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Viral Infection Simulation System (VISS)

The latest build of this program is pre-Alpha and should not be used for any production or research purposes.

VISS is an open-source simulation system for viral infections. It is a Rust-based system that uses a combination of probabilistic models to study the behavior of viruses their growth and decay over time and their impact on human populations.

VISS: Epidemiology Simulation in Rust

A fast, explainable, age-structured SEIRS ODE simulator with Erlang-stage compartments, designed to integrate with WorldPop population data and contact matrices.

Installation

cargo clean && cargo update && cargo build --release

Features

• Deterministic SEIRS with age structure and configurable Erlang stages (k_E, k_I)
• Contact-matrix-based force of infection (Prem et al.-style)
• Piecewise-constant time-varying transmission multiplier m(t)
• Simple RK4 integrator for speed and determinism
• Calibration helper: compute beta0 for a target R0 using power iteration (spectral radius)
• CSV loaders for population by age and contact matrix
• Example: single-region, multi-age simulation

Getting started

cargo run --release --bin single_region

Data inputs

• Population CSV: header row, columns: age_group,pop
• Contact matrix CSV: square matrix with header row/col optional (will try to parse numeric cells)

These can be replaced later with aggregated WorldPop outputs.

Roadmap

• Add stochastic CTMC (Gillespie)
• Add observation model (delayed NegBinon cases)
• Add multi-region coupling
• Add tests and benchmarking

License

GNU GPL v3

Diseases

VISS supports a variety of diseases that can be used to study the impact of different interventions on viral infections. These diseases include:

• HIV

SEIRS model (mathematical definition)

One of the core deterministic compartmental models used in VISS is an SEIRS model with vaccination and demographic turnover.

Let:

• S(t) be the susceptible population
• E(t) be the exposed (infected but not yet infectious) population
• I(t) be the infectious population
• R(t) be the recovered/immune population
• N(t) = S(t) + E(t) + I(t) + R(t) be the total population

The model is defined by the ODE system:

dS/dt = b (1 − ν) N − β (S I / N) − dS + αR − ρS
dE/dt = β (S I / N) − σE − dE
dI/dt = σE − γI − dI
dR/dt = b ν N + γI − dR − αR + ρS

Term definitions (typical units are per-day rates):

• b: per-capita birth rate (births occur at total rate bN)
• d: per-capita death rate (applied to all compartments)
• β: transmission rate parameter
• σ: latent progression rate (mean latent/incubation period is 1/σ)
• γ: recovery rate (mean infectious period is 1/γ)
• α: waning immunity rate (R → S)
• ν: fraction of newborns vaccinated at birth (births into R)
• ρ: vaccination rate applied to susceptibles (S → R)

The infection (incidence) term β (S I / N) corresponds to frequency-dependent transmission, i.e. the force of infection is λ(t) = β I/N and incidence is λS.

Summing the four equations gives dN/dt = (b − d) N. In particular, when b = d the total population remains constant.

Interventions Roadmap

In the future, VISS will support a variety of interventions that can be used to study the impact of different interventions on viral infections. These interventions include:

• Pre-exposure prophylaxis (PrEP)
• Post-exposure prophylaxis (PEP)
• Antiretroviral therapy (ART)
• Vaccination
• Voluntary Male Circumcision (VMMC)
• Condom-use