Optimal vaccine roll-out strategies including social distancing for pandemics.

Non-pharmacological interventions (NPIs), principally social distancing, in combination with effective vaccines, aspire to develop a protective immunity shield against pandemics and particularly against the COVID-19 pandemic. In this study, an agent-based network model with small-world topology is employed to find optimal policies against pandemics, including social distancing and vaccination strategies. The agents' states are characterized by a variation of the SEIR model (susceptible, exposed, infected, recovered). To explore optimal policies, an equation-free method is proposed to solve the inverse problem of calibrating an agent's infection rate with respect to the vaccination efficacy. The results show that prioritizing the first vaccine dose in combination with mild social restrictions, is sufficient to control the pandemic, with respect to the number of deaths. Moreover, for the same mild number of social contacts, we find an optimal vaccination ratio of 0.85 between older people of ages > 65 compared to younger ones.

Multidimensional Analysis Integrating Human T-Cell Signatures in Lymphatic Tissues with Sex of Humanized Mice for Prediction of Responses after Dendritic Cell Immunization.

Mice transplanted with human cord blood-derived hematopoietic stem cells (HSCs) became a powerful experimental tool for studying the heterogeneity of human immune reconstitution and immune responses in vivo. Yet, analyses of human T cell maturation in humanized models have been hampered by an overall low immune reactivity and lack of methods to define predictive markers of responsiveness. Long-lived human lentiviral induced dendritic cells expressing the cytomegalovirus pp65 protein (iDCpp65) promoted the development of pp65-specific human CD8+ T cell responses in NOD.Cg-Rag1 tm1Mom -Il2rγ tm1Wj humanized mice through the presentation of immune-dominant antigenic epitopes (signal 1), expression of co-stimulatory molecules (signal 2), and inflammatory cytokines (signal 3). We exploited this validated system to evaluate the effects of mouse sex in the dynamics of T cell homing and maturation status in thymus, blood, bone marrow, spleen, and lymph nodes. Statistical analyses of cell relative frequencies and absolute numbers demonstrated higher CD8+ memory T cell reactivity in spleen and lymph nodes of immunized female mice. In order to understand to which extent the multidimensional relation between organ-specific markers predicted the immunization status, the immunophenotypic profiles of individual mice were used to train an artificial neural network designed to discriminate immunized and non-immunized mice. The highest accuracy of immune reactivity prediction could be obtained from lymph node markers of female mice (77.3%). Principal component analyses further identified clusters of markers best suited to describe the heterogeneity of immunization responses in vivo. A correlation analysis of these markers reflected a tissue-specific impact of immunization. This allowed for an organ-resolved characterization of the immunization status of individual mice based on the identified set of markers. This new modality of multidimensional analyses can be used as a framework for defining minimal but predictive signatures of human immune responses in mice and suggests critical markers to characterize responses to immunization after HSC transplantation.

On the effect of the path length of small-world networks on epidemic dynamics.

We show how one can trace in a systematic way the coarse-grained solutions of individual-based stochastic epidemic models evolving on heterogeneous complex networks with respect to their topological characteristics. In particular, we illustrate the "distinct" impact of the average path length (with respect to the degree and clustering distributions) on the emergent behavior of detailed epidemic models; to achieve this we have developed an algorithm that allows its tuning at will. The framework could be used to shed more light on the influence of weak social links on epidemic spread within small-world network structures, and ultimately to provide novel systematic computational modeling and exploration of better contagion control strategies.

Epidemionics: from the host-host interactions to the systematic analysis of the emergent macroscopic dynamics of epidemic networks.

One of the most critical issues in epidemiology revolves around the bridging of the diverse space and time scales stretching from the microscopic scale, where detailed knowledge on the immune mechanisms, host-microbe and host-host interactions is often available, to the macroscopic population-scale where the epidemic emerges, the questions arise and the answers are required. In this paper we show how the so called Equation-Free approach, a novel computational framework for multi-scale analysis, can be exploited to efficiently analyze the macroscopic emergent behavior of complex epidemic models on certain type of networks by acting directly on the multi-scale simulation. The methodology can be used to bypass the need of derivation of closures for the emergent population-level equations providing a systematic computational strict approach for macroscopic-level analysis. We illustrate the methodology through a stochastic individual-based model with agents acting on two different networks: a random regular and an Erdos-Rényi network. We construct the macroscopic bifurcation diagrams and locate the critical points that mark the onset of emergent hysteresis behavior which are associated with disease outbreaks. Finally, we perform a rare-events analysis that may in principle be used to estimate the mean time of possible outbreaks of phenomenologically latent infectious diseases.