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 following sections briefly describe some of the covid19.analytics package main features, we strongly recomend users to read our paper “covid19.analytics: An R Package to Obtain, Analyze and Visualize Data from the Corona Virus Disease Pandemic” (https://arxiv.org/abs/2009.01091) where further details about the package are presented and discussed.
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 
Time Series data for specific locations  
tsToronto

time series data of confirmed cases for the city of Toronto, ON  Canada 
tsconfirmedUS

time series data of confirmed cases for the US detailed per state 
tsdeathsUS

time series data of fatal cases for the US detailed per state 
The TimeSeries data is organized in an specific manner with a given set of fields or columns, which resembles the following structure:
“Province.State”  “Country.Region”  “Lat”  “Long”  …  seq of dates  … 
If you have data structured in a data.frame organized as described above, then most of the functions provided by the “covid19.analytics” package for analyzing TimeSeries data will work with your data. In this way it is possible to add new data sets to the ones that can be loaded using the repositories predefined in this package and extend the analysis capabilities to these new datasets.
Be sure also to check the compatibility of these datasets using the Data Integrity and Consistency Checks
functions described in the following section.
Due to the ongoing and rapid changing situation with the CoViD19 pandemic, sometimes the reported data has been detected to change its internal format or even show some “anomalies” or “inconsistencies” (see https://github.com/CSSEGISandData/COVID19/issues/).
For instance, in some cumulative quantities reported in time series datasets, it has been observed that these quantities instead of continuously increase sometimes they decrease their values which is something that should not happen, (see for instance, https://github.com/CSSEGISandData/COVID19/issues/2165). We refer to this as inconsistency of “type II”.
Some negative values have been reported as well in the data, which also is not possible or valid; we call this inconsistency of “type I”.
When this occurs, it happens at the level of the origin of the dataset, in our case, the one obtained from the JHU/CCESGIS repository [1]. In order to make the user aware of this, we implemented two consistency and integrity checking functions:
consistency.check()
, this function attempts to determine whether there are consistency issues within the data, such as, negative reported value (inconsistency of “type I”) or anomalies in the cumulative quantities of the data (inconsistency of “type II”)
integrity.check()
, this determines whether there are integrity issues within the datasets or changes to the structure of the data
Alternatively we provide a data.checks()
function that will run both functions on an specified dataset.
It is highly unlikely that you would face a situation where the internal structure of the data, or its actual integrity may be compromised but if you think that this is the case or the integrity.check()
function reports this, please we urge you to contact the developer of this package (https://github.com/mponce0/covid19.analytics/issues).
Data consistency issues and/or anomalies in the data have been reported several times, see https://github.com/CSSEGISandData/COVID19/issues/. These are claimed, in most of the cases, to be missreported data and usually are just an insignificant number of the total cases. Having said that, we believe that the user should be aware of these situations and we recommend using the consistency.check()
function to verify the dataset you will be working with.
In order to deal with the different scenarios arising from incomplete, inconsistent or missreported data, we provide the nullify.data()
function, which will remove any potential entry in the data that can be suspected of these incongruencies. In addition ot that, the function accepts an optional argument stringent=TRUE
, which will also prune any incomplete cases (e.g. with NAs present).
That’s why the covid19.analytics package provides access to a good number of the genomics data currently available.
The covid19.genomic.data()
function allows users to obtain the CoViD19’s genomics data from NCBI’s databases [3]. The type of genomics data accessible from the package is described in the following table.
type  description  source 
genomic  a composite list containing different indicators and elements of the SARSCoV2 genomic information  https://www.ncbi.nlm.nih.gov/sarscov2/ 
genome  genetic composition of the reference sequence of the SARSCoV2 from GenBank  https://www.ncbi.nlm.nih.gov/nuccore/NC_045512 
fasta  genetic composition of the reference sequence of the SARSCoV2 from a fasta file  https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.2?report=fasta 
ptree  phylogenetic tree as produced by NCBI data servers  https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/precomptree 
nucleotide / protein  list and composition of nucleotides/proteins from the SARSCoV2 virus  https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/ 
nucleotidefasta / proteinfasta  FASTA sequences files for nucleotides, proteins and coding regions  https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/ 
Although the package attempts to provide the latest available genomic data, there are a few important details and differences with respect to the reported cases data. For starting, the amount of genomic information available is way larger than the data reporting the number of cases which adds some additional constraints when retrieving this data. In addition to that, the hosting servers for the genomic databases impose certain limits on the rate and amounts of downloads.
In order to mitigate these factors, the covid19.analytics package employs a couple of different strategies as summarized below: * most of the data will be attempted to be retrieved live from NCBI databases – same as using src='livedata'
* if that is not possible, the package keeps a local version of some of the largest datasets (i.e. genomes, nucleotides and proteins) which might not be uptodate – same as using src='repo'
. * the package will attempt to obtain the data from a mirror server with the datasets updated on a regular basis but not necessarily with the latest updates – same as using src='local'
.
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.Toronto.data

obtain live* data for covid19 cases in the city of Toronto, ON Canada, from the City of Toronto reports [2]  return dataframe/list with the collected data 
covid19.US.data

obtain live* US specific data for covid19 virus, from the JHU’s CCSE repository [1]  return dataframe with the collected data 
covid19.genomic.data c19.refGenome.data c19.fasta.data c19.ptree.data c19.NPs.data c19.NP_fasta.data

obtain covid19’s genomic sequencing data from NCBI [3] 
list, with the RNA seq data in the “$NC_045512.2” entry

