Dynamic SAFFRON: Disease Control Over Time via Group Testing

Relevance. Hospital-acquired acute respiratory viral infections (ARVI) are a significant cause of high morbidity in pediatric hospitals, especially in departments for small children. To control HCAI, there is still a need for evidence-based anti-epidemic and preventive measures. Mathematical modeling is known to be an attractive approach for objectively assessing infection control measures. In this study, we demonstrate effectiveness of timely isolation of the source of respiratory infection (i.e. an infected individuals) which might be revealed in a large pediatric hospital.Materials & Methods. We developed an agent-based model of the spread of nosocomial respiratory infection to assess the effectiveness of timely isolation of patients with ARVI. Primary data on the incidence of ARVI, the number of beds, and the frequency of infection in a multidisciplinary non-infectious children's hospital at the regional level were used. The model also implements the ability of the agent to change the department. To simulate the anti-epidemic measure, we included in the developed simulation model the execution of a local algorithm of actions by a doctor in the event of detection of a patient with ARVI (50% probability of non-compliance with isolation; almost all sources of infection are isolated; 100% decision-making, the ideal option).Results. The maximum absolute daily increase in ARVI cases were observed in scenarios where the rules for isolating the source of infection were violated; here the median equaled 32.9 (IQR 32.4-33.4) cases, ranging from 4.0 to 34.8 cases. With an increase in the probability of isolating a patient with ARVI, the indicator decreased: with a 50% probability of isolating the source, the median was 5.7 (5-6.5); 90% probability – 3.1 (2.8-3.6); 100% probability – 2.9 (2.6-3.4) cases. According to model estimates, when comparing the departments over time, we noted their unequal involvement in the epidemic process and wave-like spread of infection. Statistically significant differences in the predicted number of ARVI cases with different probabilities of isolation were revealed.Conclusion. We used a simulation agent-based approach to modeling nosocomial ARVI. This allowed us to correctly describe the spread of infection among patients in a children's hospital, as well as to assess the effectiveness of timely isolation of the source of infection. In our model, we took into account various options for the interaction of agents with each other. In addition, we fit importations of the infection and studied how they could facilitate the spread of the infection. The solid evidence was obtained of the effectiveness of timely isolation of the source of infection identified in healthcare settings, which can be used to justify control measures.

The dynamics of viral infection spread, whether in laboratory cultures or in naturally infected hosts, reflects a coupling of biological and physical processes that remain to be fully elucidated. Biological processes include the kinetics of virus growth in infected cells while physical processes include transport of virus progeny from infected cells, where they are produced, to susceptible cells, where they initiate new infections. Mechanistic models of infection spread have been widely developed for systems where virus growth is coupled with transport of virus particles by diffusion, but they have yet to be developed for systems where viruses move under the influence of fluid flows. Recent experimental observations of flow-enhanced infection spread in laboratory cultures motivate here the development of initial continuum and discrete virus-particle models of infection spread. The magnitude of a dimensionless group, the Damköhler number, shows how parameters that characterize particle adsorption to cells, strain rates that reflect flow profiles, and diffusivities of virus particles combine to influence the spatial pattern of infection spread.

Fatigue Life of Reciproc and Mtwo Instruments Subjected to Static and Dynamic Tests

As graphs continue to grow in size, we seek ways to effectively process such data at scale. The model of streaming graph processing, in which a compact summary is maintained as each edge insertion/deletion is observed, is an attractive one. However, few results are known for optimization problems over such dynamic graph streams.In this paper, we introduce a new approach to handling graph streams, by instead seeking solutions for the parameterized versions of these problems. Here, we are given a parameter k and the objective is to decide whether there is a solution bounded by k. By combining kernelization techniques with randomized sketch structures, we obtain the first streaming algorithms for the parameterized versions of Maximal Matching and Vertex Cover. We consider various models for a graph stream on n nodes: the insertion-only model where the edges can only be added, and the dynamic model where edges can be both inserted and deleted. More formally, we show the following results:•In the insertion only model, there is a one-pass deterministic algorithm for the parameterized Vertex Cover problem which computes a sketch using Õ(k2) space1 such that at each timestamp in time Õ(2k) it can either extract a solution of size at most k for the current instance, or report that no such solution exists. We also show a tight lower bound of Ω(k2) for the space complexity of any (randomized) streaming algorithms for the parameterized Vertex Cover, even in the insertion-only model.•In the dynamic model, and under the promise that at each timestamp there is a maximal matching of size at most k, there is a one-pass Õ(k2)-space (sketch-based) dynamic algorithm that maintains a maximal matching with worst-case update time Õ(k2). This algorithm partially solves Open Problem 64 from [1]. An application of this dynamic matching algorithm is a one-pass Õ(k2)-space streaming algorithm for the parameterized Vertex Cover problem that in time Õ(2k) extracts a solution for the final instance with probability 1 – δ/no(1), where δ < 1. To the best of our knowledge, this is the first graph streaming algorithm that combines linear sketching with sequential operations that depend on the graph at the current time.•In the dynamic model without any promise, there is a one-pass randomized algorithm for the parameterized Vertex Cover problem which computes a sketch using Õ(nk) space such that in time Õ(nk + 2k) it can either extract a solution of size at most k for the final instance, or report that no such solution exists.

