Positivity of compartments in epidemiological model

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In summary, it is important to follow proper methodology and analysis techniques, as outlined in the referenced paper, in order to determine the positivity of the compartments in this epidemiological model.
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kalish1
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Given the following dynamical model (system of ODEs):

\begin{array}
$
\frac{dA}{dt}=\Lambda-\mu A-\beta(C+D+E+F)\frac{A}{N}-\tau(B+D)\frac{A}{N} \\
\frac{dB}{dt}=\tau(B+D)\frac{A}{N}-\beta(C+D+E+F)\frac{B}{N}-(\mu+\mu_T)B, \\
\frac{dC}{dt}=\beta(C+D+E+F)\frac{A}{N}-\tau(B+D)\frac{C}{N}-(\mu+\mu_A)C, \\
\frac{dD}{dt}=\beta(C+D+E+F)\frac{B}{N}+\tau(B+D)\frac{C}{N}-(\mu+\mu_T+\mu_A+\lambda_T)D, \\
\frac{dE}{dt}=\lambda_TD-(\mu+\mu_A+\rho_1+\eta_1)E, \\
\frac{dF}{dt}=\rho_1E-(\mu+\mu_A+\rho_2+\eta_2)F, \\
\frac{dG}{dt}=\eta_1E-(\mu+\rho_1+\gamma)G, \\
\frac{dH}{dt}=\eta_2H+\rho_1G-(\mu+\rho_2+\frac{\gamma\rho_1}{\rho_1+\rho_2})H, \\
%,$
\end{array}

where $\mu, \beta, \tau, \mu_T, \mu_A, \gamma, \lambda_T, \eta_1, \eta_2, \rho_1, \rho_2 >0 \\$ and $A(0), B(0), C(0), D(0), E(0), F(0), G(0), H(0)>0,$

is it possible to show simply that $A(t),B(t),C(t),D(t),E(t),F(t),G(t),H(t) > 0?$

Or should I follow sections $II$ (Mathematical Formulation) and $III$ (Methodology) in this paper: http://www.iosrjournals.org/iosr-jm/papers/Vol5-issue5/G0554652.pdf

?

I have crossposted this question here: http://math.stackexchange.com/questions/1106786/positivity-of-compartments-in-epidemiological-model
 
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it is important to follow proper methodology in order to ensure the validity and accuracy of your findings. In this case, it would be best to follow the sections outlined in the paper you have referenced in order to analyze the dynamics of the system of ODEs and determine the positivity of the compartments.

Section II of the paper discusses the mathematical formulation of the model, which includes defining the variables, parameters, and initial conditions. This step is crucial in understanding the model and its behavior.

Section III outlines the methodology for analyzing the system of ODEs. This includes stability analysis, which is important in determining the behavior of the model over time. By analyzing the stability of the model, you can determine whether the compartments will remain positive or if they will eventually approach zero.

Additionally, it may be helpful to plot the solutions of the system of ODEs over time to visually see the behavior of the compartments. This can aid in determining the positivity of the compartments.

In conclusion, while it may be possible to show the positivity of the compartments in this model simply, it is important to follow proper methodology in order to ensure the accuracy of your results. Therefore, it would be best to follow the sections outlined in the paper and thoroughly analyze the system of ODEs in order to determine the positivity of the compartments.
 

FAQ: Positivity of compartments in epidemiological model

What is the significance of positivity of compartments in epidemiological models?

The positivity of compartments in epidemiological models refers to the number of individuals who have been exposed to and/or infected with a particular disease. This is an important factor in understanding the spread and control of a disease within a population.

How is the positivity of compartments calculated in epidemiological models?

The positivity of compartments is typically calculated by dividing the number of individuals in a specific compartment (such as exposed or infected) by the total population size. This provides a percentage or proportion of individuals who have been affected by the disease.

What is the impact of changing positivity of compartments on the model's predictions?

The positivity of compartments can have a significant impact on the predictions of an epidemiological model. A higher positivity may indicate a faster spread of the disease, while a lower positivity may suggest a slower spread. This information can help inform public health interventions and policies.

How does the positivity of compartments differ between different diseases?

The positivity of compartments can vary greatly between different diseases. Some diseases may have a higher positivity due to a shorter incubation period or higher transmission rate, while others may have a lower positivity due to longer incubation periods or lower transmission rates.

Can the positivity of compartments change over time in an epidemiological model?

Yes, the positivity of compartments can change over time in an epidemiological model. As individuals become immune to the disease or receive treatment, the number of individuals in certain compartments may decrease, resulting in a lower positivity. Additionally, public health interventions can also impact the positivity of compartments by reducing the spread of the disease.

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