Bayesian group testing with dilution effects

A Bayesian framework for group testing under dilution effects has been developed, using lattice-based models. This work has particular relevance given the pressing public health need to enhance testing capacity for coronavirus disease 2019 and future pandemics, and the need for wide-scale and repeated testing for surveillance under constantly varying conditions. The proposed Bayesian approach allows for dilution effects in group testing and for general test response distributions beyond just binary outcomes. It is shown that even under strong dilution effects, an intuitive group testing selection rule that relies on the model order structure, referred to as the Bayesian halving algorithm, has attractive optimal convergence properties. Analogous look-ahead rules that can reduce the number of stages in classification by selecting several pooled tests at a time are proposed and evaluated as well. Group testing is demonstrated to provide great savings over individual testing in the number of tests needed, even for moderately high prevalence levels. However, there is a trade-off with higher number of testing stages, and increased variability. A web-based calculator is introduced to assist in weighing these factors and to guide decisions on when and how to pool under various conditions. High-performance distributed computing methods have also been implemented for considering larger pool sizes, when savings from group testing can be even more dramatic.

Biodiversity loss and COVID-19 pandemic: The role of bats in the origin and the spreading of the disease

The loss of biodiversity in the ecosystems has created the general conditions that have favored and, in fact, made possible, the insurgence of the COVID-19 pandemic. A lot of factors have contributed to it: deforestation, changes in forest habitats, poorly regulated agricultural surfaces, mismanaged urban growth. They have altered the composition of wildlife communities, greatly increased the contacts of humans with wildlife, and altered niches that harbor pathogens, increasing their chances to come in contact with humans. Among the wildlife, bats have adapted easily to anthropized environments such as houses, barns, cultivated fields, orchards, where they found the suitable ecosystem to prosper. Bats are major hosts for αCoV and βCoV: evolution has shaped their peculiar physiology and their immune system in a way that makes them resistant to viral pathogens that would instead successfully attack other species, including humans. In time, the coronaviruses that bats host as reservoirs have undergone recombination and other modifications that have increased their ability for inter-species transmission: one modification of particular importance has been the development of the ability to use ACE2 as a receptor in host cells. This particular development in CoVs has been responsible for the serious outbreaks in the last two decades, and for the present COVID-19 pandemic.

Pharmaceutical concentration variability at sewage treatment plant outlets dominated by hydrology and other factors

A study was conducted in which the effluent at four small to medium sized sewage treatment plants (STP) in North Rhine-Westphalia, Germany was monitored for three pharmaceutical compounds (carbamazepine, diclofenac, metoprolol) over a period of four years. Grab sampling and auto sampling campaigns were accomplished with respect to various weather conditions in the catchment area. Flow volumes and hydraulic retention times (HRT) from various sampling dates which provide information on processes causing emission changes were additionally taken into account. Monitoring results showed that concentration scattering in the effluent is related to HRT in the sewage treatment plants. Dilution effects following rain events in the catchment area were analysed for the three investigated substances. Short-term emission changes explained by dilution only could be well determined by the mathematical relation between discharge and concentration, and for carbamazepine to be solely determined by the dilution effects at all HRTs. For metoprolol, a clear decrease in concentrations was observed at HRTs above 80 h, and a significant contribution of biodegradation was supported by independent biodegradation tests. For three out of the four STPs, a decrease in concentrations of diclofenac was observed at hydraulic retention times above 80 h, indicating removal, whereas the relationship between concentration and HRT of the other STP could be explained by dilution only. The study shows that emissions can vary with weather conditions, hampering the assessment of emissions and estimation of concentrations in surface waters from generic removal rates only. Furthermore, it illustrates the importance of HRT of rather stable substances in wastewater treatment.

Statistical Considerations of Food Allergy Prevention Studies

Clinical studies to prevent the development of food allergy have recently helped reshape public policy recommendations on the early introduction of allergenic foods. These trials are also prompting new research, and it is therefore important to address the unique design and analysis challenges of prevention trials. We highlight statistical concepts and give recommendations that clinical researchers may wish to adopt when designing future study protocols and analysis plans for prevention studies. Topics include selecting a study sample, addressing internal and external validity, improving statistical power, choosing alpha and beta, analysis innovations to address dilution effects, and analysis methods to deal with poor compliance, dropout, and missing data.