runDICEUserData: Main Driver for using 'DICE' for Fitting User Provided...

In predsci/DICE: DICE: Dynamics of Interacting Community Epidemics

Description

runDICEUserData reads in a user provided incidence file and uses that along with the user provided population size and value for the generation time, Tg, and if relevant the latent period sigma to fit, and if desired, forecast the incidence. Data cadence is arbitrary but at most can be monthly We support only S-I-R and S-E-I-R models for a single population with a fixed force of infection The incidence file must have two columns: dates and cases All other parameters are set by the code

Usage

runDICEUserData(filename = "data.csv", pop = 10000, epi_model = 1,
  Tg = 3, sigma = NULL)

Arguments

pop	- Integer population of the region for which incidence is provided
epi_model	- integer 1 (SIR) or 2 (SEIR), default is
Tg	- Numeric, generation time in days. Default is 3 days
sigma	- inverse of of the latent period in days. Needed only for an SEIR model. Default NULL
filenmae	String - input csv file name. Default 'data.csv'

Value

results A list with the input and entire output of the run.

Examples

Run an SEIR model using the incidence file and assuming a population of 1 million people.
The generation time and latent period are set to 2.6 days and 3 days respectively

output <- runDICEUserData(filename = 'data.csv', pop = 1e6, epi_model = 2, Tg = 2.6, sigma = 3.)

Run an SIR model using the incidence file and assuming a population of 10,000 people.
The generation time is set to 3 days. (No need to define a latent period.)
output <- runDICEUserData(filename = 'data.csv', pop = 1e5, epi_model = 1, Tg = 3.)

predsci/DICE documentation built on Aug. 9, 2019, 9:41 a.m.