metawards.Population

class metawards.Population(initial: int = 0, susceptibles: int = 0, latent: int = 0, total: int = 0, totals: Optional[Dict[str, int]] = None, other_totals: Optional[Dict[str, int]] = None, recovereds: int = 0, n_inf_wards: int = 0, scale_uv: float = 1.0, day: int = 0, date: Optional[datetime.date] = None)

This class holds information about the progress of the disease through the population

__init__(initial: int = 0, susceptibles: int = 0, latent: int = 0, total: int = 0, totals: Optional[Dict[str, int]] = None, other_totals: Optional[Dict[str, int]] = None, recovereds: int = 0, n_inf_wards: int = 0, scale_uv: float = 1.0, day: int = 0, date: Optional[datetime.date] = None) → None

Initialize self. See help(type(self)) for accurate signature.

Methods

__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(other)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__gt__(value, /)	Return self>value.
__init__([initial, susceptibles, latent, …])	Initialize self.
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(**kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
assert_sane()	Assert that this population is sane, i.e. the totals within this population and with the sub-populations all add up to the correct values.
has_equal_SEIR(other)	Return whether or not the SEIR values for this population equal ‘other’
headers()	Return a list of the headers that should be used to report data from this population.
increment_day([ndays])	Advance the day count by ‘ndays’ (default 1)
specialise(network)	Specialise this population for the passed Networks
summary([demographics])	Return a short summary string that is suitable to be printed out during a model run

Attributes

__annotations__
__dataclass_fields__
__dataclass_params__
__dict__
__doc__
__hash__
__module__
__weakref__	list of weak references to the object (if defined)
date	The date in the outbreak of this record
day	The day in the outbreak of this record (e.g.
infecteds	The number who are infected across all wards
initial	The initial population loaded into the model
latent	The number of latent infections (E)
n_inf_wards	The number infected in all wards (IW)
other_totals	The total number of individuals in other non-infected states (X)
others	Return the number who are in the ‘other’ state, i.e. not classed as susceptible, latent, infected or recovered/removed.
population	The total population in all wards
recovereds	The total number who are removed from the outbreak, either because they have recovered, or are otherwise no longer able to be infected (R)
scale_uv	The scale_uv parameter that can be used to affect the foi calculation.
subpops	The populations in each of the multi-demographic subnetworks
susceptibles	The number of members who could be infected (S)
total	The total number of infections (I)
totals	The total number of infections in other infected states (X)

__str__()

Return str(self).

assert_sane()

Assert that this population is sane, i.e. the totals within this population and with the sub-populations all add up to the correct values

date: datetime.date = None

The date in the outbreak of this record

day: int = 0

The day in the outbreak of this record (e.g. day 0, day 10 etc.)

has_equal_SEIR(other)

Return whether or not the SEIR values for this population equal ‘other’

headers()

Return a list of the headers that should be used to report data from this population. This returns a list of headers, e.g. [“S”, “E”, “I”, “R”]

increment_day(ndays: int = 1) → None

Advance the day count by ‘ndays’ (default 1)

property infecteds

The number who are infected across all wards

initial: int = 0

The initial population loaded into the model

latent: int = 0

The number of latent infections (E)

n_inf_wards: int = 0

The number infected in all wards (IW)

other_totals: Dict[str, int] = None

The total number of individuals in other non-infected states (X)

property others

Return the number who are in the ‘other’ state, i.e. not classed as susceptible, latent, infected or recovered/removed. This is the sum of states that are set as “is_infected=False” in the disease model, e.g. V (vaccinated)

property population

The total population in all wards

recovereds: int = 0

The total number who are removed from the outbreak, either because they have recovered, or are otherwise no longer able to be infected (R)

scale_uv: float = 1.0

The scale_uv parameter that can be used to affect the foi calculation. A value of 1.0 means do nothing

specialise(network)

Specialise this population for the passed Networks

subpops = None

The populations in each of the multi-demographic subnetworks

summary(demographics=None)

Return a short summary string that is suitable to be printed out during a model run

Returns

summary – The short summary string

Return type

str

susceptibles: int = 0

The number of members who could be infected (S)

total: int = 0

The total number of infections (I)

totals: Dict[str, int] = None

The total number of infections in other infected states (X)