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Science
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1780
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K
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s
:
C
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D
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19
Data
s
et
Ma
ch
in
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lear
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Pro
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alg
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im
p
o
r
tan
t
to
esti
m
ate
th
e
n
u
m
b
er
o
f
h
id
d
en
lay
e
r
s
an
d
t
h
e
ir
ch
r
o
n
o
lo
g
ical
o
r
d
e
r
[
1
6
]
.
Ot
h
er
r
esu
lts
s
u
g
g
est
s
tatis
t
ical
an
aly
s
is
,
m
o
d
elin
g
,
an
d
a
r
tific
ial
in
tellig
en
ce
co
n
tain
th
e
s
p
r
ea
d
o
f
C
OVI
D
-
1
9
an
d
h
ig
h
li
g
h
t
th
e
im
p
ac
t in
th
e
co
m
in
g
d
a
y
s
[
1
7
]
-
[
1
9
]
.
T
o
g
e
t
t
h
e
s
p
r
e
a
d
o
f
C
O
V
I
D
-
1
9
f
o
r
t
h
e
n
e
x
t
p
e
r
i
o
d
,
c
a
n
u
s
e
t
h
e
p
r
o
p
h
e
t
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m
,
w
h
e
r
e
t
h
e
p
r
o
p
h
e
t
al
g
o
r
i
t
h
m
wo
r
k
s
w
i
t
h
t
i
m
e
-
s
e
r
i
es
d
a
ta
t
h
a
t
i
s
cl
a
i
m
e
d
t
o
h
a
v
e
a
f
ai
r
l
y
h
i
g
h
l
e
v
e
l
o
f
p
r
e
d
i
ct
i
o
n
a
c
c
u
r
a
c
y
[
2
0
]
.
T
h
i
s
p
r
o
p
h
e
t
a
l
g
o
r
i
t
h
m
o
n
l
y
r
e
q
u
i
r
e
s
t
w
o
v
a
l
u
e
s
i
n
o
n
e
d
a
t
a
f
r
a
m
e
t
a
b
l
e,
n
a
m
e
l
y
a
c
o
l
u
m
n
c
o
n
t
a
i
n
i
n
g
t
h
e
t
i
m
e/
d
a
t
e
/
d
ay
a
n
d
a
c
o
l
u
m
n
c
o
n
t
a
i
n
i
n
g
a
v
a
l
u
e
t
h
a
t
w
e
wi
l
l
p
r
e
d
ic
t
[
2
0
]
.
T
h
i
s
a
l
g
o
r
i
t
h
m
i
s
c
o
n
s
i
d
e
r
e
d
ac
c
u
r
a
t
e
b
e
c
a
u
s
e
t
h
e
d
a
t
a
c
o
n
ta
in
s
t
i
m
e
v
a
r
ia
b
l
e
s
a
d
j
u
s
te
d
t
o
t
h
e
d
a
t
a
s
et
[
2
1
]
-
[
2
3
]
.
I
n
th
e
p
r
e
v
io
u
s
an
aly
s
is
with
p
r
o
p
h
et
alg
o
r
ith
m
,
t
h
e
d
ataset
was
f
r
o
m
s
ev
er
al
o
n
lin
e
p
o
r
t
als
th
at
an
n
o
u
n
ce
d
p
u
b
lic
in
f
o
r
m
atio
n
ab
o
u
t
C
OVI
D
-
1
9
in
I
n
d
o
n
esia
th
at
was
tak
en
in
1
d
a
y
o
n
7
O
cto
b
er
2
0
2
0
[
2
4
]
.
Me
an
wh
ile,
in
th
is
s
tu
d
y
,
d
at
a
was
tak
en
f
o
r
1
y
ea
r
s
.
T
h
e
u
s
e
o
f
th
e
o
r
ig
i
n
al
d
ata
a
r
e
m
o
r
e
n
u
m
er
o
u
s
an
d
d
iv
er
s
e
th
an
p
r
e
v
io
u
s
r
esear
c
h
wh
ich
in
clu
d
e
d
d
ata
o
n
we
ek
d
ay
s
an
d
h
o
lid
ay
s
is
ex
p
ec
ted
to
im
p
r
o
v
e
th
e
ac
cu
r
ac
y
o
f
th
e
m
o
d
el
p
r
o
p
h
e
t.
T
h
is
s
tu
d
y
will
p
r
ed
ict
a
p
o
s
itiv
e
n
u
m
b
er
o
f
co
n
f
ir
m
ed
,
r
ec
o
v
er
ed
,
a
n
d
d
ie
d
f
o
r
th
e
n
ex
t y
ea
r
.
2.
CO
VID
-
1
9
DATAS
E
T
T
h
e
d
ataset
u
s
ed
co
m
es
f
r
o
m
Kaw
alC
OVI
D1
9
web
s
ite.
T
h
e
d
ata
was
tak
en
f
r
o
m
2
Ma
r
c
h
2
0
2
0
to
1
2
Feb
r
u
ar
y
2
0
2
1
.
T
h
e
d
ata
o
b
tain
ed
wer
e
in
th
e
f
o
r
m
o
f
d
a
ily
ca
s
es o
f
p
o
s
itiv
e
co
v
id
p
atien
ts
.
T
h
en
,
th
e
d
ata
is
m
ad
e
in
to
d
ataset
as
s
h
o
w
i
n
T
ab
le
1
.
B
ased
o
n
th
e
T
ab
le
1
,
th
e
n
u
m
b
er
o
f
d
ata
s
ets
u
s
ed
in
th
is
s
tu
d
y
was
3
4
7
.
