UNIVERSIDAD COMPLUTENSE DE
MADRID
FACULTAD DE INFORMÁTICA
DEPARTAMENTO DE SISTEMAS INFORMÁTICOS Y
COMPUTACIÓN
T abajo Fin de G ado en Ingenie ía
In o má ica
Desa ollo de un Dashboa d pa a la E olución de la
COVID-19
De elopmen o a Dashboa d o he E olu ion o
COVID-19
Di igido po :
Sonia Es é ez Ma ín
Ad ián Ga cía Sánchez
Ha old Luis Pascua Cajucom
Dale F ancis Valencia Calicdan
Cu so académico 2020-21
Con oca o ia Junio
Acknowledgemen s
Fi s o all, we wan o gi e hanks o ou p ojec di ec o , Sonia Es-
é ez Ma in, who has always helped us when we ha e needed i and
has gi en us guidelines o de elop he p ojec in he bes possible way.
We also wan o men ion Vic o ia López, who has also helped us in
planning he wo k and choosing adequa e me ics o he co ec p epa-
a ion.
We also wan o acknowledge all he e o s made on he pa o he
pa ne g oup, composed o I án Jesús Sauga Peinado, Se gio Sánchez
O iz, and Ál a o San José Gue a, who ha e p o ided us wi h he
da ase s wi h which o wo k, a undamen al pa o he de elopmen
o he p ojec and he achie emen o he objec i es.
Finally, we wan o hank all amily and iends o all he mo al sup-
po gi en h oughou his p ojec .
2
Abs ac
The SARS-CoV-2 co ona i us is esponsible o he COVID-
19 disease ha has caused economic and heal h issues a ound he
wo ld. To en o ce measu es ha cu bs he g ow h o his disease,
his pandemic mus be moni o ed.
The objec i e o his p ojec is o c ea e an online, open sou ce
dashboa d ha moni o s he e olu ion o COVID-19 by coun y
and by i s p incipal indica o s. To do so, his p ojec has de el-
oped a se ies o Sc um sp in s, oge he wi h a ious echnologies,
such as RS udio, Shiny, and Google Si es.
The dashboa d c ea ed se es o analyze da a and o p esen i
in a clea manne because o mas e y o di e en ools, me hod-
ologies, and analy ical echniques ha ha e been used.
This dashboa d di e s om o he exis ing dashboa ds such ha
i specializes in p esen ing indica o s such as ansmission index,
dange index, and cumula i e incidence in addi ion o aw da a
like posi i e cases, dea hs, and eco e ies.
As a esul , a dashboa d was c ea ed ha can in o m use s
abou he p og ess o he pandemic in g aphic and map o ms,
especially unde he a o emen ioned indica o s.
Keywo ds
COVID-19, Co ona i us, Dashboa d, RS udio, Shiny, Da a Analysis, Accu-
mula ed Incidence, R0, Dange Index
Resumen
El SARS-CoV-2 co ona i us es esponsable de la COVID-19,
la cual ha causado p oblemas económicos y sani a ios al ededo
de odo el mundo. Pa a hace cumpli medidas que enen el
c ecimien o de la en e medad, la pandemia debe se moni o izada.
El obje i o de es e p oyec o es c ea un dashboa d online y de
código abie o que moni o ice la e olución del COVID-19 po país
y po sus p incipales indicado es. Pa a log a es o, el p oyec o
ha sido elabo ado median e una se ie de i e aciones de Sc um,
unidas a a ias ecnologías como RS udio, Shiny y Google Si es.
El dashboa d c eado si e pa a analiza los da os y pa a p e-
sen a los de una mane a cla a, debido al dominio de las di e en es
he amien as, me odologías y écnicas analí icas que han sido u i-
lizadas.
Es e dashboa d di ie e de o os exis en es en que se especial-
iza en p esen a algunos indicado es como son el índice de ans-
misión, el índice de pelig osidad y la incidencia acumulada además
de da os b u os como son los casos posi i os, las mue es y los e-
cupe ados.
Como esul ado, ha sido c eado un dashboa d que in o ma a
los usua ios sob e el p og eso de la pandemia median e mapas y
g á icos, especialmen e sob e los indicado es mencionados an e i-
o men e.
Palab as Cla e
COVID-19, Co ona i us, Dashboa d, RS udio, Shiny, Análisis de da os, In-
cidencia Acumulada, R0, Índice de pelig osidad
Con en s
1 In oduc ion 8
1.1 Mo i a ion............................. 8
1.2 Objec i es............................. 9
1.3 Wo kPlan............................. 10
1.4 Documen S uc u e . . . . . . . . . . . . . . . . . . . . . . . 11
2 S a e o he A 13
2.1 Types o COVID-19 Dashboa ds . . . . . . . . . . . . . . . . . 13
2.1.1 Types o Dashboa ds by Geog aphic Scope . . . . . . . 14
2.1.2 Types o Dashboa ds by Type o Da a Used . . . . . . 17
2.2 Applica ions o Dashboa d C ea ion and Con igu a ion . . . . 21
2.3 Technical Requi emen s o C ea ing a Dashboa d . . . . . . . 23
3 Da a o In e es o he P ojec 25
3.1 Exis ing Da abases o In e es . . . . . . . . . . . . . . . . . . 25
3.2 G aphs o In e es . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3 Va iables and Indica o s o In e es . . . . . . . . . . . . . . . 31
3.3.1 Va iables.......................... 31
3.3.2 Indica o s ......................... 33
3.3.3 Smoo hed Va iables and Indica o s . . . . . . . . . . . 35
4 Da a P epa a ion 36
4.1 Da abases Used as Bases o Da abase C ea ion . . . . . . . . 36
4.2 Da ase C ea ion o P ojec Use . . . . . . . . . . . . . . . . 37
4.2.1 Fi s Da ase ....................... 37
4.2.2 Second Da ase . . . . . . . . . . . . . . . . . . . . . . 37
4.2.3 FinalDa ase ....................... 39
4.3 Da ase Quali y E alua ion . . . . . . . . . . . . . . . . . . . 39
4

CONTENTS 5
5 P ojec De elopmen 41
5.1 P ojec Planning ......................... 41
5.1.1 De elopmen Me hodology . . . . . . . . . . . . . . . . 41
5.1.2 GoogleSi es........................ 42
5.2 P oduc Backlog ......................... 43
5.3 Fi s Sp in ............................ 44
5.4 SecondSp in ........................... 49
5.5 Thi dSp in ............................ 55
5.6 Fou hSp in ........................... 59
5.7 RS udio Lib a ies Used . . . . . . . . . . . . . . . . . . . . . . 67
5.8 Sc um Sp in Schedule . . . . . . . . . . . . . . . . . . . . . . 68
6 Resul s 70
6.1 FinalDa ase ........................... 70
6.2 Dashboa d and Websi e . . . . . . . . . . . . . . . . . . . . . 72
6.3 Example Analysis: Compa ing Spain and he Philippines . . . 75
6.4 In eg a ing he Dashboa d o O he Websi es . . . . . . . . . 79
7 Indi idual Con ibu ions 80
7.1 Con ibu ions made by Ad ián Ga cía Sánchez . . . . . . . . . 80
7.2 Con ibu ions made by Dale F ancis Valencia Calicdan . . . . 82
7.3 Con ibu ions made by Ha old Luis Pascua Cajucom . . . . . 84
8 Conclusions and Fu u e Wo k 86
8.1 Conclusions ............................ 86
8.2 Fu u eWo k............................ 87
Lis o Figu es
2.1 ISCIII COVID-19 Dashboa d (Applica ion Used: Shiny) . . . 15
2.2 Co idly Dashboa d (Applica ion Used: Mapbox) . . . . . . . . 16
2.3 Global Map ound in he COVID-19 Dashboa d by JHU (Ap-
plica ion Used: A cGIS Dashboa ds) . . . . . . . . . . . . . . 16
2.4 U.S. Map ound in he COVID-19 Dashboa d by JHU (Appli-
ca ion Used: A cGIS Dashboa ds) . . . . . . . . . . . . . . . . 17
2.5 WHO COVID-19 Dashboa d (Applica ion Used: Sp inkl ) . . 18
2.6 Co id-19 T ialsT acke Dashboa d (Applica ion Used: Tableau) 19
2.7 Co id-T ials.o g Dashboa d (Applica ion Used: Cy el) . . . . 19
2.8 Se oT acke Dashboa d (Applica ions Used: Recha s and Lea le ) 20
2.9 The Vaccine T acke (Applica ions Used: Ga sby and Reac ) . 20
2.10 Typical a chi ec u e o a Dashboa d . . . . . . . . . . . . . . . 23
3.1 Rolling 7-day a e age o COVID-19 cases as o Oc obe 31, 2020 26
3.2 Table o con i med cases and dea hs o each coun y in he
pas 14days............................ 27
3.3 Line g aph o he cumula i e con i med COVID-19 cases pe
millionpeople........................... 28
3.4 Daily new con i med COVID-19 cases pe million people, Oc
31,2020 .............................. 29
3.5 BoxPlo .............................. 30
5.1 Sc um p ocess diag am . . . . . . . . . . . . . . . . . . . . . . 42
5.2 Ini ial designs (o de ed om op o bo om, le o igh ) . . . 45
5.3 Final p o o ype design. Map View 1 . . . . . . . . . . . . . . 46
