Emil Carlsson

We evaluated aggregated, privacy-preserving smartphone-detected nocturnal cough activity from the Sleep Cycle application as a potential syndromic surveillance signal in England. Weekly cough metrics from January 2023 to January 2026 were compared with UK Health Security Agency indicators: NHS 111 acute respiratory infection (ARI) triage calls, influenza and COVID-19 PCR positivity, and hospital admission rates for influenza, COVID-19, and respiratory syncytial virus. We evaluated total cough counts alongside two population-normalised metrics, coughs per user and coughs per hour of sleep, and assessed temporal relationships nationally and regionally using cross-correlation with prewhitening. The strongest and most consistent associations were observed for NHS 111 ARI triage calls, where population-normalised cough metrics showed raw national correlations of approximately 0.95 and retained prewhitened correlations above 0.55 at lag 0. This indicates that nocturnal cough activity closely tracks short-term variation in an established syndromic surveillance indicator, beyond shared seasonality, long-term trends, and autocorrelation. Similar near-contemporaneous patterns were observed across regions. Population-normalised cough metrics also showed epidemiologically plausible leading associations with pathogen-specific indicators: coughs per hour of sleep peaked one week before influenza PCR positivity, while both coughs per user and coughs per hour of sleep peaked one week before COVID-19 PCR positivity. Hospital-based indicators showed weaker and more heterogeneous relationships, but the normalised cough metrics still showed plausible temporal alignment with influenza and COVID-19 admissions, including contemporaneous associations with influenza admissions and short leading associations with COVID-19 admissions. In contrast, unnormalised total cough counts produced less stable and often non-interpretable lag structures, consistent with sensitivity to variation in observation volume. These findings suggest that passive, near-real-time nocturnal cough monitoring can provide a population-level signal of respiratory symptom burden, with greatest utility as a broad syndromic indicator that complements surveillance sources affected by healthcare-seeking behaviour, laboratory turnaround times, backfilling, and reporting delays.

Air pollution remains one of the leading environmental determinants of health, yet the short-term effects of fine particulate matter (PM₂.₅) on early respiratory symptoms are poorly characterized owing to the limited availability of high-resolution, population-scale data. Enabled by global mobile sensing through a widely used application (Sleep Cycle) and AI algorithms that automatically detect coughs from nocturnal audio recordings, we derived nighttime cough frequency across 34 cities in 12 countries on five continents over 500 consecutive days, conducting the first multi-city assessment linking daily PM₂.₅ exposure to nocturnal respiratory symptoms. Single-city generalized additive models, multi-city panel regressions adjusting for meteorological variables and influenza rates, and quasi-experimental analyses consistently showed that same-day PM₂.₅ exposure was associated with higher cough frequency (relative risk = 1.004, 95% CI 1.001–1.008 per 10 μg/m³), with amplified effects during influenza peaks. The exposure–response curve was monotonic but nonlinear, with measurable risks evident even below the World Health Organization 24-hour guideline (15 μg/m³) and sharply increasing risks above 50 μg/m³. These findings provide real-world, population-level, multi-city evidence of acute respiratory effects from short-term PM₂.₅ exposure, reveal the absence of a safe threshold, and underscore the urgency of stringent air-quality interventions to protect respiratory health. This study also demonstrates how large-scale, AI-based mobile sensing can transform real-time environmental epidemiology by enabling population-level monitoring of early respiratory symptoms worldwide.

Recent work (Xu et al., 2020) has suggested that numeral systems in different languages are shaped by a functional need for efficient communication in an information-theoretic sense. Here we take a learning-theoretic approach and show how efficient communication emerges via reinforcement learning. In our framework, two artificial agents play a Lewis signaling game where the goal is to convey a numeral concept. The agents gradually learn to communicate using reinforcement learning and the resulting numeral systems are shown to be efficient in the information-theoretic framework of Regier et al. (2015); Gibson et al. (2017). They are also shown to be similar to human numeral systems of same type. Our results thus provide a mechanistic explanation via reinforcement learning of the recent results in Xu et al. (2020) and can potentially be generalized to other semantic domains.

It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider efficiency as instantiated in the Information Bottleneck (IB) principle, and a model of cultural evolution that combines iterated learning and communication. We show that this model, instantiated in neural networks, converges to color naming systems that are efficient in the IB sense and similar to human color naming systems. We also show that some other proposals such as iterated learning alone, communication alone, or the greater learnability of convex categories, do not yield the same outcome as clearly. We conclude that the combination of iterated learning and communication provides a plausible means by which human semantic systems become efficient.

It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider efficiency as instantiated in the Information Bottleneck (IB) principle, and a model of cultural evolution that combines iterated learning and communication. We show that this model, instantiated in neural networks, converges to color naming systems that are efficient in the IB sense and similar to human color naming systems. We also show that some other proposals such as iterated learning alone, communication alone, or the greater learnability of convex categories, do not yield the same outcome as clearly. We conclude that the combination of iterated learning and communication provides a plausible means by which human semantic systems become efficient.

In this work we introduce a structured signaling game, an extension of the classical signaling game with a similarity structure between meanings in the context, along with a variant of the Rational Speech Act (RSA) framework which we call structured-RSA (sRSA) for pragmatic reasoning in structured domains. We explore the behavior of the sRSA in the domain of color and show that pragmatic agents using sRSA on top of semantic representations, derived from the World Color Survey, attain efficiency very close to the information theoretic limit after only 1 or 2 levels of recursion. We also explore the interaction between pragmatic reasoning and learning in multi-agent reinforcement learning framework. Our results illustrate that artificial agents using sRSA develop communication closer to the information theoretic frontier compared to agents using RSA and just reinforcement learning. We also find that the ambiguity of the semantic representation increases as the pragmatic agents are allowed to perform deeper reasoning about each other during learning.

In this paper we take the first steps in studying a new approach to synthesis of efficient communication schemes in multi-agent systems, trained via reinforcement learning. We combine symbolic methods with machine learning, in what is referred to as a neuro-symbolic system. The agents are not restricted to only use initial primitives: reinforcement learning is interleaved with steps to extend the current language with novel higher-level concepts, allowing generalisation and more informative communication via shorter messages. We demonstrate that this approach allow agents to converge more quickly on a small collaborative construction task.