T
h
e
d
ata
co
n
s
is
ted
o
f
c
o
n
f
ir
m
e
d
,
r
ec
o
v
er
ed
,
an
d
d
ea
th
f
r
o
m
c
o
v
id
p
atien
ts
.
W
e
ca
n
s
ee
th
at
th
er
e
is
in
cr
ea
s
e
d
ata
o
f
c
o
n
f
ir
m
e
d
,
d
e
ath
an
d
r
ec
o
v
er
e
d
in
e
v
er
y
d
a
y
.
T
ab
le
1
.
Data
s
et
o
f
co
n
f
ir
m
e
d
,
d
ea
th
,
an
d
r
ec
o
v
er
ed
o
f
C
OVI
D
-
1
9
[
2
5
]
O
b
serv
a
t
i
o
n
D
a
t
e
C
o
n
f
i
r
me
d
D
e
a
t
h
s
R
e
c
o
v
e
r
e
d
0
M
a
r
c
h
0
2
,
2
0
2
0
2
0
0
1
M
a
r
c
h
0
3
,
2
0
2
0
2
0
0
2
M
a
r
c
h
0
4
,
2
0
2
0
2
0
0
3
M
a
r
c
h
0
5
,
2
0
2
0
2
0
0
4
M
a
r
c
h
0
6
,
2
0
2
0
4
0
0
...
...
...
...
...
3
4
5
F
e
b
r
u
a
r
y
1
1
,
2
0
2
1
1
,
1
9
1
,
9
9
0
3
2
,
3
8
1
9
9
3
,
1
1
7
3
4
6
F
e
b
r
u
a
r
y
1
2
,
2
0
2
1
1
,
2
0
1
,
8
5
9
3
2
,
6
5
6
1
,
0
0
4
.
1
1
7
3.
SPREAD
O
F
CO
VI
D
-
19
B
ased
o
n
th
e
d
ata
o
b
tain
ed
,
th
e
s
p
r
ea
d
o
f
C
OVI
D
-
19
in
I
n
d
o
n
esia
h
as
in
cr
ea
s
ed
ev
e
r
y
m
o
n
th
.
I
n
Fig
u
r
e
1
,
we
ca
n
s
ee
t
h
at
th
e
n
u
m
b
er
o
f
co
n
f
ir
m
ed
C
OVI
D
-
19
p
atien
ts
in
cr
ea
s
es
ev
er
y
m
o
n
th
.
B
y
th
e
n
u
m
b
er
co
n
f
ir
m
e
d
af
f
ec
ted
b
y
C
OVI
D
-
19
,
m
o
s
t
o
f
th
em
r
ec
o
v
er
e
d
f
r
o
m
C
OVI
D
-
19
.
Alth
o
u
g
h
it
ap
p
ea
r
s
th
at
th
e
n
u
m
b
er
o
f
d
ea
th
s
is
less
th
an
th
e
n
u
m
b
er
r
ec
o
v
er
ed
,
th
is
v
ir
u
s
ca
n
s
till
ca
u
s
e
f
atal
th
in
g
s
,
n
am
ely
d
ea
th
.
T
ab
le
2
s
h
o
ws
th
e
ad
d
itio
n
o
f
d
aily
ca
s
es
f
o
r
th
e
last
m
o
n
th
.
I
n
th
e
T
ab
le
2
,
we
ca
n
s
ee
th
at
8
6
.
8
%
o
f
p
atien
ts
wer
e
r
ec
o
v
er
ed
f
r
o
m
C
OVI
D
-
1
9
,
an
d
as
m
an
y
as
2
.
2
5
%
wer
e
d
ied
.
I
n
th
e
tim
ef
r
am
e
f
r
o
m
Ma
r
ch
2
0
2
0
to
Feb
r
u
ar
y
2
0
2
1
,
d
ata
o
n
th
e
ad
d
itio
n
o
f
th
e
h
ig
h
es
t
d
aily
ca
s
es
d
u
r
in
g
th
e
last
m
o
n
th
wer
e
o
b
tain
ed
.
I
n
T
ab
le
3
,
we
ca
n
s
ee
th
at
th
e
h
ig
h
est
p
atien
t
d
ata
in
th
e
last
m
o
n
th
was
o
n
30
J
an
u
ar
y
2
0
2
1
,
am
o
u
n
tin
g
to
1
4
,
1
5
8
p
atien
ts
af
f
ec
ted
b
y
C
OVI
D
-
19.
T
ab
le
2
.
T
h
e
av
er
a
g
e
d
aily
ca
s
e
s
ad
d
itio
n
s
o
v
er
t
h
e
last
m
o
n
t
h
(
1
2
J
an
u
ar
y
2
0
2
1
to
1
2
Feb
r
u
ar
y
2
0
2
1
)
C
a
ses
N
u
mb
e
r
o
f
C
a
ses
C
o
n
f
i
r
me
d
11
,
454
D
e
a
t
h
s
2
5
8
R
e
c
o
v
e
r
e
d
9
,
9
4
5
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
3
,
Dec
em
b
er
2
0
2
1
:
1
7
8
0
-
1
7
8
8
1782
T
ab
le
3
.
