How to avoid a local epidemic becoming a global pandemic.

Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.

Computational medicine, present and the future: obstetrics and gynecology perspective.

Medicine is, in its essence, decision making under uncertainty; the decisions are made about tests to be performed and treatments to be administered. Traditionally, the uncertainty in decision making was handled using expertise collected by individual providers and, more recently, systematic appraisal of research in the form of evidence-based medicine. The traditional approach has been used successfully in medicine for a very long time. However, it has substantial limitations because of the complexity of the system of the human body and healthcare. The complex systems are a network of highly coupled components intensely interacting with each other. These interactions give those systems redundancy and thus robustness to failure and, at the same time, equifinality, that is, many different causative pathways leading to the same outcome. The equifinality of the complex systems of the human body and healthcare system demand the individualization of medical care, medicine, and medical decision making. Computational models excel in modeling complex systems and, consequently, enabling individualization of medical decision making and medicine. Computational models are theory- or knowledge-based models, data-driven models, or models that combine both approaches. Data are essential, although to a different degree, for computational models to successfully represent complex systems. The individualized decision making, made possible by the computational modeling of complex systems, has the potential to revolutionize the entire spectrum of medicine from individual patient care to policymaking. This approach allows applying tests and treatments to individuals who receive a net benefit from them, for whom benefits outweigh the risk, rather than treating all individuals in a population because, on average, the population benefits. Thus, the computational modeling-enabled individualization of medical decision making has the potential to both improve health outcomes and decrease the costs of healthcare.

Spatial distributions of observables in systems under thermal gradients.

Departures of observables from their thermal equilibrium expectation values are studied under heat flow in steady-state nonequilibrium environments. The relation between the spatial and temperature dependence of these nonequilibrium behaviors and the underlying statistical properties are clarified from general considerations. The predictions are then confirmed in direct numerical simulations within the Fermi-Pasta-Ulam beta model. Nonequilibrium momentum distribution functions are also examined and characterized through their cumulants and the properties of higher order cumulants are discussed.

Lyapunov exponents and the extensivity of dimensional loss for systems in thermal gradients.

An explicit relation between the dimensional loss (DeltaD), entropy production, and transport is established under thermal gradients, relating the microscopic and macroscopic behaviors of the system. The extensivity of DeltaD in systems with bulk behavior follows from the relation. The maximum Lyapunov exponents in thermal equilibrium and DeltaD in nonequilibrium depend on the choice of heat baths, while their product is unique and macroscopic. Finite-size corrections are also computed and all results are verified numerically.

Giant-dipole resonance and the deformation of hot, rotating nuclei.

The development of nuclear shapes under the extreme conditions of high angular momentum and/or temperature is examined. Scaling properties are used to demonstrate universal properties of both thermal expectation values of nuclear shapes as well as the minima of the free energy, which can be used to understand the Jacobi transition. A universal correlation between the width of the giant-dipole resonance and quadrupole deformation is found, providing a novel probe to measure the nuclear deformation in hot nuclei.