Interactive stochastic spatial model

Virus Spillover Simulator

We propose a generic, pathogen-centred framework for zoonotic emergence, combining spatial layers for a reservoir host Kr(x), a spillover host Ks(x), and, when relevant, a covariate α(x). Host populations are characterized by pathogen phenotypic optima following Fisher's Geometric Model, while pathogen density P(x, θ) is structured by space and trait. Here we illustrate the framework with a toy example of Nipah virus spillover in Bangladesh, using Pteropus medius bats as reservoir hosts, humans as spillover hosts, and date-palm consumption as covariate.

This web-app was developed in the framework of the European BCOMING project.
Pathogen densityP(x, θ) = G(θ) (JD ⋆ Kr)(x)
Spillover intensityλ(x, θ) = (β0 + β1α(x)) Ks(x) P(x, θ)
Branching parameters (control transmission chain length)b(θ) = b0,   d(θ) = d0 + (θ - Os)2
Model time0.0
Spillover seeds0expected -
Active infected people0
People ever infected0
Active transmission chains0
Spatial view

Virus spillover and local transmission

Spillover intensity
--
locally supercritical locally subcritical
t = 0.0
Population trajectory

Infected people over time

actively infectedever infected
Current realization

Diagnostics

Poisson rate-seeds / model-time
Supercritical seed traits-b(θ) > d(θ)
Largest transmission chain-people ever infected