T
h
e
h
ig
h
est in
cr
ea
s
e
in
d
aily
ca
s
es d
u
r
in
g
th
e
last
m
o
n
th
(
1
2
J
an
u
a
r
y
2
0
2
1
to
1
2
Feb
r
u
ar
y
2
0
2
1
)
P
r
e
d
i
c
t
i
o
n
N
u
mb
e
r
o
f
C
a
ses
3
0
Ja
n
u
a
r
y
2
0
2
1
14
,
1
5
8
(
C
o
n
f
i
r
me
d
)
2
8
Ja
n
u
a
r
y
2
0
2
1
4
7
6
(
D
e
a
t
h
s)
8
F
e
b
r
u
a
r
y
2
0
2
1
13
,
0
3
8
(
R
e
c
o
v
e
r
e
d
)
Fig
u
r
e
1
.
Vis
u
aliza
tio
n
o
f
ca
s
e
f
r
o
m
1
M
ar
ch
2
0
2
0
u
n
til
1
2
F
eb
r
u
ar
y
2
0
2
1
4.
P
RO
P
H
E
T
AL
G
O
R
I
T
H
M
M
O
DE
L
Pro
p
h
et
is
a
p
r
o
ce
d
u
r
e
f
o
r
p
r
ed
ictin
g
tim
e
s
er
ies
d
ata
b
ase
d
o
n
an
ad
d
itiv
e
m
o
d
el
in
wh
ich
n
o
n
-
lin
ea
r
tr
en
d
s
m
atch
d
ata
o
n
an
n
u
al,
wee
k
ly
,
d
aily
,
s
ea
s
o
n
al,
an
d
h
o
lid
ay
ef
f
ec
ts
.
T
h
e
p
r
o
p
h
et'
s
alg
o
r
ith
m
f
its
m
an
y
lin
ea
r
an
d
n
o
n
lin
ea
r
f
u
n
ctio
n
s
o
f
tim
e
as
a
c
o
m
p
o
n
en
t
(
tim
e
as
r
eg
r
ess
o
r
)
.
Fo
r
e
ca
s
tin
g
FB
p
r
o
p
h
et
wo
r
k
s
b
est
with
tim
e
s
er
ie
s
t
h
at
h
av
e
s
ea
s
o
n
al
ef
f
ec
ts
(
s
tr
o
n
g
s
ea
s
o
n
al
ef
f
ec
ts
an
d
s
o
m
e
h
is
to
r
ical
s
ea
s
o
n
d
ata)
.
FB
Pro
p
h
et
is
s
tr
o
n
g
at
d
ata
lo
s
s
an
d
tr
en
d
s
h
if
ts
,
an
d
u
s
u
ally
h
a
n
d
les
o
u
tlier
s
well.
T
h
e
s
im
p
le
f
u
n
ctio
n
s
o
f
th
e
p
r
o
p
h
et
[
2
6
]
a
lg
o
r
ith
m
is
(
1
)
.
(
)
=
(
)
+
(
)
+
ℎ
(
)
+
(
)
(
1
)
W
h
er
e:
(
)
=
tr
en
d
se
(
)
=
s
ea
s
o
n
ality
ho
(
)
=
h
o
lid
ay
id
(
)
=
in
d
iv
id
u
al
Fig
u
r
e
2
is
a
v
is
u
aliza
tio
n
o
f
t
h
e
tr
ain
in
g
d
ata
an
d
test
d
ata
.
B
ec
au
s
e
th
er
e
wer
e
to
o
m
an
y
r
ea
d
y
-
m
a
d
e
d
atasets
an
d
th
is
s
tep
was
ca
r
r
ied
o
u
t
o
n
ly
to
en
s
u
r
e
th
e
ac
cu
r
ac
y
o
f
th
e
m
o
d
el,
it
was
d
ec
id
ed
to
tak
e
o
n
l
y
th
e
last
3
m
o
n
th
s
o
f
d
ata
f
r
o
m
1
1
No
v
em
b
er
2
0
2
0
to
1
8
J
an
u
ar
y
2
0
2
1
f
o
r
tr
ai
n
d
ata
an
d
1
9
J
an
u
ar
y
2
0
2
1
to
1
2
Feb
r
u
ar
y
2
0
2
1
f
o
r
test
d
ata.
T
h
er
e
i
s
a
d
if
f
er
e
n
ce
o
f
2
5
d
ay
s
b
etwe
en
th
e
tr
ai
n
d
ata
a
n
d
th
e
test
d
ata,
f
r
o
m
th
is
g
a
p
th
e
m
o
d
el
will
b
e
s
et
t
o
p
r
e
d
ict
f
o
r
th
e
n
ex
t
2
5
d
ay
s
with
th
e
tr
ain
d
ata
in
p
u
t.
Af
ter
in
p
u
ttin
g
th
e
tr
ain
d
ata,
th
e
m
o
d
el
alr
ea
d
y
h
as
p
r
e
d
ictio
n
s
f
o
r
t
h
e
n
ex
t
2
5
d
ay
s
.
I
f
th
e
m
o
d
el
o
f
p
r
ed
ictio
n
(
b
lu
e
lin
e)
is
b
elo
w
f
r
o
m
th
e
r
ea
l
d
ata
(
r
ed
lin
e)
,
t
h
en
th
e
m
o
d
el
ca
n
b
e
u
s
ed
to
d
eter
m
in
e
p
r
ed
ictio
n
s
o
f
t
h
e
n
ex
t
s
p
r
ea
d
o
f
C
OVI
D
-
1
9
.
I
n
Fig
u
r
e
2
,
it
ca
n
b
e
s
ee
n
th
at
th
e
p
r
ed
ictio
n
m
o
d
el
o
f
c
o
n
f
ir
m
ed
,
d
ie
d
,
an
d
r
ec
o
v
e
r
ed
ar
e
b
elo
w
th
e
r
e
al
d
ata
s
o
th
at
it
ca
n
b
e
u
s
ed
to
p
r
ed
ictio
n
s
th
e
s
p
r
ea
d
o
f
C
OVI
D
-
1
9
.
