Combining participatory influenza surveillance with modeling and forecasting

Background: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. Objectives: Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. Methods: We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), InfluenzaNet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using InfluenzaNet and Flu Near You). Results: WISDM based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. InfluenzaNet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities; and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information. Conclusions: While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long term forecasting of Influenza activity in data poor parts of the world

Quantifying the effect of temporal resolution on time-varying networks

Time-varying networks describe a wide array of systems whose constituents and interactions evolve over time. They are defined by an ordered stream of interactions between nodes, yet they are often represented in terms of a sequence of static networks, each aggregating all edges and nodes present in a time interval of size Δt. In this work we quantify the impact of an arbitrary Δt on the description of a dynamical process taking place upon a time-varying network. We focus on the elementary random walk, and put forth a simple mathematical framework that well describes the behavior observed on real datasets. The analytical description of the bias introduced by time integrating techniques represents a step forward in the correct characterization of dynamical processes on time-varying graphs

Contagion dynamics in time-varying metapopulation networks

The metapopulation framework is adopted in a wide array of disciplines to describe systems of well separated yet connected subpopulations. The subgroups or patches are often represented as nodes in a network whose links represent the migration routes among them. The connections have been so far mostly considered as static, but in general evolve in time. Here we address this case by investigating simple contagion processes on time-varying metapopulation networks. We focus on the SIR process and determine analytically the mobility threshold for the onset of an epidemic spreading in the framework of activity-driven network models. We find profound differences from the case of static networks. The threshold is entirely described by the dynamical parameters defining the average number of instantaneously migrating individuals and does not depend on the properties of the static network representation. Remarkably, the diffusion and contagion processes are slower in time-varying graphs than in their aggregated static counterparts, the mobility threshold being even two orders of magnitude larger in the first case. The presented results confirm the importance of considering the time-varying nature of complex networks

Autoimmune and autoinflammatory mechanisms in uveitis

The eye, as currently viewed, is neither immunologically ignorant nor sequestered from the systemic environment. The eye utilises distinct immunoregulatory mechanisms to preserve tissue and cellular function in the face of immune-mediated insult; clinically, inflammation following such an insult is termed uveitis. The intra-ocular inflammation in uveitis may be clinically obvious as a result of infection (e.g. toxoplasma, herpes), but in the main infection, if any, remains covert. We now recognise that healthy tissues including the retina have regulatory mechanisms imparted by control of myeloid cells through receptors (e.g. CD200R) and soluble inhibitory factors (e.g. alpha-MSH), regulation of the blood retinal barrier, and active immune surveillance. Once homoeostasis has been disrupted and inflammation ensues, the mechanisms to regulate inflammation, including T cell apoptosis, generation of Treg cells, and myeloid cell suppression in situ, are less successful. Why inflammation becomes persistent remains unknown, but extrapolating from animal models, possibilities include differential trafficking of T cells from the retina, residency of CD8(+) T cells, and alterations of myeloid cell phenotype and function. Translating lessons learned from animal models to humans has been helped by system biology approaches and informatics, which suggest that diseased animals and people share similar changes in T cell phenotypes and monocyte function to date. Together the data infer a possible cryptic infectious drive in uveitis that unlocks and drives persistent autoimmune responses, or promotes further innate immune responses. Thus there may be many mechanisms in common with those observed in autoinflammatory disorders

The role of endogenous and exogenous mechanisms in the formation of R&D networks

We develop an agent-based model of strategic link formation in Research and Development (R&D)networks. Empirical evidence has shown that the growth of these networks is driven by mechanisms whichare both endogenous to the system (that is, depending on existing alliances patterns) and exogenous (that is, driven by an exploratory search for newcomer firms). Extant research to date has not investigated both mechanisms simultaneously in a comparative manner. To overcome this limitation, we develop a general modeling framework to shed light on the relative importance of these two mechanisms. We test our model against a comprehensive dataset, listing cross-country and cross-sectoral R&D alliances from 1984 to 2009. Our results show that by fitting only three macroscopic properties of the network topology, this framework is able to reproduce a number of micro-level measures, including the distributions of degree, local clustering, path length and component size, and the emergence of network clusters. Furthermore, by estimating the link probabilities towards newcomers and established firms from the data, we find that endogenous mechanisms are predominant over the exogenous ones in the network formation, thus quantifying the importance of existing structures in selecting partner firms

