Inferring spatial source of disease outbreaks using maximum entropy

Mathematical modeling of disease outbreaks can infer the future trajectory of an epidemic, allowing for making more informed policy decisions. Another task is inferring the origin of a disease, which is relatively difficult with current mathematical models. Such frameworks, across varying levels of complexity, are typically sensitive to input data on epidemic parameters, case counts, and mortality rates, which are generally noisy and incomplete. To alleviate these limitations, we propose a maximum entropy framework that fits epidemiological models, provides calibrated infection origin probabilities, and is robust to noise due to a prior belief model. Maximum entropy is agnostic to the parameters or model structure used and allows for flexible use when faced with sparse data conditions and incomplete knowledge in the dynamical phase of disease-spread, providing for more reliable modeling at early stages of outbreaks. We evaluate the performance of our model by predicting future disease trajectories based on simulated epidemiological data in synthetic graph networks and the real mobility network of New York State. In addition, unlike existing approaches, we demonstrate that the method can be used to infer the origin of the outbreak with accurate confidence. Indeed, despite the prevalent belief on the feasibility of contact-tracing being limited to the initial stages of an outbreak, we report the possibility of reconstructing early disease dynamics, including the epidemic seed, at advanced stages

Markovian approach to tackle the interaction of simultaneous diseases

The simultaneous emergence of several abrupt disease outbreaks or the extinction of some serotypes of multistrain diseases are fingerprints of the interaction between pathogens spreading within the same population. Here, we propose a general and versatile benchmark to address the unfolding of both cooperative and competitive interacting diseases. We characterize the explosive transitions between the disease-free and the epidemic regimes arising from the cooperation between pathogens and show the critical degree of cooperation needed for the onset of such abrupt transitions. For the competing diseases, we characterize the mutually exclusive case and derive analytically the transition point between the full-dominance phase, in which only one pathogen propagates, and the coexistence regime. Finally, we use this framework to analyze the behavior of the former transition point as the competition between pathogens is relaxed

Physics of interdependent dynamical processes.

La emergencia de fenómenos colectivos a escalas macroscópicas no observados en escalas microscópicas cuestiona la validez de las teorías reduccionistas. Para explicar estos fenómenos se necesitan enfoques sistémicos que den cuenta de los patrones de interacción no triviales existentes entre los constituyentes de los sistemas sociales, biológicos o económicos, lo que ha dado lugar al nacimiento de la disciplina conocida como ciencia de los sistemas complejos. Una vía habitual para caracterizar los sistemas complejos ha sido la búsqueda de la conexión entre la estructura de interacciones y el comportamiento colectivo observado en sistemas reales mediante el estudio individual de dinámicas aisladas. No obstante, los sistemas complejos no son inmutables y se encuentran constantemente intercambiando información mediante estímulos internos y externos. Esta tesis se centra en la adaptación de modelos sobre diferentes dinámicas en el campo de los sistemas complejos para caracterizar el impacto de este flujo de información, ya sea entre escalas microscópicas y macroscópicas de un mismo sistema o mediante la existencia de interdependencias entre procesos dinámicos que se propagan de forma simultánea.La primera parte de la tesis aborda el estudio dinámicas acopladas en redes de contacto estáticas. Adaptando los modelos compartimentales introducidos en el siglo XX a la naturaleza de cada dinámica, caracterizamos cuatro problemas diferentes: la propagación de patógenos que interactúan, cuya coexistencia puede ser beneficiosa o perjudicial para su evolución, el control de brotes epidémicos con el uso del rastreo de contactos digital, la aparición de movimientos sociales desencadenados por pequeñas minorías sociales bien coordinadas y la competencia entre honestidad y la corrupción en las sociedades modernas. En todas estas dinámicas, encontramos que el flujo de información cambia las propiedades críticas del sistema así como algunas de las conclusiones extraídas sobre el papel de la estructura de contactos al estudiar cada dinámica de forma individual.La segunda parte de la tesis se centra en el impacto de la movilidad recurrente en la propagación de epidemias en entornos urbanos. Derivamos un modelo sencillo que permite incorporar fácilmente la distribución de la población en las ciudades reales y sus patrones habituales de desplazamiento sin ninguna pérdida de información. Demostramos que los efectos de las políticas de contención basadas en la reducción de la movilidad no son universales y dependen en gran medida de las características estructurales de las ciudades y los parámetros epidemiológicos del virus circulante en la población. En particular, descubrimos y caracterizamos un nuevo fenómeno, el detrimento epidémico, que refleja el efecto beneficioso de la movilidad en algunos escenarios para contener un brote epidémico. Por último, exploramos tres casos de estudio reales, mostrando que nuestro modelo permite capturar algunos de los mecanismos que han convertido a los núcleos urbanos en importantes focos de contagio en recientes epidemias y que el modelo desarrollado puede servir como base para desarrollar marcos teóricos más realistas que reproducen la evolución de distintas enfermedades como la COVID-19 o el dengue.<br /