5.4 Final p o o ype design. Map View 2 . . . . . . . . . . . . . . 46
5.5 Final p o o ype design. Map View 3 . . . . . . . . . . . . . . 47
5.6 Final p o o ype design. G aph View . . . . . . . . . . . . . . 47
6
LIST OF FIGURES 7
5.7 Sp in 1 esul (g aph iew) . . . . . . . . . . . . . . . . . . . 48
5.8 Sp in 1 esul (map iew) . . . . . . . . . . . . . . . . . . . . 48
5.9 Re ised p o o ype design (g aph iew) . . . . . . . . . . . . . 51
5.10 Re ised p o o ype design (map iew) . . . . . . . . . . . . . . 51
5.11 Sp in 2 esul (line g aph) . . . . . . . . . . . . . . . . . . . . 52
5.12 Sp in 2 esul (box plo ) . . . . . . . . . . . . . . . . . . . . . 53
5.13 Sp in 2 esul (map iew) . . . . . . . . . . . . . . . . . . . . 54
5.14 Re ised p o o ype design (g aph iew) . . . . . . . . . . . . . 56
5.15 Re ised p o o ype design (map iew) . . . . . . . . . . . . . . 56
5.16 Sp in 3 esul (g aph iew) . . . . . . . . . . . . . . . . . . . 58
5.17 Sp in 3 esul (map iew) . . . . . . . . . . . . . . . . . . . . 58
5.18 Dashboa d design (g aph iew) . . . . . . . . . . . . . . . . . 61
5.19 Dashboa d design (map iew) . . . . . . . . . . . . . . . . . . 62
5.20 Websi e design (home iew) . . . . . . . . . . . . . . . . . . . 63
5.21 Websi e implemen a ion (home iew) . . . . . . . . . . . . . . 65
5.22 Websi e implemen a ion(abou us iew) . . . . . . . . . . . . . 65
5.23 Websi e implemen a ion (abou his p ojec iew) . . . . . . . 66
5.24 Dashboa d a chi ec u e . . . . . . . . . . . . . . . . . . . . . . 68
5.25 Realized sp in da es . . . . . . . . . . . . . . . . . . . . . . . 69
6.1 Inc o Spain om July 2020 o May 2021, as shown in Dashboa d 72
6.2 Inc o Spain om July 2020 o May 2021 . . . . . . . . . . . . 72
6.3 Websi e wi h in eg a ed Dashboa d . . . . . . . . . . . . . . . 73
6.4 Func ions a ailable in he G aph View . . . . . . . . . . . . . 74
6.5 G aphs compa ing Spain and he Philippines unde R0 . . . . 76
6.6 Map compa ing Spain and he Philippines unde R0 on June
8,2020............................... 76
6.7 G aphs compa ing Spain and he Philippines unde DI . . . . 77
6.8 Map compa ing Spain and he Philippines unde DI on June
8,2020............................... 77
6.9 G aphs compa ing Spain and he Philippines unde Inc . . . . 78
6.10 Map compa ing Spain and he Philippines unde Inc on June
8,2020............................... 79
Chap e 1
In oduc ion
COVID-19 [1] is an in ec ious disease caused by he co ona i us called Se-
e e Acu e Respi a o y Synd ome Co ona i us 2 (SARS-CoV-2). I is held
esponsible o he pandemic ha was happening a ound he wo ld decla ed
by he Wo ld Heal h O ganiza ion (WHO) on Ma ch 11, 2020. An a icle
published by WHO [2] sugges ed ha he s a o he ou b eak o his i us
o igina ed om Wuhan, China a a local ma ke a ound la e Decembe 2019.
E iden ly, he COVID-19 pandemic b ough de as a ing e ec s o e e y-
one a ound he globe. Public heal h acili ies we e b ough o hei limi s
[3], [4], economies wo ldwide ook a massi e hi [5], and, mos impo an ly,
many li es ha e been los . WHO had s a ed ha , as o No embe 1, 2020,
nea ly 46 million posi i e cases and 1.2 million dea hs ha e been epo ed
om a ound he wo ld [6].
As such, he global phenomenon has challenged e e y s a e and coun y
o o mula e hei own coun e measu es and impede he a e a which ci izens
a e being in ec ed. While some o hese implemen a ions ha e been p o en o
be e ec i e, o he ac o s such as inexpe ienced heal h wo ke s, poo heal h
sys ems, lack o social dis ancing, and sca ce knowledge o he co ona i us
make his p oblem all he mo e di icul o esol e.
1.1 Mo i a ion
Va ious dashboa ds, bo h online and o line, ha e al eady been de eloped o
analy ical use, wi h some ha ing been de eloped o isually ep esen he de-
elopmen o he COVID-19 pandemic in nume ous aspec s. Va ious ea u es
8
CHAPTER 2. STATE OF THE ART 15
Figu e 2.1: ISCIII COVID-19 Dashboa d (Applica ion Used: Shiny)
may o may no be de ailed. Fo example, a dashboa d ha uses da a s o ed
in he cloud will ha e he capaci y o p esen da a in a deg ee o speci ici y
simila o dashboa ds o na ional scope, hanks o he abili y o cloud-based
pla o ms o ha e a i ually limi less amoun o s o age [9], while dashboa ds
ha a e mo e limi ed in s o age may op o show summa ies o da a o each
coun y ins ead.
Co idly [10] (see Figu e 2.2) is an example o a dashboa d wi h a global
scope, p ojec ing da a, such as cases, dea hs, eco e ies, and o he a iables
ela ed o he pandemic, summa ized o each coun y.
Hyb id Scope
While he e a e dashboa ds ha ei he ha e a na ional scope o a global
one, he e a e ce ainly dashboa ds ha a e a mix o he wo, ca ying bo h
na ional da a o a ious coun ies and local da a o ce ain coun ies, as seen
in he wo maps p esen ed by he COVID-19 Dashboa d by he Cen e o
Sys ems Science and Enginee ing (CSSE) a Johns Hopkins Uni e si y (JHU)
[11] ha show bo h he na ional da a o di e en coun ies and he da a o
he coun ies in he Uni ed S a es o Ame ica (see Figu es 2.3 and 2.4).

CHAPTER 2. STATE OF THE ART 16
Figu e 2.2: Co idly Dashboa d (Applica ion Used: Mapbox)
As wi h dashboa ds o global scope, he speci ici y o he da a on a pe -
coun y le el may depend on he capaci y o he da abase being used o he
applica ion.
Figu e 2.3: Global Map ound in he COVID-19 Dashboa d by JHU (Appli-
ca ion Used: A cGIS Dashboa ds)
CHAPTER 2. STATE OF THE ART 17
Figu e 2.4: U.S. Map ound in he COVID-19 Dashboa d by JHU (Applica-
ion Used: A cGIS Dashboa ds)
2.1.2 Types o Dashboa ds by Type o Da a Used
In e ms o he da a ha a e o en analyzed and p esen ed in exis ing dash-
boa ds, i can be obse ed ha dashboa ds use one o he ou : epidemiolog-
ical da a, da a ega ding clinical ials, da a ha desc ibe he se op e alence
o he Co ona i us in a ce ain a ea, and da a on he p og ess o accine
de elopmen .
Epidemiological Da a T acke
This is he ype o dashboa d ha is usually associa ed wi h he e m
“COVID-19 dashboa d”. dashboa ds cen e ed a ound epidemiological da a
ocus mos ly on he numbe o con i med cases and dea hs, shown bo h on a
daily basis and as an accumula ion o e a ce ain pe iod o ime. O he da a
ela ed o he de elopmen o he pandemic, including es ing a e, hospi al-
iza ion coun , in ensi e ca e uni (ICU) admission coun , and eco e ies, may
also be included and p esen ed o p o ide use s a mo e in-dep h analysis.
As p e iously men ioned, Epidemiological Da a T acke s a e he mos
common ype o COVID-19 dashboa ds. Hence, many well-known dash-
boa ds, such as he Dashboa d o JHU and he Wo ld Heal h O ganiza ion
(WHO) COVID-19 Dashboa d [12] (see Figu e 2.5) a e good examples.
CHAPTER 2. STATE OF THE ART 18
Figu e 2.5: WHO COVID-19 Dashboa d (Applica ion Used: Sp inkl )
Clinical T ial T acke
This ype o dashboa d aims o p esen use s wi h upda es ega ding he
clinical ial esea ch o COVID-19 in e en ion me hods like accines, an-
i i al d ugs, and e en adi ional Chinese medicine. Usual p esen ed da a
include ial ID, ial loca ion, ial s a da e, ial comple ion da e (i ap-
plicable), ype o ea men , p esen ed ou come and he URL o he esul s.
Examples include he Co id-19 T ialsT acke [13] (see Figu e 2.6), a p od-
uc o The Da aLab a he Uni e si y o Ox o d. I uses da a om he In e -
na ional Clinical T ials Regis y Pla o m (ICTRP) o display clinical ials
ela ed o he ea men o COVID-19 h ough ables and g aphs. Ano he
example is Co id-T ials.o g [14, 15](see Figu e 2.7), whose unc ionali y is
simila , wi h he excep ion o being able o display he da a in a map as well.
Se op e alence Dashboa d
Se op e alence Dashboa ds, such as Se oT acke [16] (see Figu e 2.8), o-
cus on se op e alence o COVID-19 an ibodies. This means ha dashboa ds
o his ype p esen he equency o indi iduals in a ce ain popula ion ha
con ain COVID-19 an ibodies.