T
h
e
lev
el
o
f
ac
cu
r
ac
y
is
n
ee
d
e
d
to
s
ee
h
o
w
v
alid
th
e
p
r
ed
ictio
n
m
o
d
el
we
g
et
f
r
o
m
th
e
o
r
ig
in
al
d
ata
.
T
h
e
g
r
ea
ter
th
e
v
alu
e
o
f
ac
cu
r
ac
y
,
th
e
m
o
r
e
v
alid
th
e
p
r
e
d
ic
tio
n
m
o
d
el
th
at
h
as
b
ee
n
o
b
tain
ed
.
T
h
e
f
o
llo
win
g
is
a
f
o
r
m
u
la
f
o
r
d
ete
r
m
in
in
g
th
e
ac
cu
r
ac
y
v
alu
e:
−
=
1
−
=
ℎ
ℎ
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
Tr
en
d
o
f th
e
s
p
r
ea
d
o
f COVI
D
-
1
9
in
I
n
d
o
n
esia
u
s
in
g
t
h
e
ma
ch
in
e
lea
r
n
in
g
p
r
o
p
h
et
a
lg
o
r
ith
m
(
N
u
r
Ha
ya
ti
)
1783
(
a)
(
b
)
(
c)
Fig
u
r
e
2
.
Pre
d
ictio
n
m
o
d
el
v
er
s
u
s
:
(
a)
r
ea
l d
ata
(
test
d
ata)
o
f
co
n
f
ir
m
e
d
ca
s
es,
(
b
)
d
ea
th
s
ca
s
es,
an
d
(
c)
r
ec
o
v
er
ed
ca
s
es
5.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
r
esear
ch
u
s
es
G
o
o
g
le
C
o
lab
with
p
y
th
o
n
p
r
o
g
r
am
m
in
g
lan
g
u
ag
e.
I
n
th
is
r
esear
ch
,
a
f
o
r
ec
asti
n
g
was
ca
r
r
ied
o
u
t
f
o
r
th
e
n
ex
t
1
y
ea
r
o
f
d
ata
s
tar
tin
g
f
r
o
m
1
3
F
eb
r
u
ar
y
2
0
2
1
to
1
2
F
eb
r
u
ar
y
2
0
2
2
.
T
h
er
e
ar
e
3
d
ata
th
at
will
b
e
p
r
ed
icted
(
ac
co
r
d
in
g
to
th
e
d
ataset
in
th
e
o
r
ig
in
al
d
ata)
,
n
am
el
y
co
n
f
ir
m
ed
d
ata,
r
ec
o
v
e
r
ed
an
d
d
ied
.
T
h
e
f
o
llo
win
g
is
th
e
p
r
ed
ictio
n
d
ata
f
o
r
1
y
ea
r
.
I
n
T
ab
le
4
we
ca
n
s
ee
th
at
th
e
p
r
ed
ictio
n
f
o
r
th
e
s
p
r
ea
d
o
f
C
OVI
D
-
1
9
will
in
cr
ea
s
ed
in
ev
er
y
d
ay
.
Fro
m
d
ataset
p
r
ed
ictio
n
we
c
an
p
r
ed
icted
th
e
p
er
ce
n
ta
g
e
o
f
th
r
ee
m
o
n
t
h
s
as
f
o
llo
win
g
T
ab
le
5
.
B
ased
o
n
T
ab
le
5
,
th
e
p
e
r
ce
n
tag
e
o
f
th
r
ee
m
o
n
th
s
f
o
r
c
o
n
f
ir
m
ed
p
ati
en
ts
was
2
2
.
6
0
-
4
2
.
1
1
%,
d
ie
d
was
2
1
.
6
7
-
3
9
.
0
0
%,
an
d
r
ec
o
v
e
r
ed
was
2
2
.
5
3
-
4
1
.
8
2
%.
Fu
r
th
er
m
o
r
e,
to
p
r
ed
ict
th
e
av
er
ag
e
ad
d
itio
n
o
f
d
aily
c
ases
ca
n
b
e
s
ee
n
in
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
3
,
Dec
em
b
er
2
0
2
1
:
1
7
8
0
-
1
7
8
8
1784
th
e
f
o
llo
win
g
T
ab
le
6
.
I
n
T
a
b
le
6
,
we
ca
n
s
ee
th
at
th
e
p
r
ed
icted
p
er
ce
n
tag
e
o
f
th
e
av
e
r
ag
e
ca
s
es
d
y
in
g
is
2
.
4
3
%,
an
d
ca
s
es
r
ec
o
v
er
e
d
b
y
8
0
.
7
1
%
f
o
r
ea
ch
d
a
y
.
Fu
r
th
er
m
o
r
e,
we
ca
n
s
ee
th
e
p
r
ed
ic
tio
n
o
f
th
e
h
ig
h
est
in
cr
ea
s
e
in
d
aily
ca
s
es in
th
e
T
ab
le
7
.
T
ab
le
4
.
T
h
e
p
r
e
d
ictio
n
d
ata
o
f
co
n
f
ir
m
ed
,
d
ea
th
an
d
r
ec
o
v
e
r
f
o
r
1
y
ea
r
O
b
serv
a
t
i
o
n
D
a
t
e
C
o
n
f
i
r
me
d
D
e
a
t
h
s
R
e
c
o
v
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r
e
d
3
4
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1
3
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e
b
r
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1
1
,
1
7
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,
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4
32
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350
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7
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e
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0
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a
b
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.
