An Investigation into current CoronaVirus spread with 'Covid-19.analysis' package in R
Mirza Wasim Ahmed
11 November 2020
The "covid19.analytics" R package provides live data worldwide from the novel CoronaVirus, known as CoViD-19, as published by the John Hopkins University. This package aslo gives some primary analytical tools to look into the data.
https://www.rdocumentation.org/packages/covid19.analytics/versions/1.0
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####Install Library####
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library(covid19.analytics)Obtaining Covid information
## Obtain all the records combined for "confirmed", "deaths" and "recovered" cases -- *aggregated* data##
ALLcases<- covid19.data()## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## ================================================================================
## ================================================================================
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~View(ALLcases)## obtain time series data for "confirmed" cases ##
confirmed_cases <- covid19.data(case="ts-confirmed")## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## --------------------------------------------------------------------------------View(confirmed_cases)## Death Count / reads time series data for casualties ##
death_count <- covid19.data(case = "ts-deaths")## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## --------------------------------------------------------------------------------View(death_count)Summary and graphical overview of the covid data for top 5 countries/regions.
## Overview of top 5 regions ##
report.summary(Nentries = 5,
graphical.output = T)## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## --------------------------------------------------------------------------------
## ################################################################################
## ##### TS-CONFIRMED Cases -- Data dated: 2020-11-10 :: 2020-11-12 01:00:35
## ################################################################################
## Number of Countries/Regions reported: 191
## Number of Cities/Provinces reported: 82
## Unique number of distinct geographical locations combined: 269
## --------------------------------------------------------------------------------
## Worldwide ts-confirmed Totals: 51456775
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals GlobalPerc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 US 10252129 19.92 136325 119944 109780 102946 78258 40562
## 2 India 8636011 16.78 44281 38073 45903 50210 49881 55342
## 3 Brazil 5699005 11.08 23973 10917 10554 23976 28629 8429
## 4 France 1810653 3.52 0 19836 125414 40753 34848 42956
## 5 Russia 1802762 3.50 20765 21577 20248 19483 15886 13406
## --------------------------------------------------------------------------------
## Global Perc. Average: 0.37 (sd: 1.77)
## Global Perc. Average in top 5 : 10.96 (sd: 7.5)
## --------------------------------------------------------------------------------
## ================================================================================
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## --------------------------------------------------------------------------------
## ################################################################################
## ##### TS-DEATHS Cases -- Data dated: 2020-11-10 :: 2020-11-12 01:00:36
## ################################################################################
## Number of Countries/Regions reported: 191
## Number of Cities/Provinces reported: 82
## Unique number of distinct geographical locations combined: 269
## --------------------------------------------------------------------------------
## Worldwide ts-deaths Totals: 1272094
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals Perc LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 US 239671 2.34 1415 477 462 1104 996 323
## 2 Brazil 162802 2.86 174 231 128 610 510 201
## 3 India 127571 1.48 512 448 490 704 517 706
## 4 Mexico 95842 9.79 815 0 219 635 495 164
## 5 United Kingdom 49770 4.03 532 194 156 492 310 50
## --------------------------------------------------------------------------------
## --------------------------------------------------------------------------------
## ================================================================================
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## --------------------------------------------------------------------------------
