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Baltazar Espinoza

Publications and source records attributed to Baltazar Espinoza.

15 recordsLinked to original sources
arXiv2026

Bayesian calibration of adaptive-behavior SIR models for multi-wave COVID-19 incidence in New York City

Epidemic incidence reflects both transmission dynamics and adaptive human behavior, yet these mechanisms may be difficult to distinguish from aggregate case data alone. We calibrated four susceptible--infected--recovered (SIR) specifications to weekly confirmed COVID-19 incidence in New York City from June to December 2020, comparing a single continuous SIR trajectory, a wave-initialized SIR model, and two adaptive-behavior models with either shared or wave-specific transmission. Inference was performed using rejection Approximate Bayesian Computation (ABC), and in-sample reconstruction was assessed using root mean squared error (RMSE) and the weighted interval score (WIS). Reinitializing the epidemic state by wave produced the largest structural improvement over the continuous SIR trajectory, reducing mean-based RMSE by 48.6\% and WIS by 17.6\%. Adding delayed prevalence-dependent behavioral adaptation with shared transmission further reduced mean-based RMSE by 23.4\%, but yielded essentially unchanged WIS relative to the wave-initialized SIR model. Allowing transmission to vary by wave did not provide a consistent additional advantage and produced strongly asymmetric posterior-simulation trajectories. Behavioral sensitivity, response midpoint, and delay remained only weakly to partially identified. The clearest posterior structure was a negative association between transmission intensity and the behavioral midpoint, indicating that higher transmission could be compensated by behavioral responses activated at lower prevalence. Sensitivity to ordered behavioral priors further showed that reconstruction and behavioral inference depend materially on structural prior assumptions. These results suggest that adaptive mechanisms can improve multi-wave incidence reconstruction, while aggregate incidence alone is insufficient to sharply separate transmission from behavioral adaptation.

stat.AP↗
arXiv2026

EpiFlow: A framework for improving the utility of wastewater signals for disease forecasting

Wastewater-based surveillance is an effective tool for disease monitoring and can provide early warning of outbreaks. Although wastewater viral loads (WVL) correlate with disease burden, their utility for improving real-time forecasting remains under investigation. During the early phases of an epidemic, many indicators can effectively monitor disease spread, but their reliability may decline because of reporting fatigue and low prevalence. Hospital burden can vary substantially even during low-prevalence periods, making accurate forecasting of burden indicators essential for minimizing disease impacts. In this paper, we present principled approaches for processing wastewater data, characterizing its relationship with burden indicators, and generating real-time forecasts. We assess the predictability of WVL using entropy measures. We analyze the relationship between WVL and burden indicators using causality tests that capture temporal dynamics and the leading-indicator behavior of WVL. We incorporate these insights into a time-varying forecasting model that accounts for the evolving relationship between the signals. We also evaluate the effects of delays in WVL reporting through simulations. We test the utility of our methods by forecasting COVID-19 hospital admissions across Virginia and its health regions during periods of varying disease prevalence. Incorporating WVL improves forecast accuracy relative to baseline models, particularly during critical epidemic phases, and results in a 20 percentage point improvement in forecast coverage. Our results demonstrate that WVL signals can improve infectious disease forecasting even under conditions of low prevalence or delayed reporting.

cs.LG↗
arXiv2026

Agentic Framework for Epidemiological Modeling

Epidemic modeling is essential for public health planning, yet traditional approaches rely on fixed model classes that require manual redesign as pathogens, policies, and scenario assumptions evolve. We introduce EPIAGENT, an agentic framework that automatically synthesizes, calibrates, verifies, and refines epidemiological simulators by modeling disease progression as an iterative program synthesis problem. A central design choice is an explicit epidemiological flow graph intermediate representation that links scenario specifications to model structure and enables strong, modular correctness checks before code is generated. Verified flow graphs are then compiled into mechanistic models supporting interpretable parameter learning under physical and epidemiological constraints. Evaluation on epidemiological scenario case studies demonstrates that EPIAGENT captures complex growth dynamics and produces epidemiologically consistent counterfactual projections across varying vaccination and immune escape assumptions. Our results show that the agentic feedback loop prevents degeneration and significantly accelerates convergence toward valid models by mimicking professional expert workflows.

cs.LG↗
arXiv2025

Localized risk perception triggers early behavioral adaptations in epidemics on networks

The contact structure of the population shapes the progression of epidemics. Nonetheless, the joint evolution of individual behavioral adaptations and disease dynamics on networks remains poorly understood. We use a behavioral-epidemiological model to study the joint evolution of human behavior and epidemic dynamics on networks. Our results reveal how the adaptation of local social structures, influenced by risk-benefit trade-offs, affects the dynamics of epidemics. We allow the epidemic and population-level behavior dynamics to emerge from the heterogeneous behavioral responses of individuals. Our framework assumes that individuals adjust their contact structure by temporarily dropping or maintaining connections based on perceived benefits and risks. Our results show that behavioral responses induced by localized risk perceptions lead to premature population-level responses relative to epidemic dynamics. Specifically, individual efforts peak at the epidemic maximum, while population-level efforts remain modest. We explore the robustness and extensions incorporating heterogeneous subpopulations.