CHAPTER 2. STATE OF THE ART 19
Figu e 2.6: Co id-19 T ialsT acke Dashboa d (Applica ion Used: Tableau)
Figu e 2.7: Co id-T ials.o g Dashboa d (Applica ion Used: Cy el)
Fo heal h au ho i ies, se op e alence may be an in e es ing a iable o
obse e, as i plays a ole in an ibody es ing, a c ucial ac o in bo h moni o -
ing he e olu ion o he Co ona i us and es ima ing he a ali y a e caused
by in ec ions [17].
CHAPTER 2. STATE OF THE ART 20
Figu e 2.8: Se oT acke Dashboa d (Applica ions Used: Recha s and
Lea le )
Vaccine T acke
This ype o dashboa d acks he p og ess o he de elopmen o ac-
cines agains he Co ona i us. The name o he accine and he cu en
s a us/phase o he accine in he de elopmen p ocess is included. Addi-
ionally, accines can be il e ed acco ding o hei cu en s a us.
A good example o his is The Vaccine T acke [18] (see Figu e 2.9).
Figu e 2.9: The Vaccine T acke (Applica ions Used: Ga sby and Reac )

CHAPTER 2. STATE OF THE ART 21
2.2 Applica ions o Dashboa d C ea ion and
Con igu a ion
The e a e nume ous applica ions a ailable on he ma ke ha specialize on
he c ea ion and con igu a ion o dashboa ds, bo h paid and ee. Fo illus-
a ion pu poses, six well-known applica ions a e discussed: Shiny, Powe BI,
G a ana, Sisense, Domo, and A cGIS.
Shiny
Shiny [19] is an open-sou ce package based on R [20], a language and
en i onmen commonly used o s a is ical p og amming ha allows an easy
c ea ion and con igu a ion o in e ac i e web-based applica ions, including
dashboa ds. Shiny applica ions can be made using RS udio [21], he in e-
g a ed de elopmen en i onmen (IDE) o R, and hey can also be ex ended
h ough CSS, h mlwidge s, and Ja aSc ip .
A huge ad an age ha Shiny has o e o he applica ions o dashboa d
c ea ion is ha i is ee o use, wi hou imposing limi a ions o any so on
i s comple e unc ionali y. Any use can c ea e a ully unc ional dashboa d
h ough Shiny wi hou incu ing any cos s. De eloped p oduc s can ei he
be deployed o he Shinyapps.io cloud, wi h bo h ee and paid op ions [22],
o be in eg a ed on-p emises wi h an exis ing se e .
Powe BI
Powe BI [23] is a business in elligence (BI) so wa e c ea ed by Mic oso .
I allows da a modeling and isualiza ion h ough he use o a i icial in-
elligence (AI). In addi ion, Powe BI p o ides in e ope abili y wi h o he
Mic oso applica ions such as Azu e and Excel, end- o-end da a p o ec ion,
and nume ous da a connec o s ha pe mi connec ions o da a sou ces such
as Azu e SQL Da abase and Excel.
While Powe BI is a paid se ice, an op ion o a ee ial is also a ailable
o hose who wan o ge an idea o how he applica ion wo ks i s hand.
G a ana
G a ana [24] is a ee and open-sou ce pla o m ha ea u es suppo
o o e 30 da abases, including G aphi e, In luxDB, and P ome heus, o
CHAPTER 2. STATE OF THE ART 22
use in a single dashboa d and has a wide ange o dashboa ds and plugins
a ailable in i s o icial lib a y. I also allows he de elopmen o dashboa ds
ia collabo a ion and he de ini ion o ale s ( o example, when a ce ain
h eshold alue is su passed o he da a being moni o ed) ha no i ies he
use s.
Sisense
Sisense [25] is a cloud-na i e pla o m ha gi es i s use s he unc ions
necessa y o c ea e powe ul analy ical solu ions such as embedded analy ics,
da a mashups, access con ols a sys em, objec , da a, and p ocess le els, and
cus omized ale s. I o e s P oduc s can be deployed ei he ia a p i a e
cloud, on commodi y ha dwa e, o by aking a hyb id app oach. Sisense also
o e s da a connec o s o AWS, Snow lake, and Google BigQue y.
Being a BI pla o m ha ocuses on analy ics o en e p ise da a, his
applica ion does no o e any ee e sions.
Domo
Domo [26] is a cloud-based BI pla o m ha consis s o h ee laye s: da a
in eg a ion, business in elligence and analy ics, and he c ea ion o in elligen
applica ions. I boas s o as pe o mance, unlimi ed scalabili y, da a au-
oma ion, and a la ge a ay o da a connec o s, om da abases o e en social
ne wo ks, allowing use s om businesses o so wa e de elope s o ocus on
c ea ing he p oduc wi hou wo ying abou he unde lying in as uc u e.
As wi h Powe BI, Domo also has a ee ial op ion o use s who wan
o y i s capabili ies in connec ing, ans o ming, and isualizing da a.
A cGIS Dashboa ds
An online and a desk op pla o m c ea ed by Es i, A cGIS Dashboa ds
[27] allows he c ea ion o dashboa ds ha ea u e loca ion-based analy ics as
i s main ad an age o e o he pla o ms. I also has lexibili y, con igu abili y,
a sui e o a ailable and eady- o-use da a isualiza ion ools like maps, lis s,
and cha s, as well as ools ha allow use s o in e ac wi h dashboa ds
c ea ed h ough his applica ion.
This applica ion usually equi es o be pu chased o use, bu i also o e s
a 21-day ee ial pe iod, gi en ha i is o non-p oduc ion use only.
CHAPTER 2. STATE OF THE ART 23
2.3 Technical Requi emen s o C ea ing a Dash-
boa d
The ypical a chi ec u e o a dashboa d consis s o ou laye s, as shown in
Figu e 2.10 [28]. As such, hese will make up he echnical equi emen s ha
mus be complied in o de o c ea e a ully unc ioning dashboa d.
Figu e 2.10: Typical a chi ec u e o a Dashboa d
F on End
Being he pa o he applica ion wi h which he use in e ac s he mos ,
i is undeniably he mos impo an pa o he dashboa d. The in e ace
implemen ed in his laye mus be use - iendly and in e ac i e. Ca e ul
layou planning and selec ion o g aphs and o he ypes o isual da a play a
signi ican ole in helping he use s unde s and and in e p e clea ly he da a
being shown. In addi ion, accessibili y ea u es, such as app op ia ely sized
ex and labels and g aph anno a ions, mus also be conside ed.
Back End
The back end is whe e he se e pe o ms mos o i s ope a ions. I
con ains a ious sc ip s and sou ce codes o pe o ming analyses and ans-
o ma ions on he da a collec ed, as well as sc ip s o making p edic ions
CHAPTER 2. STATE OF THE ART 24
and decisions based on he da a. In he case o dashboa ds, he main goal o
his laye is o p epa e all da a o isualiza ion in he on end.
Mechanisms o da a s o age, such as da abases used by he se e , can
also be ound he e. As such, knowledge o basic da abase concep s such as
c ea ion, inse ion, dele ion, agg ega ion, and p ojec ion a e a mus . I is
also impo an o know he ad an ages and disad an ages o ela ional and
non- ela ional da abases o de e mine which ype o da abase is bes sui ed
o he applica ion. In he case o dashboa ds, ela ional da abases, many
o which a e c ea ed using SQL, a e ideal as his makes da a analysis and
p esen a ion easie o do.
Da a Acquisi ion
As explained by i s name, he da a acquisi ion laye consis s o sc ip s
whose objec i e is o ob ain da a om sou ce sys ems and place ha da a
as me ic alues in he s o age ound in he back end. The e a e mul iple
me hods o acqui ing da a om hese sou ces, om s a ic analysis o da a
mining. Hence, knowledge o hese p ocesses, as well as knowledge o he
s uc u es ound in he sou ce sys ems, is a p e equisi e o his laye o
unc ion p ope ly.
Sou ce Sys ems
While sou ce sys ems a e no ac ually pa o he dashboa d, hey s ill
hold a c ucial pa in i s ope a ions. These sys ems s o e da a ha may
be acqui ed o pe o m analyses, ans o ma ions, and isualiza ions. Ex-
amples include code eposi o ies like Gi Hub and e en da abases om o he
websi es.
CHAPTER 3. DATA OF INTEREST FOR THE PROJECT 31
3.3 Va iables and Indica o s o In e es
Since he beginning o he COVID-19 pandemic, a ious models ha e been
de eloped in an a emp o accu a ely desc ibe and p edic he e olu ion
o he i us. These models include di e en a iables, some o which we e
disco e ed and calcula ed by means o scien i ic in es iga ions. As he model
o be used o he dashboa d is inspi ed by he low ne wo k models p esen ed
by López and Cukić [34, 35], as well as Kucha ski e al [36], he eam would
use no only a iables commonly used in exis ing da abases o COVID-19
su eillance, bu also hose ha a e ela ed o he model and can be de i ed
ma hema ically om he commonly ound a iables, such as accumula ed
cases and dea hs, as well as indica o s such as he ansmission and dange
indices and he accumula ed incidence, which will be discussed sho ly.