## ################################################################################
## ##### TS-RECOVERED Cases -- Data dated: 2020-11-10 :: 2020-11-12 01:00:38
## ################################################################################
## Number of Countries/Regions reported: 191
## Number of Cities/Provinces reported: 68
## Unique number of distinct geographical locations combined: 256
## --------------------------------------------------------------------------------
## Worldwide ts-recovered Totals: 33544236
## --------------------------------------------------------------------------------
## Country.Region Province.State Totals LastDayChange t-2 t-3 t-7 t-14 t-30
## 1 India 8013783 54377 42033 48405 55331 56480 77760
## 2 Brazil 5183970 20744 16054 8531 17465 33044 0
## 3 US 3961873 33028 47354 30026 38397 30474 31651
## 4 Russia 1341868 15300 10640 11321 15182 12067 3785
## 5 Argentina 1081897 8320 10666 9598 8369 9803 11202
## --------------------------------------------------------------------------------
## --------------------------------------------------------------------------------
## ================================================================================
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## ================================================================================
## ================================================================================
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY CONFIRMED Cases -- Data dated: 2020-09-16 :: 2020-11-12 01:00:38
## ############################################################################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 559
## Unique number of distinct geographical locations combined: 3939
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Maharashtra, India 1097856 3.78 30409 2.77 775273 70.62 292174 26.61
## 2 Sao Paulo, Brazil 901271 3.11 32963 3.66 763246 84.69 105062 11.66
## 3 South Africa 651521 2.24 15641 2.40 583126 89.50 52754 8.10
## 4 Andhra Pradesh, India 583925 2.01 5041 0.86 486531 83.32 92353 15.82
## 5 Argentina 577338 1.99 11852 2.05 438883 76.02 126603 21.93
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY DEATHS Cases -- Data dated: 2020-09-16 :: 2020-11-12 01:00:38
## ############################################################################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 559
## Unique number of distinct geographical locations combined: 3939
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 England, United Kingdom 323029 1.11 36996 11.45 0 0.00 286033 88.55
## 2 Sao Paulo, Brazil 901271 3.11 32963 3.66 763246 84.69 105062 11.66
## 3 Maharashtra, India 1097856 3.78 30409 2.77 775273 70.62 292174 26.61
## 4 Iran 407353 1.40 23453 5.76 349984 85.92 33916 8.33
## 5 Rio de Janeiro, Brazil 244418 0.84 17180 7.03 220651 90.28 6587 2.69
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY RECOVERED Cases -- Data dated: 2020-09-16 :: 2020-11-12 01:00:38
## ############################################################################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 559
## Unique number of distinct geographical locations combined: 3939
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Maharashtra, India 1097856 3.78 30409 2.77 775273 70.62 292174 26.61
## 2 Sao Paulo, Brazil 901271 3.11 32963 3.66 763246 84.69 105062 11.66
## 3 South Africa 651521 2.24 15641 2.40 583126 89.50 52754 8.10
## 4 Andhra Pradesh, India 583925 2.01 5041 0.86 486531 83.32 92353 15.82
## 5 Tamil Nadu, India 514208 1.77 8502 1.65 458900 89.24 46806 9.10
## ============================================================================================================================================## ############################################################################################################################################
## ##### AGGREGATED Data -- ORDERED BY ACTIVE Cases -- Data dated: 2020-09-16 :: 2020-11-12 01:00:38
## ############################################################################################################################################
## Number of Countries/Regions reported: 188
## Number of Cities/Provinces reported: 559
## Unique number of distinct geographical locations combined: 3939
## --------------------------------------------------------------------------------------------------------------------------------------------
## Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1 Lima, Peru 340912 1.17 13780 4.04 0 0.00 327132 95.96
