Risk-based cost-benefit analysis of alternative vaccines against COVID-19 in Brazil: Coronavac vs. Astrazeneca vs. Pfizer

We propose a probabilistic model to quantify the cost-benefit of mass Vaccination Scenarios (VSs) against COVID-19. Through this approach, we conduct a six-month simulation, from August 31st, 2021 to March 3rd, 2022, of nine VSs, i.e., the three primary vaccine brands in Brazil (CoronaVac, AstraZeneca and Pfizer), each with three different vaccination rates (2nd doses per week). Since each vaccine has different individual-level effectiveness, we measure the population-level benefit as the probability of reaching herd immunity (HI). We quantify and categorize the cost-benefit of VSs through risk graphs that show: (i) monetary cost vs. probability of reaching HI; and (ii) number of new deaths vs. probability of reaching HI. Results show that AstraZeneca has the best cost-benefit when prioritizing acquisition costs, while Pfizer is the most cost-beneficial when prioritizing the number of deaths. This work provides helpful information that can aid public health authorities in Brazil to better plan VSs. Furthermore, our approach is not restricted to Brazil, the COVID-19 pandemic, or the mentioned vaccine brands. Indeed, the method is flexible so that this study can be a valuable reference for future cost-benefit analyses in other countries and pandemics, especially in the early stages of vaccination, when data is scarce and uncertainty is high.     

Correcting forensic DNA errors

DNA mixture interpretation can produce opposing conclusions by qualified forensic analysts, even within the same laboratory. The long-delayed publication of the National Institutes of Standards and Technology (NIST) study of 109 North American crime laboratories in this journal demonstrates this most clearly. This latest study supports earlier work that shows common methods such as the Combined Probability of Inclusion (CPI) have wrongly included innocent people as contributors to DNA mixtures. The 2016 President's Council of Advisors on Science and Technology report concluded, “In summary, the interpretation of complex DNA mixtures with the CPI statistic has been an inadequately specified—and thus inappropriately subjective—method. As such, the method is clearly not foundationally valid” [7]. The adoption of probabilistic genotyping by many laboratories will certainly prevent some of these errors from occurring in the future, but the same laboratories that produced past errors can also now review old cases with their new software—without additional bench work. It is critical that laboratories adopt procedures and policies to do this.     

STRmix™ put to the test: 300 000 non-contributor profiles compared to four-contributor DNA mixtures and the impact of replicates

Probabilistic genotyping approaches are increasingly used for the interpretation of DNA mixtures. To explore the specificity of one of these systems (STRmix^™), we conducted an extensive study using 24 complex mixtures: all were known or apparent 4-person mixtures with at least one contributor representing less than 20% of total DNA, and all mixtures had at least one contributor with suboptimal DNA quantity. Those mixtures were either generated in-house or from casework. All the mixtures were compared to 300,000 virtual non-contributors, resulting in a dataset of 7.2 million comparisons. The great majority of the non-contributor comparisons led to a LR lower than 1 for a specificity of 99.1%. The effect of using replicate amplifications to calculate the LR of non-contributors was also assessed as triplicates were used and led to an increased specificity of 99.8%. The very large extent of the analyzed data shows that STRmix^™ has an excellent ability to discriminate non-contributors from complex DNA mixtures.     

Multimodal, Multidimensional Models of Mouse Brain

Summary:  Naturally occurring mutants and genetically manipulated strains of mice are widely used to model a variety of human diseases. Atlases are an invaluable aid in understanding the impact of such manipulations by providing a standard for comparison and to facilitate the integration of anatomic, genetic, and physiologic observations from multiple subjects and experiments. We have developed digital atlases of the C57BL/6J mouse brain (adult and neonate) as comprehensive frameworks for storing and accessing the myriad types of information about the mouse brain. Along with raw and annotated images, these contain database management systems and a set of tools for comparing information from different techniques and different animals. Each atlas establishes a canonical representation of the mouse brain and provides the tools for the manipulation and analysis of new data. We describe both these atlases and discuss how they may be put to use in organizing and analyzing data from mouse models of epilepsy.     

On a relaxation-labelling algorithm for quantitative assessment of tumour vasculature in tissue section images

Although tumour vasculature constitutes a biological factor playing a crucial role in the radiation response of tumours, the current procedures of assessment are semiquantitative, typically employing visual examination of stained histological material. Such techniques are also time consuming, and inefficient of extracting essential information on the vascular network. Image analysis has yet to contribute significantly in this direction, and most studies to date focus on blood vessel segmentation through empirical, user-selected thresholds. The present paper proposes an alternative segmentation approach, based on a probabilistic relaxation algorithm, applied in microscopic images of stained tissues. After image partitioning various information is obtained, such as vascular domains and geometrical characteristics of vessels.     

