Fabio Sanchez

The basic reproduction number, R₀, is routinely reported as though it were an intrinsic property of a pathogen, a biological constant to be looked up and endured. It is not. R₀ is an average over a population, and its value is set largely by how that population lives: household size and age structure, density, housing and sanitation, work and mobility, and access to care. The same pathogen therefore carries different reproduction numbers across social settings, and the scalar R₀ obscures the contact structure that determines who is infected and where transmission will concentrate. None of this is news to epidemiologists; the confusion lives downstream, where a working average is repeated as though it were a fixed trait of the microbe. This essay argues that R₀ is best read not as a verdict a virus hands down but as a summary of the conditions a society arranges for it: in an outbreak, a map of where transmission will run hot; over the longer run, a lever held largely by sectors well beyond emergency response.

The basic reproduction number R ₀ confounds pathogen biology with adaptive human contact behavior. Earlier epidemiological–economic theory predicted a forward-looking behavioral contact response but could not test it in the absence of appropriate behavioral data. Using directly measured mobility as an observable proxy for contact, we (i) estimate the behavioral response function directly from data; (ii) show that the biology/behavior decomposition and hence the behavioral correction to R ₀ is not identified from an epidemic trajectory, the apparent constant-contact R ₀ being one endpoint of an observational-equivalence class that fits the factual curve identically yet diverges under counterfactual; and (iii) characterize that divergence (“what R ₀ deletes”) as state-dependent , unimodal in counterfactual severity and vanishing when behavior saturates. We then show that, across US jurisdictions, the correction is empirically bounded because risk-responsiveness and behavioral non-saturation are confounded ( r = −0.57, n = 51): where behavior could compensate, it was already maximal, and where it was not maximal it did not respond. What R ₀ deletes is thus real and structurally characterizable yet empirically modest here, for reasons the framework itself supplies.

Epidemic models are essential tools for understanding the spread of infectious diseases and evaluating containment measures. The COVID-19 pandemic highlighted the critical role of population movement in shaping epidemic dynamics, emphasizing the need for models that incorporate mobility effects. In this work, we study disease transmission between two interconnected populations using a stochastic framework. Building on a deterministic model, we introduce a continuous-time Markov chain stochastic model and compare it with three approximations. While continuous-time Markov chains provide a natural stochastic counterpart to deterministic models, they pose challenges in scalability and implementation for large systems. To address these issues, we explore simplified approximations that retain key stochastic features while reducing computational complexity. Our analysis focuses on the impact of movement on disease persistence, particularly in source-sink scenarios where one population serves as a reservoir of infection. We show that stochastic effects can lead to extinction events absent in deterministic models, underscoring the importance of randomness in epidemic forecasting. Numerical simulations illustrate each approach, providing insights into the interplay between mobility and epidemic spread.

Disease outbreaks in wild populations worldwide can result in widespread mortality within populations, with the recovery of individuals being rare. An example of this population is the sunflower sea star Pycnopodia helianthioides o to an unidentified disease known as sea star wasting disease. Pycnopodia down control of kelp grazers in rocky reefs across the Northeastern Pacific coast. This, combined with the massive declines in kelp coverage observed during the 2015–2016 marine heat wave observed in the Northeastern Pacific, has sparked an interest in reintroducing Pycnopodia individuals on the coast to potentially assist in the recovery of the populations. However, the epidemiological implications of reintroducing healthy sea stars into the wild populations are an under-explored question. This work explores this question using a dynamical population model of Pycnopodia. We also find. This analysis provides valuable information on timing and intensity for restoring Pycnopodia populations. This article is part of the theme issue ‘Managing infectious marine diseases in wild populations’.

This article has three objectives: 1) modeling the climate-dengue relationship at the smallest administrative division (districts) using high-resolution data; 2) use of an objective algorithm for the selection of predictors that results in parsimonious models, cross-validated to prevent overfitting; and 3) using estimates from CMIP6 climate models to provide mid-century (2035-2065) potential dengue incidence projections under a pessimistic scenario (SSP5-8.5) with seasonal windows actionable by region in Costa Rica to guide preparedness decisions. Results show that temperature and precipitation data are significantly related to dengue incidence. Projections of dengue cases for mid-century show increments of up to 42 more cases in some districts compared to the historical scenario. It should be remembered that ultimately dengue variations and change are related to climatic and non-climatic factors and the results presented here represent a future potential increase of the dissemination of the disease, based on the projected climate change of the most pessimistic scenario.

Population growth models typically incorporate attributes observable at the population scale, often overlooking the trade-off between individual-level reproductive and behavioral traits and their influence on population size. Individuals’ survival and reproductive abilities are expected to dynamically evolve depending on the population size, which is affected by the aggregation of individual decisions. Reconciling individual-level incentives with population-level dynamics requires an integrative framework that explicitly addresses the intertwined relationships between population growth and individual decision-making processes. We formulate a multiscale modeling framework that integrates the logistic population growth model with an optimal foraging model to study the interplay between individual-level behavioral incentives and population growth dynamics. Specifically, we explicitly model individuals’ decision-making process, which shapes their reproductive fitness and, ultimately, influences population growth. Moreover, we incorporate the concept of resource limitations from the logistic growth model to account for dynamic incentives that depend on population size. Our results yield insights into the multiscale processes, such as the selection pressure of behavioral choices and the cost-benefit of social activities that influence population robustness beyond mere size and aggregated reproductive traits. We found that populations exhibiting similar limiting sizes may undergo significantly different transient dynamics. This variation may be induced by environments imposing distinct behavioral cost-benefit trade-offs that require individuals to exert different levels of foraging effort to maintain reproductive viability.

Vaping has rapidly become a global public health concern, particularly among adolescents and young adults. Initially promoted as a smoking alternative, its widespread adoption is driven by digital marketing, flavored high-nicotine products, and social media influence. Evidence links vaping to nicotine addiction, respiratory and cardiovascular risks, and long-term health concerns. While regulations vary globally, gaps in digital advertising oversight and disposable e-cigarette policies contribute to rising youth consumption. This paper examines vaping trends, health risks, and regulatory challenges, emphasizing the role of mathematical and epidemiological models in understanding its impact and informing policy interventions.