An autonomous compartmental model for accelerating epidemics.
Ontology highlight
ABSTRACT:
SUBMITTER: Budanur NB
PROVIDER: S-EPMC9292088 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Simultaneously controlling COVID-19 epidemics and limiting economic and societal impacts presents a difficult challenge, especially with limited public health budgets. Testing, contact tracing, and isolating/quarantining is a key strategy that has been used to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 and other pathogens. However, manual contact tracing is a time-consuming process and as case numbers increase a smaller fraction of cases' contacts can be traced, leading to additional virus spread. Delays between symptom onset and being tested (and receiving results), and a low fraction of symptomatic cases being tested and traced can also reduce the impact of contact tracing on transmission. We examined the relationship between increasing cases and delays and the pathogen reproductive number Rt, and the implications for infection dynamics using deterministic and stochastic compartmental models of SARS-CoV-2. We found that Rt increased sigmoidally with the number of cases due to decreasing contact tracing efficacy. This relationship results in accelerating epidemics because Rt initially increases, rather than declines, as infections increase. Shifting contact tracers from locations with high and low case burdens relative to capacity to locations with intermediate case burdens maximizes their impact in reducing Rt (but minimizing total infections may be more complicated). Contact tracing efficacy decreased sharply with increasing delays between symptom onset and tracing and with lower fraction of symptomatic infections being tested. Finally, testing and tracing reductions in Rt can sometimes greatly delay epidemics due to the highly heterogeneous transmission dynamics of SARS-CoV-2. These results demonstrate the importance of having an expandable or mobile team of contact tracers that can be used to control surges in cases. They also highlight the synergistic value of high capacity, easy access testing and rapid turn-around of testing results, and outreach efforts to encourage symptomatic cases to be tested immediately after symptom onset.
| S-EPMC8241027 | biostudies-literature
Project description:Accelerating materials research by integrating automation with artificial intelligence is increasingly recognized as a grand scientific challenge to discover and develop materials for emerging and future technologies. While the solid state materials science community has demonstrated a broad range of high throughput methods and effectively leveraged computational techniques to accelerate individual research tasks, revolutionary acceleration of materials discovery has yet to be fully realized. This perspective review presents a framework and ontology to outline a materials experiment lifecycle and visualize materials discovery workflows, providing a context for mapping the realized levels of automation and the next generation of autonomous loops in terms of scientific and automation complexity. Expanding autonomous loops to encompass larger portions of complex workflows will require integration of a range of experimental techniques as well as automation of expert decisions, including subtle reasoning about data quality, responses to unexpected data, and model design. Recent demonstrations of workflows that integrate multiple techniques and include autonomous loops, combined with emerging advancements in artificial intelligence and high throughput experimentation, signal the imminence of a revolution in materials discovery.
| S-EPMC7020936 | biostudies-literature
Project description:The anterior region of the Drosophila embryo is patterned by the concentration gradient of the homeodomain transcription factor bicoid (Bcd). The Bcd gradient was the first identified morphogen gradient and continues to be a subject of intense research at multiple levels, from the mechanisms of RNA localization in the oocyte to the evolution of the Bcd-mediated patterning events in multiple Drosophila species. Critical assessment of the mechanisms of the Bcd gradient formation requires biophysical models of the syncytial embryo. Most of the proposed models rely on reaction-diffusion equations, but their formulation and applicability at high nuclear densities is a nontrivial task. We propose a straightforward alternative in which the syncytial blastoderm is approximated by a periodic arrangement of well-mixed compartments: a single nucleus and an associated cytoplasmic region. We formulate a compartmental model, constrain its parameters by experimental data, and demonstrate that it provides an adequate description of the Bcd gradient dynamics.
| S-EPMC2919596 | biostudies-literature
Project description:A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations.
| S-EPMC8520832 | biostudies-literature
Project description:Frequently, we encounter the phenomenon of hysteresis in kinetic-dynamic modeling. The hysteresis loop in the concentration-effect curve suggests a time discrepancy caused by various pharmacokinetic and pharmacodynamic factors. To collapse the hysteresis loop and to simplify the concentration-effect relationship, several kinetic-dynamic modeling approaches including the effect compartment link model, turnover model (indirect response model), and tolerance/rebound model, have been used. The semicompartmental model is one method to describe the hysteresis of the pharmacokinetic-pharmacodynamic relationship. Furthermore, this semi-compartmental model differs from other models (full parametric approaches) as it does not require pharmacokinetic parameters to estimate pharmacodynamic parameters and ke0 . Therefore, we could employ a semi-compartmental approach in case it is difficult to apply the compartment model to pharmacokinetic data, as required for the pharmacodynamic analysis of inhalational anesthetics.
| S-EPMC7713860 | biostudies-literature
Project description:Antibodies are under development to treat a variety of cancers, such as lymphomas, colon, and breast cancer. A major limitation to greater efficacy for this class of drugs is poor distribution in vivo. Localization of antibodies occurs slowly, often in insufficient therapeutic amounts, and distributes heterogeneously throughout the tumor. While the microdistribution around individual vessels is important for many therapies, the total amount of antibody localized in the tumor is paramount for many applications such as imaging, determining the therapeutic index with antibody drug conjugates, and dosing in radioimmunotherapy. With imaging and pretargeted therapeutic strategies, the time course of uptake is critical in determining when to take an image or deliver a secondary reagent. We present here a simple mechanistic model of antibody uptake and retention that captures the major rates that determine the time course of antibody concentration within a tumor including dose, affinity, plasma clearance, target expression, internalization, permeability, and vascularization. Since many of the parameters are known or can be estimated in vitro, this model can approximate the time course of antibody concentration in tumors to aid in experimental design, data interpretation, and strategies to improve localization.