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h
e
p
r
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c
t
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o
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t
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e
t
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t
a
l
c
o
n
f
i
r
m
e
d
c
a
s
es
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1
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e
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r
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a
r
y
2
0
2
1
t
o
1
2
F
e
b
r
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a
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y
2
0
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)
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a
t
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f
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me
d
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t
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a
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r
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to
1
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b
r
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)
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e
d
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c
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ab
le
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.
T
h
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p
r
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d
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g
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est ad
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d
aily
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t
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J
a
n
u
a
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,
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,
2
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8
Ja
n
u
a
r
y
2
0
2
2
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,
1
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,
2
1
,
2
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Ja
n
u
a
r
y
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0
2
2
5
,
1
2
F
e
b
r
u
a
r
y
2
0
2
2
4
,
1
1
F
e
b
r
u
a
r
y
2
0
2
2
4
,
1
1
F
e
b
r
u
a
r
y
2
0
2
2
I
n
T
ab
le
7
we
ca
n
s
ee
th
e
p
r
ed
ictio
n
o
f
th
e
h
ig
h
est
ad
d
it
io
n
o
f
co
n
f
ir
m
ed
ca
s
es
o
f
C
OVI
D
-
19
o
cc
u
r
r
in
g
o
n
Satu
r
d
ay
s
ex
ce
p
t
5
M
ar
c
h
2
0
2
1
.
T
h
at
m
ea
n
s
an
in
cr
ea
s
e
in
th
e
n
u
m
b
er
o
f
co
n
f
ir
m
e
d
C
OVI
D
-
19
,
d
ied
a
n
d
r
ec
o
v
er
ed
d
u
r
in
g
h
o
lid
a
y
s
wh
ich
is
o
n
e
o
f
th
e
p
ar
am
eter
s
o
f
th
e
tr
en
d
o
n
t
h
e
p
r
o
p
h
et
alg
o
r
ith
m
.
T
h
e
r
esu
lts
o
f
th
e
p
r
ed
ictio
n
o
f
th
e
h
ig
h
est
d
aily
in
cr
ea
s
e
o
f
ca
s
es
f
r
o
m
th
e
1
-
y
ea
r
s
im
u
latio
n
ar
e
9
,
4
5
5
p
eo
p
le
f
o
r
co
n
f
i
r
m
ed
ca
s
es,
2
3
2
p
e
o
p
le
f
o
r
d
ea
d
ca
s
es
an
d
7
,
5
5
2
p
e
o
p
le
f
o
r
r
ec
o
v
er
ed
ca
s
es.
Fo
r
a
v
is
u
aliza
tio
n
o
f
th
e
1
-
y
ea
r
p
r
e
d
ictio
n
,
we
d
is
p
lay
it in
Fig
u
r
e
3
.
B
ased
o
n
Fig
u
r
e
3
,
th
e
b
lack
lin
e
s
h
o
ws
th
e
d
ataset
u
s
ed
as
tr
ain
in
g
d
ata,
w
h
ile
th
e
b
lu
e
lin
e
is
th
e
p
r
ed
ictio
n
m
o
d
el
o
f
th
e
Pr
o
p
h
et
alg
o
r
ith
m
.
B
ec
au
s
e
th
e
tr
ain
in
g
d
ata
is
alr
ea
d
y
o
n
th
e
lin
e
o
f
th
e
p
r
e
d
ictio
n
m
o
d
el,
we
ca
n
u
s
e
th
is
p
r
e
d
ictio
n
m
o
d
el
f
o
r
t
h
e
p
r
ed
ictio
n
p
r
o
ce
s
s
f
o
r
t
h
e
n
e
x
t
1
y
ea
r
.
Fo
r
b
lu
e
s
h
ad
in
g
is
th
e
l
o
wer
lim
it
an
d
u
p
p
e
r
lim
it
o
f
th
e
p
r
ed
ictio
n
d
ata
d
ata.
T
h
e
l
o
n
g
er
th
e
p
r
ed
ictio
n
tim
e,
th
e
b
ig
g
er
o
f
th
e
lo
wer
lim
it
an
d
u
p
p
er
lim
it.
Af
ter
o
b
tain
in
g
a
p
r
ed
ictio
n
m
o
d
el
an
d
p
r
ed
ictiv
e
d
ata
f
o
r
th
e
s
p
r
ea
d
o
f
C
OVI
D
-
1
9
,
th
e
au
th
o
r
s
th
en
co
m
p
a
r
e
an
d
ca
lc
u
late
th
e
ac
cu
r
ac
y
v
alu
e
o
f
th
e
p
r
ed
ictio
n
d
ata
f
o
r
th
e
n
e
x
t 1
y
ea
r
with
r
ea
l d
ata
th
at
is
cu
r
r
en
tly
r
u
n
n
in
g
f
r
o
m
1
3
Feb
r
u
a
r
y
2
0
2
1
to
3
Ma
y
2
0
2
1
.
B
ased
o
n
th
e
T
ab
le
8
,
it
ca
n
b
e
s
ee
n
th
at
th
e
ac
cu
r
ac
y
r
ate
o
f
p
atien
ts
with
co
n
f
ir
m
e
d
C
OVI
D
-
1
9
is
b
elo
w
5
0
%,
n
am
el
y
4
3
.
9
7
%.
