sample.input

#runtime 1 single dimenstion
runtime 730

# tolerance of the runge kutta algorithm
error_tolerance 1e-8

# number of varants and number of classes
n_variants 2
n_classes 3

# Intrinsic reproduction rate, 1 by variant
Rzero 1.2 1.5

# Variant introduction date, 1 by variant
variant_introduction_date 0. 150.

# Total population
total_population 6.7e7

# Partition into classes, one value per class, sum of values has to be 1
population_class_distribution 0.239 0.554 0.207

# Contact affinities Classes, non symmetric, Matrix [Classes x Classes]
contacts_between_classes 5.29 5.52 0.34  2.3 10.06 1.05  0.5 3.68 1.91

# Initial Fraction in E [classes(rows) x variants(columns)]  
initial_intermediate_fraction 2.9850746e-05 2.9850746e-05  2.9850746e-05 2.9850746e-05  2.9850746e-05 2.9850746e-05

# Initial Fraction in I [classes(rows) x variants(columns)]  
initial_infected_fraction 1.4925373e-05 1.4925373e-05  1.4925373e-05 1.4925373e-05   1.4925373e-05 1.4925373e-05

# cross immunity [variants x variants]
epsilon 1. 1.  1. 1.  

# pre immune fraction [ classes ]
pre_immune_fraction 0.1 0.1 0.1

# infection affinity for classes [ variants(rows) x classes(columns) ]
sigma 0.39 0.82 0.77  0.39 0.82 0.77

# E -> I delta 1 value per variant
conversion_rate 0.34483 0.34483

# I -> R gamma 1 value per variant
recovery_rate 0.2 0.2

# R -> S eta [ classes(rows) x variants(columns) ]  
immune_drain 0.01 0.01  0.01 0.01  0.01 0.01


# vaccination
# vaccine logistic maximum [ variants(rows) x classes(columns) ] deactivate = 0
vaccine_logistic_maximum 0 0 0  0 0 0

# V -> s [ classes(rows) x variants(columns) ]  
vaccine_drain 0.005 0.005  0.005 0.005  0.005 0.005

# vaccine logistic growth [ variants(rows) x classes(columns) ]
vaccine_logistic_growth 0.0292 0.0292 0.0292  0.0292 0.0292 0.0292 

# vaccine logistic midpoint [ variants(rows) x classes(columns) ]
vaccine_logistic_midpoint 192.755 192.755 192.755  192.755 192.755 

# reporting rate
reporting_rate 0.5
# proportion of asymptotics [ classes(rows) x variants(columns) ]
asymptomatics_fraction 0.5 0.5  0.5 0.5  0.5 0.5

# vaccine efficiency [ classes(row) x variants(columns) ]
vaccine_efficiency 0.8 0.6  0.8 0.6  0.8 0.6