## 2 Maharashtra, India 1097856 3.78 30409 2.77 775273 70.62 292174 26.61
## 3 England, United Kingdom 323029 1.11 36996 11.45 0 0.00 286033 88.55
## 4 Los Angeles, California, US 255049 0.88 6273 2.46 0 0.00 248776 97.54
## 5 Miami-Dade, Florida, US 164688 0.57 2923 1.77 0 0.00 161765 98.23
## ============================================================================================================================================
## Confirmed Deaths Recovered Active
## Totals
## 29022050 900783 16849185 NA
## Average
## 7367.87 228.68 4277.53 NA
## Standard Deviation
## 39633.3 1364.62 31226.25 NA
##
##
## * Statistical estimators computed considering 3939 independent reported entries
##
##
## ********************************************************************************
## ******************************** OVERALL SUMMARY********************************
## ********************************************************************************
## **** Time Series Worldwide TOTS ****
## ts-confirmed ts-deaths ts-recovered
## 51456775 1272094 33544236
## 2.47% 65.19%
## **** Time Series Worldwide AVGS ****
## ts-confirmed ts-deaths ts-recovered
## 191289.13 4728.97 131032.17
## 2.47% 68.5%
## **** Time Series Worldwide SDS ****
## ts-confirmed ts-deaths ts-recovered
## 912555.23 21097.55 659067.35
## 2.31% 72.22%
##
##
## * Statistical estimators computed considering 269/269/256 independent reported entries per case-type
## ********************************************************************************Plot total cases across the world
total_ts <- covid19.data(case = "ts-ALL")## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv## Data retrieved on 2020-11-12 01:00:40 || Range of dates on data: 2020-01-22--2020-11-10 | Nbr of records: 269## --------------------------------------------------------------------------------## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv
## Data retrieved on 2020-11-12 01:00:40 || Range of dates on data: 2020-01-22--2020-11-10 | Nbr of records: 269## --------------------------------------------------------------------------------## Data being read from JHU/CCSE repository## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv## Data retrieved on 2020-11-12 01:00:40 || Range of dates on data: 2020-01-22--2020-11-10 | Nbr of records: 256## --------------------------------------------------------------------------------totals.plt(total_ts)## Loading required package: plotly## Loading required package: ggplot2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layout
New Zealand
tots.per.location(confirmed_cases,geo.loc = "New Zealand" )## [1] "NEWZEALAND"
## NEW ZEALAND -- 1988
## =============================== running models...===============================
## Linear Regression (lm):
##
## Call:
## lm(formula = y.var ~ x.var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -573.31 -247.96 -47.94 258.20 610.15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 182.5474 39.0828 4.671 4.58e-06 ***
## x.var 7.1923 0.2297 31.316 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 334.2 on 292 degrees of freedom
## Multiple R-squared: 0.7706, Adjusted R-squared: 0.7698
## F-statistic: 980.7 on 1 and 292 DF, p-value: < 2.2e-16
##
## --------------------------------------------------------------------------------
## Linear Regression (lm):
##
## Call:
## lm(formula = y.var ~ x.var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3033 -1.4789 0.0082 1.5781 2.8202
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.41348 0.21110 11.43 <2e-16 ***
## x.var 0.02405 0.00124 19.39 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.805 on 292 degrees of freedom
## Multiple R-squared: 0.5628, Adjusted R-squared: 0.5613
## F-statistic: 375.9 on 1 and 292 DF, p-value: < 2.2e-16
##
## --------------------------------------------------------------------------------
## GLM using Family [1] "poisson" :
##
## Call:
## glm(formula = y.var ~ x.var, family = family)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -34.104 -11.927 0.704 11.714 21.539
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.098e+00 4.309e-03 1415.0 <2e-16 ***
## x.var 6.086e-03 2.104e-05 289.3 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 186762 on 293 degrees of freedom
## Residual deviance: 96424 on 292 degrees of freedom
## AIC: 98648
##
## Number of Fisher Scoring iterations: 5
##
## --------------------------------------------------------------------------------
Calculation and visualizations of changes and growth rates in New Zealand
growth.rate(confirmed_cases, geo.loc = "New Zealand")## [1] "NEWZEALAND"
