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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.

Patrick Moss (1947--2024) had two distinct lives as a mathematician. The first was as a ring theorist in the late 1970s, in which he worked with Ginn and Lenagan as a student. After a long career as an inspirational mathematics teacher, Patrick completed a doctorate under my supervision in 2003. This led to a second mathematical life in arithmetic dynamics almost forty years after his first period of research. This is a short obituary of his remarkable contributions, several of which have stimulated further research., Comment: 9 pages

We construct a set of topological generators for the monoid of time-changes preserving the space of dynamical zeta functions.

Subterrestrial neutron spectra show weak but consistent anomalies at multiplicities ~100 and above [1-3]. The data of the available measurements are of low statistical significance [4] but indicate an excess of events not correlated with the muon flux. The origin of the anomalies remains ambiguous but could be a signature of WIMP annihilation-like interaction with a Pb target. In this paper, we outline a model consistent with this hypothesis, the extended Standard Model (SM) approach called the Radiation Gauge Model (RGM) [5]. The RGM identifies scalar neutrino-antineutrino wave function components of WIMP Dark Matter (DM) responsible for the weak interaction leading to annihilation with ordinary matter. The model assigns neutrino-nucleon(target) charged current (CC) transitions to the observed anomalies. If the existence of the anomalies is confirmed and the model interpretation is positively verified, this will be the first terrestrial indirect detection of DM., Comment: TAUP 2023 proceedings

The spin, charge, and lattice degrees of freedom and their interplay in high entropy oxides were intensively investigated in recent years. However, how the orbital degree of freedom is affected by the extreme disorder in high entropy oxides hasn't been studied. In this work, using perovskite structured \textit{R}VO$_3$ as a materials playground, we report how the disorder arising from mixing different rare earth ions at the rare earth site affects the orbital ordering of V$^{3+}$ t$_{2g}$-electrons. Since each member of \textit{R}VO$_3$ crystallizes into the same orthorhombic \textit{Pbnm} structure, the configurational entropy should not be critical for the success synthesis of (\textit{R}$_1$,...,\textit{R}$_n$)VO$_3$. The spin and orbital ordering was studied by measuring magnetic properties and specific heat of single crystals. Rather than the number and type of rare earth ions, the average ionic radius and size variance are the key factors determining the spin and orbital order in (\textit{R}$_1$,...,\textit{R}$_n$)VO$_3$. When the size variance is small, the average ionic radius takes precedence in dictating spin and orbital order. Increasing size variance suppresses the G-type orbital order and C-type magnetic order but favors the C-OO/G-AF state and the spin-orbital entanglement. These findings suggest that the extreme disorder introduced by mixing multiple rare earth ions in high entropy perovskites might be employed to preserve the orbital degree of freedom to near the magnetic ordering temperature, which is necessary for the electronic driven orbital ordering in a Kugel-Khomskii compound., Comment: 3 figures, 7 pages

Accurate and representative data is vital for precisely reporting the impact of influenza in healthcare systems. Northern hemisphere winter 2022/23 experienced the most substantial influenza wave since the COVID-19 pandemic began in 2020. Simultaneously, new data streams become available within health services because of the pandemic. Comparing these data, surveillance and administrative, supports the accurate monitoring of population level disease trends. We analysed admissions rates per capita from four different collection mechanisms covering National Health Service hospital Trusts in England over the winter 2022/23 wave. We adjust for difference in reporting and extracted key epidemic characteristics including the maximum admission rate, peak timing, cumulative season admissions and growth rates by fitting generalised additive models at national and regional levels. By modelling the admission rates per capita across surveillance and administrative data systems we show that different data measuring the epidemic produce different estimates of key quantities. Nationally and in most regions the data correspond well for the maximum admission rate, date of peak and growth rate, however, in subnational analysis discrepancies in estimates arose, particularly for the cumulative admission rate. This research shows that the choice of data used to measure seasonal influenza epidemics can influence analysis substantially at sub-national levels. For the admission rate per capita there is comparability in the sentinel surveillance approach (which has other important functions), rapid situational reports, operational databases and time lagged administrative data giving assurance in their combined value. Utilising multiple sources of data aids understanding of the impact of seasonal influenza epidemics in the population.

In the presence of external magnetic field, the Kitaev model could either hosts gapped topological anyon or gapless Majorana fermions. In $\alpha$-RuCl$_3$, the gapped and gapless cases are only separated by a thirty-degree rotation of the in-plane magnetic field vector. The presence/absence of the spectral gap is key for understanding the thermal transport behavior in $\alpha$-RuCl$_3$. Here, we study the anisotropy of the oscillatory features of thermal conductivity in $\alpha$-RuCl$_3$. We examine the oscillatory features of thermal conductivities (k//a, k//b) with fixed external fields and found distinct behavior for the gapped (B//a) and gapless (B//b) scenarios. Furthermore, we track the evolution of thermal resistivity ($\lambda_{a}$) and its oscillatory features with the rotation of in-plane magnetic fields from B//b to B//a. The thermal resistivity $\lambda (B,\theta)$ display distinct rotational symmetries before and after the emergence of the field induced Kitaev spin liquid phase. These experiment data suggest close correlations between the oscillatory features of thermal conductivity, the underlying Kitaev spin liquid phase and the fermionic excitation it holds.

Quantum machine learning (QML) is a fast-growing discipline within quantum computing. One popular QML algorithm, quantum kernel estimation, uses quantum circuits to estimate a similarity measure (kernel) between two classical feature vectors. Given a set of such circuits, we give a heuristic, predictive model for the total circuit execution time required, based on a recently-introduced measure of the speed of quantum computers. In doing so, we also introduce the notion of an "effective number of quantum volume layers of a circuit", which may be of independent interest. We validate the performance of this model using synthetic and real data by comparing the model's predictions to empirical runtime data collected from IBM Quantum computers through the use of the Qiskit Runtime service. At current speeds of today's quantum computers, our model predicts data sets consisting of on the order of hundreds of feature vectors can be processed in order a few hours. For a large-data workflow, our model's predictions for runtime imply further improvements in the speed of circuit execution -- as well as the algorithm itself -- are necessary., Comment: 8.5 pages of main text + 1.5 pages of appendices. 7 figures & 3 tables

Hospitalisations from COVID-19 with Omicron sub-lineages have put a sustained pressure on the English healthcare system. Understanding the expected healthcare demand enables more effective and timely planning from public health. We collect syndromic surveillance sources, which include online search data, NHS 111 telephonic and online triages. Incorporating this data we explore generalised additive models, generalised linear mixed-models, penalised generalised linear models and model ensemble methods to forecast over a two-week forecast horizon at an NHS Trust level. Furthermore, we showcase how model combinations improve forecast scoring through a mean ensemble, weighted ensemble, and ensemble by regression. Validated over multiple Omicron waves, at different spatial scales, we show that leading indicators can improve performance of forecasting models, particularly at epidemic changepoints. Using a variety of scoring rules, we show that ensemble approaches outperformed all individual models, providing higher performance at a 21-day window than the corresponding individual models at 14-days. We introduce a modelling structure used by public health officials in England in 2022 to inform NHS healthcare strategy and policy decision making. This paper explores the significance of ensemble methods to improve forecasting performance and how novel syndromic surveillance can be practically applied in epidemic forecasting., Comment: arXiv admin note: substantial text overlap with arXiv:2303.12037