3.3.1 Va iables
Daily Posi i e Cases
This pe ains o he numbe o posi i e COVID-19 cases eco ded in a
24-hou in e al. Fu he mo e, his a iable can be classi ied acco ding o
he he ype o diagnos ic es used o con i m he posi i e cases [37]:
•An igen Tes s: Using samples collec ed h ough nasal o h oa swabs,
an igen es s a e used o de ec a p o ein ha makes up pa o he
co ona i us and a e use ul o iden i ying cases ha a e app oaching
s a es o peak in ec ion. Compa ed o he o he ypes o es s, an igen
es s a e cheape and as e , bu a e also less accu a e, being able o
p oduce alse posi i es and alse nega i es.
•Molecula /PCR Tes s: Also using nasal and h oa swabs, molec-
ula es s ocus on iden i ying he gene ic makeup o he co ona i us
using di e en me hods, wi h he mos amous one being he poly-
me ase chain eac ion (PCR). While some molecula es s ha e shown
alse nega i e esul s o up o 20% o he ime, his ype o es emains
mo e accu a e in iden i ying posi i e cases han an igen es s.

CHAPTER 3. DATA OF INTEREST FOR THE PROJECT 32
Dea hs
These a e he numbe o dea hs caused by he co ona i us disease. We
assume hese igu es a e epo ed by he hospi als daily.
Reco e ies
These a e pa ien s who ha e been discha ged om he hospi al o hose
ha a e home-qua an ined and ha e been conside ed eco e ed by hei a -
ending physicians. This a iable will emain use ul when aking in o accoun
he o al numbe o eco e ies epo ed.
Accumula ed Posi i e Cases
As explained in i s name, his a iable ga he s he o al numbe o posi i e
cases epo ed so a . This is calcula ed by e ie ing he sum o daily posi i e
case coun s epo ed om s a o p esen .
Ac i e Cases
These a e posi i e cases ha a e nei he coun ed as a dea h no a eco e y,
meaning ha hese a e ongoing cases o COVID-19.
As a pe son in ec ed wi h COVID-19 may expe ience mild o se e e symp-
oms, i is ecommended ha no all ac i e COVID-19 cases be conside ed
as hospi aliza ions, bu a he be b oken down in o he ollowing ca ego ies:
•Home-Qua an ined: Pa ien s who expe ience mild symp oms a e no
ecommended o be hospi alized ( o allow hospi als o accommoda e
cases o highe se e i y), so hey a e qua an ined a home ins ead. They
a e usually moni o ed by hei espec i e physicians h ough emo e
means such as examina ions o e he phone.
•Hospi alized: Pa ien s who expe ience se e e symp oms and a e in
need o hospi al ca e all unde his ca ego y. These pa ien s s ay in
his s a e un il hey eco e and a e deemed sa e o discha ge o un il
hei condi ion wo sens in o a c i ical s a e.
•C i ical: C i ical cases in ol e pa ien s who a e expe iencing ex eme
symp oms and equi e immedia e a en ion by being admi ed in o in-
ensi e ca e uni s (ICUs).
CHAPTER 3. DATA OF INTEREST FOR THE PROJECT 33
Numbe o Tes s Done
This consis s o he accumula ed numbe o es s pe o med in a ce ain
a ea. This will include bo h es s ha e u ned posi i e esul s as well as
nega i e ones. To p oduce mo e comp ehensible numbe s, his numbe may
be p esen ed as a a io o es s pe 1M/100,000 inhabi an s.
Numbe o Cases pe 1M/100,000 Inhabi an s
Compa ing he numbe o cases (be i ac i e cases, numbe o dea hs, o
numbe o eco e ies) may be di icul o unde s and o he a e age use .
This a iable aims o p ojec hese numbe s as a a io o numbe o cases
o numbe o inhabi an s, p o iding a densi y o cases wi hin a pa o he
popula ion.
Popula ion-Weigh ed Densi y (PWD)
PWD is one o he a iables ha explain he sp ead o he COVID-19
pandemic [38]. As his a iable desc ibes he densi y a which an a e age
pe son li es, i can be obse ed ha his in luences he a e o dea hs caused
by he co ona i us. Inc eased social dis ancing esul s in a educ ion in PWD
(albei empo a ily), indica ing e ec i eness in measu es aken by a uni o
go e nmen .
3.3.2 Indica o s
T ansmission Index (R0)
The ansmission index [34] ep esen s he powe o i us ansmission
and can be in e ed by calcula ing he a io be ween he numbe o in ec ions
eco ded a a ce ain day and he numbe o in ec ions eco ded 14 days
be o e , as seen in Equa ion 3.2. A alue o 1 o g ea e indica es dange in
he ansmission o he i us while a alue o less han 1 indica es no dange .
c
R0( ) = In ( )
In ( −14) (3.2)
CHAPTER 3. DATA OF INTEREST FOR THE PROJECT 34
Cumula i e Incidence pe 100,000 Inhabi an s (Inc)
As seen in Equa ion 3.3, his index de e mines he cumula i e numbe o
posi i e COVID-19 cases in he pas 14 days pe 100,000 inhabi an s [34, 35].
As his a iable akes in o conside a ion he popula ion o he coun y,
i is use ul o making ai e compa isons be ween he si ua ions o di e en
coun ies wi h ega ds o he pandemic. Howe e , i s disad an age comes
om he ac ha his indica o only conside s he numbe o in ec ed people.
Inci= (
14
X
j=1
Ci−j)∗100000/P, ∀i≥15 (3.3)
Dange Index (DI)
The dange index [34] is used o de e mine he le el o dange a low
ne wo k p esen s. Using Equa ion 3.4, he DI ob ained indica es li le o no
dange i i s alue is equal o o less han 0, while a highe DI indica es a
mo e dange ous low ne wo k.
While bo h R0 and Inc only conside s he numbe o in ec ed cases, DI has
an ad an age such ha i conside s he numbe o posi i e cases, he numbe
o dea hs, and he numbe o eco e ies, each o which can be weighed o
p o ide mo e adequa e alues depending on he si ua ion. Howe e , com-
pa ed o he Inc indica o , he popula ion o he coun y is no aken in o
conside a ion.
DI( ) = In ( ) + F( )−Rec( ) = X
x∈V
x,INF ( ) + X
x∈V
x,F ( )−X
x∈V
x,R( )
(3.4)
CHAPTER 3. DATA OF INTEREST FOR THE PROJECT 35
3.3.3 Smoo hed Va iables and Indica o s
In ob aining da a on COVID-19, he e a e imes when la ge peaks a e ol-
lowed by sudden d ops in alue, possibly esul ing in o noisy da a [39]. To
coun e his, da a smoo hing is applied by using an algo i hm o emo e noise
om a da ase and allow pa e ns o s and ou mo e clea ly.
In his case, he a iables and indica o s men ioned p e iously we e smoo hed
by calcula ing he 7-day mo ing a e ages, ound in Equa ion 3.5.
∀i≥4, mm(xi) = 1
7
i+3
X
k=i−3
(xi)(3.5)
Chap e 4
Da a P epa a ion
A e ha ing a lis o possible sou ces o da a o he p ojec , he da a would
need o be p epa ed o use in he dashboa d. This p epa a ion p ocess
includes ope a ions such as uni ying he o ma o da a, cleaning ou in alid
da a, and placing he esul s oge he in a ile ha he dashboa d can access
and use o p esen he da a isually.
In his pa o he p ojec , a collabo a ion was ini ia ed wi h ano he
g oup, whose goal is o sea ch and analyze a ailable in o ma ion on he
COVID-19 pandemic. They would ul ima ely be in cha ge o p o iding he
da ase o be used by he dashboa d.
4.1 Da abases Used as Bases o Da abase C e-
a ion
As p e iously explained, he pa ne g oup was in cha ge o ob aining a ail-
able in o ma ion on he si ua ions o each coun y. While mos in o ma ion
can be ound on he websi e o each coun y’s heal h agency (which was
he case o majo coun ies wi h conside ably eliably go e nmen s such as
Spain, Japan, and Russia), he ollowing eposi o ies a e highligh ed due o
he ac ha hey con ain in o ma ion ha has al eady been compiled and
is cons an ly upda ed o con enience in da a ga he ing:
•h ps://gi hub.com/da ase s/co id-19
•h ps://gi hub.com/cssegisandda a/co id-19
36

CHAPTER 4. DATA PREPARATION 37
4.2 Da ase C ea ion o P ojec Use
4.2.1 Fi s Da ase
In o de o ini ialize he he de elopmen o he p ojec , ano he da ase
mus be empo a ily used o es ing he unc ionali y o he dashboa d. Fo
his pu pose, a da ase p o ided by he p ojec di ec o s ha con ained da a
on he pandemic si ua ion in 14 coun ies was used.
The ollowing columns a e ound in his da ase :
•COUNTRY: coun y in which he da a was eco ded
•FECHA: da e o eco ding
•CONTAGIADOS: daily numbe o posi i e cases o COVID-19
•FALLECIDOS: daily numbe o dea hs caused by COVID-19
•HOSPITALIZADOS: daily numbe o hospi aliza ions due o COVID-19
•UCIs: daily numbe o COVID-19 pa ien s admi ed in o ICUs
4.2.2 Second Da ase
C ea ed by he pa ne g oup, his da ase is sepa a ed in o wo iles: one
con aining da a pe aining o he Uni ed S a es alone and ano he con aining
da a o he es o he wo ld. Compa ed o he p e ious da ase , his con-
ains in o ma ion on mo e coun ies, as well as mo e columns ha conside
a iables ha we e p e iously no p esen , such as he numbe o ac i e cases
and he dange index [34, 35]. Ano he di e ence is ha , when applicable,
da a is also o ganized by he p o inces o he in ol ed coun ies.