Me
an
wh
ile,
th
e
ac
cu
r
ac
y
o
f
d
ata
r
ec
o
v
er
e
d
an
d
d
ied
is
ab
o
v
e
5
0
%.
T
h
e
h
ig
h
er
th
e
le
v
el
o
f
ac
cu
r
ac
y
o
b
tain
ed
,
th
e
m
o
r
e
ac
cu
r
ate
th
e
p
r
ed
ictio
n
m
o
d
el
will
b
e
m
a
d
e
with
th
e
r
ea
l
d
ata.
T
h
e
lev
el
o
f
ac
cu
r
ac
y
ca
n
b
e
d
o
n
e
f
o
r
th
e
s
am
e
tim
e
s
p
an
,
f
o
r
ex
am
p
le
o
n
e
y
ea
r
s
tar
tin
g
f
r
o
m
th
e
i
n
itial
p
r
ed
ictio
n
d
ata
an
d
th
en
c
o
m
p
a
r
in
g
it
with
th
e
o
r
ig
in
al
d
ata
th
a
t
is
cu
r
r
en
tly
r
u
n
n
in
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
Tr
en
d
o
f th
e
s
p
r
ea
d
o
f COVI
D
-
1
9
in
I
n
d
o
n
esia
u
s
in
g
t
h
e
ma
ch
in
e
lea
r
n
in
g
p
r
o
p
h
et
a
lg
o
r
ith
m
(
N
u
r
Ha
ya
ti
)
1785
(
a)
(
b
)
(
c)
Fig
u
r
e
3
.
T
h
e
Fo
r
ec
ast is
(
a)
c
o
n
f
ir
m
e
d
ca
s
es,
(
b
)
d
ea
th
s
ca
s
es,
an
d
(
c)
r
ec
o
v
e
r
ed
ca
s
es f
o
r
1
y
ea
r
(
1
2
Feb
r
u
ar
y
2
0
2
1
-
1
2
Feb
r
u
a
r
y
2
0
2
2
)
T
ab
le
8
.
T
h
e
ac
cu
r
ac
y
o
f
co
n
f
i
r
m
ed
,
d
ea
t
h
s
an
d
r
ec
o
v
er
at
1
3
Feb
r
u
ar
y
2
0
2
1
to
3
Ma
y
2
0
2
1
P
r
e
d
i
c
t
i
o
n
N
u
mb
e
r
o
f
C
a
ses
C
o
n
f
i
r
me
d
4
3
.
9
7
%
D
e
a
t
h
7
2
.
5
0
%
R
e
c
o
v
e
r
e
d
8
4
.
2
4
%
6.
RE
S
E
ARCH
M
E
T
H
O
D
B
ased
o
n
Fig
u
r
e
4
,
th
e
r
esear
ch
m
eth
o
d
is
d
iv
id
ed
in
to
s
e
v
er
al
p
ar
ts
,
n
am
ely
d
ata
g
ath
er
in
g
,
d
ata
p
r
ep
r
o
ce
s
s
in
g
,
m
o
d
elin
g
,
an
d
ev
alu
atio
n
.
I
n
th
e
f
ir
s
t
s
tag
e,
it
b
eg
in
s
b
y
tak
in
g
a
d
ataset
f
r
o
m
d
at
a
K
awal
C
OVI
D
19
web
s
ite
.
Dat
a
g
ath
er
in
g
s
tar
ts
with
tak
in
g
th
e
m
ain
d
ataset
th
en
f
ilter
in
g
th
e
m
ain
d
ataset
an
d
th
en
cr
ea
tin
g
a
n
ew
d
atas
et
to
r
etr
iev
e
th
e
attr
ib
u
tes
n
ee
d
ed
b
y
th
e
p
r
e
d
ictio
n
/f
o
r
ec
asti
n
g
m
o
d
el,
af
ter
th
e
n
ew
d
ataset
h
as
b
ee
n
cr
ea
ted
,
th
e
d
ataset
is
r
ea
d
y
an
d
th
e
n
ew
d
ataset
w
ill
b
e
ap
p
lied
to
th
e
m
o
d
el.
Nex
t,
we
d
o
d
ata
p
r
e
p
r
o
ce
s
s
in
g
with
m
an
ip
u
late
th
e
f
ilter
ed
d
ataset
b
ef
o
r
e
in
p
u
ttin
g
it
to
th
e
FB
Pro
p
h
et
m
o
d
el.
FB
Pro
p
h
et
o
n
ly
ac
ce
p
ts
2
co
lu
m
n
s
o
f
in
p
u
t,
t
h
e
d
ate
co
lu
m
n
(
o
b
s
er
v
atio
n
d
ate
)
m
u
s
t
b
e
in
th
e
id
ea
l
f
o
r
m
,
n
am
ely
th
e
d
ata
f
r
am
e
a
n
d
th
e
d
ata
ty
p
e
m
u
s
t
b
e
d
atetim
e6
4
[
n
s
]
th
en
h
a
v
e
th
e
YYYY
-
MM
-
DD
f
o
r
m
at
an
d
th
e
co
lu
m
n
y
o
u
wan
t
to
p
r
e
d
ic
t
m
u
s
t
b
e
n
u
m
er
ic.
Af
te
r
p
r
e
p
r
o
ce
s
s
in
g
,
th
e
d
ataset
is
r
ea
d
y
t
o
b
e
in
p
u
t
in
t
o
th
e
mod
el.