physics.soc-ph↗
arXiv2025

The nexus between disease surveillance, adaptive human behavior and epidemic containment

Epidemics exhibit interconnected processes that operate at multiple time and organizational scales, a hallmark of complex adaptive systems. Modern epidemiological modeling frameworks incorporate feedback between individual-level behavioral choices and centralized interventions. Nonetheless, the realistic operational course for disease detection, planning, and response is often overlooked. Disease detection is a dynamic challenge, shaped by the interplay between surveillance efforts and transmission characteristics. It serves as a tipping point that triggers emergency declarations, information dissemination, adaptive behavioral responses, and the deployment of public health interventions. Evaluating the impact of disease surveillance systems as triggers for adaptive behavior and public health interventions is key to designing effective control policies. We examine the multiple behavioral and epidemiological dynamics generated by the feedback between disease surveillance and the intertwined dynamics of information and disease propagation. Specifically, we study the intertwined dynamics between: $(i)$ disease surveillance triggering health emergency declarations, $(ii)$ risk information dissemination producing decentralized behavioral responses, and $(iii)$ centralized interventions. Our results show that robust surveillance systems that quickly detect a disease outbreak can trigger an early response from the population, leading to large epidemic sizes. The key result is that the response scenarios that minimize the final epidemic size are determined by the trade-off between the risk information dissemination and disease transmission, with the triggering effect of surveillance mediating this trade-off. Finally, our results confirm that behavioral adaptation can create a hysteresis-like effect on the final epidemic size.

math.DS↗
arXiv2024

Wastewater-based Epidemiology for COVID-19 Surveillance and Beyond: A Survey

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

stat.AP↗
arXiv2024

Interplay between Foraging Choices and Population Growth Dynamics

In this study, we couple a population dynamics model with a model for optimal foraging to study the interdependence between individual-level cost-benefits and population-scale dynamics. Specifically, we study the logistic growth model, which provides insights into population dynamics under resource limitations. Unlike exponential growth, the logistic model incorporates the concept of carrying capacity, thus offering a more realistic depiction of biological populations as they near environmental limits. We aim to study the impact of individual-level incentives driving behavioral responses in a dynamic environment. Specifically, explore the coupled dynamics between population density and individuals' foraging times. Our results yield insights into the effects of population size on individuals' optimal foraging efforts, which impacts the population's size.

q-bio.PE↗
arXiv2024

The Complex Interplay Between Risk Tolerance and the Spread of Infectious Diseases

Risk-driven behavior provides a feedback mechanism through which individuals both shape and are collectively affected by an epidemic. We introduce a general and flexible compartmental model to study the effect of heterogeneity in the population with regards to risk tolerance. The interplay between behavior and epidemiology leads to a rich set of possible epidemic dynamics. Depending on the behavioral composition of the population, we find that increasing heterogeneity in risk tolerance can either increase or decrease the epidemic size. We find that multiple waves of infection can arise due to the interplay between transmission and behavior, even without the replenishment of susceptibles. We find that increasing protective mechanisms such as the effectiveness of interventions, the number of risk-averse people in the population, and the duration of intervention usage reduces the epidemic overshoot. When the protection is pushed past a critical threshold, the epidemic dynamics enter an underdamped regime where the epidemic size exactly equals the herd immunity threshold. Lastly, we can find regimes where epidemic size does not monotonically decrease with a population that becomes increasingly risk-averse.

physics.soc-ph↗
arXiv2019

Mobility restrictions for the control of epidemics: When do they work?

Mobility restrictions - travel advisories, trade and travel bans, border closures and, in extreme cases, area quarantines or cordons sanitaires - are among the most widely used measures to control infectious diseases. Restrictions of this kind were important in the response to epidemics of SARS (2003), H1N1 influenza (2009), and Ebola (2014). However, they do not always work as expected. The imposition of a cordon sanitaire to control the 2014 West African Ebola outbreak, for example, is argued to have led to a higher-than-expected number of cases in the quarantined area. To determine when mobility restrictions reduce the size of an epidemic, we use a model of disease transmission within and between economically heterogeneous locally connected communities. One community comprises a low-risk, resource-rich, low-density population with access to effective medical resources. The other comprises a high-risk, resource-poor, high-density population without access to effective medical resources. We find that the overall size of an epidemic centered in the high-risk community is sensitive to the stringency of mobility restrictions between the two communities. Unrestricted mobility between the two risk communities increases the number of secondary cases in the low-risk community but reduces the overall epidemic size. By contrast, the imposition of a cordon sanitaire around the high-risk community reduces the number of secondary infections in the low-risk community but increases the overall epidemic size. The degree to which mobility restrictions increase or decrease the overall epidemic size depends on the level of risk in each community and the characteristics of the disease.

q-bio.PE↗
arXiv2019

Epidemics on Networks: Reducing Disease Transmission Using Health Emergency Declarations and Peer Communication