This da ase consis s o he ollowing columns:
•Coun y_Region: coun y/ egion in which he da a was eco ded
•P o ince: p o ince o he coun y in which he da a was eco ded, i
a ailable
•La : la i udinal coo dina e o he a ea’s loca ion
•Long: longi udinal coo dina e o he a ea’s loca ion
CHAPTER 4. DATA PREPARATION 38
•Las _Upda e1: da e o eco ding
•Con i med: cumula i e numbe o con i med posi i e COVID-19 cases
•Dea hs: cumula i e numbe o dea hs caused by COVID-19
•Reco e ed: cumula i e numbe o COVID-19 eco e ies
•Ac i e: cumula i e numbe o ac i e COVID-19 cases
•Daily_Con i med: daily numbe o con i med posi i e COVID-19 cases
•Daily_Dea hs: daily numbe o dea hs caused by COVID-19
•Daily_Reco e ed: daily numbe o COVID-19 eco e ies
•Daily_Ac i e: daily numbe o ac i e COVID-19 cases
•IP: dange index
•R0: ansmission index
•Daily_Con i medMM: mo ing a e age o he con i med posi i e COVID-
19 cases
•Daily_Mue osMM: mo ing a e age o he dea hs cause by COVID-19
•Daily_Recupe adosMM: mo ing a e age o he COVID-19 eco e ies
•Daily_Ac i osMM: mo ing a e age o he ac i e COVID-19 cases
•IPMM: mo ing a e age o he dange index
•R0MM: mo ing a e age o he ansmission index
Meanwhile, he da ase pe aining o he Uni ed S a es con ained addi-
ional columns:
•Daily_Tes ed: daily numbe o people who ook he COVID-19 es
•Daily_Hospi alized: daily numbe o people admi ed o he hospi al
due o COVID-19
•People_Tes ed: cumula i e numbe o people who ook he COVID-19
es
CHAPTER 4. DATA PREPARATION 39
•People_Hospi alized: cumula i e numbe o people admi ed o he
hospi al due o COVID-19
4.2.3 Final Da ase
Upon s udying he p e ious da ase , he ollowing issues we e encoun e ed:
•The da ase con ained columns deemed necessa y o he dashboa d
implemen a ion (i.e. La , Long).
•The iles con aining he da ase has a o al size equal o 50MB. This
was a po en ial p oblem wi h ega ds o he limi in he size o he
applica ion o be uploaded in he se e .
•Some o he da a con ain names o coun ies ha do no ma ch wi h
hose used in he dashboa d, pa icula ly i s map unc ionali y.
•The INC indica o , which was a a iable o in e es , was no included.
•The calcula ion o he mo ing a e age was done by egion, his was a
p oblem as he dashboa d being implemen ed was o ganized by coun y.
The eam had c ea ed an R sc ip named da aT ans o m.R o sol e hese
issues. This sc ip pa ses he da a om bo h da ase iles and pe o ms he
necessa y ans o ma ions (i.e. column enaming, da a me ging, calcula ion
o ex a a iables and smoo hing a iables and indica o s using mo ing a -
e ages), c ea ing he inal da ase , da ase CODA.cs . The s uc u e o his
da ase is explained in he Chap e 6.
4.3 Da ase Quali y E alua ion
C ea ing he da ase o he dashboa d in ol ed mul iple asks ha equi ed
no only ime and e o , bu also he equi ed knowledge o pe o m such
asks and in o de o edac he da ase e icien ly. No only does he da ase
ha e o be well-de ined, i also has o be eliable. One impo an ask in his
p ocess is he e alua ion o he quali y o hese da ase s. I gi es he idea o
how alid he in o ma ion inside a da ase can be. This can be es ima ed by
some quali a i e ac o s p esen in he da a.
CHAPTER 4. DATA PREPARATION 40
An impo an aspec abou dashboa ds is ha pa o hei quali y and
e ec i eness is de i ed om he da ase i uses o p esen in o ma ion. As
such, i is impo an o e alua e a leas he inal da ase used.
Wo king wi h his da ase , he ollowing obse a ions, some o which led
o modi ica ions in he p ojec , we e made:
•Some coun ies do no ha e upda ed da a.
•Some alues, like eco dings o nega i e dea hs, do no make sense and
can be a ibu ed o e o s in eco ding da a.
•Mos coun ies con ain ou lie da a due o inconsis encies such as in-
co po a ing da a eco ded in a ce ain day in o ha o a la e da e.
•Da a om coun ies such as No h Ko ea, An a c ica, Wes e n Saha a
and Tu kmenis an a e una ailable and he e o e could no be shown in
he dashboa d.
CHAPTER 5. PROJECT DEVELOPMENT 47
Figu e 5.5: Final p o o ype design. Map View 3
Figu e 5.6: Final p o o ype design. G aph View

CHAPTER 5. PROJECT DEVELOPMENT 48
Imp o emen s o Dashboa d Implemen a ion
As ag eed in he p o o ype design, he dashboa d was spli in o wo sc eens/ abs,
one o p esen ing use s he g aphs ha show he e olu ion o he di e en
a iables in a ious coun ies (see Figu e 5.7) and ano he one o displaying
he cumula i e numbe s o each coun y in a map (see Figu e 5.8). Addi ion-
ally, he g aph unc ion allows use s o see he e olu ion o he pandemic in
mul iple coun ies o mul iple a iables o allow compa isons.
Figu e 5.7: Sp in 1 esul (g aph iew)
Figu e 5.8: Sp in 1 esul (map iew)
CHAPTER 5. PROJECT DEVELOPMENT 49
Sp in E alua ion
A he end o he sp in , some o he asks included in he backlog o his
i e a ion we e no implemen ed, such as he upda ing Lis View UI in he
Map View. Ins ead, only an In o ma ion Panel UI ha con ained no da a
was displayed. A mee ing was hen held wi h he p ojec di ec o o p o ide
upda es on he p og ess o he p ojec , as well as p esen he dashboa d in i s
s a e a e he sp in . Feedback was ecei ed om he di ec o , which would
hen be aken in o conside a ion upon beginning he nex sp in . Some o
hese included changing he names o he coun ies in he map iew o hei
English names, as hey we e o iginally p esen ed as hei espec i e names
in hei own languages, selec ing mo e han jus wo coun ies o a iables,
including a legend o g aphs, and p esen ing a summa y o all da a in he
in o ma ion panel by means o p esen ing, o example, he coun ies wi h
he mos accumula ed numbe o posi i e cases.
5.4 Second Sp in
Sp in Planning
A e conduc ing a mee ing a he end o he i s sp in , we ha e selec ed
asks o be included in he second sp in backlog as shown in Table 5.3. Also
lea ning om ou expe ience in he i s sp in , some o he asks ha e been
sligh ly modi ied o accommoda e he limi a ions p esen ed by Shiny, such as
di icul ies in laye ing componen s o he dashboa d UI. As p e iously done,
he asks ha e been di ided in o smalle ones o ensu e manageabili y by he
eam membe s.
Design
As p e iously s a ed, he second sp in ocuses on adjus ing he imple-
men a ion o he dashboa d o he design limi a ions o Shiny and on e ining
he dashboa d con en s as much as possible. As such, he design p o o ypes
ha e been e ined, as seen in Figu es 5.9 and 5.10.
CHAPTER 5. PROJECT DEVELOPMENT 50
S o y Task
1
A. Implemen mouse ho e unc ion o show da a a a speci ic
poin in he g aph
B. Implemen legends in he g aph
C. Show he axis labels bigge
D. Fix da e axis scale o 1 week
E. Fix da e axis angle o 45 deg ees
F. Add con ols o isualize g aph (zoom in/ou , pan, au o-scale)
G. Reloca e he panel o il e s abo e he g aph.
2
A. Se colo di ision o pe cen ages depending on he highes
numbe o accumula ed cases o he chosen a iable
B. Implemen mouse ho e unc ion o show he coun y’s name
and he alue o he a iable chosen
C. Change he map o show all coun ies in English
8
A. Limi o 5 coun ies o choose
B. Remo e Single Va iable/Coun y Mode Radio Bu on and
se de aul mode o Mul iple Coun y
9
A. Limi o 5 a iables o choose
B. Es ablish a pale e o colo s o be used o e e y di e en line
o be d awn in he g aph.
10
A. Change UI o slide o bo h Map and G aph Views
B. Pu con ols o modi y bo h he s a and end da e in he G aph
View
11
A. In es iga e o he g aphs ha could well desc ibe some a iables
B. Implemen a adio bu on UI o choose be ween some
ype o g aphs
C. Implemen se e unc ions o isualize selec ed g aph
12
A. Implemen a able showing 5 coun ies wi h he
highes numbe o accumula ed cases o he chosen a iable (o he
i s a iable chosen in mul iple a iables mode) in he G aph View
B. Implemen he same able in he Map View
C. Implemen ano he In o ma ion Panel UI in he G aph View
ha shows he o al numbe o in ec ed and dea h cases
D. Implemen se e unc ions o calcula e hese a iables
E. Reloca e he In o ma ion Panel and Fil e Selec Box UI in he
Map View and place i o e he map
Table 5.3: Second sp in backlog
CHAPTER 5. PROJECT DEVELOPMENT 51
Figu e 5.9: Re ised p o o ype design (g aph iew)
Figu e 5.10: Re ised p o o ype design (map iew)
CHAPTER 5. PROJECT DEVELOPMENT 52
Imp o emen s o Dashboa d Implemen a ion
One o he bigges changes in oduced in he dashboa d implemen a ion
o his sp in is he op ion o selec mo e han 2 coun ies when selec ing he
"mul iple coun ies" op ion. Use s would now be allowed o choose up o 5
coun ies o be displayed in he g aph. This limi was placed o allow use s
o compa e di e en coun ies wi hou possibly o e c owding he esul ing
g aph wi h a ious lines ha may become un eadable.