B
u
t
to
en
s
u
r
e
th
e
ac
cu
r
ac
y
o
f
th
e
m
o
d
el,
we
m
u
s
t
tr
ain
th
e
m
o
d
el
an
d
test
th
e
m
o
d
el
with
th
e
in
p
u
t
d
ataset
an
d
th
en
th
e
r
esu
lts
will
b
e
co
m
p
ar
ed
.
Ne
x
t,
c
o
m
p
a
r
in
g
th
e
r
esu
lts
o
f
th
e
p
r
ed
icti
o
n
o
f
2
5
d
a
y
s
with
th
e
2
5
d
a
y
s
o
r
ig
i
n
al
d
ata
test
.
I
f
p
r
e
d
ictio
n
d
ata
is
u
n
d
er
o
r
ig
in
al
d
ata
test
,
it
m
ea
n
s
th
e
m
o
d
el
alr
ea
d
y
to
p
r
ed
ictio
n
f
o
r
th
e
n
e
x
t
o
n
e
y
e
ar
s
.
Af
ter
p
r
e
d
ictio
n
,
t
h
en
ca
l
cu
late
th
e
ac
cu
r
ac
y
v
alu
e
f
r
o
m
th
e
p
r
ed
icted
d
ata.
T
h
e
h
ig
h
er
o
f
ac
cu
r
ac
y
v
alu
e
,
m
ea
n
th
e
clo
s
er
o
f
p
r
ed
ictio
n
d
ata
to
r
ea
l
d
ata.
T
h
e
Fig
u
r
e
4
is
a
f
lo
wch
ar
t
o
f
th
e
r
esear
ch
m
eth
o
d
o
lo
g
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
3
,
Dec
em
b
er
2
0
2
1
:
1
7
8
0
-
1
7
8
8
1786
Fig
u
r
e
4
.
R
esear
ch
m
eth
o
d
s
7.
CO
NCLU
SI
O
N
T
o
r
ed
u
ce
th
e
s
p
r
ea
d
o
f
C
OVI
D
-
1
9
an
d
m
ak
e
th
e
r
ig
h
t
d
ec
i
s
io
n
s
f
o
r
th
e
g
o
v
er
n
m
e
n
t,
p
r
e
d
ictio
n
s
o
f
f
u
tu
r
e
ca
s
es
ar
e
n
ee
d
ed
.
T
h
er
ef
o
r
e,
th
e
p
r
o
p
h
et
al
g
o
r
ith
m
i
s
u
s
ed
to
p
r
e
d
ict
th
e
s
p
r
ea
d
o
f
C
OVI
D
-
1
9
f
o
r
th
e
n
ex
t
o
n
e
y
ea
r
.
T
h
e
a
u
th
o
r
s
ch
o
o
s
e
p
r
o
p
h
et
alg
o
r
ith
m
b
e
ca
u
s
e
th
is
alg
o
r
ith
m
h
as
a
f
air
ly
h
ig
h
lev
el
o
f
ac
cu
r
ac
y
.
Fo
r
t
h
e
au
th
o
r
s
,
th
is
r
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was to
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ased
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ich
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ate
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ith
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ict
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OVI
D
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1
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in
I
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d
o
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till
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alid
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o
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ith
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D
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r
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th
e
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o
v
er
n
m
en
t
.
RE
F
E
R
E
NC
E
S
[1
]
Wi
n
d
h
u.
“
Distrib
u
ti
o
n
o
f
COV
ID
-
1
9
He
a
lt
h
M
a
teria
ls
Up
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te
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p
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0
.
A
c
c
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ss
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d
:
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0
2
,
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0
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0
.
[On
li
n
e
].
Av
a
il
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le:
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o
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d
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r
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2
0
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0
.
[2
]
“
Risk
Zo
n
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ti
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n
M
a
p
.
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c
o
v
id
1
9
.
g
o
.
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d
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c
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:
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a
r.
0
2
,
2
0
2
0
.
[O
n
li
n
e
].
Av
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il
a
b
le
:
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tt
p
s:/
/www
.
c
o
v
i
d
1
9
.
g
o
.
i
d
/
p
e
ta
-
risik
o
.
[3
]
L.
Jia
,
K.
Li
,
Y.
Jia
n
g
,
X.
G
u
o
,
a
n
d
T
.
Z
h
a
o
,
“
P
re
d
ictio
n
a
n
d
An
a
l
y
sis
o
f
C
o
ro
n
a
v
ir
u
s
Dise
a
se
2
0
1
9
,
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PL
OS
ONE
,
2
0
2
0
,
d
o
i
:
1
0
.
1
3
7
1
/
jo
u
rn
a
l.
p
o
n
e
.
0
2
3
9
9
6
0
.
[4
]
Q.
Li
a
n
d
W.
F
e
n
g
,
“
Tren
d
a
n
d
F
o
re
c
a
stin
g
o
f
t
h
e
COV
ID
-
1
9
O
u
tb
re
a
k
in
C
h
in
a
,
”
J
o
u
rn
a
l
o
f
I
n
fec
ti
o
n
,
v
o
l.
8
,
n
o
.
4
,
p
p
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4
6
9
-
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9
6
,
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0
2
0
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o
i:
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0
.
1
0
1
6
/j
.
ji
n
f
.
2
0
2
0
.
0
2
.
0
1
4
.
[5
]
B.
P
a
v
l
y
sh
e
n
k
o
,
“
M
a
c
h
in
e
-
Lea
rn
in
g
M
o
d
e
ls
fo
r
S
a
les
Ti
m
e
S
e
ries
F
o
re
c
a
stin
g
,
”
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t
a
,
v
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l.
