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Changes in human behaviors, such as reductions of physical contacts and the adoption of preventive measures, impact the transmission of infectious diseases considerably. Behavioral adaptations may be the result of individuals aiming to protect themselves or mere responses to public containment measures, or a combination of both. What drives autonomous and policy-induced adaptation, how they are related and change over time is insufficiently understood. Here, we develop a framework for more precise analysis of behavioral adaptation, focusing on confluence, interactions and time variance of autonomous and policy-induced adaptation. We carry out an empirical analysis of Germany during the fall of 2020 and beyond. Subsequently, we discuss how behavioral adaptation processes can be better represented in behavioral-epidemiological models. We find that our framework is useful to understand the interplay of autonomous and policy-induced adaptation as a "moving target". Our empirical analysis suggests that mobility patterns in Germany changed significantly due to both autonomous and policy-induced adaption, with potentially weaker effects over time due to decreasing risk signals, diminishing risk perceptions and an erosion of trust in the government. We find that while a number of simulation and prediction models have made great efforts to represent behavioral adaptation, the interplay of autonomous and policy-induced adaption needs to be better understood to construct convincing counterfactual scenarios for policy analysis. The insights presented here are of interest to modelers and policy makers aiming to understand and account for behaviors during a pandemic response more accurately. [ABSTRACT FROM AUTHOR]

Objective: To explore a new technique for reconstructing and measuring three-dimensional (3D) models of orthodontic plaster casts using multi-baseline digital close-range photogrammetry (MBDCRP) with a single-lens reflex camera. Study design: Thirty sets of orthodontic plaster casts that do not exhibit severe horizontal overlap (>2 mm) between any two teeth were recorded by a single-lens reflex camera with 72 pictures taken in different directions. The 3D models of these casts were reconstructed and measured using the open source software MeshLab. These parameters, including mesio-distal crown diameter, arch width, and arch perimeter, were recorded six times on both the 3D digital models and on plaster casts by two examiners. Statistical analysis was carried out using the Bland–Altman method by calculating the differences between mean values. Results: The average differences between the measurements of the photogrammetric 3D models and the plaster casts were 0.011–0.402mm. The mean differences between measurements obtained by the photogrammetric 3D models and the dental casts were not significant except for the lower arch perimeter (P>0.05), and all the differences were regarded as clinically acceptable (<0.5 mm). Conclusions: Measurements obtained by MBDCRP are compared well with those obtained from plaster casts, indicating that MBDCRP is an alternate way to store and measure dental plaster casts without severe horizontal overlap between any two teeth. [ABSTRACT FROM AUTHOR]

Online social networks such as Facebook, Twitter and Gowalla allow people to communicate and interact across borders. In past years online social networks have become increasingly important for studying the behavior of individuals, group formation, and the emergence of online societies. Here we focus on the characterization of the average growth of online social networks and try to understand which are possible processes behind seemingly long-range temporal correlated collective behavior. In agreement with recent findings, but in contrast to Gibrat's law of proportionate growth, we find scaling in the average growth rate and its standard deviation. In contrast, Renren and Twitter deviate, however, in certain important aspects significantly from those found in many social and economic systems. Whereas independent methods suggest no significance for temporally long-range correlated behavior for Renren and Twitter, a scaling analysis of the standard deviation does suggest long-range temporal correlated growth in Gowalla. However, we demonstrate that seemingly long-range temporal correlations in the growth of online social networks, such as in Gowalla, can be explained by a decomposition into temporally and spatially independent growth processes with a large variety of entry rates. Our analysis thus suggests that temporally or spatially correlated behavior does not play a major role in the growth of online social networks. [ABSTRACT FROM AUTHOR]