Article: ‘EpiLPS: A Quick and Versatile Bayesian Tool for Estimating the Fluctuating Reproduction Number’

Article: ‘EpiLPS: A Quick and Versatile Bayesian Tool for Estimating the Fluctuating Reproduction Number’

Introduction:

Introducing the episim() routine, this article explores the simulation of epidemic data by specifying a serial interval distribution and choosing from available patterns for the true reproduction number curve. The simulated outbreak lasts for 40 days and generates a figure summarizing the incidence time series, a bar plot for the serial interval distribution, and the true underlying reproduction number curve. The article then discusses the use of the epilps() routine to smooth the epidemic curve and estimate the reproduction number using the LPSMAP and LPSMALA methods. It provides code examples and figures for visualizing the results and compares the estimated reproduction number with the true underlying curve.

Full Article: Article: ‘EpiLPS: A Quick and Versatile Bayesian Tool for Estimating the Fluctuating Reproduction Number’

Estimating Reproduction Number Using Epidemic Data: A Simulation Approach

In order to simulate a set of epidemic data, researchers can use the episim() routine. This involves specifying a serial interval distribution and selecting one of several available patterns for the true reproduction number curve. For this simulation, pattern number 5, which corresponds to a rather wiggly curve, was chosen. The simulated outbreak in this case lasts for a duration of 40 days.

To obtain an overview of the generated incidence time series, users can simply type a command.

Smoothing the Epidemic Curve and Estimating Reproduction Number

To smooth the epidemic curve and estimate the reproduction number, the epilps() routine is used. There are two methods available: LPSMAP (a fully sampling-free approach) and LPSMALA (a fully stochastic approach relying on a MCMC algorithm with Langevin dynamics). The chain length is set to 10000 and the burn-in is set to 4000.

The LPSMAP_fit object provides information about the inference method chosen, the total number of days, the mean reproduction number discarding the first 7 days, and the mean 95% CI of the reproduction number discarding the first 7 days. The LPSMALA_fit object includes similar information, as well as the MCMC acceptance rate.

Once the routines are executed, a figure can be generated to visualize the smoothed epidemic curve and the estimated reproduction number. Users have the option to customize the figure by specifying the theme and other parameters such as whether or not to show incidence bars and the colors of the curves and intervals.

Comparison of Estimated Reproduction Number with True Curve

Users can also extract information directly from the LPSMAP_fit and LPSMALA_fit objects to plot the estimated reproduction number values and their associated credible intervals for each day. In the figure generated, the estimated reproduction numbers obtained with LPSMAP and LPSMALA are compared to the true underlying reproduction number curve. The fit is quite good.

Accessing Specific Results

Specific results can be accessed by typing commands. For example, to obtain the estimated reproduction numbers for the last week of the epidemic, the command “round(tail(LPSMAP_fit$epifit[, 1:4], 7), 3)” can be used. Similarly, to obtain the estimated mean number of cases for the last week, the command “round(tail(LPSMAP_fit$epifit[, 5:7], 7))” can be used.

In conclusion, the episim() and epilps() routines provide valuable tools for simulating epidemic data, smoothing the epidemic curve, and estimating the reproduction number. The generated figures and extracted results aid in visualizing and analyzing the dynamics of an epidemic outbreak.

Summary: Article: ‘EpiLPS: A Quick and Versatile Bayesian Tool for Estimating the Fluctuating Reproduction Number’

The episim() routine can be used to simulate a set of epidemic data by specifying a serial interval distribution and a pattern for the true reproduction number curve. The routine returns a figure summarizing the incidence time series, a bar plot for the serial interval distribution, and the true underlying reproduction number curve. The epilps() routine can then be used to smooth the epidemic curve and estimate the reproduction number using LPSMAP or LPSMALA methods. The routine prints a summary of the method chosen and provides basic information such as the mean reproduction number. The plot() routine can be used to customize and visualize the smoothed epidemic curve and estimated reproduction number. The figure shows the estimated reproduction number values and their credible intervals for each day. The fit is quite good. The results of the last week of the epidemic can be accessed by typing specific commands.

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