Two elementary parameters for quantifying viral infection and shedding are viral load and whether samples yield a replicating virus isolate in cell culture. We examined 25,381 German SARS-CoV-2 cases, including 6110 from test centres attended by pre-symptomatic, asymptomatic, and mildly-symptomatic (PAMS) subjects, 9519 who were hospitalised, and 1533 B.1.1.7 lineage infections. The youngest had mean log10 viral load 0.5 (or less) lower than older subjects and an estimated ~78% of the peak cell culture replication probability, due in part to smaller swab sizes and unlikely to be clinically relevant. Viral loads above 109 copies per swab were found in 8% of subjects, one-third of whom were PAMS, with mean age 37.6. We estimate 4.3 days from onset of shedding to peak viral load (8.1) and cell culture isolation probability (0.75). B.1.1.7 subjects had mean log10 viral load 1.05 higher than non-B.1.1.7, with estimated cell culture replication probability 2.6 times higher.

As children are under-represented in current studies aiming to analyse transmission of SARS-coronavirus 2 (SARS-CoV-2), their contribution to transmission is unclear. Viral load, as measured by RT-PCR, can inform considerations regarding transmission, especially if existing knowledge of viral load in other respiratory diseases is taken into account. RT-PCR threshold cycle data from 3303 patients who tested positive for SARS-CoV-2 (out of 77,996 persons tested in total, drawn from across Germany) were analysed to examine the relationship between patient age and estimated viral load. Two PCR systems were used. In data from the PCR system predominantly used for community and cluster screening during the early phase of the epidemic (Roche LightCycler 480 II), when such screening was frequent practice, viral loads do not differ significantly in three comparisons between young and old age groups (differences in log10 viral loads between young and old estimated from raw viral load data and a Bayesian mixture model of gamma distributions collectively range between -0.11 and -0.43). Data from a second type of PCR system (Roche cobas 6800/8800), introduced into diagnostic testing on March 16, 2020 and used during the time when household and other contact testing was reduced, show a credible but small difference in the three comparisons between young and old age groups (differences, measured as above, collectively range between -0.43 and -0.83). This small difference may be due to differential patterns of PCR instrument utilization rather than to an actual difference in viral load. Considering household transmission data on influenza, which has a similar viral load kinetic to SARS-CoV-2, the viral load differences between age groups observed in this study are likely to be of limited relevance. Combined data from both PCR instruments show that viral loads of at least 250,000 copies, a threshold we previously established for the isolation of infectious virus in cell culture at more than 5% probability, were present across the study period in 29.0% of kindergarten-aged patients 0-6 years old (n=38), 37.3% of those aged 0-19 (n=150), and in 51.4% of those aged 20 and above (n=3153). The differences in these fractions may also be due to differences in test utilization. We conclude that a considerable percentage of infected people in all age groups, including those who are pre- or mild-symptomatic, carry viral loads likely to represent infectivity. Based on these results and uncertainty about the remaining incidence, we recommend caution and careful monitoring during gradual lifting of non-pharmaceutical interventions. In particular, there is little evidence from the present study to support suggestions that children may not be as infectious as adults.

Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyper-activity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups.

When we make decisions, the benefits of an option often need to be weighed against accompanying costs. Little is known, however, about the neural systems underlying such cost-benefit computations. Using functional magnetic resonance imaging and choice modeling, we show that decision making based on cost-benefit comparison can be explained as a stochastic accumulation of costbenefit difference. Model-driven functional MRI shows that ventromedial and left dorsolateral prefrontal cortex compare costs and benefits by computing the difference between neural signatures of anticipated benefits and costs from the ventral striatum and amygdala, respectively. Moreover, changes in blood oxygen level dependent (BOLD) signal in the bilateral middle intraparietal sulcus reflect the accumulation of the difference signal from ventromedial prefrontal cortex. In sum, we show that a neurophysiological mechanism previously established for perceptual decision making, that is, the difference-based accumulation of evidence, is fundamental also in value-based decisions. The brain, thus, weighs costs against benefits by combining neural benefit and cost signals into a single, differencebased neural representation of net value, which is accumulated over time until the individual decides to accept or reject an option.hen we make decisions, the benefits of a decision option often need to be weighed against accompanying costs. Cost-benefit integration, thus, is an important aspect of decision making. However, value-based decision making is typically investigated in the context of decision uncertainty (1-3), so little is known about the neural mechanisms underlying the integration of costs and benefits as such.Cost-benefit-based decision making involves the binary decision to either accept or reject a choice option based on two competing attributes-the option's expected rewards and losses. Such binary accept-versus-reject decisions bear a strong resemblance to twoalternative choices in perceptual decision making (4, 5). For example, when monkeys performed a direction-of-motion discrimination task in which they had to decide whether a noisy field of dots was moving in one direction or its opposite direction (e.g., leftward or rightward) and indicated their choice with a quick eye movement to the target on the respective side, motion-sensitive neurons in middle temporal visual area MT either respond to leftward motion or to rightward motion. Prefrontal and parietal neurons, in contrast, form a decision by accumulating the difference in the activities of populations of neurons in area MT that code for opposite directions of motion. The monkey's saccade response is faster when more dots are moving in one direction, and this effect is predicted by the strength of the accumulated neuronal difference signal (6). A difference-based decision mechanism has also been identified in the human dorsolateral prefrontal cortex (DLPFC) during perceptual face-house decisions (4, 7). Thus, we hypothesized that cost-benefit decisions involve an analogous decisio...