R: 'EpiInvertForecast' computes a 28-day forecast of the...

EpiInvertForecast {EpiInvert}

R Documentation

`EpiInvertForecast` computes a 28-day forecast of the restored incidence curve including a 95 using the weekly seasonality, from the forecasted restored incidence curve we also estimate a 28-day forecast of the original incidence curve.

Description

EpiInvertForecast computes a 28-day forecast of the restored incidence curve including a 95 using the weekly seasonality, from the forecasted restored incidence curve we also estimate a 28-day forecast of the original incidence curve.

Usage

EpiInvertForecast(
  EpiInvert_result,
  restored_incidence_database,
  type = "median",
  trend_sentiment = 0,
  NumberForecastAdditionalDays = 0
)

Arguments

`EpiInvert_result`	output list of the EpiInvert execution including, in particular, the restored incidence curve and the seasonality.
`restored_incidence_database`	a database including 27,418 samples of different restored incidence curves computed by EpiInvert using real data. Each restored incidence curve includes the last 56 values of the sequence. That is this database can be viewed as a matrix of size 27,418 X 56
`type`	string with the forecast option. It can be "mean" or "median".
`trend_sentiment`	"a priori" knowledge about the future incidence evolution. == 0 means that you are neutral about the future trend > 0 means that you expect that the future trend is higher than the expected one using all database curves. the value represents the percentage of database curves removed before computing the forecast The curves removed are the ones with lowest growth in the last 28 days. < 0 means that you expect that the future trend is higher than the expected one using all database curves. the meaning of the value is similar to the previous case, but removing the curves with the highest growth in the last 28 days.
`NumberForecastAdditionalDays`	The number of forecast days is 28. With this parameter you can add extra forecast days using linear extrapolation.

Details

EpiInvertForecast estimates a forecast of the restored incidence curve using a weighted average of 27,418 restored incidence curves previously estimated by EpiInvert and stored in the database "restored_incidence_database". The weight, in the average computation, of each restored incidence curve of the database depends on the similarity between the current curve in the last 28 days and the first 28 days of the database curve. Each database curve contains 56 days. The first 28 days are used for comparison with the current curve and the last 28 days are used for forecasting.

Value

a list with components:

dates: a vector of dates corresponding to the forecast days.
i_restored_forecast: a numeric vector with the forecast of the restored incidence curve for the next 28 days.
i_original_forecast: a numeric vector with the forecast of the original incidence curve for the next 28 days.
i_restored_forecast_CI50: radius of an empiric confidence interval, with percentile 50, for the restored incidence forecast following the number of days passed since the current day.
i_restored_forecast_CI75: radius of an empiric confidence interval, with percentile 75, for the restored incidence forecast following the number of days passed since the current day.
i_restored_forecast_CI90: radius of an empiric confidence interval, with percentile 90, for the restored incidence forecast following the number of days passed since the current day.
i_restored_forecast_CI95: radius of an empiric confidence interval, with percentile 95, for the restored incidence forecast following the number of days passed since the current day.

Author(s)

Luis Alvarez lalvarez@ulpgc.es

References

[1] Alvarez, L.; Colom, M.; Morel, J.D.; Morel, J.M. Computing the daily reproduction number of COVID-19 by inverting the renewal equation using a variational technique. Proc. Natl. Acad. Sci. USA, 2021.

[2] Alvarez, Luis, Jean-David Morel, and Jean-Michel Morel. "Modeling COVID-19 Incidence by the Renewal Equation after Removal of Administrative Bias and Noise" Biology 11, no. 4: 540. 2022.

[3] Ritchie, H. et al. Coronavirus Pandemic (COVID-19), OurWorldInData.org. Available online: https://ourworldindata.org/coronavirus-source-data (accessed on 5 May 2022).

[4] Alvarez, Luis, Jean-David Morel, and Jean-Michel Morel. EpiInvertForecast Available online: https://ctim.ulpgc.es/covid19/EpiInvertForecastPaper.html

Examples

## load data on COVID-19 daily incidence up to 2022-05-05 for France, 
## and Germany (taken from the official government data) and for UK and 
## the USA taken from reference [3]
data(incidence)

## load of the database of restored incidence curves. 
data("restored_incidence_database")

## EpiInvert execution for USA with no festive days specification
## using the incidence 90 days in the past
res <- EpiInvert(incidence$USA,
"2022-05-05",
"1000-01-01",
select_params(list(max_time_interval = 90))
)

## EpiInvertForecast execution using the EpiInvert results obtained by USA
forecast <-  EpiInvertForecast(res,restored_incidence_database)

[Package EpiInvert version 0.3.1 Index]

EpiInvertForecast computes a 28-day forecast of the restored incidence curve including a 95 using the weekly seasonality, from the forecasted restored incidence curve we also estimate a 28-day forecast of the original incidence curve.