Biology subjects

Velasco-Hernandez, J. X.

Publications and source records attributed to Velasco-Hernandez, J. X..

6 recordsLinked to original sources

bioRxiv2026

COUPLING OF ENVIRONMENTAL AND DIRECT TRANSMISSIONMECHANISMS: ANALYSIS OF A SIMPLE MODEL

AO_SCPLOWBSTRACTC_SCPLOWWe study an extension of an environmentally mediated epidemiological model that incorporates direct human-to-human transmission. While the original formulation accounted for environmental exposure, it did not include direct transmission between individuals. Allowing both transmission routes to interact leads to significant qualitative changes in the system dynamics. The analysis reveals multiple dynamical regimes governed by environmental and combined threshold quantities. The stability of the disease-free equilibrium is controlled by an environmental threshold, whereas a combined reproduction number determines the onset of multistability. For certain parameter ranges, endemic equilibria coexist with the disease-free equilibrium, giving rise to backward-type bifurcation behavior and sensitivity to initial conditions. Moreover, the direct transmission rate acts as an organizing parameter by inducing the emergence of an environmental-free equilibrium when exceeding its classical threshold. These results highlight how environmentally coupled transmission mechanisms can generate rich dynamics in low-dimensional models.

systems biology↗

bioRxiv2025

Multiscale Modeling of Vector-Borne Diseases: The Role of Dose-Dependent Transmission

The transmission of infectious diseases involves complex interactions across multiple biological scales, from within-host immunological processes to between-host transmission dynamics. While multiscale models have the potential to capture these interactions more accurately, they are often hindered by increased complexity and limited data availability. In this study, we develop a multiscale epidemic model linking host-vector population-level transmission dynamics to within-host and within-vector pathogen dynamics. Our model captures key features of within-vector viral progression and allows bidirectional coupling between within-host and between-host processes. The scales are linked under the assumption of dose-dependent transmission, with the functional form informed by empirical viremia-infectiousness data from arbovirus transmission. Focusing on Dengue, Zika, and West Nile viruses as case studies, we assess how different functional forms of the coupling affect the number of equilibria of the epidemic model. We find that when the transmission is modeled using linear coupling functions, the multiscale model yields the same bifurcation structure of the simpler, uncoupled model, indicating that the linking of scales does not alter the range of possible long-term epidemiological states in such cases. However, nonlinear coupling can induce complex behaviors such as multiple endemic equilibria and backward bifurcations, which the uncoupled model does not capture. These results underscore the importance of carefully selecting coupling functions and provide guidance on when multiscale modeling is essential for understanding and managing vector-borne diseases.

systems biology↗

bioRxiv2025

A general allometric rule predicts sustainable growth across societies

Balancing population growth against finite resources remains a foundational challenge in sustainability science. Although qualitative sustainability principles have matured over decades, translating these principles into universal quantitative rules has been challenging due to the diverse environmental and cultural contexts under which human societies develop. Here, we demonstrate that a general sustainability rule emerges naturally when population-resource feedbacks follow empirically observed allometric scaling laws, condensing ecological, technological, and social complexities into four fundamental parameters. This rule predicts "sustainability corridors" within which societies must remain to ensure long-term persistence. Testing our theoretically derived rule against ethnographic data from 299 hunter-gatherer societies across varied ecological and social environments, we find consistent alignment: these societies tend to occupy the predicted corridors despite substantial contextual differences. By grounding sustainability in biophysical scaling rather than context-specific variables, our approach bridges ecology and sustainability science, suggesting that despite cultural and technological differences, all societies face fundamental constraints for sustainable growth.

ecology↗

bioRxiv2018

Regional reinfection by Dengue: a network approach using data from Mexico

Most of the recent epidemic outbreaks in the world have a strong immigration component as a trigger rather than the dynamics implied by the basic reproduction number. In this work we present and discuss an approach to the problem of pathogen reinfections in a given area that associates people mobility and transmission of dengue, using a Markov-chain Susceptible-Infected-Susceptible (SIS) metapopulation model over a network. Our model postulates a parameter that we have named the effective inoculum size which represents a local measure of the population size of infected hosts that arrive at a given location as a function of population size, current incidence at neighboring locations and the connectivity of the patches. This parameter can be interpreted as an indicator of outbreak risk of any location. Our model also incorporates climate variability represented by an index based upon precipitation data. We replicate observed patterns of incidence at a regional scale using data from epidemics in Mexico.

epidemiology↗

bioRxiv2017

A mathematical model for Dengue and Chikungunya inMexico.

We present a model that incorporates two co-circulating viral diseases, Dengue and Chikungunya, where we allow secondary infections from either of the two diseases. We only consider one vector population, Ae. aegypti since in the Mexican region where we set our scenarios, only this species has been reported to transmit both viruses. We estimate the basic reproduction number and perform numerical simulations for different scenarios where we may observe coexistence of Dengue and Chikungunya; we also compare the results of the model with Dengue and Chikungunya data from Mexico 2015 and we obtain a good model fit. To complete our findings we perform a sensitivity analysis, and calculate the partial rank correlation coefficients (PRCCs) to determine the parameter values influence on the reproduction numbers and predict fate of the diseases.\n\nWe show that R0 for each one of the viruses is highly sensitive to the mosquito biting rate and the transmission rates for both diseases with positive influence and the average lifespan of mosquito along with the human recovery rate with negative influence on both diseases. Our results are consistent with those of previous authors.

epidemiology↗

bioRxiv2017

Inferring a qualitative contact rate index of uncertain epidemics

We will inevitably face new epidemic outbreaks where the mechanisms of transmission are still uncertain, making it difficult to obtain quantitative predictions. Thus we present a novel algorithm that qualitatively predicts the start, relative magnitude and decline of uncertain epidemic outbreaks, requiring to know only a few of its \\macroscopic\" parameters. The algorithm is based on estimating exactly the time-varying contact rate of a canonical but time-varying Susceptible-Infected-Recovered epidemic model parametrized to the particular outbreak. The algorithm can also be extended to other canonical epidemic models. Even if dynamics of the outbreak deviates significantly from the underlying epidemic model, we show the predictions of the algorithm remain robust. We validated our algorithm using real time-series data of measles, dengue and the current zika outbreak, comparing its performance to existing algorithms that also use a few macroscopic parameters (e.g., those estimating reproductive numbers) and to those using a thorough understanding of the mechanisms of the epidemic outbreak. We show our algorithm can outperform existing algorithms using a few macroscopic parameters, providing an informative qualitative evaluation of the outbreak.

epidemiology↗