Confirming the function of a Final Bronze Age wine processing site in the Nuraghe Genna Maria in Villanovaforru (South Sardinia)

The stone artefact in the hut γ of the NuragheGenna Maria, object of this study, is part of a compound still unpublished today and dated to the Nuragic period. It was found during a 1991 excavation, revealing a situation unchanged since the collapse occurred between the 10th and 9th century B.C., thus preserving the situation at the time of the collapse to this day. The presence of tartaric acid - the marker considered to determinate the presence of wines or products deriving from grapes - has been determined using HPLC-DAD and UHPLC-HQOMS. So the findings under examination, together with the overall evaluation of the archaeological aspects examined, suggests to positively consider the stone artifact as a "laccus" (the latin word for wine presses, still used in the Sardinian language today ) for grape crushing. The internal slope of the floor of the "laccus" allowed the extraction of juice with rapid separation of juice from berry skins. The presence in Sardinia of a large number of "stone wine presses" ("palmenti" in Italian) such as that of the Nuraghe Genna Maria studied in this article, brings a contribution to their dating and confirm the existence of an oenological industry on the island in the Archaic period (9th-10th century B.C.)

Photochemical dihydrogen production using an analogue of the active site of [NiFe] hydrogenase

The photoproduction of dihydrogen (H2) by a low molecular weight analogue of the active site of [NiFe] hydrogenase has been investigated by the reduction of the [NiFe2] cluster, 1, by a photosensitier PS (PS = [ReCl(CO)3(bpy)] or [Ru(bpy)3][PF6]2). Reductive quenching of the 3MLCT excited state of the photosensitiser by NEt3 or N(CH2CH2OH)3 (TEOA) generates PS•−, and subsequent intermolecular electron transfer to 1 produces the reduced anionic form of 1. Time-resolved infrared spectroscopy (TRIR) has been used to probe the intermediates throughout the reduction of 1 and subsequent photocatalytic H2 production from [HTEOA][BF4], which was monitored by gas chromatography. Two structural isomers of the reduced form of 1 (1a•− and 1b•−) were detected by Fourier transform infrared spectroscopy (FTIR) in both CH3CN and DMF (dimethylformamide), while only 1a•− was detected in CH2Cl2. Structures for these intermediates are proposed from the results of density functional theory calculations and FTIR spectroscopy. 1a•− is assigned to a similar structure to 1 with six terminal carbonyl ligands, while calculations suggest that in 1b•− two of the carbonyl groups bridge the Fe centres, consistent with the peak observed at 1714 cm−1 in the FTIR spectrum for 1b•− in CH3CN, assigned to a ν(CO) stretching vibration. The formation of 1a•− and 1b•− and the production of H2 was studied in CH3CN, DMF and CH2Cl2. Although the more catalytically active species (1a•− or 1b•−) could not be determined, photocatalysis was observed only in CH3CN and DMF

Low Fidelity Imitation of Atypical Biological Kinematics in Autism Spectrum Disorders Is Modulated by Self-Generated Selective Attention.

We examined whether adults with autism had difficulty imitating atypical biological kinematics. To reduce the impact that higher-order processes have on imitation we used a non-human agent model to control social attention, and removed end-state target goals in half of the trials to minimise goal-directed attention. Findings showed that only neurotypical adults imitated atypical biological kinematics. Adults with autism did, however, become significantly more accurate at imitating movement time. This confirmed they engaged in the task, and that sensorimotor adaptation was self-regulated. The attentional bias to movement time suggests the attenuation in imitating kinematics might be a compensatory strategy due to deficits in lower-level visuomotor processes associated with self-other mapping, or selective attention modulated the processes that represent biological kinematics

Real-time numerical forecast of global epidemic spreading: Case study of 2009 A/H1N1pdm

Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. Methods We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Results Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Conclusions Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models