## Processing... NEW ZEALAND## Loading required package: pheatmap## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess
## $Changes
## geo.loc 2020-01-23 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28
## 1 NEW ZEALAND 0 0 0 0 0 0
## 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04
## 1 0 0 0 0 0 0 0
## 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11
## 1 0 0 0 0 0 0 0
## 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18
## 1 0 0 0 0 0 0 0
## 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25
## 1 0 0 0 0 0 0 0
## 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03
## 1 0 0 1 0 0 0 0
## 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10
## 1 2 0 1 1 0 0 0
## 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17
## 1 0 0 0 1 2 0 4
## 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24
## 1 8 8 11 13 50 0 53
## 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31
## 1 50 78 85 83 63 75 58
## 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07
## 1 61 89 71 82 89 67 54
## 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14
## 1 50 29 44 29 18 19 17
## 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21
## 1 20 15 8 13 9 9 5
## 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28
## 1 6 5 5 9 -1 3 2
## 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05
## 1 2 3 6 2 0 -1 2
## 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12
## 1 1 1 2 2 3 0 0
## 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19
## 1 0 1 0 1 0 0 4
## 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26
## 1 0 1 0 0 0 0 0
## 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02
## 1 0 0 0 0 0 0 0
## 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09
## 1 0 0 0 0 0 0 0
## 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16
## 1 0 0 0 0 0 2 0
## 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23
## 1 1 0 2 2 2 2 1
## 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30
## 1 3 1 2 4 2 0 0
## 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07
## 1 2 0 0 3 1 2 1
## 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14
## 1 3 2 1 1 0 1 2
## 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21
## 1 1 1 1 3 1 1 0
## 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28
## 1 0 1 0 0 0 1 2
## 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04
## 1 1 0 2 3 2 0 2
## 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11
## 1 0 0 0 0 0 1 0
## 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18
## 1 19 13 7 13 9 12 6
## 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25
## 1 5 11 6 3 9 7 5
## 2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01
## 1 7 12 13 2 9 14 5
## 2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08
## 1 2 5 3 5 4 6 6
## 2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15
## 1 4 1 2 2 1 3 1
## 2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22
## 1 7 0 2 4 0 0 9
## 2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29
## 1 3 2 2 2 0 2 1
## 2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06
## 1 12 0 1 5 1 3 3
## 2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13
## 1 3 2 4 1 0 1 2
## 2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20
## 1 2 4 3 3 0 1 25
## 2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27
## 1 2 9 11 1 5 1 2
## 2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03
## 1 6 1 7 2 4 5 3
## 2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10
## 1 2 1 2 6 4 1 1
##
## $Growth.Rate
## geo.loc 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29
## 1 NEW ZEALAND NaN NaN NaN NaN NaN NaN
## 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04 2020-02-05
## 1 NaN NaN NaN NaN NaN NaN NaN
## 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11 2020-02-12
## 1 NaN NaN NaN NaN NaN NaN NaN
## 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18 2020-02-19
## 1 NaN NaN NaN NaN NaN NaN NaN
## 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25 2020-02-26
## 1 NaN NaN NaN NaN NaN NaN NaN
## 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03 2020-03-04
## 1 NaN NA 0 NaN NaN NaN NA
## 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11
## 1 0 NA 1 0 NaN NaN NaN
## 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18
## 1 NaN NaN NA 2 0 NA 2
## 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24 2020-03-25
## 1 1 1.375 1.181818 3.846154 0 NA 0.9433962
## 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31 2020-04-01
## 1 1.56 1.089744 0.9764706 0.7590361 1.190476 0.7733333 1.051724
## 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07 2020-04-08
## 1 1.459016 0.7977528 1.15493 1.085366 0.752809 0.8059701 0.9259259
## 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14 2020-04-15
## 1 0.58 1.517241 0.6590909 0.6206897 1.055556 0.8947368 1.176471
## 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21 2020-04-22
## 1 0.75 0.5333333 1.625 0.6923077 1 0.5555556 1.2
## 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28 2020-04-29
## 1 0.8333333 1 1.8 -0.1111111 -3 0.6666667 1
## 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05 2020-05-06
## 1 1.5 2 0.3333333 0 -Inf -2 0.5
## 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12 