Based on the analysis, it is established that traditional approaches, such as the SIR (Susceptible-Infectious-Covered) and SEIR (Susceptible-Exposed-Infectious-Covered) models, do not provide sufficient forecasting accuracy and do not take into account the complex dynamics of the spread of infectious diseases. The need to develop a method that will improve forecasting accuracy and provide support for managerial decision-making to predict the spread of epidemiological threats based on the telegraphic equation is substantiated. The developed system allows for making effective management decisions aimed at reducing the negative impact of the epidemic on the population and medical infrastructure. The use of the telegraphic level allows us to predict the wave spread of infection, spatial and temporal delays, as well as sources of new infections, which ensures accurate forecasting of peak periods, the duration of the epidemic, and the workload of medical facilities. The developed method integrates the classical SIR model with the telegraphic level, which allows the modelling the dynamics of infection spread in a spatio-temporal environment. This method provides forecasting of the spatial and temporal dynamics of infection spread, taking into account wave effects, delays, and the influence of external factors. It provides an opportunity to accurately analyze key epidemic indicators, such as the peak of the disease, its duration, and the distribution of the burden on hospitals. The developed method and mathematical model based on the telegraphic level provided an appropriate level of accuracy in predicting the spatial and temporal dynamics of the spread of epidemiological threats. Testing the model on historical COVID-19 data showed that the average forecast error was 5...10%. This indicates the model's adequacy. In the case of high population mobility, the model accurately described the wave dynamics of the infection. The proposed decision support system includes a user-friendly interface with four tabs for entering model parameters, analyzing results, visualizing them, and generating recommendations. It allows to improve the accuracy of estimating the duration of the epidemic, peak loads, and some resources. The developed system is a tool for managers to support the adoption of governmental decisions aimed at predicting the infection of the population of regions and optimizing the use of medical resources. The results of the study can be used to plan epidemic response measures at the local, regional, and global levels. The proposed system ensures efficiency, flexibility, and accuracy, which are key to managing epidemiological situations in the face of modern challenges.

As graphs continue to grow in size, we seek ways to effectively process such data at scale. The model of streaming graph processing, in which a compact summary is maintained as each edge insertion/deletion is observed, is an attractive one. However, few results are known for optimization problems over such dynamic graph streams.In this paper, we introduce a new approach to handling graph streams, by instead seeking solutions for the parameterized versions of these problems. Here, we are given a parameter k and the objective is to decide whether there is a solution bounded by k. By combining kernelization techniques with randomized sketch structures, we obtain the first streaming algorithms for the parameterized versions of Maximal Matching and Vertex Cover. We consider various models for a graph stream on n nodes: the insertion-only model where the edges can only be added, and the dynamic model where edges can be both inserted and deleted. More formally, we show the following results:• In the insertion only model, there is a one-pass deterministic algorithm for the parameterized Vertex Cover problem which computes a sketch using O(k2) space such that at each timestamp in time O(2k) it can either extract a solution of size at most k for the current instance, or report that no such solution exists. We also show a tight lower bound of Ω(k2) for the space complexity of any (randomized) streaming algorithms for the parameterized Vertex Cover, even in the insertion-only model.• In the dynamic model, and under the promise that at each timestamp there is a maximal matching of size at most k, there is a one-pass O(k2)-space (sketch-based) dynamic algorithm that maintains a maximal matching with worst-case update time O(k2). This algorithm partially solves Open Problem 64 from [1]. An application of this dynamic matching algorithm is a one-pass O(k2)-space streaming algorithm for the parameterized Vertex Cover problem that in time O(2k) extracts a solution for the final instance with probability 1 − δ/nO(1), where δ

