An agent-based model of monocyte differentiation into tumour-associated macrophages in chronic lymphocytic leukemia.

Monocyte-derived macrophages help maintain tissue homeostasis and defend the organism against pathogens. In tumors, recent studies have uncovered complex macrophage populations, including tumor-associated macrophages, which support tumorigenesis through cancer hallmarks such as immunosuppression, angiogenesis, or matrix remodeling. In the case of chronic lymphocytic leukemia, these macrophages are known as nurse-like cells (NLCs) and they protect leukemic cells from spontaneous apoptosis, contributing to their chemoresistance. We propose an agent-based model of monocyte differentiation into NLCs upon contact with leukemic B cells in vitro. We performed patient-specific model optimization using cultures of peripheral blood mononuclear cells from patients. Using our model, we were able to reproduce the temporal survival dynamics of cancer cells in a patient-specific manner and to identify patient groups related to distinct macrophage phenotypes. Our results show a potentially important role of phagocytosis in the polarization process of NLCs and in promoting cancer cells' enhanced survival.

Unraveling the seasonal epidemiology of pneumococcus.

Infections caused by Streptococcus pneumoniae-including invasive pneumococcal diseases (IPDs)-remain a significant public health concern worldwide. The marked winter seasonality of IPDs is a striking, but still enigmatic aspect of pneumococcal epidemiology in nontropical climates. Here we confronted age-structured dynamic models of carriage transmission and disease with detailed IPD incidence data to test a range of hypotheses about the components and the mechanisms of pneumococcal seasonality. We find that seasonal variations in climate, influenza-like illnesses, and interindividual contacts jointly explain IPD seasonality. We show that both the carriage acquisition rate and the invasion rate vary seasonally, acting in concert to generate the marked seasonality typical of IPDs. We also find evidence that influenza-like illnesses increase the invasion rate in an age-specific manner, with a more pronounced effect in the elderly than in other demographics. Finally, we quantify the potential impact of seasonally timed interventions, a type of control measures that exploit pneumococcal seasonality to help reduce IPDs. Our findings shed light on the epidemiology of pneumococcus and may have notable implications for the control of pneumococcal infections.

Characterizing and Comparing the Seasonality of Influenza-Like Illnesses and Invasive Pneumococcal Diseases Using Seasonal Waveforms.

The seasonalities of influenza-like illnesses (ILIs) and invasive pneumococcal diseases (IPDs) remain incompletely understood. Experimental evidence indicates that influenza-virus infection predisposes to pneumococcal disease, so that a correspondence in the seasonal patterns of ILIs and IPDs might exist at the population level. We developed a method to characterize seasonality by means of easily interpretable summary statistics of seasonal shape-or seasonal waveforms. Nonlinear mixed-effects models were used to estimate those waveforms based on weekly case reports of ILIs and IPDs in 5 regions spanning continental France from July 2000 to June 2014. We found high variability of ILI seasonality, with marked fluctuations of peak amplitudes and peak times, but a more conserved epidemic duration. In contrast, IPD seasonality was best modeled by a markedly regular seasonal baseline, punctuated by 2 winter peaks in late December to early January and January to February. Comparing ILI and IPD seasonal waveforms, we found indication of a small, positive correlation. Direct models regressing IPDs on ILIs provided comparable results, even though they estimated moderately larger associations. The method proposed is broadly applicable to diseases with unambiguous seasonality and is well-suited to analyze spatially or temporally grouped data, which are common in epidemiology.

An agent-based model simulation of influenza interactions at the host level: insight into the influenza-related burden of pneumococcal infections.

BACKGROUND: Host-level influenza virus-respiratory pathogen interactions are often reported. Although the exact biological mechanisms involved remain unelucidated, secondary bacterial infections are known to account for a large part of the influenza-associated burden, during seasonal and pandemic outbreaks. Those interactions probably impact the microorganisms' transmission dynamics and the influenza public health toll. Mathematical models have been widely used to examine influenza epidemics and the public health impact of control measures. However, most influenza models overlooked interaction phenomena and ignored other co-circulating pathogens. METHODS: Herein, we describe a novel agent-based model (ABM) of influenza transmission during interaction with another respiratory pathogen. The interacting microorganism can persist in the population year round (endemic type, e.g. respiratory bacteria) or cause short-term annual outbreaks (epidemic type, e.g. winter respiratory viruses). The agent-based framework enables precise formalization of the pathogens' natural histories and complex within-host phenomena. As a case study, this ABM is applied to the well-known influenza virus-pneumococcus interaction, for which several biological mechanisms have been proposed. Different mechanistic hypotheses of interaction are simulated and the resulting virus-induced pneumococcal infection (PI) burden is assessed. RESULTS: This ABM generates realistic data for both pathogens in terms of weekly incidences of PI cases, carriage rates, epidemic size and epidemic timing. Notably, distinct interaction hypotheses resulted in different transmission patterns and led to wide variations of the associated PI burden. Interaction strength was also of paramount importance: when influenza increased pneumococcus acquisition, 4-27% of the PI burden during the influenza season was attributable to influenza depending on the interaction strength. CONCLUSIONS: This open-source ABM provides new opportunities to investigate influenza interactions from a theoretical point of view and could easily be extended to other pathogens. It provides a unique framework to generate in silico data for different scenarios and thereby test mechanistic hypotheses.