2020-05-13
## 1 1 2 1 1.5 0 NaN NaN
## 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19 2020-05-20
## 1 NA 0 NA 0 NaN NA 0
## 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26 2020-05-27
## 1 NA 0 NaN NaN NaN NaN NaN
## 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02 2020-06-03
## 1 NaN NaN NaN NaN NaN NaN NaN
## 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09 2020-06-10
## 1 NaN NaN NaN NaN NaN NaN NaN
## 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16 2020-06-17
## 1 NaN NaN NaN NaN NA 0 NA
## 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23 2020-06-24
## 1 0 NA 1 1 1 0.5 3
## 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30 2020-07-01
## 1 0.3333333 2 2 0.5 0 NaN NA
## 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07 2020-07-08
## 1 0 NaN NA 0.3333333 2 0.5 3
## 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14 2020-07-15
## 1 0.6666667 0.5 1 0 NA 2 0.5
## 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21 2020-07-22
## 1 1 1 3 0.3333333 1 0 NaN
## 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28 2020-07-29
## 1 NA 0 NaN NaN NA 2 0.5
## 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04 2020-08-05
## 1 0 NA 1.5 0.6666667 0 NA 0
## 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11 2020-08-12
## 1 NaN NaN NaN NaN NA 0 NA
## 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18 2020-08-19
## 1 0.6842105 0.5384615 1.857143 0.6923077 1.333333 0.5 0.8333333
## 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25 2020-08-26
## 1 2.2 0.5454545 0.5 3 0.7777778 0.7142857 1.4
## 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01 2020-09-02
## 1 1.714286 1.083333 0.1538462 4.5 1.555556 0.3571429 0.4
## 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08 2020-09-09
## 1 2.5 0.6 1.666667 0.8 1.5 1 0.6666667
## 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15 2020-09-16
## 1 0.25 2 1 0.5 3 0.3333333 7
## 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22 2020-09-23
## 1 0 NA 2 0 NaN NA 0.3333333
## 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29 2020-09-30
## 1 0.6666667 1 1 0 NA 0.5 12
## 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06 2020-10-07
## 1 0 NA 5 0.2 3 1 1
## 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13 2020-10-14
## 1 0.6666667 2 0.25 0 NA 2 1
## 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20 2020-10-21
## 1 2 0.75 1 0 NA 25 0.08
## 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27 2020-10-28
## 1 4.5 1.222222 0.09090909 5 0.2 2 3
## 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03 2020-11-04
## 1 0.1666667 7 0.2857143 2 1.25 0.6 0.6666667
## 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10 NA
## 1 0.5 2 3 0.6666667 0.25 1 NASimulating the Virus spread in NZ on the basis of time series data for confirmed cases
generate.SIR.model(confirmed_cases,'New Zealand', tot.population=5084300)## ################################################################################
## ################################################################################
## [1] "NEWZEALAND"
## Processing... NEW ZEALAND
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31] 0 0 0 0 0 0 0 1 1 1 1 1 3 3 4
## [46] 5 5 5 5 5 5 5 6 8 8 12 20 28 39 52
## [61] 102 102 155 205 283 368 451 514 589 647 708 797 868 950 1039
## [76] 1106 1160 1210 1239 1283 1312 1330 1349 1366 1386 1401 1409 1422 1431 1440
## [91] 1445 1451 1456 1461 1470 1469 1472 1474 1476 1479 1485 1487 1487 1486 1488
## [106] 1489 1490 1492 1494 1497 1497 1497 1497 1498 1498 1499 1499 1499 1503 1503
## [121] 1504 1504 1504 1504 1504 1504 1504 1504 1504 1504 1504 1504 1504 1504 1504
## [136] 1504 1504 1504 1504 1504 1504 1504 1504 1504 1504 1506 1506 1507 1507 1509
## [151] 1511 1513 1515 1516 1519 1520 1522 1526 1528 1528 1528 1530 1530 1530 1533
## [166] 1534 1536 1537 1540 1542 1543 1544 1544 1545 1547 1548 1549 1550 1553 1554
## [181] 1555 1555 1555 1556 1556 1556 1556 1557 1559 1560 1560 1562 1565 1567 1567
## [196] 1569 1569 1569 1569 1569 1569 1570 1570 1589 1602 1609 1622 1631 1643 1649
## [211] 1654 1665 1671 1674 1683 1690 1695 1702 1714 1727 1729 1738 1752 1757 1759
## [226] 1764 1767 1772 1776 1782 1788 1792 1793 1795 1797 1798 1801 1802 1809 1809
## [241] 1811 1815 1815 1815 1824 1827 1829 1831 1833 1833 1835 1836 1848 1848 1849
## [256] 1854 1855 1858 1861 1864 1866 1870 1871 1871 1872 1874 1876 1880 1883 1886
## [271] 1886 1887 1912 1914 1923 1934 1935 1940 1941 1943 1949 1950 1957 1959 1963
## [286] 1968 1971 1973 1974 1976 1982 1986 1987 1988
## [1] 58
## [1] 28 39 52 102 102 155 205 283 368 451 514 589 647 708 797
## [16] 868 950 1039 1106 1160 1210 1239 1283 1312 1330 1349
## ------------------------ Parameters used to create model ------------------------
## Region: NEW ZEALAND
## Time interval to consider: t0=58 - t1= ; tfinal=90
## t0: 2020-03-20 -- t1:
## Number of days considered for initial guess: 26
## Fatality rate: 0.02
## Population of the region: 5084300
## --------------------------------------------------------------------------------
## [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
## beta gamma
## 0.5849109 0.4150902
## R0 = 1.40911755495443
## Max nbr of infected: 238455.14 ( 4.69 %)
## Max nbr of casualties, assuming 2% fatality rate: 4769.1
## Max reached at day : 63 ==> 2020-05-22
## ================================================================================
## $Infected
## [1] 28 39 52 102 102 155 205 283 368 451 514 589 647 708 797
## [16] 868 950 1039 1106 1160 1210 1239 1283 1312 1330 1349
##
## $model
## time S I R
## 1 1 5084272 28.00000 0.000000e+00
## 2 2 5084254 33.18245 1.266769e+01
## 3 3 5084233 39.32401 2.768000e+01
## 4 4 5084208 46.60216 4.547083e+01
## 5 5 5084178 55.22718 6.655438e+01
## 6 6 5084143 65.44827 9.153999e+01
## 7 7 5084101 77.56068 1.211497e+02
## 8 8 5084052 91.91420 1.562392e+02
## 9 9 5083993 108.92335 1.978222e+02
## 10 10 5083924 129.07918 2.471002e+02
## 11 11 5083842 152.96343 3.054966e+02
## 12 12 5083744 181.26524 3.746981e+02
## 13 13 5083628 214.80093 4.567030e+02
## 14 14 5083492 254.53734 5.538788e+02
## 15 15 5083329 301.61947 6.690304e+02
## 16 16 5083137 357.40319 8.054803e+02
## 17 17 5082909 423.49374 9.671643e+02
## 18 18 5082639 501.79135 1.158744e+03
## 19 19 5082320 594.54489 1.385740e+03
## 20 20 5081941 704.41520 1.654690e+03
## 21 21 5081492 834.54962 1.973333e+03
## 22 22 5080960 988.66959 2.350832e+03
## 23 23 5080331 1171.17352 2.798031e+03
## 24 24 5079585 1387.25734 3.327760e+03
## 25 25 5078702 1643.05544 3.955195e+03
## 26 26 5077656 1945.80505 4.698283e+03
## 27 27 5076418 2304.03733 5.578234e+03
## 28 28 5074952 2727.79864 6.620109e+03
## 29 29 5073218 3228.90560 7.853492e+03
## 30 30 5071165 3821.23733 9.313294e+03
## 31 31 5068738 4521.06794 1.104067e+04
## 32 32 5065868 5347.44137 1.308409e+04
## 33 33 5062477 6322.58922 1.550058e+04
## 34 34 5058470 7472.38957 1.835713e+04
## 35 35 5053741 8826.86112 2.173231e+04
## 36 36 5048161 10420.68118 2.571811e+04
## 37 37 5041584 12293.70809 3.042196e+04
## 38 38 5033840 14491.47762 3.596901e+04
## 39 39 5024730 17065.62795 4.250455e+04
## 40 40 5014029 20074.18824 5.019664e+04
## 41 41 5001480 23581.64156 5.923875e+04
## 42 42 4986789 27658.64305 6.985243e+04
## 43 43 4969629 32381.24039 8.228972e+04
## 44 44 4949636 37829.40831 9.683508e+04
## 45 45 4926409 44084.67641 1.138066e+05
## 46 46 4899517 51226.60987 1.335560e+05
## 47 47 4868505 59327.90717 1.564667e+05
## 48 48 4832902 68447.92689 1.829499e+05
## 49 49 4792238 78624.56806 2.134372e+05
## 50 50 4746066 89864.62683 2.483698e+05
## 51 51 4693983 102133.05170 2.881836e+05
## 52 52 4635669 115341.91648 3.332891e+05
## 53 53 4570912 129340.38728 3.840478e+05
## 54 54 4499649 143907.39702 4.407440e+05
## 55 55 4421996 158749.02544 5.035552e+05
## 56 56 4338275 173502.53719 5.725224e+05
## 57 57 4249028 187748.68174 6.475236e+05
## 58 58 4155013 201032.48869 7.282546e+05
## 59 59 4057189 212891.78087 8.142196e+05
## 60 60 3956674 222890.66554 9.047354e+05
## 61 61 3854697 230654.07945 9.989493e+05
## 62 62 3752531 235898.60348 1.095871e+06
## 63 63 3651430 238455.13551 1.194415e+06
## 64 64 3552567 238280.24139 1.293453e+06
## 65 65 3456982 235454.95865 1.391864e+06
## 66 66 3365545 230171.94447 1.488583e+06
## 67 67 3278940 222713.62716 1.582647e+06
## 68 68 3197653 213425.05118 1.673222e+06
## 69 69 3121988 202685.29171 1.759627e+06
## 70 70 3052079 190880.78385 1.841340e+06
## 71 71 2987920 178382.93868 1.917997e+06
## 72 72 2929387 165531.31163 1.989381e+06
## 73 73 2876267 152622.59226 2.055410e+06
## 74 74 2828283 139904.94114 2.116112e+06
## 75 75 2785113 127576.75113 2.171610e+06
## 76 76 2746412 115788.72672 2.222099e+06
## 77 77 2711826 104648.18920 2.267826e+06
## 78 78 2681000 94224.65289 2.309075e+06
## 79 79 2653590 84555.91293 2.346154e+06
## 80 80 2629268 75654.09072 2.379378e+06
## 81 81 2607723 67511.27043 2.409065e+06
## 82 82 2588669 60104.51378 2.435526e+06
## 83 83 2571840 53400.15697 2.459060e+06
## 84 84 2556993 47357.37609 2.479950e+06
## 85 85 2543908 41931.06000 2.498461e+06
## 86 86 2532387 37074.06044 2.514839e+06
## 87 87 2522250 32738.90297 2.529311e+06
## 88 88 2513337 28879.04531 2.542084e+06
## 89 89 2505505 25449.76565 2.553345e+06
## 90 90 2498626 22408.75544 2.563265e+06
##
## $params
## $params$beta
## beta
## 0.5849109
##
## $params$gamma
## gamma
## 0.4150902
##
## $params$R0
## R0
## 1.409118