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$R_0=1$ is the threshold separating outbreak decline from growth. If $R_0 < 1$, each case infects fewer than one other person on average, causing transmission chains to break and the outbreak to fade.

Conditions: The video states the growth side only until immunity builds up.; The example value shown is $R_0=2.5$.

$R_0=1$ is the threshold separating outbreak decline from growth. If $R_0 < 1$, each case infects fewer than one other person on average, causing transmission chains to break and the outbreak to fade.

Conditions: The video states the growth side only until immunity builds up.; The example value shown is $R_0=2.5$.

$R_0$ should be used early in an outbreak when the whole population is susceptible and when a growth rate and serial interval are available. Once some immunity builds up in the population, one should switch to using the effective reproduction number instead.

Conditions: Use $R_0$ early in an outbreak with a fully susceptible population.; Use it when a growth rate and serial interval are available.; Switch to effective $R$ once immunity builds.

$R_0$ should be used early in an outbreak when the whole population is susceptible and when a growth rate and serial interval are available. Once some immunity builds up in the population, one should switch to using the effective reproduction number instead.

Conditions: Use $R_0$ early in an outbreak with a fully susceptible population.; Use it when a growth rate and serial interval are available.; Switch to effective $R$ once immunity builds.

Under the simple SIR model, $R_0$ is approximated using the formula $R_0 = 1 + r \cdot T_s$, where $r$ is the growth rate and $T_s$ is the serial interval. This approximation assumes exponentially distributed generation times.

Conditions: Stated as an approximation under the simple SIR model.; The on-screen note says it assumes exponentially distributed generation times.

Under the simple SIR model, $R_0$ is approximated using the formula $R_0 = 1 + r \cdot T_s$, where $r$ is the growth rate and $T_s$ is the serial interval. This approximation assumes exponentially distributed generation times.

Conditions: Stated as an approximation under the simple SIR model.; The on-screen note says it assumes exponentially distributed generation times.

When $R_0 > 1$, each generation of infections is larger than the previous one because every case produces more than one new infection on average. This repeated multiplication by a factor greater than one results in exponential growth of the outbreak.

Conditions: Assumes the same average transmission per case across generations.; The worked example uses $R_0=2.5$.

When $R_0 > 1$, each generation of infections is larger than the previous one because every case produces more than one new infection on average. This repeated multiplication by a factor greater than one results in exponential growth of the outbreak.

Conditions: Assumes the same average transmission per case across generations.; The worked example uses $R_0=2.5$.