outbreakSim

Worked examples of simulating a SARS-CoV-2 outbreak in a real city and watching it through clinical and wastewater surveillance. This is a Modjadji project built on covasim4wastewater, the Andersen lab's wastewater extension of Covasim.

Examples

Each example is a script in src/simulation_examples/<place>/ that writes its figures next to itself:

python src/simulation_examples/sandiego/sandiego_simulation.py
python src/simulation_examples/lusakacity/lusakacity_simulation.py

Both follow the same steps: simulate an outbreak on a map, track viral shedding by sewershed, introduce variants with real genomes, simulate amplicon sequencing reads, compare clinical and wastewater surveillance, and draw the transmission phylogeny.

• San Diego (sandiego/): San Diego County, with clinical cases by ZIP code and wastewater by treatment-plant sewershed. Written as a step-by-step tutorial. make_sewershed_map.py and clip_worldpop.py build the sewershed and population-raster inputs.
• Lusaka (lusakacity/): Lusaka, Zambia, with the same workflow using administrative boundaries, sewershed catchments and a population raster.

To model another place, supply a boundary file, a sewershed file and a density raster to cv.Sim (boundary_shapefile, sewershed_file, density_raster).

Installation

Install covasim4wastewater following the instructions in its repository. The scripts need that build of covasim, plus geopandas, rasterio, matplotlib, pillow, baltic and bygul.

What covasim4wastewater adds

Covasim is an agent-based COVID-19 model. covasim4wastewater adds:

• spatial populations placed using a density raster and region boundaries
• sewershed assignment, so shedding can be summed per treatment plant
• per-agent viral shedding over time
• sequence evolution, with variants seeded from real genomes and transmissibility derived from their mutations
• clinical and wastewater samplers, and amplicon read simulation with Bygul
• an infection log for building transmission phylogenies

Definitions and calculations

• Rescaling:

• Parameters : pop_scale=1, scaled_pop= None, rescale=TRUE, rescale_threshold=0.05, rescale_factor=1.02
• Functions: rescale(self)
• Depends on: self.people.false('naive')= who can get infected?,self['pop_size'] how many agents?
• What it does: In Covasim, pop_scale is how many real people each simulated agent represents, giving you the total scaled_pop size. When rescale is turned on, the model watches for when too many agents catch the virus or get vaccinated—once that number hits the rescale_threshold, it uses the rescale_factor to prune non-susceptible agents and bump up the scale, keeping the simulation fast without running out of memory.

Basic disease transmission

• Parameters: beta=0.016,n_import=0 (there will be random number of infected cases drawn from a poisson distribution even if set to zero),beta_dist='neg_binomial', par1=1.0, par2=0.45, step=0.01,viral_dist=frac_time=0.3, load_ratio=2, high_cap=4, asymp_factor=1
• what is does: transmission is set and controlled using a combination of a baseline transmissibility parameter (beta), network layer weights (beta_layer), intrahost viral dynamics, and modifying factor