Zombie Invasion Calculator
Set your starting human and zombie populations, configure how fast and deadly the horde is, and watch the simulation play out day by day. The calculator uses a population-dynamics model inspired by predator-prey equations to show you whether humanity survives, when the outbreak peaks, and how many days either side has left. Adjust any parameter and the results update instantly.
How the simulation works
The zombie invasion calculator runs a discrete-time population model inspired by predator-prey dynamics and classic epidemic (SIR-style) equations. Each simulated day, every zombie encounters humans at the rate you set, and those encounters split into flee attempts or fights based on the human defense stance. Failed flees and lost fights produce human casualties, a configurable fraction of which rise as new zombies (the transformation rate). Meanwhile, humans who win fights destroy zombies permanently, reduced by the resurrection rate. If starvation is enabled, idle zombies that did not feed degrade by a fixed daily fraction. This continues until one side collapses below 0.01% of its starting population, or the 365-day ceiling is reached.
Key parameters and their effects
Attack frequency has the largest single impact on the timeline: doubling it roughly halves how many days humanity has left. Transformation probability determines how fast the horde snowballs from casualties; above roughly 80% the zombie side gains population faster than humans can kill them. Human fighting skill matters most in scenarios where humans choose to fight rather than flee: a jump from average (5/10) to elite (10/10) can swing an otherwise losing battle into a decisive victory. Zombie resurrection rate is a multiplier on how efficiently humans can reduce the horde: at 50% resurrection, humans must win two fights to permanently kill one zombie. Finally, zombie speed punishes a flee-heavy strategy and forces a re-evaluation of the defense stance.
Outbreak archetypes from popular fiction
Classic shamblers (Night of the Living Dead) are slow but nearly always convert their kills, making them dangerous over a long siege. Rage-infected (28 Days Later) are fast and highly contagious but fragile when outmaneuvered. The Walking Dead horde is the middle ground used as the default: moderate threat individually but overwhelming in large numbers. Plug these archetypes into the reference table above to reproduce the feel of your favorite zombie story, or mix parameters to invent a new strain.
What the chart tells you
The population chart plots human (blue) and zombie (red) counts for every simulated day. The crossover point, if one appears, marks when zombies outnumber humans. A rapid early spike in zombies followed by a crash indicates that the horde burned through its food supply, especially if starvation is on. A slow, sustained rise in zombies with a mirror decline in humans is the most dangerous pattern: it means the horde is feeding efficiently and converting enough victims to outpace human kills. The day number of peak zombie population shown in the results tells you when the outbreak is at its worst.
Outbreak parameter guide
| Zombie type | Speed | Attack frequency | Transform % | Resurrect % |
|---|---|---|---|---|
| Classic shambler (Night of the Living Dead) | Slow | 0.2 | 80 | 5 |
| Rage infected (28 Days Later) | Fast | 1.5 | 95 | 0 |
| Modern horde (The Walking Dead) | Slow | 0.5 | 70 | 10 |
| Smart zombie (World War Z) | Moderate | 1.0 | 85 | 0 |
| Videogame horde (Resident Evil) | Moderate | 0.8 | 60 | 15 |
Use these reference values to calibrate your simulation to real zombie-fiction archetypes.
Frequently asked questions
What mathematical model does this calculator use?
It uses a discrete-time predator-prey model loosely analogous to SIR (Susceptible-Infected-Recovered) epidemic equations, but adapted for zombie fiction. Each day the population of each side updates based on encounters, fight outcomes, transformation probability, and starvation. The model is deterministic given fixed parameters, but the reference ODE model (used in academic zombie-outbreak papers) would produce a similar qualitative outcome.
Why does a small change in transformation probability have such a big effect?
Because conversion is exponential. Each human death that produces a new zombie increases the number of attackers for the next day, which produces more deaths and more conversions. A 70% transformation rate is already powerful; 90% typically means the horde doubles in a few days even when humans fight well. Small reductions, say from 80% to 60%, can shift a loss into a stalemate because of this compounding effect.
What does zombie starvation represent?
In many zombie-fiction universes, zombies are animated by a pathogen or energy source that degrades over time without feeding. Starvation in this model removes a fraction of the idle zombie population each day (those that did not make kills). Enabling "yes, quickly" (8%/day) can dramatically reduce long-running hordes, especially if humans adopt a defensive strategy and deny the zombies easy targets.
Can humans ever win against a large starting horde?
Yes, especially with elite fighting skills, a fight-oriented stance, low transformation probability, fast zombie starvation, and a low resurrection rate. The model shows that a 10,000-human population can defeat an initial horde of 500 fast zombies if humans fight well enough and conversions are low. Flee-heavy strategies are almost always losing plays unless zombies are shamblers that starve quickly.
What happens when the simulation reaches 365 days?
If neither population collapses within a year the simulation reports a stalemate. This can happen with highly conservative settings on both sides, for example slow shamblers that starve quickly vs. humans who mostly flee but do not engage enough to eliminate the horde. In a real extended scenario, resource depletion and disease would shift the balance, but the model caps at one year to keep results readable.
Is this based on any real academic research?
The basic structure mirrors a 2009 paper by Munz, Hudea, Imad, and Smith in the journal Infectious Disease Modelling Research Progress, which applied SIR compartmental equations to a hypothetical zombie outbreak and concluded humans lose almost universally unless they strike early and decisively. The qualitative findings of that paper, that fast response and high kill rates are essential, are reflected in this calculator.