Zombie Outbreak Simulator: Can Humanity Survive?

Start with a human population, a zombie population, and the number of days you want to simulate. Then decide how often humans fight, how successful fighting and escape attempts are, how quickly losses transform, and how much of the horde disappears through daily decay.

The simulator advances one day at a time and updates both populations after every round of encounters. It stops early when humans, zombies, or both fall below the practical extinction threshold.

Use a quick preset for an immediate scenario, then change one parameter at a time to see which assumptions alter the outcome.

Slow Burn starts with a small, slow-moving horde, strong human escapes, and noticeable decay. Movie Night is the balanced default. Sprinters creates frequent encounters, difficult escapes, rapid transformation, and little decay. Last Stand gives humans a much more aggressive and effective fighting response.

Presets are starting points, not predictions. Loading one simply fills the visible model inputs, and every value can still be edited.

Because the simulation is deterministic, rerunning a preset produces the same timeline. That makes it easier to compare a changed strategy against the original without random luck hiding the effect.

The number of daily attacks is the smaller of the remaining human population and the zombie population multiplied by attacks per zombie. This prevents the model from attacking more humans than still exist.

The chosen fight share divides encounters into fights and escape attempts. Failed fights and failed escapes become human losses. Successful fights remove zombies.

A selected share of human losses transforms into new zombies. Daily zombie decay is then applied to the surviving and newly transformed horde.

Choose to fight controls the percentage of encounters handled as fights. The rest automatically become escape attempts.

A high fight share can remove zombies faster when fight success is strong, but unsuccessful fights also increase human losses. Fleeing can protect humans when escape success is high, but it does not directly reduce the horde.

There is no universal best setting independent of the other inputs. A strategy that contains slow, decaying zombies may fail against a fast-growing horde with a high transformation chance.

Attacks per zombie per day controls average encounter pressure. A value below one means each zombie creates less than one encounter per day on average; a value above one allows the same zombie population to create multiple encounters.

Losses transformed determines how many human losses join the horde. A lower value removes humans without creating as many new zombies, while a high value turns casualties into additional attackers.

Zombie decay removes a percentage of the post-combat horde every day. It can represent fictional decomposition, starvation, environmental losses, accidents, or any other non-combat removal mechanism in the scenario.

Contained means the expected zombie population falls below 0.5 and is treated as zero. Overrun means the human population reaches that threshold first. Mutual collapse means both cross it on the same day. Ongoing means both populations remain when the selected time limit ends.

Peak horde reports the largest zombie population and the day it occurs. The after-action report also identifies when humanity falls below half strength, when zombies first equal or outnumber humans, and when the horde first becomes smaller than it was the previous day.

A first decline does not guarantee final containment. A horde can shrink temporarily and later grow again if transformations exceed eliminations and decay.

The green line follows the expected human population and the red line follows the expected zombie population. The red marker identifies the peak horde.

A rapid upward red curve indicates that transformations are replacing zombies faster than fighting and decay remove them. A sustained downward curve indicates that removals are winning under the selected assumptions.

Long simulations are sampled for drawing performance, but the model still calculates every daily update and uses the full timeline for outcomes, peaks, totals, and milestones.

The tool uses deterministic averages. If one day produces an expected 2.4 human losses, the model carries that fractional value forward rather than randomly choosing two or three people.

Fractions make repeated strategy comparisons stable and show the average direction of the rules, but they should not be interpreted as literal partial people.

When an expected population falls below 0.5, the simulator rounds it to practical extinction and stops when that determines the outcome.

Many games use random rolls for every fight, escape, and transformation. This simulator instead multiplies encounter counts by the selected success rates.

Deterministic results make sensitivity testing easier: if fight success changes from 50% to 55%, any change in the timeline comes from that setting rather than a different sequence of lucky rolls.

The trade-off is that the tool does not show the range of outcomes that a stochastic or agent-based simulation could produce.

Traditional compartment models may divide a population into susceptible, infected, recovered, removed, quarantined, or treated groups and describe transitions with differential equations.

Agent-based and pedestrian models simulate individual movement, local contact, obstacles, density, and pursuit. Those systems can produce spatial effects that a two-population daily average cannot represent.

This tool uses a simpler custom encounter model designed for transparent interactive comparisons. It borrows the educational idea of studying fictional zombies with population mathematics, but it does not implement the equations of any cited paper.

Researchers and educators have used fictional zombie outbreaks to demonstrate population dynamics, differential equations, contagion, stability, crowd movement, and the consequences of model assumptions.

The famous When Zombies Attack! chapter explored susceptible, zombie, and removed populations with variants involving treatment, quarantine, latent infection, and repeated attacks.

Later work has explored conditions under which human survival can become stable, evolutionary invasion, military assistance, pedestrian avoidance, and building evacuation. These studies are mathematical thought experiments, not evidence that fictional undead outbreaks are real.

A model answers only the fictional question created by its rules. Increasing fight success here does not model ammunition, fatigue, training, coordination, injuries, equipment, terrain, or the possibility that one encounter involves a group.

Likewise, decay is a simple daily percentage rather than a biological process, and transformation happens on the same daily step without an incubation delay.

Use the result to explore cause and effect inside the simulator—not to make claims about real epidemics, disasters, public safety, or emergency response.

Load a preset or create a baseline scenario, note the final outcome and peak horde, then change one variable while leaving the others unchanged.

Compare not only who wins, but how quickly the decision occurs, how many humans remain, whether the horde ever outnumbers humanity, and the total transformations and eliminations.

Changing several assumptions at once can produce a more entertaining scenario, but it makes it harder to identify which parameter caused the difference.