Static and dynamic tests were conducted at the time of driving on each of two instrumented piles driven into cohesive soils to embedded depths of 64 and 74 feet. The tests were repeated 11 days later in order to investigate the response of the reconsolidated soil. The purpose of this study was to investigate the time dependent force displacement response of cohesive soils induced by dynamic pile loads and to compare dynamic response with static response. All past attempts to achieve such comparisons were based either upon an assumed dynamic soil model and/or a simulated applied dynamic force estimated from the weight and speed of the driving hammer. In order to eliminate such assumptions in this study, the induced dynamic force was measured at five strategic locations along each instrumented pile. An accelerometer was used to record the motion at the head of the pile. This motion, together with the induced forces, were utilized in a multi degree spring-mass, system to directly compute the displacements of the masses representing various pile segments. The result of this analysis showed that when the applied loads were maximum, each pile experienced What may be described as rigid body motion. Consequently, a rigid pile model was utilized in a single-degree of freedom system to compute the dynamic soil resistance. Since both the dynamic soil resistance and the pile motions were known, a rigorous, or closed form solution of the equation of motion leads to a direct computation of the static component of soil resistance. This static component was found to approximate the static ultimate bearing capacity when the pile had experienced significant relative motion with respect to the soil. Introduction The bearing capacity of piles supporting Offshore structures is often determined by the so called static method. It is noteworthy that this method relies on correlations between soil shear strength, as determined from laboratory or in situ tests, to skin friction along the pile shaft and to end bearing below the pile point. Such relations have been reported by Terzaghi and Peck (15). Skempton (13), Tomlinson (16), Peck (9). Woodard et al. (18). and by Coyle and Reese (2). All of these studies undoubtedly advanced the state of the art. but uncertainties still remain. The overriding cause for uncertainty in the static method is that pile driving changes the shear strength of the clay, and it is difficult to estimate the strength of the clay after driving. In contrast to land piles, it is difficult to verify the design of offshore piles by static load tests, due to prohibitive costs and to high design loads that are difficult to attain. An alternate approach to the static field load test would consist of utilizing the resistance of the pile during driving as a dynamic field test to compute the static ultimate bearing capacity. The dynamic test may be conducted at any time after driving, and may therefore include some of the effects of soil reconsolidation and setup. However, dynamic test methods are not problem free for several reasons. First, the forcing function induced between hammer and pile is difficult to evaluate theoretically. Second, the relation between static and dynamic resistance is usually empirically defined by use of a damping coefficient which is held constant throughout the duration of impact. Even though this is an accepted procedure in the analysis of 1ineari1y elastic solids, it may not be sufficient for ultimate bearing capacity determinations in clay, because the material properties are vastly different.

A dynamic information-structure mutual exclusion algorithm is presented for distributed systems whose information-structure evolves with time as sites learn about the state of the system through messages. An interesting feature of the algorithm is that it adapts itself to heterogeneous or fluctuating traffic conditions to optimize the performance (the number of messages exchanged). The performance of the algorithm is studied by simulation technique and compared to the performance of a well-known mutual exclusion algorithm. The impact message loss and site failures on the algorithm is discussed and methods to tolerate these failures are proposed. >

In this paper, we present and evaluate dynamic dictionary-based estimation methods for joint model order and parameter estimation. In dictionary-based estimation, a continuous parameter space is discretized, and vector-valued dictionary elements are formed for specific parameter values. A linear combination of a subset of dictionary elements is used to represent the model, where the number of elements used is the estimated model order, and the parameters corresponding to the selected elements are the parameter estimates. In static-based methods, the dictionary is fixed; while in the dynamic methods proposed here, the parameter sampling, and hence the dictionary, adapt to the data. We propose two dynamic dictionary-based estimation algorithms in which the dictionary elements are dynamically adjusted to improve parameter estimation performance. We examine the performance of both static and dynamic algorithms in terms of probability of correct model order selection and the root mean-squared error of parameter estimates. We show that dynamic dictionary methods overcome the problem of estimation bias induced by quantization effects in static dictionary-based estimation, and we demonstrate that dictionary-based estimation methods are capable of parameter estimation performance comparable to the Cramér-Rao lower bound and to traditional ML-based model estimation over a wide range of signal-to-noise ratios.

Mechanical and biomechanical testing provides crucial information about the safety, effectiveness and function of spinal implants. Mainly static and dynamic tests are carried out. While mechanical tests may be carried out according to testing standards or in some cases to individual testing procedures, biomechanical tests should be conducted according to published recommendations or in case of dynamic testing, as individual test procedures. In order to allow for direct comparison between testing laboratories, it should be strived for standardised testing. However, as standardised loading often simplifies the in vivo occurring conditions, more physiological testing can be carried out additionally and may become the next improved testing standard. Mechanical testing focuses mainly on the safety issue, while effectiveness and function of an implant can be tested in a biomechanical setup. Each type of implant generally requires specific mechanical and biomechanical tests depending on its design, material, indication and function. In general, mechanical testing can be subdivided in static and dynamic fatigue testing as well as special types of testing such as wear or corrosion testing. Biomechanical testing concentrates on quasi-static and short-term dynamic testing mostly in interaction with biological tissue.

