A tool to estimate Instantaneous Reproduction Number(Rt)

cal_r() provide real-time estimation of time-varying distribution of Rt and infected numbers from a range of epidemic observations (e.g., number of onsets and confirmed cases).

Usage

dart = DARt(GT,D_s,Filename)

cal_r()

Arguments

GT: generation time distribution; GT[0] represents the probability that the secondary infection case occurs 1 day after the primary infection.

D_s: delay time distribution; D_s[0] represents the probability that an individual infected is observed after 1 day.

Filename: input file; this file should contain date and corresponding observations, the date should be sorted from the oldest to the latest. See 'Example_input.csv'.

References:

This tool is described in the following paper:'Revealing the Transmission Dynamics of COVID-19: A Bayesian Framework for R_t Estimation'

Examples

::

GT = [0, 0, 0.165720874545241, 0.226350757019051, 0.245007574714227, 0.213515210247327,
      0.149405583474155]  # 1:7; non-zero index 3:7
D_s = np.array([0, 0, 0, 0, 0, 0, 0.0996906, 0.1130266, 0.1143032, 0.1069238, 0.0937167999999999])
dart = DARt(GT=GT, D_s=D_s, filename='Example_input.csv')
dart.cal_r()