Understanding individual decisions in a world where communications and information move instantly via cell phones and the internet, contributes to the development and implementation of policies aimed at stopping or ameliorating the spread of diseases. In this manuscript, the role of official social network perturbations generated by public health officials to slow down or stop a disease outbreak are studied over distinct classes of static social networks. The dynamics are stochastic in nature with individuals (nodes) being assigned fixed levels of education or wealth. Nodes may change their epidemiological status from susceptible, to infected and to recovered. Most importantly, it is assumed that when the prevalence reaches a pre-determined threshold level, P*, information, called awareness in our framework, starts to spread, a process triggered by public health authorities. Information is assumed to spread over the same static network and whether or not one becomes a temporary informer, is a function of his/her level of education or wealth and epidemiological status. Stochastic simulations show that threshold selection P* and the value of the average basic reproduction number impact the final epidemic size differentially. For the Erdos-Renyi and Small-world networks, an optimal choice for P* that minimize the final epidemic size can be identified under some conditions while for Scale-free networks this is not case.

physics.soc-ph↗
arXiv2018

Assessing the impact of non-vaccinators: quantifying the average length of infection chains in outbreaks of vaccine-preventable disease

Analytical expressions for the basic reproduction number, R0, have been obtained in the past for a wide variety of mathematical models for infectious disease spread, along with expressions for the expected final size of an outbreak. However, what has so far not been studied is the average number of infections that descend down the chains of infection begun by each of the individuals infected in an outbreak (we refer to this quantity as the "average number of descendant infections" per infectious individual, or ANDI). ANDI includes not only the number of people that an individual directly contacts and infects, but also the number of people that those go on to infect, and so on until that particular chain of infection dies out. Quantification of ANDI has relevance to the vaccination debate, since with ANDI one can calculate the probability that one or more people are hospitalised (or die) from a disease down an average chain of infection descending from an infected un-vaccinated individual. Here we obtain estimates of ANDI using both deterministic and stochastic modelling formalisms. With both formalisms we find that even for relatively small community sizes and under most scenarios for R0 and initial fraction vaccinated, ANDI can be surprisingly large when the effective reproduction number is >1, leading to high probabilities of adverse outcomes for one or more people down an average chain of infection in outbreaks of diseases like measles.

q-bio.PE↗
arXiv2016

Role of Mobility and Health Disparities on the Transmission Dynamics of Tuberculosis

The transmission dynamics of Tuberculosis (TB) involve complex epidemiological and socio-economical interactions between individuals living in highly distinct regional conditions. The level of exogenous reinfection and first time infection rates within high-incidence settings may influence the impact of control programs on TB prevalence. This study aims at enhancing the understanding of TB dynamics via the study of scenarios, within {\it simplified}, two patch, risk-defined environments, in the presence of short term mobility and variations in reinfection and infection rates. The modeling framework captures the role of individuals' `daily' dynamics within and between places of residency, work or business via the proportion of time spent in residence and as visitors to TB-risk environments (patches). As a result, the {\it effective population size} of Patch $i$ (home of $i$-residents) at time $t$ must account for visitors and residents of Patch $i$, at time $t$. The impact that {\it effective population size} and the distribution of {\it individuals' residence times} in different patches have on TB transmission and control are studied using selected scenarios where risk is defined by the estimated or perceive first time infection and/or exogenous re-infection rates. Our results suggest that, under certain conditions, allowing infected individuals to move from high to low TB prevalence areas (for example via the sharing of treatment and isolation facilities) may lead to a reduction in the total TB prevalence in the overall, here two-patch, population.

q-bio.PE↗
arXiv2016

Role of short-term dispersal on the dynamics of Zika virus

In November 2015, El Salvador reported their first case of Zika virus (Zv) leading to an explosive outbreak that in just two months had over 6000 suspected cases. Many communities along with national agencies initiated the process to implement control measures that ranged from vector control and the use of repellents to the suggestion of avoiding pregnancies for two years, the latter one, in response to the growing number of microcephaly cases in Brazil. In our study, we explore the impact of short term mobility between two idealized interconnected communities where disparities and violence contribute to the Zv epidemic. Using a Lagrangian modeling approach in a two-patch setting, it is shown via simulations that short term mobility may be beneficial in the control of a Zv outbreak when risk is relative low and patch disparities are not too extreme. However, when the reproductive number is too high, there seems to be no benefits. This paper is dedicated to the inauguration of the Centro de Modelamiento Matemático Carlos Castillo-Chávez at Universidad Francisco Gavidia in San Salvador, El Salvador.

q-bio.PE↗
arXiv2015

Assessing the Efficiency of \textit{Cordon Sanitaire} as a Control Strategy of Ebola

We formulate a two-patch mathematical model for Ebola Virus Disease dynamics in order to evaluate the effectiveness of \textit{cordons sanitaires}, mandatory movement restrictions between communities while exploring their role on disease dynamics and final epidemic size. Simulations show that severe restrictions in movement between high and low risk areas of closely linked communities may have a deleterious impact on the overall levels of infection in the total population.

q-bio.PE↗