| S-EPMC3729444 | biostudies-literature
Project description:In the light-harvesting antenna of the Synechocystis PCC 6803 phycobilisome (PB), the core consists of three cylinders, each composed of four disks, whereas each of the six rods consists of up to three hexamers (Arteni et al., Biochim Biophys Acta 1787(4):272-279, 2009). The rods and core contain phycocyanin and allophycocyanin pigments, respectively. Together these pigments absorb light between 400 and 650 nm. Time-resolved difference absorption spectra from wild-type PB and rod mutants have been measured in different quenching and annihilation conditions. Based upon a global analysis of these data and of published time-resolved emission spectra, a functional compartmental model of the phycobilisome is proposed. The model describes all experiments with a common set of parameters. Three annihilation time constants are estimated, 3, 25, and 147 ps, which represent, respectively, intradisk, interdisk/intracylinder, and intercylinder annihilation. The species-associated difference absorption and emission spectra of two phycocyanin and two allophycocyanin pigments are consistently estimated, as well as all the excitation energy transfer rates. Thus, the wild-type PB containing 396 pigments can be described by a functional compartmental model of 22 compartments. When the interhexamer equilibration within a rod is not taken into account, this can be further simplified to ten compartments, which is the minimal model. In this model, the slowest excitation energy transfer rates are between the core cylinders (time constants 115-145 ps), and between the rods and the core (time constants 68-115 ps).
| S-EPMC5784004 | biostudies-literature
Project description:For decades, mathematical models of disease transmission have provided researchers and public health officials with critical insights into the progression, control, and prevention of disease spread. Of these models, one of the most fundamental is the SIR differential equation model. However, this ubiquitous model has one significant and rarely acknowledged shortcoming: it is unable to account for a disease's true infectious period distribution. As the misspecification of such a biological characteristic is known to significantly affect model behavior, there is a need to develop new modeling approaches that capture such information. Therefore, we illustrate an innovative take on compartmental models, derived from their general formulation as systems of nonlinear Volterra integral equations, to capture a broader range of infectious period distributions, yet maintain the desirable formulation as systems of differential equations. Our work illustrates a compartmental model that captures any Erlang distributed duration of infection with only 3 differential equations, instead of the typical inflated model sizes required by traditional differential equation compartmental models, and a compartmental model that captures any mean, standard deviation, skewness, and kurtosis of an infectious period distribution with 4 differential equations. The significance of our work is that it opens up a new class of easy-to-use compartmental models to predict disease outbreaks that do not require a complete overhaul of existing theory, and thus provides a starting point for multiple research avenues of investigation under the contexts of mathematics, public health, and evolutionary biology.
| S-EPMC8449186 | biostudies-literature
Project description:Compartmental model diagrams have been used for nearly a century to depict causal relationships in infectious disease epidemiology. Causal directed acyclic graphs (DAGs) have been used more broadly in epidemiology since the 1990s to guide analyses of a variety of public health problems. Using an example from chronic disease epidemiology, the effect of type 2 diabetes on dementia incidence, we illustrate how compartmental model diagrams can represent the same concepts as causal DAGs, including causation, mediation, confounding, and collider bias. We show how to use compartmental model diagrams to explicitly depict interaction and feedback cycles. While DAGs imply a set of conditional independencies, they do not define conditional distributions parametrically. Compartmental model diagrams parametrically (or semiparametrically) describe state changes based on known biological processes or mechanisms. Compartmental model diagrams are part of a long-term tradition of causal thinking in epidemiology and can parametrically express the same concepts as DAGs, as well as explicitly depict feedback cycles and interactions. As causal inference efforts in epidemiology increasingly draw on simulations and quantitative sensitivity analyses, compartmental model diagrams may be of use to a wider audience. Recognizing simple links between these two common approaches to representing causal processes may facilitate communication between researchers from different traditions.
| S-EPMC6294476 | biostudies-other
Project description:The glymphatic system is the subject of numerous pieces of research in biology. Mathematical modelling plays a considerable role in this field since it can indicate the possible physical effects of this system and validate the biologists' hypotheses. The available mathematical models that describe the system at the scale of the brain (i.e. the macroscopic scale) are often solely based on the diffusion equation and do not consider the fine structures formed by the perivascular spaces. We therefore propose a mathematical model representing the time and space evolution of a mixture flowing through multiple compartments of the brain. We adopt a macroscopic point of view in which the compartments are all present at any point in space. The equations system is composed of two coupled equations for each compartment: One equation for the pressure of a fluid and one for the mass concentration of a solute. The fluid and solute can move from one compartment to another according to certain membrane conditions modelled by transfer functions. We propose to apply this new modelling framework to the clearance of 14C-inulin from the rat brain.
| S-EPMC9990927 | biostudies-literature