Ano he majo change in he g aph iew o he dashboa d is he inclusion
o box plo s. As discussed in Sec ion 3.2, box plo s a e used o de e mine
he de ia ion o he da a and he nume ical ange hey occupy, which use s
may ind in e es ing.
The inal majo change in he g aph iew o he dashboa d is ha he
in o ma ion panel om he p e ious sp in was eplaced wi h wo smalle
in o ma ion panels ha p esen summa ies abou he cu en pandemic si u-
a ion globally. Fo example, use s could o see he o al numbe o cases and
dea hs eco ded wo ldwide. They could also ind a lis o he op coun ies
in e ms o numbe o posi i e COVID-19 cases.
Figu e 5.11: Sp in 2 esul (line g aph)
Wi h espec o he map iew o he dashboa d, he coun y names we e
all enamed o hei English names. This was done by eplacing he map

CHAPTER 5. PROJECT DEVELOPMENT 53
Figu e 5.12: Sp in 2 esul (box plo )
used wi h he one by Es i, which also changed o he colo pale e o shades
o blue. A legend was also placed o indica e he alues a ce ain colo in he
map ep esen s. The panel o il e s oge he wi h he In o ma ion Panel
we e placed o e he map o maximize he space o be occupied by he map
i sel . Finally, he in o ma ion panel, implemen ed in he 1s i e a ion and
ha con ained da a, is epu posed o p esen he coun ies wi h he highes
numbe o COVID-19 in ec ions.
Sp in E alua ion
Feedback om he p ojec di ec o included changing he colo s om
shades o blue o shades o ed, as his would appea mo e ala ming and
would gi e he imp ession ha he e is an ongoing h ea , he inclusion o
digi g oup sepa a o s in he numbe s p esen ed in he dashboa d o imp o e
eadabili y, and changing he labels in he in o ma ion panels o he G aph
View (i.e. changing "To al cases" o "Global numbe o cases") o p e en
con usion. Addi ionally, i would p esen a bigge con enience o use s o
he dashboa d o ha e a unc ion ha cap u es he g aph being shown, be i
he en i e g aph o a po ion o i .
CHAPTER 5. PROJECT DEVELOPMENT 54
Figu e 5.13: Sp in 2 esul (map iew)
CHAPTER 5. PROJECT DEVELOPMENT 55
5.5 Thi d Sp in
Sp in Planning
The sp in began by selec ing he asks o be execu ed, elabo a ed in he
sp in backlog ound in Table 5.4
S o y Task
1
A. P ocess he new da ase o adap i wi h he unc ions
o he dashboa d
B. Add he accumula ed a iables in he selec box UI
C. Include Dange Index o he lis o a iables ha can be shown
as g aphs
2A. Change he colo pale e o he map o indica e in ensi y o
numbe s by shades o ed
5
A. Implemen UI o iew Coun y Table in he Map View
B. Implemen Upda e Coun y Table UI se e unc ion
C. Implemen map click unc ion o enable his ea u e
12 A. Imp o e he o ma ing o numbe s by placing digi g oup
sepa a o s
Table 5.4: Thi d sp in backlog
Design
Imp o ing on he dashboa d designs c ea ed in he p e ious sp in and
conside ing he eedback ecei ed in he p e ious sp in e alua ion, he p e-
sen a ion o he da a and he labels ound in he g aph iew o he dashboa d
we e enhanced (see Figu e 5.14). The colo s used in he map we e changed o
shades o ed and a panel ha upda es whene e he use clicks on a coun y
in he map o see mo e in o ma ion was included (see Figu e 5.15).
CHAPTER 5. PROJECT DEVELOPMENT 56
Figu e 5.14: Re ised p o o ype design (g aph iew)
Figu e 5.15: Re ised p o o ype design (map iew)
CHAPTER 5. PROJECT DEVELOPMENT 63
Figu e 5.20: Websi e design (home iew)

CHAPTER 5. PROJECT DEVELOPMENT 64
Imp o emen s o Dashboa d Implemen a ion
The mos signi ican implemen a ion in his sp in is he c ea ion o he
websi e in Google Si es. In he websi e, bo h he comple ely de eloped dash-
boa d and addi ional in o ma ion on bo h he p ojec i sel and he eam
ha made he dashboa d we e placed. By c ea ing and using a CSS ile
s yle.css o indica e he s yle adjus men s and desc ibe how HTML ele-
men s will be displayed in he web page (ma gins, paddings, e c.), he design
o he websi e was made such ha i allowed he dashboa d o i wi hin he
alloca ed space when in eg a ed. The CSS ile is included along wi h he
sou ce code.
Ano he impo an change is ha he sou ce code was e ac o ed, esul -
ing in a mo e e icien ou pu o da a by he dashboa d. The memo y usage
o he dashboa d is also kep below 1 GB, allowing he eam o con inue
using he ee plan o Shinyapps.io.
O he changes made in his i e a ion include he addi ion o he Dange
Index, R0 and Inc a iables in he g aph iew o he dashboa d. The alues
unde he Dange Index, R0, Inc columns we e calcula ed using Equa ions
3.5, 3.2, 3.3 ound in Chap e 3. In addi ion, he Wo ld Popula ions P ospec s
2019 da ase published by he Uni ed Na ions [42] was u ilized o ob ain he
popula ions o each coun y, which we e used o calcula e Inc. Some mino
design changes, such as he swapping o o al and pa ial ables in he map
iew o he dashboa d and he addi ion o da a poin s in he plo o allow
u he isualiza ion o he g aph, we e also made.
CHAPTER 5. PROJECT DEVELOPMENT 65
Figu e 5.21: Websi e implemen a ion (home iew)
Figu e 5.22: Websi e implemen a ion(abou us iew)
CHAPTER 5. PROJECT DEVELOPMENT 66
Figu e 5.23: Websi e implemen a ion (abou his p ojec iew)
CHAPTER 5. PROJECT DEVELOPMENT 67
Sp in E alua ion
In his sp in e alua ion, he web page’s design was well- ecei ed. The
la es de elopmen s done in he dashboa d’s implemen a ion and i s design
we e all accep ed.
Mino adjus men s, like co ec ing g amma ical e o s in labels in he
dashboa d and changing he adio bu ons o a d op-down lis o he a i-
ables in he map, would ha e o be made. Some addi ional in o ma ion, such
as he use manual, we e also equi ed in he Abou This P ojec page o
he websi e. These mino co ec ions we e hen made immedia ely and we e
included in he sp in backlog o his i e a ion.
Wi h all c i e ia lis ed in he P oduc Backlog ul illed and e alua ed,
he de elopmen phase o he dashboa d was concluded and deployed o
assessmen .
5.7 RS udio Lib a ies Used
Aside om he base RS udio lib a y, which is used whene e he RS udio
IDE is launched, he ollowing lib a ies we e u ilized in he de elopmen o
he dashboa d:
•shiny: R amewo k used o build he applica ion’s UI and se e
in e ac ions
•shinydashboa d: used o design he s uc u e o he panels o he
applica ion
•dply : used o pa sing, p ocessing and c ea ing da a ames in he
Rs udio IDE
•lea le : used o c ea ing and isualizing he map
•ggplo 2: used o c ea ing and isualizing he g aph
• gdal: used o pa sing a shape ile which is hen needed o c ea e and
d aw polygon laye s on he map
• eshape2: used o me ging da a ames mo e e ec i ely han dply
CHAPTER 5. PROJECT DEVELOPMENT 68
•h ml ools: used o manipula ing HTML elemen s such as he ho e
ool on he map
•plo ly: used o he addi ional ea u es o he g aph such as he oolba
and he ho e unc ion
In summa y, he dashboa d con ains he a chi ec u e seen in Figu e 5.24.
Figu e 5.24: Dashboa d a chi ec u e
5.8 Sc um Sp in Schedule
Figu e 5.25 p esen s he da es in which each sp in was execu ed. The du a-
ion o each sp in was app oxima ely wo o h ee weeks.
Al hough he i s sp in , ha las ed om Janua y 11, 2021 o Feb ua y
19, 2021, seemed ex ensi e, he e ec i e de elopmen ime on he p ojec was
2 weeks, as his pe iod coincided wi h he comple ion o nume ous academic
equi emen s.

CHAPTER 5. PROJECT DEVELOPMENT 69
Figu e 5.25: Realized sp in da es
Chap e 6
Resul s
In his chap e , he esul s o he de elopmen o he dashboa d and he
co esponding web page a e p esen ed.
To p ope ly in e p e hese esul s, some assump ions mus be made.
One assump ion is ha he da a p o ided by each coun y is upda ed almos
daily. Ano he assump ion is ha he da a u h ully e lec s he si ua ion
o he pandemic o each coun y. The inal assump ion is ha he alues
eco ded o each day do no con ain e o s ha d as ically a ec p e ious
da a. Howe e , in some pe iods o ime, some coun ies ha e p o ided da a
ha iola e hese assump ions, as hey we e published in such manne .
