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Related Concept Videos

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

239
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

121
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

83
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Causality in Epidemiology01:21

Causality in Epidemiology

726
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
726
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

176
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
176
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

105
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Related Experiment Video

Updated: Sep 2, 2025

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
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A Multiscale Model of COVID-19 Dynamics.

Xueying Wang1, Sunpeng Wang2, Jin Wang3

  • 1Department of Mathematics and Statistics, Washington State University, Pullman, WA, 99163, USA. xueying@math.wsu.edu.

Bulletin of Mathematical Biology
|August 9, 2022
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Summary

A new multiscale model reveals the environment

Keywords:
Between-host dynamicsCOVID-19Multiscale modelWithin-host dynamics

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Infectious Disease Dynamics

Background:

  • COVID-19 (caused by SARS-CoV-2) presents global health challenges.
  • Understanding the interplay between pathogen, host, and environment is crucial.
  • Existing models often lack a multiscale approach integrating within-host and between-host dynamics.

Purpose of the Study:

  • To develop and analyze a multiscale model for COVID-19 dynamics.
  • To investigate the influence of environmental transmission routes.
  • To evaluate the impact of antiviral treatments and public health interventions.

Main Methods:

  • Developed a multiscale mathematical model coupling within-host and between-host COVID-19 dynamics.
  • Incorporated multiple transmission routes (human-to-human and environment-to-human).
  • Analyzed model dynamics across different scales (individual and population levels) and fitted to virological and epidemiological data.

Main Results:

  • The coupled model exhibits complex dynamics, including bifurcations, highlighting the interaction between viral infection and disease spread.
  • Environmental transmission plays a significant role in COVID-19.
  • Antiviral treatments can delay outbreaks but are insufficient alone.

Conclusions:

  • Comprehensive interventions targeting both airborne and environmental transmission are essential for controlling COVID-19.
  • Environmental disinfection and personal hygiene are critical alongside measures like social distancing and mask-wearing.
  • The developed multiscale framework can be applied to other infectious diseases with environmental reservoirs.