The “covid19.analytics” R package allows users to obtain live* worldwide data from the novel CoronaVirus Disease originally reported in 2019, CoViD19, as published by the JHU CCSE repository [1], as well as, provide basic analysis tools and functions to investigate these datasets.
The goal of this package is to make the latest data promptly available to researchers and the scientific community.
The covid19.data()
function allows users to obtain realtime data about the CoViD19 reported cases from the JHU’s CCSE repository, in the following modalities: * “aggregated” data for the latest day, with a great ‘granularity’ of geographical regions (ie. cities, provinces, states, countries) * “time series” data for larger accumulated geographical regions (provinces/countries)
The datasets also include information about the different categories (status) “confirmed”/“deaths”/“recovered” of the cases reported daily per country/region/city.
This dataacquisition function, will first attempt to retrieve the data directly from the JHU repository with the latest updates. If for what ever reason this fails (eg. problems with the connection) the package will load a preserved “image” of the data which is not the latest one but it will still allow the user to explore this older dataset. In this way, the package offers a more robust and resilient approach to the quite dynamical situation with respect to data availability and integrity.
argument  description 

aggregated

latest number of cases aggregated by country 
Time Series data  
tsconfirmed

time series data of confirmed cases 
tsdeaths

time series data of fatal cases 
tsrecovered

time series data of recovered cases 
tsALL

all time series data combined 
Deprecated data formats  
tsdepconfirmed

time series data of confirmed cases as originally reported (deprecated) 
tsdepdeaths

time series data of deaths as originally reported (deprecated) 
tsdeprecovered

time series data of recovered cases as originally reported (deprecated) 
Combined  
ALL

all of the above 
The covid19.genomic.data()
allows users to obtain the covid19’s genomic sequencing data from NCBI [2].
In addition to the access and retrieval of the data, the package includes some basics functions to estimate totals per regions/country/cities, growth rates and daily changes in the reported number of cases.
Function  Description  Main Type of Output 

Data Acquisition  
covid19.data

obtain live* worldwide data for covid19 virus, from the JHU’s CCSE repository [1]  return dataframes/list with the collected data 
covid19.genomic.data

obtain covid19’s genomic sequencing data from NCBI [2] 
list, with the RNA seq data in the “$NC_045512.2” entry

Analysis  
report.summary

summarize the current situation, will download the latest data and summarize different quantities  on screen table and static plots (pie and bar plots) with reported information, can also output the tables into a text file 
tots.per.location

compute totals per region and plot time series for that specific region/country  static plots: data + models (exp/linear, Poisson, Gamma), mosaic and histograms when more than one location are selected 
growth.rate

compute changes and growth rates per region and plot time series for that specific region/country  static plots: data + models (linear,Poisson,Exp), mosaic and histograms when more than one location are selected 
Graphics and Visualization  
total.plts

plots in a static and interactive plot total number of cases per day, the user can specify multiple locations or global totoals  static and interactive plot 
live.map

generates an interactive map displaying cases around the world  static and interactive plot 
Modelling  
generate.SIR.model

generates a SIR (SusceptibleInfectedRecovered) model  list containing the fits for the SIR model 
plt.SIR.model

plot the results from the SIR model  static and interactive plots 
The report.summary()
generates an overall report summarizing the different datasets. It can summarize the “Time Series” data (cases.to.process="TS"
), the “aggregated” data (cases.to.process="AGG"
) or both (cases.to.process="ALL"
). It will display the top 10 entries in each category, or the number indicated in the Nentries
argument, for displaying all the records set Nentries=0
.
In each case (“TS” or/and “AGG”) will present tables ordered by the different cases included, i.e. confirmed infected, deaths, recovered and active cases.
The dates when the report is generated and the date of the recorded data will be included at the beginning of each table.
It will also compute the totals, averages, standard deviations and percentages of various quantities: * it will determine the number of unique locations processed within the dataset * it will compute the total number of cases per case
Typical structure of a summary.report()
output for the Time Series data:
###############################################################################
##### TSCONFIRMED Cases  Data dated: 20200404 :: 20200405 17:27:17
################################################################################
Number of Countries/Regions reported: 181
Number of Cities/Provinces reported: 82
Unique number of geographical locations combined: 259

