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For $\dot{i}=\beta i(1-i)-\gamma i$, a positive endemic equilibrium requires $R_0=\beta /\gamma >1$ and gives susceptible fraction $s_{\ast }=\gamma /\beta$. Below threshold the disease-free susceptible fraction is one. The reciprocal relation is a property of this homogeneous scalar SIS model, not a universal identity for heterogeneous populations.

Let $i=I/N$ be the infectious proportion in a closed population. With homogeneous frequency-dependent transmission rate $\beta$ and recovery rate $\gamma >0$, the SIS model isThe basic reproduction number is the expected number of secondary infections produced by a typical newly infected individual in an otherwise susceptible population under the specified contact conditions. Here the infectious duration has mean $1/\gamma$, so $R_0=\beta /\gamma$.

For $R_0>1$ the attracting endemic equilibrium has $i_{\ast }=1-\gamma /\beta$. The homogeneous SIS susceptible fraction is thereforeFor $R_0\le 1$ the physical equilibrium is disease-free, $i_{\ast }=0$, $s_{\ast }=1$. At equality approach to zero is algebraic. Thus $s_{\ast }=1/R_0$ is an endemic relation, not a formula demanding more than one susceptible person per person below the invasion threshold.

For $rN>a$ and $p>0$, a nonzero infected equilibrium requires $S_{\ast }=a/r$. Using $a{I}_{\ast }=p{R}_{\ast }$ and population conservation givesEliminate $R=N-S-I$ before assessing stability, since perturbations must preserve the total population. The resulting Jacobian matrix at this endemic equilibrium isIts trace is negative and its determinant positive, so both eigenvalues have negative real part: the endemic equilibrium is locally asymptotically stable on the fixed-population plane. The full three-variable system has a neutral direction corresponding to changing the conserved population.

Writing $\kappa =rN-a>0$, as $p\to 0$ the trace is $-prN/a+O(p^2)$ and the determinant is $p\kappa$. Hence the discriminant $(\mathrm{tr}J{)}^2-4\det J=-4p\kappa +O(p^2)<0$. As $p\to \infty$, the trace is $-p-\kappa +O(p^{-1})$ and the determinant is again $p\kappa$, giving a positive discriminant $p^2+O(p)$. Both quantities are $O(p)$ in both limits, but the eigenvalues are complex for sufficiently small $p$ and real for sufficiently large $p$.

At an endemic equilibrium, $I_{\ast }>0$, so $\beta S_{\ast }=\mu +\nu$. Substitution into the susceptible equation givesThese values are physically positive exactly when $R_0>1$. The Jacobian matrix at this equilibrium isBoth eigenvalues have negative real parts, so the endemic equilibrium is locally asymptotically stable. Its decay exponents areThe regime $\nu \gg \mu$ means recovery is rapid compared with demographic replacement. For fixed $R_0>1$ away from the threshold, the exponents form a complex conjugate pair with frequency approximately $\sqrt{\mu \nu (R_0-1)}$. HenceStrictly, oscillations require $\mu (\mu +\nu )(R_0-1)>{\mu }^2{R}_0^2/4$; $\nu \gg \mu$ alone does not ensure this uniformly for every $R_0$ arbitrarily close to one or arbitrarily large.

Vaccinating a fraction $v$ of newborns replaces the susceptible input by $\mu N(1-v)$. The effective basic reproduction number is $R_v=(1-v)R_0$, and the same calculation replaces $R_0$ by $R_v$. Provided $R_v>1$ and the oscillatory approximation remains applicable, vaccination lengthens the oscillation period, because $R_v-1$ is smaller. Extremely near eradication the exponents become real and there is no oscillation period to extrapolate indefinitely.

The fixed-point conditions areOn $I=0$ they give $(S,I)=(0,0)$ and the disease-free equilibrium $(1,0)$. An interior fixed point requires $I=(\beta -1)S$, so it exists only for $\beta >1$; substitution in the first equation gives the endemic equilibrium

The Jacobian matrix isAt $(1,0)$ its eigenvalues are $-1$ and $\beta -1$. Hence $(1,0)$ is asymptotically stable for $\beta <1$ and a saddle point for $\beta >1$. At the endemic equilibrium, the equivalent $(N,\theta )$ coordinates give eigenvalues $-1$ and $1-\beta$, so it is a stable node whenever it exists. The origin is unstable because every solution with $N(0)>0$ has $N(t)\to 1$.

For the phase portrait, the line $I=0$ is invariant and points toward $(1,0)$. Alsoso trajectories move toward the line $S+I=1$. If $\beta <1$, then $\dot{I}<0$ throughout the interior and every nonzero solution approaches $(1,0)$. If $\beta >1$, the additional infective nullcline is the ray $I=(\beta -1)S$; $I$ increases below this ray and decreases above it, and every trajectory with $I(0)>0$ approaches $(1/\beta ,1-1/\beta )$. At $\beta =1$ the disease-free and endemic branches exchange stability in a transcritical bifurcation.

At an endemic equilibrium, $I^{\ast }>0$, so $0=\dot{I}=I^{\ast }(\beta S^{\ast }-\gamma )$ forcesThe equation $\dot{R}=0$ gives $R^{\ast }=\gamma I^{\ast }/\alpha$. The conservation law $S^{\ast }+I^{\ast }+R^{\ast }=N$ therefore yieldsA positive endemic state exists exactly when $\beta N>\gamma$, equivalently when the basic reproduction number ${\mathcal{R}}_0=\beta N/\gamma$ exceeds one.

At an endemic equilibrium, $I^{\ast }>0$. The equation $\dot{I}=0$ then givesSince also $S^{\ast }>0$, the equation $\dot{S}=0$ givesThis infective population is positive precisely whenwhich is exactly the assumed instability condition for the disease-free equilibrium. Thus the endemic equilibrium of the susceptible-infective model with exponential birth exists.

At this equilibrium the relations above reduce the Jacobian matrix toHenceBy the trace-determinant stability criterion, both eigenvalues have negative real part. The linearization stability theorem therefore proves that the endemic equilibrium is locally asymptotically stable.

For a heterogeneous SIS model with common recovery rate and a positive endemic equilibrium $I_{\ast }$, force balance gives $\mathrm{diag}(s_{\ast })K{I}_{\ast }=I_{\ast }$. When the transmission matrix is irreducible and nonnegative, the positive equilibrium vector and the Perron–Frobenius theorem imply the displayed spectral radius identity. It replaces the homogeneous scalar relation $s_{\ast }=1/R_0$: different recipient types generally have different susceptible fractions.