentially lower sensitivity and increased costs to implementation in some settings. Assessments of this trade-off typically assume pooled specimens are independent and identically distributed. Yet, in the context of COVID-19, these assumptions are often violated: testing done on networks (housemates, spouses, co-workers) captures correlated individuals, while infection risk varies substantially across time, place and individuals. Neglecting dependencies and heterogeneity may bias established optimality grids and induce a sub-optimal implementation of the procedure. As a lesson learned from this pandemic, this paper highlights the necessity of integrating field sampling information with statistical modeling to efficiently optimize pooled testing. Using real data, we show that (a) greater gains can be achieved at low logistical cost by exploiting natural correlations (non-independence) between samples -- allowing improvements in sensitivity and efficiency of up to 30% and 90% respectively; and (b) these gains are robust despite substantial heterogeneity across pools (non-identical). Our modeling results complement and extend the observations of Barak et al (2021) who report an empirical sensitivity well beyond expectations. Finally, we provide an interactive tool for selecting an optimal pool size using contextual information
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When testing for a disease such as COVID-19, the standard method is individual testing: we take a sample from each individual and test these samples separately. An alternative is pooled testing (or group testing), where samples are mixed together in different pools, and those pooled samples are tested. When the prevalence of the disease is low and the accuracy of the test is fairly high, pooled testing strategies can be more efficient than individual testing. In this chapter, we discuss the mathematics of pooled testing and its uses during pandemics, in particular the COVID-19 pandemic. We analyse some one- and two-stage pooling strategies under perfect and imperfect tests, and consider the practical issues in the application of such protocols.
The problem of skewness is common among clinical trials and survival data which has being the research focus derivation and proposition of different flexible distributions. Thus, a new distribution called Extended Rayleigh Lomax distribution is constructed from Rayleigh Lomax distribution to capture the excessiveness of some survival data. We derive the new distribution by using beta logit function proposed by Jones (2004). Some statistical properties of the distribution such as probability density function, cumulative density function, reliability rate, hazard rate, reverse hazard rate, moment generating functions, likelihood functions, skewness, kurtosis and coefficient of variation are obtained. We also performed the expected estimation of model parameters by maximum likelihood; goodness of fit and model selection criteria including Anderson Darling (AD), CramerVon Misses (CVM), Kolmogorov Smirnov (KS), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Consistent Akaike Information Criterion (CAIC) are employed to select the better distribution from those models considered in the work. The results from the statistics criteria show that the proposed distribution performs better with better representation of the States in Nigeria COVID-19 death cases data than other competing models.
We propose `Tapestry, a novel approach to pooled testing with application to COVID-19 testing with quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR) that can result in shorter testing time and conservation of reagents and testing kits. Tapestry combines ideas from compressed sensing and combinatorial group testing with a novel noise model for RT-PCR used for generation of synthetic data. Unlike Boolean group testing algorithms, the input is a quantitative readout from each test and the output is a list of viral loads for each sample relative to the pool with the highest viral load. While other pooling techniques require a second confirmatory assay, Tapestry obtains individual sample-level results in a single round of testing, at clinically acceptable false positive or false negative rates. We also propose designs for pooling matrices that facilitate good prediction of the infected samples while remaining practically viable. When testing $n$ samples out of which $k ll n$ are infected, our method needs only $O(k log n)$ tests when using random binary pooling matrices, with high probability. However, we also use deterministic binary pooling matrices based on combinatorial design ideas of Kirkman Triple Systems to balance between good reconstruction properties and matrix sparsity for ease of pooling. In practice, we have observed the need for fewer tests with such matrices than with random pooling matrices. This makes Tapestry capable of very large savings at low prevalence rates, while simultaneously remaining viable even at prevalence rates as high as 9.5%. Empirically we find that single-round Tapestry pooling improves over two-round Dorfman pooling by almost a factor of 2 in the number of tests required. We validate Tapestry in simulations and wet lab experiments with oligomers in quantitative RT-PCR assays. Lastly, we describe use-case scenarios for deployment.
A mathematical model for the COVID-19 pandemic spread, which integrates age-structured Susceptible-Exposed-Infected-Recovered-Deceased dynamics with real mobile phone data accounting for the population mobility, is presented. The dynamical model adjustment is performed via Approximate Bayesian Computation. Optimal lockdown and exit strategies are determined based on nonlinear model predictive control, constrained to public-health and socio-economic factors. Through an extensive computational validation of the methodology, it is shown that it is possible to compute robust exit strategies with realistic reduced mobility values to inform public policy making, and we exemplify the applicability of the methodology using datasets from England and France. Code implementing the described experiments is available at https://github.com/OptimalLockdown.
Following the onset of the COVID-19 pandemic and subsequent lockdowns, software engineers daily life was disrupted and they were abruptly forced into working remotely from home. Across one exploratory and one confirmatory study (N = 482), we tested whether a typical working day is different to pre-pandemic times and whether specific tasks are associated with task-specific satisfaction and productivity. To explore the subject domain, we first run a two-wave longitudinal study, where we found that the time software engineers spent doing specific tasks (e.g., coding, bugfixing, helping others) from home was similar to pre-pandemic times. Also, the amount of time developers spent on each task was unrelated to their general well-being, perceived productivity, and other variables such as basic needs. In our confirmatory study, we found that task satisfaction and productivity are predicted by task-specific variables (e.g., how much autonomy software engineers had during coding) but not by task-independent variables such as general resilience or a good work-life balance. Additionally, we found that satisfaction and autonomy were significantly higher when software engineers were helping others and lower when they were bugfixing. Also, contrary to anecdotal evidence, software engineers satisfaction and productivity during meetings is not lower compared to other tasks. Finally, we discuss implications for software engineers, management, and researchers.
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