Worldwide tsconfirmed Totals: 1197405

Country.Region Province.State Totals GlobalPerc LastDayChange
1 US 308850 25.79 33264
2 Spain 126168 10.54 6969
3 Italy 124632 10.41 4805
4 Germany 96092 8.03 4933

Global Perc. Average: 0.39 (sd: 2.02)
Global Perc. Average in top 10 : 7.98 (sd: 7)

.
.
.
Typical structure of a summary.report()
output for the Aggregated data:
##########################################################################################################################
##### AGGREGATED Data  ORDERED BY CONFIRMED Cases  Data dated: 20200404 :: 20200405 17:27:19
##########################################################################################################################
Number of Countries/Regions reported: 181
Number of Cities/Provinces reported: 137
Unique number of geographical locations combined: 316

Country_Region Province_State Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
1 Spain 126168 10.54 11947 9.47 34219 27.12 80002 63.41
2 Italy 124632 10.41 15362 12.33 20996 16.85 88274 70.83
3 Germany 96092 8.03 1444 1.50 26400 27.47 68248 71.02
.
.
.
A full example of this report for today can be seen here (updated twice a day, daily).
In addition to this, the function will also generate some graphical outputs, including pie and bar charts representing the top regions in each category.
It is possible to dive deeper into a particular location by using the tots.per.location()
and growth.rate()
functions. Theses functions are capable of processing different types of data, as far as these are “Time Series” data. It can either focus in one category (eg. “TSconfirmed”,“TSrecovered”,“TSdeaths”,) or all (“TSall”). When these functions detect different type of categories, each category will be processed separatedly. Similarly the functions can take multiple locations, ie. just one, several ones or even “all” the locations within the data. The locations can either be countries, regions, provinces or cities. If an specified location includes multiple entries, eg. a country that has several cities reported, the functions will group them and process all these regions as the location requested.
This function will plot the number of cases as a function of time for the given locations and type of categories, in two plots: a logscale scatter one a linear scale bar plot one.
When the function is run with multiple locations or all the locations, the figures will be adjusted to display multiple plots in one figure in a mosaic type layout.
Additionally, the function will attempt to generate different fits to match the data: * an exponential model using a Linear Regression method * a Poisson model using a General Linear Regression method * a Gamma model using a General Linear Regression method The function will plot and add the values of the coefficients for the models to the plots and display a summary of the results in screen.
It is possible to instruct the function to draw a “confidence band” based on a moving average, so that the trend is also displayed including a region of higher confidence based on the mean value and standard deviation computed considering a time interval set to equally dividing the total range of time over 10 equally spaced intervals.
The function will return a list combining the results for the totals for the different locations as a function of time.
The growth.rate()
function allows to compute daily changes and the growth rate defined as the ratio of the daily changes between two consecutive dates.
The growth.rate()
shares all the features of the tots.per.location()
function, i.e. can process the different types of cases and multiple locations.
The graphical output will display two plots per location: * a scatter plot with the number of changes between consecutive dates as a function of time, both in linear scale (left vertical axis) and logscale (right vertical axis) combined * a bar plot displaying the growth rate for the particular region as a function of time.
When the function is run with multiple locations or all the locations, the figures will be adjusted to display multiple plots in one figure in a mosaic type layout. In addition to that, when there is more than one location the function will also generate two different styles of heatmaps comparing the changes per day and growth rate among the different locations (vertical axis) and time (horizontal axis).