In he i s sec ion, he inal da ase is explained, along wi h i s s uc u e.
The second sec ion p esen s he comple ed dashboa d and websi e, along
wi h a sho guide ha explains hei usage. To u he demons a e he
use o he dashboa d and he websi e, he hi d sec ion p esen s an example
analysis ha compa es he si ua ion o he pandemic in Spain and in he
Philippines. The inal sec ion o his chap e p o ides a guide on in eg a ing
he dashboa d o o he websi es.
6.1 Final Da ase
The inal da ase c ea ed o he use o he dashboa d was also made a ailable
in a public Google D i e olde , ound a :
•h ps:// inyu l.com/CODAda ase .
This da ase con ains he ollowing columns, each o which may be used
o u he analyses:
70
CHAPTER 6. RESULTS 71
•COUNTRY: coun y/ egion in which he da a was eco ded
•DATE: da e o eco ding
•DAILY_CONFIRMED: daily numbe o con i med posi i e COVID-19 cases
•DAILY_DEATHS: daily numbe o dea hs caused by COVID-19
•DAILY_RECOVERED: daily numbe o COVID-19 eco e ies
•ACTIVE: cumula i e numbe o ac i e COVID-19 cases
•CUMULATIVE_CONFIRMED: cumula i e numbe o con i med posi i e COVID-
19 cases
•CUMULATIVE_DEATHS: cumula i e numbe o dea hs caused by COVID-
19
•CUMULATIVE_RECOVERED: cumula i e numbe o COVID-19 eco e ies
•DANGER_INDEX: dange index as o he da e o eco ding
•R0: ansmission index as o he da e o eco ding
•INC: cumula i e incidence pe 100,000 habi an s as o he da e o eco d-
ing
•SMOOTH_*: smoo hed a iable/indica o using mo ing a e age
Addi ionally, he Inc alues o Spain in his da ase (see Figu e 6.1) was
compa ed o he g aph c ea ed using he o icial published da a o he Spanish
Minis y o Heal h [43] (see Figu e 6.2) o e i y he accu acy o he calcu-
la ions done o ob ain he indica o . The wo se s o da a co e he same
ange o da es, om July 2020 o May 2021. As a esul , he alues ound in
he dashboa d a e sligh ly highe han hose ound in he o icial da a, which
may be a ibu ed o sligh ly di e en alues used o he popula ion o Spain.
None heless, he pa e n c ea ed by he g aph in he dashboa d ma ches ha
in he o icial g aph, meaning ha he calcula ions we e success ul.
CHAPTER 6. RESULTS 72
Figu e 6.1: Inc o Spain om July 2020 o May 2021, as shown in Dashboa d
Figu e 6.2: Inc o Spain om July 2020 o May 2021
6.2 Dashboa d and Websi e
The websi e c ea ed h oughou he du a ion o his p ojec (see Figu e 6.3)
was published online.
The ollowing links may be accessed o ind he dashboa d and websi e,
as well as he sou ce code o he p ojec :
CHAPTER 6. RESULTS 79
Figu e 6.10: Map compa ing Spain and he Philippines unde Inc on June 8,
2020
6.4 In eg a ing he Dashboa d o O he Web-
si es
As he de eloped dashboa d, being deployed in Shinyapps.io, is cloud-based,
use s can in eg a e he dashboa d in o hei own websi es by simply adding
i as an HTML elemen using he ollowing code:
<i ame heigh ="720" wid h="100%" amebo de ="no"
s c="h ps://co id-2019-dashboa d.shinyapps.io/dashboa d" />
The ad an age o his is ha , using he heigh and wid h a ibu es,
use s may also change he dimensions o he dashboa d o help i i in any
websi e layou .

Chap e 7
Indi idual Con ibu ions
Th oughou he en i e de elopmen o he p ojec , each membe has con-
ibu ed in e e y phase, be i h ough esea ch, h ough implemen a ion,
o h ough e i ica ion. Fo ull anspa ency, each eam membe has ex-
pounded his own con ibu ions in his chap e .
7.1 Con ibu ions made by Ad ián Ga cía Sánchez
A e he es ablishmen o he main heme o he p ojec , he objec i es and
mo i a ion, I was in cha ge o es ablishing he wo k plan oge he wi h he
s uc u e o he documen , which has allowed us du ing he whole p ojec o
be able o be o ganized and ocused on ou objec i es, hus a oiding some
isks ela ed o he non-achie emen o he objec i es o o he diso ganiza-
ion and was e o ime and e o .
In he esea ch phase I ocused on he echnical equi emen s o he
c ea ion o a dashboa d, such as he di e en laye s ha e e y unc ional
dashboa d equi es. These a e he backend, which is he pa in cha ge o
making all he logic o a page wo k, he on end, which is he pa o he
so wa e ha in e ac s wi h he use s, and o cou se he da a om which he
dashboa d is ed. Toge he wi h Ha old I was in esponsible o esea ching
abou he exis ing da abases on he in e ne ela ed o his opic and he
mos use ul ypes o g aphics o his p ojec , such as line g aphs, maps o
box plo s among o he s. All his allowed us o ge an idea o he main poin s
o a good dashboa d and hose hings we should a oid in ou p ojec .
In he c ea ion o he da ase , I helped in he e alua ion o he da ase s,
80
CHAPTER 7. INDIVIDUAL CONTRIBUTIONS 81
o ind possible isks in hei c ea ion o ela ed o duplica es o a bad use o
space, since shiny has a 1GB limi o i s ee plan, which is he one we use.
I did his by examining he con en s o he da ase ho oughly using R and
RS udio.
A e he esea ch phase, he h ee o us c ea ed he planning o ollow,
bo h he ools o use (R s udio, Shiny, Google Si es) and he me hodology
o use (Sc um) and also he du a ion o each i e a ion and he di ision o
asks. I was in cha ge o dis ibu ing and planning each o he i e a ions, he
p oduc backlog ha includes he assignmen o p io i ies o he asks, based
on he ini ial planning and on he p oblems ha a ose in he de elopmen
ha made us adap he planning om ime o ime. We chose Sc um o e
o he agile me hodologies o ha e his lexibili y when changing he planning.
I also did he dashboa d design o each i e a ion, based on he limi a ions
encoun e ed in he de elopmen and abo e all o make an in ui i e and simple
ool o all he public.
In he de elopmen phase, I helped in implemen ing he map by s udying
he lea le lib a y and he laye s ha can be included wi h he map such
as he legends and he polygons and shapes he de ine each coun y. I also
pe o med es ing and debugging asks o ind he bugs and hings o imp o e
in he sou ce code. A e each o he i e a ions we me wi h he di ec o and
es ablished he conclusions and p epa ed he basis o he nex i e a ion.
Rega ding he websi e I ook ca e o he c ea ion and he design and
o ganiza ion o he pages, aking in o accoun he many limi a ions ha
google si es has, bu enough o ou pu pose. The si e was o ganized ocusing
on he dashboa d, which is he mos impo an piece and occupies mos o
he main page. We added se e al ex a pages explaining a ious aspec s
o he wo k, such as i s use manual o da a eposi o ies. We also ound
ways o con ac he membe s o he g oup. I also poin ed ou he possible
modi ica ions and adjus men s conce ning he aes he ics such as he on
sizes, he posi ioning o he panels, he colo s, he ma gins, e c. This is o
imp o e he isualiza ion o he dashboa d in he web page.
Finally, we es ablished oge he he conclusions, whe e we commen ed
all he knowledge acqui ed du ing he elabo a ion o he p ojec , bo h a he
le el o ools and da a analysis o me hodology. Then we de ined some u u e
wo k, whe e we es ablish possible lines o ac ion o he con inua ion o he
p ojec . I was also in cha ge o he modi ica ion o he epo , in la ex and
h ough he O e lea pla o m, adjus ing he o ma and ixing he e o s.
CHAPTER 7. INDIVIDUAL CONTRIBUTIONS 82
7.2 Con ibu ions made by Dale F ancis Valen-
cia Calicdan
A he s a o he p ojec , i s hing we did was speci y he main opic
o his p ojec . We de ined he subjec his applica ion was abou which
was he co ona i us, i s de ini ion, o igin, and he impo ance o s udying i s
e olu ion. Wi h ha , I de ined he objec i es his whole p ojec will enci cle
in o and he main goals he eam should achie e by he end o his p ojec ’s
du a ion.
As we go on in o he in es iga ion phase o he p ojec , I was in cha ge
o s udying he di e en a iables in ol ed in s udying he e olu ion o he
co ona i us disease. I spen ime eading a icles and looking h ough he
a ious dashboa ds my colleagues we e in es iga ing o iden i y he common
a iables and o he s ha we e signi ican . I e alua ed hem by which I
chose he mos impo an ones which could be o in e es in de eloping he
dashboa d.
Upon he end o he in es iga ion phase, we mo ed on in o he de elop-
men phase. In his phase, we i s laid ou he plan he eam should ollow,
he me hodology o use, which was Sc um me hodology, he du a ion o each
i e a ion, and he pa i ion o asks. In each i e a ion I was p ima ily ocused
in building he sou ce code o he p og am in R language, while s ic ly ol-
lowing he sp in backlog and he design es ablished o e e y i e a ion. This
also included in esea ching and s udying a ious lib a ies which could be
o use in de eloping he dashboa d. By he end o e e y i e a ion, I also
epo ed he a ious limi a ions I ha e encoun e ed. This led in o sugges-
ions on how o imp o e he dashboa d o he nex i e a ion. By he las
i e a ion, I uploaded he dashboa d in o he dedica ed se e which included
signing up an accoun , uploading he sou ce code and esou ces, and inally,
deploying he applica ion.
