Biological variability of the most common biochemical parameters

The results of laboratory tests are analyzed against reference values that are determined in a population of healthy people prepared for the test in accordance with the relevant guidelines. Such a reference system works perfectly when analyzing the results of tests of patients from whom the material for determinations was collected in similar conditions. To better match the reference values ranges are stratified, most often by gender, age, or race of the patient – the most common and the most significant biological variability. The values of the measured parameter are also influenced by within-subject biological variability to e.g. the time of the day, food consumption, or physical exercise. This variability influences the results of random testing, often performed in patients with emergencies. The measure of both of these variations is the index of individuality, i.e. the ratio of within-subject biological variability to between-subject biological variability. In the present work, the factors influencing the circadian, seasonal, and between-subject biological variations of the selected clinical chemistry parameters are presented. Knowledge about these variations is important for the physician and the supporting laboratory diagnostician, particularly helpful in the analysis of pathological or inconsistent with the clinicians' expectations results to distinguish results related to the disease from results related to biological variability.

Sentiment Analysis of Social Media Content for Music Recommendation

There is a growing use of social media for communication and entertainment. The information obtained from these social media platforms like Facebook, Linkedln, Twitter and so on can be used for inferring users’ emotional state. Users express their emotions on social media such as Twitter through text and emojis. Such expression can be harvested for the development of a recommender system. In this work, live tweets of users were harvested for the development of an emotion-based music recommender system. The emotions captured in this work include happy, fear, angry disgusted and sad. Users tweets in the form of emojis or text were matched with predefined variables to predict the emotion of users. Random testing of live tweets using the system was conducted and the result showed high predictability.

Analysis of the time course of COVID-19 cases and deaths from countries with extensive testing allows accurate early estimates of the age specific symptomatic CFR values

Background Knowing the true infected and symptomatic case fatality ratios (IFR and CFR) for COVID-19 is of high importance for epidemiological model projections. Early in the pandemic many locations had limited testing and reporting, so that standard methods for determining IFR and CFR required large adjustments for missed cases. We present an alternate approach, based on results from the countries at the time that had a high test to positive case ratio to estimate symptomatic CFR. Methods We calculated age specific (0–69, 70–79, 80+ years old) time corrected crude symptomatic CFR values from 7 countries using two independent time to fatality correction methods. Data was obtained through May 7, 2020. We applied linear regression to determine whether the mean of these coefficients had converged to the true symptomatic CFR values. We then tested these coefficients against values derived in later studies as well as a large random serological study in NYC at that time. Results The age dependent symptomatic CFR values accurately predicted the percentage of the population infected as reported by two random testing studies in NYC. They also were in good agreement with later studies that estimated age specific IFR and CFR values from serological studies and more extensive data sets available later in the pandemic. Conclusions We found that for regions with extensive testing it is possible to get early accurate symptomatic CFR coefficients. These values, in combination with an estimate of the age dependence of infection, allows symptomatic CFR values and percentage of the population that is infected to be determined in similar regions with limited testing.

Quarantine, Contact Tracing, and Testing: Implications of an Augmented SEIR Model

I incorporate quarantine, contact tracing, and random testing in the basic SEIR model of infectious disease diffusion. A version of the model that is calibrated to known characteristics of the spread of COVID-19 is used to estimate the transmission rate of COVID-19 in the United States in 2020. The transmission rate is then decomposed into a part that reflects observable changes in employment and social contacts, and a residual component that reflects disease properties and all other factors that affect the spread of the disease. I then construct counterfactuals for an alternative employment path that avoids the sharp employment decline in the second quarter of 2020, but also results in higher cumulative deaths due to a higher contact rate. For the simulations a modest permanent increase of quarantine effectiveness counteracts the increase in deaths, and the introduction of contact tracing and random testing further reduces deaths, although at a diminishing rate. Using a conservative assumption on the statistical value of life, the value of improved health outcomes from the alternative policies far outweighs the economic gains in terms of increased output and the potential fiscal costs of these policies.

The impact of digital contact tracing on the SARS-CoV-2 pandemic—a comprehensive modelling study

Contact tracing is one of several strategies employed in many countries to curb the spread of SARS-CoV-2. Digital contact tracing (DCT) uses tools such as cell-phone applications to improve tracing speed and reach. We model the impact of DCT on the spread of the virus for a large epidemiological parameter space consistent with current literature on SARS-CoV-2. We also model DCT in combination with random testing (RT) and social distancing (SD).Modelling is done with two independently developed individual-based (stochastic) models that use the Monte Carlo technique, benchmarked against each other and against two types of deterministic models.For current best estimates of the number of asymptomatic SARS-CoV-2 carriers (approximately 40%), their contagiousness (similar to that of symptomatic carriers), the reproductive number before interventions (${R_{0}}$ R 0 at least 3) we find that DCT must be combined with other interventions such as SD and/or RT to push the reproductive number below one. At least 60% of the population would have to use the DCT system for its effect to become significant. On its own, DCT cannot bring the reproductive number below 1 unless nearly the entire population uses the DCT system and follows quarantining and testing protocols strictly. For lower uptake of the DCT system, DCT still reduces the number of people that become infected.When DCT is deployed in a population with an ongoing outbreak where $\mathcal{O}$ O (0.1%) of the population have already been infected, the gains of the DCT intervention come at the cost of requiring up to 15% of the population to be quarantined (in response to being traced) on average each day for the duration of the epidemic, even when there is sufficient testing capability to test every traced person.