Multimodal architectonic mapping of human superior temporal gyrus

Although it is generally accepted that human superior temporal gyrus is activated by a huge variety of auditory and linguistic tasks, little is known about the exact positions and extents of cortical areas that are located on the lateral convexity of the gyrus (e.g., Brodmann’s area 22). Such information, however, is relevant for a rigorous testing of structural-functional relationships in both normal volunteers and patients suffering from disorders of auditory and language perception. The present combined cytoarchitectonic and receptorarchitectonic study identifies a distinct area (Te3) in the lateral bulge of the superior temporal gyrus by using an algorithm-based approach for the detection of cortical borders. Our mapping data show that, in contrast to Brodmann’s area (BA) 22, only small portions of Te3 reach the dorsal and ventral banks of the gyrus. Therefore, we labelled the newly defined area as “Te3” and not as “BA 22”. The cytoarchitectonically defined borders of Te3 coincide with abrupt changes in the receptorarchitecture of several classical neurotransmitters, suggesting that Te3 represents a functionally relevant area of the human superior temporal gyrus. Since position and extent of area Te3 varied considerably between subjects, probability maps were created that show for each voxel of the standard references space, the frequency with which Te3 was present in it. These maps, in combination with previously published maps of the primary auditory cortex, can directly be compared with functional imaging data, and may open new perspectives for the analysis of structural-functional correlations in the human auditory and language systems.     

Arsenic exposure and tobacco consumption: Biomarkers and risk assessment

Arsenic is measurable in tobacco and cigarette mainstream smoke (MSS). Whether arsenic has an independent role in diseases associated with tobacco consumption is not known. Epidemiology and biomonitoring data and probabilistic risk assessment (PRA) methods were used to investigate this potential association. Analysis of data from the National Health and Nutrition Examination Survey (NHANES) showed that urine arsenic concentrations in tobacco consumers were not different or were lower than levels in non-consumers of tobacco. Additionally, urine arsenic levels from NHANES tobacco consumers were five-times or more lower than levels reported in epidemiology studies to be associated with adverse health effects. Results of PRA indicated that mean non-cancer hazard estimates and mean incremental lifetime cancer risk estimates were within accepted ranges. Taken together, these results suggest that arsenic may not be independently associated with tobacco consumption or diseases related to tobacco consumption.     

Statistics for Risk Assessment of Chemical Carcinogens∗

Risk assessment is a scientific process of evaluation of potential health risks of chemical exposures to humans from available information. It involves analysis of the relationship between exposure and health related outcomes to derive an allowable exposure level. Because of lack of human exposure data, the major source of information for studying potential health effects of chemicals on humans is generally obtained from animal dose response experiments. Animal data are often evaluated in two aspects via statistical analysis: qualitative testing and quantitative estimation. The qualitative testing is to determine if the chemical causes an adverse health effect, i.e., if there is a statistically significant difference between treated and control animals. Quantitative estimation involves fitting a dose-response model to derive an allowable exposure level for humans. This paper reviews statistical principles and procedures for qualitative and quantitative approaches to human risk assessment.     

A stochastic model validated with human test data causally associating target vehicle Delta V,occupant cervicocranial biomechanics,and injury during rear-impact crashes

Even at low to moderate-speeds, rear-end motor vehicle crashes have been strongly associated with occupant cervicocranial biomechanics that lead to head and neck injury. In this paper, we present the development of an analytic mechanics model of occupant head and neck motion as associated with modeled target vehicle Delta V during rear-end vehicular crashes. The inclusion of stochastic mechanical input variables further developed the model beyond the deterministic framework by reflecting aspects of the random nature of real-world crashes and the resulting injuries. This approach led to the characterization of 1000 crash simulations, quantifying Delta V and the resulting probabilistic occupant biomechanics. The model was validated through the direct comparison with 86 published human subject crash tests. Overall, the model slightly underestimated by −2.6% the magnitude of peak head accelerations identified in the literature. The utility of the model allows a forensic biomechanical investigator to customize some of the fundamental input crash parameters and appropriately explore the resulting vehicular mechanics and their direct influence on injury biomechanics.     

Describing disease processes using a probabilistic logic of qualitative time

BackgroundClinical knowledge about progress of diseases is characterised by temporal information as well as uncertainty. However, precise timing information is often unavailable in medicine. In previous research this problem has been tackled using Allen's qualitative algebra of time, which, despite successful medical application, does not deal with the associated uncertainty.ObjectivesIt is investigated whether and how Allen's temporal algebra can be extended to handle uncertainty to better fit available knowledge and data of disease processes.MethodsTo bridge the gap between probability theory and qualitative time reasoning, methods from probabilistic logic are explored. The relation between the probabilistic logic representation and dynamic Bayesian networks is analysed. By studying a typical, and clinically relevant problem, the detection of exacerbations of chronic obstructive pulmonary disease (COPD), it is determined whether the developed probabilistic logic of qualitative time is medically useful.ResultsThe probabilistic logic extension of Allen's temporal algebra, called Qualitative Time CP-logic provides a tool to model disease processes at a natural level of abstraction and is sufficiently powerful to reason with imprecise, uncertain knowledge. The representation of the COPD disease process gives evidence that the framework can be applied functionally to a clinical problem.ConclusionThe combination of qualitative time and probabilistic logic offers a useful framework for modelling knowledge and data to describe disease processes in clinical medicine.