A he begginning o his phase, we wo ked on an ini ial da abase p o ided
by he di ec o s o he p ojec , which is a small da ase con aining eco dings
o co ona i us cases o some coun ies. By he 3 d i e a ion, he i s e sion
o he p oposed da ase , c ea ed by he o he g oup, was sen o us. The
o ma o his da ase (numbe o columns, column names, a iables a ailable,
e c.) was di e en om he one we p e iously wo ked on. I es uc u ed he
code and adap ed i o he o ma he p oposed da ase used.
La e on i was seen ha he da a in he da ase p o ided by he pa ne
CHAPTER 7. INDIVIDUAL CONTRIBUTIONS 83
g oup was o ganized by egions while he dashboa d is s uc u ed o show
da a by coun y. We also lea ned ha calcula ions done inside he sou ce
code akes a lo o memo y. Wi h hese indings, he eam decided o c ea e
ano he da ase by ans o ming he ones we we e p o ided. I c ea ed he
sc ip which pe o ms he necessa y ans o ma ion o he da ase , as well as
he calcula ions o some a iables, o a oid unnecessa y ope a ions while he
applica ion is deployed in he se e .
By he end o he de elopmen phase, my colleagues we e implemen ing
he web page o he dashboa d. A e discussing some modi ica ions and
possible adjus men s o he HTML elemen s o he dashboa d, o i o i
in he alloca ed window in he web page, I c ea ed a CSS ile and adjus ed
he ma gins and paddings o he dashboa d as well as some o i s aes he ics
o u he imp o e i s accessibili y and usabili y.
A e he de elopmen phase, I con ibu ed in edac ing he documen a-
ion o his p ojec , assis ing in he chap e s I was p ima ily ocused a . I
explained he inal da ase ha was one o he esul s o his p ojec , and
p oo ead he en i e pa o Chap e 5 explaining he p ojec ’s de elopmen .
CHAPTER 7. INDIVIDUAL CONTRIBUTIONS 84
7.3 Con ibu ions made by Ha old Luis Pascua
Cajucom
I s a ed by spea heading he esea ch on he di e en ypes o dashboa ds
a ailable, as well as he examples o each one. I ini ially ound ha mos
dashboa ds a e used in businesses and ha he classi ica ions in ha con ex
do no wo k wi h he con ex o moni o ing COVID-19 and his led me o
analyze he exis ing dashboa ds based on hei common ac o s: geog aphic
scope and ype o da a used. A e ha , I esea ched on he ypes o so wa e
ools a ailable, especially o c ea ing and con igu ing dashboa ds.
I also con ibu ed o he esea ch o da a o in e es , namely he exis en
da abases o in e es and he g aphs o in e es . These we e da abases and
g aphs ha may be pu in o conside a ion o inclusion in he dashboa d. In
addi ion, I also esea ched on some a iables o in e es : R0, DI, and Inc.
As o he da a p epa a ion s age o he p ojec , I ocused on e alua ing
he da ase s, checking o poin s ha would become p oblema ic o conce n-
ing when in eg a ed wi h he dashboa d o display da a.
Fo he ini ial planning o he implemen a ion o he p ojec , I con ibu ed
in deciding he di e en speci ica ions. Fo example, we decided o apply he
Sc um me hodology, as we had some expe ience wi h his me hodology om
ou expe ience in lea ning So wa e Enginee ing. We also decided a his
poin o implemen he dashboa d using he RS udio IDE, oge he wi h
Shiny and he o he RS udio lib a ies used. Finally, we decided o embed
he dashboa d on o a Google Si es websi e, as we wan ed a websi e ha
could easily be con igu ed so we could ocus mo e on he de elopmen o he
dashboa d.
Also in he planning s age, we decided on wha ype o COVID-19 dash-
boa d we wan ed he inal p oduc o be. Based on he in o ma ion we
ecei ed om he esea ch on he di e en ypes o dashboa ds, we decided
o mainly ocus on epidemiological da a wi h a global scope, as we wan ed
he dashboa d o be eachable o as many people as possible.
Th oughou he de elopmen phase, I con ibu ed o all de elopmen
sp in s by es ing and debugging he dashboa d and by o mula ing he con-
clusions o each sp in . I was h ough his debug p ocess ha we we e able
o de ec he anomalies ound du ing de elopmen ha would ha e o be
esol ed by he ollowing sp in s.
I also con ibu ed in de eloping he dashboa d by implemen ing he g aphs,

CHAPTER 7. INDIVIDUAL CONTRIBUTIONS 85
especially he box plo s ha was allowed u he analysis o a iables. By
he end o his phase, I managed o e ac o he sou ce code o imp o e i s
comp ehensibili y. This includes de ining he auxilia y unc ions ound in he
unc ions.Rsc ip , such as he c ea ion o ables and e ching o in o ma ion
om he da ase .
In he p esen a ion o esul s, I p epa ed he eposi o y ha con ained he
da ase s used o he dashboa d, making hose da ase s a ailable o public
use. Addi ionally, I c ea ed he use manual ha would help o ien a e he
use s on he di e en a ailable unc ions o he dashboa d and acili a ed
he example analysis o he si ua ions o Spain and he Philippines. This
would help acili a e he use s in knowing how o use he dashboa d o ge
he esul s hey need and p ope ly in e p e ing hose esul s o ge insigh s.
Toge he wi h my colleagues, we o mula ed he conclusions made pos -
de elopmen . We mainly ocused on he hings we lea ned om he di e en
ields o dashboa d de elopmen , which includes p epa ing da a, adap ing o
using di e en lib a ies and echnologies, and da a analysis. We also c ea ed
he ecommenda ions o u u e wo k, some o which we e ac ions ha we
ini ially wan ed o ake bu we e unable o due o ime cons ain s.
Finally, I was in cha ge o asks ela ed o he documen a ion o he
p ojec . This included he compila ion o sou ces ci ed in his wo k in o he
bibliog aphy and p oo eading he wo k in gene al, making su e ha i was
ee o any g amma ical e o s and ha e e y de ail abou he p ojec was
well-explained.
Chap e 8
Conclusions and Fu u e Wo k
8.1 Conclusions
A dashboa d was c ea ed ha can in o m use s abou he p og ess o he
pandemic in g aphic and map o ms, especially unde indica o s such as
ansmission index, dange index, and cumula i e incidence.
Th oughou he p ojec , he eam has used R and Shiny, as well as li-
b a ies such as Plo ly and Lea le o de elop he dashboa d. Balsamiq Wi e-
ames was also used o c ea e wi e ames and p o o ypes o he design o
bo h he dashboa d and he websi e. Addi ionally, LaTeX was lea ned o
he c ea ion o his wo k.
Applying he Sc um me hodology in his p ojec was also an impo an
p ac ice lea ned. As Sc um has become a s anda d in mos companies, lea n-
ing how o employ his Agile me hodology in a p ac ical se ing p o es o be
use ul in p epa ing o wo k unde employmen .
Ano he c ucial skill aken om his p ojec is he applica ion o a ious
analy ical echniques. Ha ing mul iple ypes o g aphs p esen in he dash-
boa d allowed o he unde s anding o di e en aspec s o he da a (e.g. i s
e olu ion o e ime, a iance in alues). Explo a o y da a analysis also plays
a pa due o he na u e o he dashboa d o being based on a ma hema ical
model. As he sou ces o da a ound in he da ase mainly consis o simple
eco ded alues such as posi i e cases and dea hs, indica o s such as he R0,
DI, and Inc we e ma hema ically de i ed om hese eco ds.
The inal conclusion is ha , as impo an i is o analyze da a ho oughly
and ex ensi ely, making hese da a unde s andable and accessible o he pub-
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CHAPTER 8. CONCLUSIONS AND FUTURE WORK 87
lic is equally impo an . The use s o he dashboa d will consis o people o
di e en backg ounds and o di e en pe spec i es on he pandemic. Hence,
i is essen ial ha he same idea is objec i ely impa ed o hem by he
dashboa d o a ain i s pu pose.
8.2 Fu u e Wo k
Despi e ha ing success ully c ea ed a ully unc ioning COVID-19 dashboa d,
he eam s ongly belie es ha he p ojec s ill con ains oom o imp o e-
men .
The i s ecommended ac ion is he au oma ion o he da a acquisi ion
p ocess. Cu en ly, he dashboa d uses only one da ase , meaning ha he
da a used could easily become obsole e in a ma e o days i he da ase
is no cons an ly upda ed. Manually upda ing his da ase by uploading a
la e e sion a e a gi en ime in e al is a iable ye s enuous op ion in
esol ing his issue. Thus, au oma ing his p ocess will allow he dashboa d
o s ay up o da e wi hou equi ing a la ge amoun o e o om he eam
in e ms o main enance.
Ano he ecommended ac ion is inco po a ing anima ions in o he map
iew o he dashboa d, which could ampli y i s da a isualiza ion capabili ies.
This would allow use s o see he e olu ion o he pandemic in di e en
coun ies mo e clea ly, p o iding u he in e ac ion o he applica ion.
Bibliog aphy
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[7] Sand a Du ce ic. Types o dashboa ds: S a egic, ope a ional & an-
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