The function will return a list combining the results for the “changes per day” and the “growth rate” as a function of time.
The function totals.plt()
will generate plots of the total number of cases as a function of time. It can be used for the total data or for an specific or multiple locations. The function can generate static plots and/or interactive ones, as well, as linear and/or semilog plots.
The function live.map()
will display the different cases in each corresponding location all around the world in an interactive map of the world. It can be used with time series data or aggregated data, aggregated data offers a much more detailed information about the geographical distribution.
We are working in the development of modelling capabilities. A preliminary prototype has been included and can be accessed using the generate.SIR.model
function, which implements a simple SIR (SusceptibleInfectedRecovered) ODE model using the actual data of the virus.
This function will try to identify the data points where the onset of the epidemy began and consider the following data points to generate a proper guess for the two parameters describing the SIR ODE system. After that, it will solve the different equations and provide details about the solutions as well as plot them in a static and interactive plot.
We will continue working on adding and developing new features to the package, in particular modelling and predictive capabilities.
For using the “covi19.analytics” package, first you will need to install it.
The stable version can be downloaded from the CRAN repository:
To obtain the development version you can get it from the github repository, i.e.
# need devtools for installing from the github repo
install.packages("devtools")
# install bioC.logs
devtools::install_github("mponce0/covid19.analytics")
For using the package, either the stable or development version, just load it using the library function:
# obtain all the records combined for "confirmed", "deaths" and "recovered" cases  *aggregated* data
covid19.data.ALLcases < covid19.data()
# obtain time series data for "confirmed" cases
covid19.confirmed.cases < covid19.data("tsconfirmed")
# reads all possible datasets, returning a list
covid19.all.datasets < covid19.data("ALL")
# reads the latest aggregated data
covid19.ALL.agg.cases < covid19.data("aggregated")
# reads time series data for casualties
covid19.TS.deaths < covid19.data("tsdeaths")
Read covid19’s genomic data
# obtain covid19's genomic data
covid19.gen.seq < covid19.genomic.data()
# display the actual RNA seq
covid19.gen.seq$NC_045512.2
# a quick function to overview top cases per region for time series and aggregated records
report.summary()
# save the tables into a text file named 'covid19SummaryReport_CURRENTDATE.txt'
# where *CURRRENTDATE* is the actual date
report.summary(saveReport=TRUE)
# totals for confirmed cases for "Ontario"
tots.per.location(covid19.confirmed.cases,geo.loc="Ontario")
# total for confirmed cases for "Canada"
tots.per.location(covid19.confirmed.cases,geo.loc="Canada")
# total nbr of deaths for "Mainland China"
tots.per.location(covid19.TS.deaths,geo.loc="China")
# total nbr of confirmed cases in Hubei including a confidence band based on moving average
tots.per.location(covid19.confirmed.cases,geo.loc="Hubei", confBnd=TRUE)
The figures show the total number of cases for different cities (provinces/regions) and countries: one the upper plot in logscale with a linear fit to an exponential law and in linear scale in the bottom panel. Details about the models are included in the plot, in particular the growth rate which in several cases appears to be around 1.2+ as predicted by some models. Notice that in the case of Hubei, the values is closer to 1, as the dispersion of the virus has reached its logistic asymptote while in other cases (e.g. Germany and Italy –for the presented dates–) is still well above 1, indicating its exponential growth.
IMPORTANT Please notice that the “linear exponential” modelling function implements a simple (naive) and straightforward linear regression model, which is not optimal for exponential fits. The reason is that the errors for large values of the dependent variable weight much more than those for small values when apply the exponential function to go back to the original model. Nevertheless for the sake of a quick interpretation is OK, but one should bare in mind the implications of this simplification.
