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Epidemiological delays, such as incubation periods, serial intervals, and hospital lengths of stay, are among key quantities in infectious disease epidemiology that inform public health policy and clinical practice. This information is used to inform mathematical and statistical models, which in turn can inform control strategies. There are three main challenges that make delay distributions difficult to estimate. First, the data are commonly censored (e.g., symptom onset may only be reported by date instead of the exact time of day). Second, delays are often right truncated when being estimated in real time (not all events that have occurred have been observed yet). Third, during a rapidly growing or declining outbreak, overrepresentation or underrepresentation, respectively, of recently infected cases in the data can lead to bias in estimates. Studies that estimate delays rarely address all these factors and sometimes report several estimates using different combinations of adjustments, which can lead to conflicting answers and confusion about which estimates are most accurate. In this work, we formulate a checklist of best practices for estimating and reporting epidemiological delays with a focus on the incubation period and serial interval. We also propose strategies for handling common biases and identify areas where more work is needed. Our recommendations can help improve the robustness and utility of reported estimates and provide guidance for the evaluation of estimates for downstream use in transmission models or other analyses.

Cancer poses danger because of its unregulated growth, development of resistant subclones, and metastatic spread to vital organs. Although the major transitions in cancer development are increasingly well understood, we lack quantitative theory for how chemoprevention is predicted to affect survival. We employ master equations and probability generating functions, the latter well known in statistical physics, to derive the dynamics of tumor growth as a mean-field approximation. We also study numerically the associated stochastic birth-death process. Our findings predict exponential tumor growth when a cancer is in its early stages of development and hyper-exponential growth thereafter. Numerical simulations are in general agreement with our analytical approach. We evaluate how constant, low impact treatments affect both neoplastic growth and the frequency of chemoresistant clones. We show that therapeutic outcomes are highly predictable for treatments starting either sufficiently early or late in terms of initial tumor size and the initial number of chemoresistant cells, whereas stochastic dynamics dominate therapies starting at intermediate neoplasm sizes, with high outcome sensitivity both in terms of tumor control and the emergence of resistant subclones. The outcome of chemoprevention can be understood in terms of both minimal physiological impacts resulting in long-term control and either preventing or slowing the emergence of resistant subclones. We argue that our model and results can also be applied to the management of early, clinically detected cancers after tumor excision.

We consider the dynamics of an extension of the influential Granovetter model of social behavior, where individuals are affected by their personal preferences and observation of the neighbors' behavior. Individuals are arranged in a network (usually, the square lattice) and each has a state and a fixed threshold for behavior changes. We simulate the system asynchronously either by picking a random individual and either update its state or exchange it with another randomly chosen individual (mixing). We describe the dynamics analytically in the fast-mixing limit by using the mean-field approximation and investigate it mainly numerically in case of a finite mixing. We show that the dynamics converge to a manifold in state space, which determines the possible equilibria, and show how to estimate the projection of manifold by using simulated trajectories, emitted from different initial points. We show that the effects of considering the network can be decomposed into finite-neighborhood effects, and finite-mixing-rate effects, which have qualitatively similar effects. Both of these effects increase the tendency of the system to move from a less-desired equilibrium to the "ground state". Our findings can be used to probe shifts in behavioral norms and have implications for the role of information flow in determining when social norms that have become unpopular (such as foot binding or female genital cutting) persist or vanish., Comment: 8 pages, 6 figures

We address the question of optimization of the microalgal biomass long term productivity in the framework of production in photobioreactors under the influence of day/night cycles. For that, we propose a simple bioreactor model accounting for light attenuation in the reactor due to biomass density and obtain the control law that optimizes productivity over a single day through the application of Pontryagin's maximum principle, with the dilution rate being the main control. An important constraint on the obtained solution is that the biomass in the reactor should be at the same level at the beginning and at the end of the day so that the same control can be applied everyday and optimizes some form of long term productivity. Several scenarios are possible depending on the microalgae's strain parameters and the maximal admissible value of the dilution rate: bang-bang or bang-singular-bang control or, if the growth rate of the algae is very strong in the presence of light, constant maximal dilution. A bifurcation diagram is presented to illustrate for which values of the parameters these different behaviors occur. Finally, a simple sub-optimal bang-bang strategy is proposed that numerically achieves productivity levels that almost match those of the optimal strategy.

We address the question of optimization of the biomass long term productivity in the framework of microalgal biomass production in photobioreactors under the influence of day/night cycles. For that, we propose a simple bioreactor model accounting for light attenuation in the reactor due to biomass density and obtain the control law that optimizes productivity over a single day through the application of Pontryagin's maximum principle, with the dilution rate being the control. An important constraint on the obtained solution is that the biomass in the reactor should be at the same level at the beginning and at the end of the day so that the same control can be applied everyday and optimizes the long term productivity. Several scenarios are possible depending on the microalgae's strain parameters and the maximal admissible value of the dilution rate: bang-bang or bang-singular-bang control or, if the growth rate of the algae is very strong in the presence of light, constant maximal dilution. A bifurcation diagram is presented to illustrate for which values of the parameters these different behaviors occur.

In this work we study the process of mutant invasion on an example of a consumer-resource system with annual character of the behavior. Namely, individuals are active during seasons of fixed length separated by winter periods. All individuals die at the end of the season and the size of the next generation is determined by the number of offspring produced during the past season. The rate at which the consumers produce immature offspring depends on their internal energy which can be increased by feeding. The reproduction of the resource simply occurs at a constant rate. At the beginning, we consider a population of consumers maximizing their common fitness, all consumers being individuals having the same goal function and acting for the common good. We suppose that a small fraction of the consumer population may appear at the beginning of one season and start to behave as mutants in the main population. We study how such invasion occurs.