Disease Dynamics
Disease Dynamics - Related Videos
10:38
Microtubule Plus-End Dynamics Visualization in Huntington's Disease Model based on Human Primary Skin Fibroblasts
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Cited by 2 •
2022
This protocol is dedicated to the microtubule plus-end visualization by EB3 protein transfection to study their dynamic properties in primary cell culture. The protocol was implemented on human primary skin fibroblasts obtained from Huntington's disease patients.
10:28
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Cited by 24 •
2019
This protocol offers a digitization of portions of traditional clinical tasks commonly used to measure cognition and motor control in Parkinson’s disease. Clinical tasks are digitized while biophysical rhythms are co-registered from different functional levels of the nervous systems, ranging from voluntary, spontaneous, automatic to autonomic.
06:37
Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
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Cited by 13 •
2022
Differential dynamic microscopy (DDM) combines features of dynamic light scattering and microscopy. Here, the process of using DDM to characterize reconstituted cytoskeleton networks by quantifying the subdiffusive and caged dynamics of particles in vimentin networks and the ballistic motion of active myosin-driven actin-microtubule composites is presented.
12:03
Dynamics of Structures
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2023
Source: Roberto Leon, Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA It is rare nowadays that a whole year goes by without a major earthquake event wreaking havoc somewhere around the world. In some cases, like the 2005 Banda Ache earthquake in Indonesia, the damage involved large geographic areas and casualties in the six figures. In general, the number and intensity of earthquakes is not increasing, however, the vulnerability of the built environment is...
02:20
Dynamic Equilibrium
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2020
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
FAQs
Compartmental models organize a population into groups defined by disease-related states, then describe movement between those groups over time. Statistical parameters can represent transmission, recovery, immunity, and mortality, while observed case data help estimate their values. This structure allows researchers to examine how changes in one process affect overall disease patterns.
Probability distributions represent the variation inherent in observed disease data and estimated epidemiological rates. Rather than treating each estimate as exact, statistical analysis quantifies uncertainty around parameters and predicted patterns. This helps researchers judge whether apparent changes reflect meaningful evidence or could plausibly result from random variation in the available observations.
Time-series analysis examines incidence measurements across successive points in time, making it possible to identify changes in disease patterns that may not be evident from a single observation. Statistical comparisons help distinguish persistent or meaningful signals from short-term random variation, supporting surveillance and interpretation of changing population-level conditions.
A typical analysis begins with observed case data and a clearly specified statistical representation of transmission, recovery, immunity, mortality, or incidence. Researchers then estimate relevant rates or model parameters, quantify uncertainty, and compare the resulting patterns with the observations. The analysis can subsequently support evaluation of interventions or forecasting.
Regression analysis evaluates relationships between disease outcomes and potential risk factors within observed data. By estimating how patterns in incidence or related outcomes vary across those factors, researchers can identify associations that merit epidemiological attention. This complements transmission modeling by focusing on measurable differences in risk rather than only population-wide disease change.
Statistical disease models help surveillance systems interpret incoming case patterns, assess whether observed changes are likely meaningful, and estimate relevant transmission or outcome rates. Researchers can also use these analyses to evaluate interventions and support forecasting. The resulting evidence informs public-health decisions by connecting uncertain observations with quantified epidemiological patterns.