We also provide two additional models, as shown in the figures above, using the Generalized Linear Model glm()
function, using a Poisson and Gamma family function. In particular, the tots.per.location
function will determine when is possible to automatically generate each model and display the information in the plot as well as details of the models in the console.
# read the time series data for all the cases
all.data < covid19.data('tsALL')
# run on all the cases
tots.per.location(all.data,"Japan")
It is also possible to run the tots.per.location
(and growth.rate
) functions, on the whole data set, for which a quite large but complete mosaic figure will be generated, e.g.
# total for death cases for "ALL" the regions
tots.per.location(covid19.TS.deaths)
# or just
tots.per.location(covid19.data("tsconfirmed"))
# read time series data for confirmed cases
TS.data < covid19.data("tsconfirmed")
# compute changes and growth rates per location for all the countries
growth.rate(TS.data)
# compute changes and growth rates per location for 'Italy'
growth.rate(TS.data,geo.loc="Italy")
# compute changes and growth rates per location for 'Italy' and 'Germany'
growth.rate(TS.data,geo.loc=c("Italy","Germany"))
The previous figures show on the upper panel the number of changes on a daily basis in linear scale (thin line, left yaxis) and log scale (thicker line, right yaxis), while the bottom panel displays the growth rate for the given country/region/city.
Combining multiple geographical locations:
# obtain Time Series data
TSconfirmed < covid19.data("tsconfirmed")
# explore different combinations of regions/cities/countries
# when combining different locations, heatmaps will also be generated comparing the trends among these locations
growth.rate(TSconfirmed,geo.loc=c("Italy","Canada","Ontario","Quebec","Uruguay"))
growth.rate(TSconfirmed,geo.loc=c("Hubei","Italy","Spain","United States","Canada","Ontario","Quebec","Uruguay"))
growth.rate(TSconfirmed,geo.loc=c("Hubei","Italy","Spain","US","Canada","Ontario","Quebec","Uruguay")
# retrieve time series data
TS.data < covid19.data("tsALL")
# static and interactive plot
totals.plt(TS.data)
# totals for Ontario and Canada, without displaying totals and one plot per page
totals.plt(TS.data, c("Canada","Ontario"), with.totals=FALSE,one.plt.per.page=TRUE)
# totals for Ontario, Canada, Italy and Uruguay; including global totals with the linear and semilog plots arranged one next to the other
totals.plt(TS.data, c("Canada","Ontario","Italy","Uruguay"), with.totals=TRUE,one.plt.per.page=FALSE)
# totals for all the locations reported on the dataset, interactive plot will be saved as "totalsall.html"
totals.plt(TS.data, "ALL", fileName="totalsall")
# retrieve aggregated data
data < covid19.data("aggregated")
# interactive map of aggregated cases  with more spatial resolution
live.map(data)
# or
live.map()
# interactive map of the time series data of the confirmed cases with less spatial resolution, ie. aggregated by country
live.map(covid19.data("tsconfirmed"))
Interactive examples can be seen at https://mponce0.github.io/covid19.analytics/
# read time series data for confirmed cases
data < covid19.data("tsconfirmed")
# run a SIR model for a given geographical location
generate.SIR.model(data,"Hubei", t0=1,t1=15)
generate.SIR.model(data,"Germany",tot.population=83149300)
generate.SIR.model(data,"Uruguay", tot.population=3500000)
generate.SIR.model(data,"Ontario",tot.population=14570000)
# the function will aggregate data for a geographical location, like a country with multiple entries
generate.SIR.model(data,"Canada",tot.population=37590000)
# modelling the spread for the whole world, storing the model and generating an interactive visualization
world.SIR.model < generate.SIR.model(data,"ALL", t0=1,t1=15, tot.population=7.8e9, staticPlt=FALSE)
# plotting and visualizing the model
plt.SIR.model(world.SIR.model,"World",interactiveFig=TRUE,fileName="world.SIR.model")
(*) Data can be upto 24 hs delayed wrt the latest updates.
[1] 2019 Novel CoronaVirus CoViD19 (2019nCoV) Data Repository by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) https://github.com/CSSEGISandData/COVID19
[2] Severe acute respiratory syndrome coronavirus 2 isolate WuhanHu1, complete genome NCBI Reference Sequence: NC_045512.2 https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.2
SourceCredit: CDC/ Alissa Eckert, MS; Dan Higgins, MAMS