Data Quality Assessment  
data.checks

run integrity and consistency checks on a given dataset  diagnostics about the dataset integrity and consistency 
consistency.check

run consistency checks on a given dataset  diagnostics about the dataset consistency 
integrity.check

run integrity checks on a given dataset  diagnostics about the dataset integrity 
nullify.data

remove inconsistent/incomplete entries in the original datasets  original dataset (dataframe) without “suspicious” entries 
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 
single.trend mtrends

visualize different indicators of the “trends” in daily changes for a single or mutliple locations  compose of static plots: total number of cases vs time, daily changes vs total changes in different representations 
estimateRRs

compute estimates for fatality and recovery rates on a rollingwindow interval  list with values for the estimates (mean and sd) of reported cases and recovery and fatality rates 
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 
itrends

generates an interactive plot of daily changes vs total changes in a loglog plot, for the indicated regions  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 
sweep.SIR.model

generate multiple SIR models by varying parameters used to select the actual data  list containing the values parameters, \(\beta, \gamma\) and \(R_0\) 
Auxiliary functions  
geographicalRegions

determines which countries compose a given continent  list of countries 
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
.
The function can also target specific geographical location(s) using the geo.loc
argument. When a geographical location is indicated, the report will include an additional “Rel.Perc” column for the confirmed cases indicating the relative percentage among the locations indicated. Similarly the totals displayed at the end of the report will be for the selected locations.
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
for the “Confirmed” cases, as the ratio between the corresponding number of cases and the total number of cases, i.e. a sort of “global percentage” indicating the percentage of infected cases wrt the rest of the world
for “Confirmed” cases, when geographical locations are specified, a “Relative percentage” is given as the ratio of the confirmed cases over the total of the selected locations
for the other categories, “Deaths”/“Recovered”/“Active”, the percentage of a given category is computed as the ratio between the number of cases in the corresponding category divided by the “Confirmed” number of cases, i.e. a relative percentage with respect to the number of confirmed infected cases in the given region
Typical structure of a summary.report()
output for the Time Series data:
################################################################################
##### TSCONFIRMED Cases  Data dated: 20200412 :: 20200413 12:02:27
################################################################################
Number of Countries/Regions reported: 185
Number of Cities/Provinces reported: 83
Unique number of geographical locations combined: 264

Worldwide tsconfirmed Totals: 1846679

Country.Region Province.State Totals GlobalPerc LastDayChange t2 t3 t7 t14 t30
1 US 555313 30.07 28917 29861 35098 29595 20922 548
2 Spain 166831 9.03 3804 4754 5051 5029 7846 1159
3 Italy 156363 8.47 4092 4694 3951 3599 4050 3497
4 France 132591 7.18 2937 4785 7120 5171 4376 808
5 Germany 127854 6.92 2946 2737 3990 3251 4790 910
.
.
.

Global Perc. Average: 0.38 (sd: 2.13)
Global Perc. Average in top 10 : 7.85 (sd: 8.18)

********************************************************************************
******************************** OVERALL SUMMARY********************************
********************************************************************************
**** Time Series TOTS ****
tsconfirmed tsdeaths tsrecovered
1846679 114091 421722
6.18% 22.84%
**** Time Series AVGS ****
tsconfirmed tsdeaths tsrecovered
6995 432.16 1686.89
6.18% 24.12%
**** Time Series SDS ****
tsconfirmed tsdeaths tsrecovered
39320.05 2399.5 8088.55
6.1% 20.57%
* Statistical estimators computed considering 250 independent reported entries
********************************************************************************
Typical structure of a summary.report()
output for the Aggregated data:
#################################################################################################################################
##### AGGREGATED Data  ORDERED BY CONFIRMED Cases  Data dated: 20200412 :: 20200413 12:02:29
#################################################################################################################################
Number of Countries/Regions reported: 185
Number of Cities/Provinces reported: 138
Unique number of geographical locations combined: 2989

Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
1 Spain 166831 9.03 17209 10.32 62391 37.40 87231 52.29
2 Italy 156363 8.47 19899 12.73 34211 21.88 102253 65.39
3 France 132591 7.18 14393 10.86 27186 20.50 91012 68.64
4 Germany 127854 6.92 3022 2.36 60300 47.16 64532 50.47
5 New York City, New York, US 103208 5.59 6898 6.68 0 0.00 96310 93.32
.
.
.
=================================================================================================================================
Confirmed Deaths Recovered Active
Totals
1846680 114090 421722 1310868
Average
617.83 38.17. 141.09 438.56
Standard Deviation
6426.31 613.69 2381.22 4272.19
* Statistical estimators computed considering 2989 independent reported entries
In both cases an overall summary of the reported cases is presented by the end, displaying totals, average and standard devitation of the computed quantities.
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.
We provide three different functions to visualize the trends in daily changes of reported cases from time series data.
single.trend
, allows to inspect one single location, this could be used with the worldwide data sliced by the corresponding location, the Toronto data or the user’s own data formatted as “Time Series” data.
mtrends
, similar to single.trend function, but accepts multiple or single locations generating one plot per location requested
itrends
, function to generate an interactive plot of the trend in daily changes representing changes in number of cases vs total number of cases in logscale using splines techniques to smooth the abrupt variations in the data
The first two functions will generate “static” plots in a compose with different insets:  the main plot represents daily changes as a function of time  the inset figures in the top, from left to right:  total number of cases (in linear and semilog scales),  changes in number of cases vs total number of cases  changes in number of cases vs total number of cases in logscale  the second row of insets, represent the “growth rate” (as defined above) and the “normalized” growth rate defined as the growth rate divided by the maximum growth rate reported for this location
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.
For exploring the parameter space of the SIR model, it is possible to produce a series of models by varying the conditions, i.e. range of dates considered for optimizing the parameters of the SIR equation, which will effectively sweep a range for the parameters \(\beta, \gamma\) and \(R_0\). This is implemented in the function sweep.SIR.models()
, which takes a range of dates to be used as starting points for the number of cases used to feed into the generate.SIR.model()
producing as many models as different ranges of dates are indicated. One could even use this in combination to other resampling or Monte Carlo techniques to estimate statistical variability of the parameters from the model.
We will continue working on adding and developing new features to the package, in particular modelling and predictive capabilities.
Please contact us if you think of a functionality or feature that could be useful to add.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 covid19.analytics from github
devtools::install_github("mponce0/covid19.analytics")
For using the package, either the stable or development version, just load it using the library function:
Further examples and details about the covid19.analytics package are provided in our manuscript, https://arxiv.org/abs/2009.01091 .
# 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
# 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)
# summary report for an specific location with default number of entries
report.summary(geo.loc="Canada")
# summary report for an specific location with top 5
report.summary(Nentries=5, geo.loc="Canada")
# it can combine several locations
report.summary(Nentries=30, geo.loc=c("Canada","US","Italy","Uruguay","Argentina"))
# 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.
# 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")
# single location trend, in this case using data from the City of Tornto
tor.data < covid19.Toronto.data()
single.trend(tor.data[tor.data$status=="Active Cases",])
# or data from the province of Ontario
ts.data < covid19.data("tsconfirmed")
ont.data < ts.data[ ts.data$Province.State == "Ontario",]
single.trend(ont.data)
# or from Italy
single.trend(ts.data[ ts.data$Country.Region=="Italy",])
# multiple locations
ts.data < covid19.data("tsconfirmed")
mtrends(ts.data, geo.loc=c("Canada","Ontario","Uruguay","Italy")
# multiple cases
single.trend(tor.data)
# interactive plot of trends
# for all locations and all type of cases
itrends(covid19.data("tsALL"),geo.loc="ALL")
# or just for confirmed cases and some specific locations, saving the result in an HTML file named "itrends_ex.html"
itrends(covid19.data("tsconfirmed"), geo.loc=c("Uruguay","Argentina","Ontario","US","Italy","Hubei"), fileName="itrends_ex")
# interactive trend for Toronto cases
itrends(tor.data[,ncol(tor.data)])
# 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] COVID19: Status of Cases in Toronto – City of Toronto https://www.toronto.ca/home/covid19/covid19latestcityoftorontonews/covid19statusofcasesintoronto/
[3] 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
If you are using this package please cite our main publication about the covid19.analytics package:
https://arxiv.org/abs/2009.01091
You can also ask for this citation information in R:
> citation("covid19.analytics")
To cite covid19.analytics in publications use:
Marcelo Ponce, Amit Sandhel (2020). covid19.analytics: An R Package
to Obtain, Analyze and Visualize Data from the Corona Virus Disease
Pandemic. URL https://arxiv.org/abs/2009.01091
A BibTeX entry for LaTeX users is
@Article{,
title = {covid19.analytics: An R Package to Obtain, Analyze and Visualize Data from the Corona Virus Disease Pandemic},
author = {Marcelo Ponce and Amit Sandhel},
journal = {preprint},
year = {2020},
url = {https://arxiv.org/abs/2009.01091},
}