BackgroundAccess to qualitative HIV PCRs for early infant diagnosis (EID) is restricted in resource-limited settings due to cost. We hypothesised that pooling of dried blood spots (DBS), defined as combining multiple patient samples in a single test with subsequent individual testing of positive pools, would be cost saving while retaining clinical accuracy compared to individual patient testing.MethodsCost savings: A model was developed to simulate reagent and consumable cost saving of pooled compared to individual sample testing. Daily sample/result data of a public health laboratory in South Africa were used to illustrate outputs from the model. Samples were randomly allocated to pools and the process was repeated 1000 times to measure variation in estimates due to this stochasticity.Clinical accuracy: 1170 patient samples were tested using the Roche CAP/CTM Qual assay in pools of five 50 μl DBS. Negative pools comprised DBS previously tested in single reactions; positive pools included 1 positive sample.ResultsPooling would have saved 64% of laboratory costs in 2015. The model is published as an R-based web tool, into which the user enters sample/positivity estimates and workflow management parameters to obtain cost saving estimates at an optimal pool size.Sensitivity of pooled testing was 98.8% overall; 100% for strongly reactive pools. One pool tested false positive which would not impact clinical specificity as individual patient testing is performed prior to reporting.ConclusionsPooled PCR testing for EID remains accurate and dramatically reduces costs in settings with moderate to low prevalence rates and sufficient sample numbers.

The prevalence and pathogenic effects of trypanosomosis were determined in cattle, goats and pigs reared in Kasese, Jinja and Rakai districts, Uganda; presence of trypanosomes was detected by buffy coat technique (BCT). The overall prevalence of trypanosomosis in cattle was 7.6% (144/1,891), 0.7% in goats (4/573) and 2.3% in pigs (9/386). Internal transcribed spacer 1 (ITS1) of ribosomal DNA polymerase chain reaction was utilised to identify trypanosomes to species level and revealed infections in 108 of the 144 trypanosome-positive cattle while all infected goats and pigs gave amplicons. Trypanosoma vivax was the most prevalent trypanosome species in cattle in single and mixed infections compared to infections involving Trypanosoma congolense and Trypanosoma brucei; in pigs, eight were mixed infections with one single T. vivax infection. No predominant trypanosome species was detected in goats. Anaemia, the main trypanosomosis pathological feature, was investigated by determining packed cell volume (PCV). Mean PCV values by t test in infected individuals were significantly lower than non-infected individuals (P<0.05) for all animal species. However, the proportion of anaemic animals was not significantly different in infected and non-infected individuals. In addition, the percent of infected animals by Fisher's exact test depended on district of origin and species but not sex. These findings show that trypanosomosis is a major cause of anaemia in livestock in endemic areas. Cattle were the major animal species affected by trypanosomosis; similar genotypes of trypanosomes were detected in the three animal species. BCT was more effective than ITS1 rDNA detecting trypanosomes in naturally infected cattle.

This work is contextualized in research in modeling and simulation of infection spread within a community or population, with the objective to provide a public health and policy tool in assessing the dynamics of infection spread and the qualitative impacts of public health interventions. This work uses the integration of real data sources into an Agent Based Model (ABM) to simulate respiratory infection spread within a small municipality. Novelty is derived in that the data sources are not necessarily obvious within ABM infection spread models. The ABM is a spatial-temporal model inclusive of behavioral and interaction patterns between individual agents on a real topography. The agent behaviours (movements and interactions) are fed by census / demographic data, integrated with real data from a telecommunication service provider (cellular records) and person-person contact data obtained via a custom 3G Smartphone application that logs Bluetooth connectivity between devices. Each source provides data of varying type and granularity, thereby enhancing the robustness of the model. The work demonstrates opportunities in data mining and fusion that can be used by policy and decision makers. The data become real-world inputs into individual SIR disease spread models and variants, thereby building credible and non-intrusive models to qualitatively simulate and assess public health interventions at the population level.

A flow-regulated infection rate is defined for a Susceptible-Infected-Susceptible (SIS) model revealing the network structural properties that influence the spread of infections. The infection rate is linked to the flow between compartments in the associated positive compartmental system, providing a self-regulatory effect on the spreading dynamics. This translates to infection carriers preferring to visit healthier sites over more infected ones. A flow-independent epidemic threshold is defined that sets the conditions on the graph’s structure and the aggregate infection rate for a disease to either spread or die out. An individual-based mean-field approach returns results comparable to models with constant infection rates. This approach lends itself well to model the spread of infectious diseases as well as threats in IT networks, pharmacokinetics and the spread of disruptions on infrastructure networks.

Strategies to minimize preventable morbidity and mortality resulting from pandemics like COVID-19.