4
,
n
o
.
1
,
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0
1
9
.
doi
:
1
0
.
3
3
9
0
/
d
a
ta4
0
1
0
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1
5
.
[6
]
J.
Ku
m
a
r
a
n
d
K.
P
.
S
.
S
.
He
m
b
ra
m
,
“
Ep
id
e
m
io
lo
g
ica
l
S
t
u
d
y
o
f
No
v
e
l
Co
r
o
n
a
Vir
u
s
(COV
ID
-
1
9
),
”
In
te
rn
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Co
mm
u
n
it
y
M
e
d
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e
a
n
d
Pu
b
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lt
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.
8
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1
8
2
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-
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0
4
0
.
i
jcm
p
h
2
0
2
1
0
8
2
8
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
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N:
2502
-
4
7
5
2
Tr
en
d
o
f th
e
s
p
r
ea
d
o
f COVI
D
-
1
9
in
I
n
d
o
n
esia
u
s
in
g
t
h
e
ma
ch
in
e
lea
r
n
in
g
p
r
o
p
h
et
a
lg
o
r
ith
m
(
N
u
r
Ha
ya
ti
)
1787
[7
]
J.
F
a
tt
a
h
,
L.
Ezz
in
e
,
Z.
Am
a
n
,
H.
El
M
o
u
ss
a
m
i
,
a
n
d
A.
Lac
h
h
a
b
,
“
F
o
re
c
a
st
in
g
o
f
d
e
m
a
n
d
u
sin
g
ARIMA
m
o
d
e
l,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
n
g
in
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rin
g
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si
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t
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o
i:
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0
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1
7
7
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8
4
7
9
7
9
0
1
8
8
0
8
6
7
3
.
[8
]
B.
M
.
P
a
v
l
y
sh
e
n
k
o
,
“
Li
n
e
a
r,
M
a
c
h
in
e
Lea
rn
in
g
An
d
P
r
o
b
a
b
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Ap
p
ro
a
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s
F
o
r
Ti
m
e
S
e
ries
An
a
ly
sis,”
I
n
2
0
1
6
IEE
E
Fi
rs
t
I
n
ter
n
a
ti
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l
Co
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fer
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D
a
ta
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tre
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m
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P
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2
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5
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.
[9
]
B.
S
u
v
a
rn
a
,
T.
M
.
P
a
d
m
a
ja,
V
.
Do
n
d
e
ti
,
H.
Tela
p
r
o
lu
,
a
n
d
H.
P
a
p
p
u
la
,
“
M
a
c
h
in
e
Lea
rn
i
n
g
Alg
o
rit
h
m
fo
r
P
re
d
ictin
g
Nu
m
b
e
r
o
f
C
o
v
id
-
1
9
Ca
se
s,”
J
o
u
rn
a
l
o
f
X
i’a
n
Un
ive
rs
it
y
o
f
Arc
h
it
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c
tu
re
&
T
e
c
h
n
o
lo
g
y
,
v
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l
.
12
,
n
o
.
6
,
p
p
.
1
1
7
6
-
1
1
8
6
,
2
0
2
0
.
[1
0
]
P
.
Wan
g
,
X
.
Z
h
e
n
g
,
J.
L
i,
a
n
d
B.
Zh
u
,
"
P
re
d
icti
o
n
o
f
e
p
i
d
e
m
ic
tren
d
s
in
COV
ID
-
1
9
wit
h
l
o
g
isti
c
m
o
d
e
l
a
n
d
m
a
c
h
in
e
lea
rn
in
g
tec
h
n
ics
,
"
Ch
a
o
s,
S
o
l
it
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s &
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c
t
a
ls
,
v
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l.
1
3
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,
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8
.
[1
1
]
S
.
T
u
li
,
S
.
Tu
li
,
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.
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u
li
,
a
n
d
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.
S
.
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il
l,
"
P
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th
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ro
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d
tren
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ID
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1
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p
a
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ic
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,
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ter
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t.
2
0
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2
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.
[1
2
]
K.
Ku
m
a
r
a
n
d
D.
P
.
G
a
n
d
h
m
a
l
,
“
An
in
telli
g
e
n
t
i
n
d
ian
sto
c
k
m
a
rk
e
t
fo
re
c
a
stin
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m
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n
g
L
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TM
d
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p
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rn
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g
,
”
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ter
n
a
t
io
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l
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o
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e
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E
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ter
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(IJ
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)
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2
1
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o
.
2
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p
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1
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2
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3
]
N.
Ch
i
n
tala
p
u
d
i,
G
.
Ba
tt
in
e
n
i,
a
n
d
F
.
Am
e
n
ta,
“
Co
v
id
-
1
9
Viru
s
O
u
tb
re
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k
F
o
re
c
a
stin
g
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Re
g
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d
a
n
d
Re
c
o
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e
re
d
Ca
se
s
Afte
r
S
ix
ty
Da
y
Lo
c
k
d
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w
n
in
Ital
y
:
A
Da
ta
Driv
e
n
M
o
d
e
l
Ap
p
ro
a
c
h
.
,
”
J
.
M
icr
o
b
io
l
.
Imm
u
n
o
l.
In
fec
t.
,
v
o
l.
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,
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o
.
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p
p
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/
j.
jmii.
2
0
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0
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0
4
.
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0
4
.
[1
4
]
D.
F
a
n
e
ll
i,
a
n
d
F
.
P
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5
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A.
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7
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9
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.
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[2
3
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4
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W.
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so
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2
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:
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le:
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tt
p
s://
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.
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