Related Experiment Video

Updated: Sep 20, 2025

An In Vitro Model for Measuring Immune Responses to Malaria in the Context of HIV Co-infection
08:14

An In Vitro Model for Measuring Immune Responses to Malaria in the Context of HIV Co-infection

Published on: October 6, 2015

10.4K

HIV and COVID-19 co-infection: A mathematical model and optimal control

N Ringa1,2, M L Diagne3, H Rwezaura4

  • 1Data and Analytic Services, British Columbia Centre for Disease Control, 655 W 12th Ave, Vancouver, BC, Canada V5Z 4R4.

Informatics in Medicine Unlocked
|June 6, 2022
PubMed

Insights

This study models COVID-19 and HIV/AIDS co-infection, finding that prevention strategies for both diseases significantly reduce new co-infection cases. HIV prevention and COVID-19 treatment are key to managing the dual epidemic burden.

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • Investigating the epidemiologic synergy between COVID-19 and HIV/AIDS is crucial for understanding disease dynamics.
  • Co-infections pose significant public health challenges, necessitating integrated modeling approaches.

Purpose of the Study:

  • To develop and analyze a mathematical model assessing the impact of COVID-19 on HIV dynamics and vice-versa.
  • To evaluate the effectiveness of various intervention strategies against COVID-19 and HIV co-infections.

Main Methods:

  • Analysis of sub-models for HIV-only and COVID-19-only dynamics, including computation of basic reproduction numbers.
  • Local and global asymptotic stability analysis of disease-free and endemic equilibria.
  • Model fitting to real-world COVID-19 data from South Africa using MATLAB's fmincon function.

Main Results:

  • HIV prevention measures substantially decrease the burden of COVID-19 co-infections.
  • Effective COVID-19 treatment reduces co-infections with opportunistic diseases like HIV/AIDS.
  • COVID-19 and HIV prevention strategies individually averted approximately 10,500 new co-infection cases.

Conclusions:

  • Integrated prevention and treatment strategies are vital for managing co-infections.
  • Mathematical modeling provides valuable insights into disease synergy and intervention impact.
  • Targeted interventions for either COVID-19 or HIV can yield significant reductions in co-infection rates.

Keywords:
Basic reproduction numberCOVID-19Co-infectionHIVOptimal control

More Related Videos

Humanized NOD/SCID/IL2rγnull (hu-NSG) Mouse Model for HIV Replication and Latency Studies07:10

Humanized NOD/SCID/IL2rγnull (hu-NSG) Mouse Model for HIV Replication and Latency Studies

Published on: January 7, 2019

15.8K
Oral Combinational Antiretroviral Treatment in HIV-1 Infected Humanized Mice06:07

Oral Combinational Antiretroviral Treatment in HIV-1 Infected Humanized Mice

Published on: October 6, 2022

2.5K

Related Concept Videos

Causality in Epidemiology01:21

Causality in Epidemiology

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...
929
Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
2.1K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
561
Retrovirus Life Cycles01:10

Retrovirus Life Cycles

Retroviruses have a single-stranded RNA genome that undergoes a special form of replication. Once the retrovirus has entered the host cell, an enzyme called reverse transcriptase synthesizes double-stranded DNA from the retroviral RNA genome. This DNA copy of the genome is then integrated into the host’s genome inside the nucleus via an enzyme called integrase. Consequently, the retroviral genome is transcribed into RNA whenever the host’s genome is transcribed, allowing the...
47.0K
Fundamental Mathematical Principles in Pharmacokinetics: Mathematical Expressions and Units01:19

Fundamental Mathematical Principles in Pharmacokinetics: Mathematical Expressions and Units

Mathematical principles play a crucial role in pharmacokinetics, providing a framework for understanding and quantifying drug distribution and elimination dynamics in the body. By utilizing mathematical expressions and units, pharmacologists can accurately characterize the behavior of drugs, optimize dosing regimens, and predict therapeutic outcomes.
One significant application of mathematics in pharmacokinetics is the characterization of drug distribution through the volume of distribution...
981
Controls in Experiments01:13

Controls in Experiments

When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
12.5K