Emergence, survival, and segregation of competing gangs

In this paper, we approach the phenomenon of criminal activity from an infectious perspective by using tailored compartmental agent-based models that include the social flavor of the mechanisms governing the evolution of crime in society. Specifically, we focus on addressing how the existence of competing gangs shapes the penetration of crime. The mean-field analysis of the model proves that the introduction of dynamical rules favoring the simultaneous survival of both gangs reduces the overall number of criminals across the population as a result of the competition between them. The implementation of the model in networked populations with homogeneous contact patterns reveals that the evolution of crime substantially differs from that predicted by the mean-field equations. We prove that the system evolves toward a segregated configuration where, depending on the features of the underlying network, both gangs can form spatially separated clusters. In this scenario, we show that the beneficial effect of the coexistence of two gangs is hindered, resulting in a higher penetration of crime in the population

Polarized opinion states in static networks driven by limited information horizons

The widespread emergence of opinion polarization is often attributed to the rise of social media and the internet. These platforms can promote selective exposure, leading to the formation of echo chambers where individuals are only exposed to viewpoints that reinforce their existing beliefs. However, experimental evidence shows that exposure to opposing views through cross-cutting ties is common in both online and offline social contexts, which frequently involve long-standing personal relationships. To account for these facts, we have developed an opinion model that applies to static contact structures. In this model, an agent's influence over their neighbors depends on the similarity of their opinions. Our findings suggest that polarization can indeed emerge in such static structures and, driven by an increased narrow-mindedness, even in presence of non-negligible cross-cutting ties. Interestingly, the polarized opinion distributions generated by our model closely resemble those obtained in surveys about highly polarized issues. This has allowed us to categorize various issues based on their controversial nature, shedding light on the factors that contribute to opinion polarization

A metapopulation approach to identify targets for Wolbachia-based dengue control

Over the last decade, the release of Wolbachia-infected Aedes aegypti into the natural habitat of this mosquito species has become the most sustainable and long-lasting technique to prevent and control vector-borne diseases, such as dengue, zika, or chikungunya. However, the limited resources to generate such mosquitoes and their effective distribution in large areas dominated by the Aedes aegypti vector represent a challenge for policymakers. Here, we introduce a mathematical framework for the spread of dengue in which competition between wild and Wolbachia-infected mosquitoes, the cross-contagion patterns between humans and vectors, the heterogeneous distribution of the human population in different areas, and the mobility flows between them are combined. Our framework allows us to identify the most effective areas for the release of Wolbachia-infected mosquitoes to achieve a large decrease in the global dengue prevalence. © 2022 Author(s)

Modeling the Spatiotemporal Epidemic Spreading of COVID-19 and the Impact of Mobility and Social Distancing Interventions

On 31 December, 2019, an outbreak of a novel coronavirus, SARS-CoV-2, that causes the COVID-19 disease, was first reported in Hubei, mainland China. This epidemics'' health threat is probably one of the biggest challenges faced by our interconnected modern societies. According to the epidemiological reports, the large basic reproduction number R0~3.0, together with a huge fraction of asymptomatic infections, paved the way for a major crisis of the national health capacity systems. Here, we develop an age-stratified mobility-based metapopulation model that encapsulates the main particularities of the spreading of COVID-19 regarding (i) its transmission among individuals, (ii) the specificities of certain demographic groups with respect to the impact of COVID-19, and (iii) the human mobility patterns inside and among regions. The full dynamics of the epidemic is formalized in terms of a microscopic Markov chain approach that incorporates the former elements and the possibility of implementing containment measures based on social distancing and confinement. With this model, we study the evolution of the effective reproduction number R(t), the key epidemiological parameter to track the evolution of the transmissibility and the effects of containment measures, as it quantifies the number of secondary infections generated by an infected individual. The suppression of the epidemic is directly related to this value and is attained when R<1. We find an analytical expression connecting R with nonpharmacological interventions, and its phase diagram is presented. We apply this model at the municipality level in Spain, successfully forecasting the observed incidence and the number of fatalities in the country at each of its regions. The expression for R should assist policymakers to evaluate the epidemics'' response to actions, such as enforcing or relaxing confinement and social distancing

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

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