This is a table of type proper and their frequencies. Use it to search & browse the list to learn more about your study carrel.
proper | frequency |
---|---|
SIR | 1832 |
COVID-19 | 671 |
S | 437 |
Fig | 372 |
_ | 341 |
Eq | 330 |
N | 239 |
T | 215 |
China | 201 |
SIS | 194 |
• | 192 |
May | 182 |
− | 177 |
β | 175 |
Italy | 170 |
D | 160 |
el | 159 |
SARS | 152 |
γ | 148 |
J | 145 |
SEIR | 141 |
∞ | 133 |
March | 132 |
I(t | 129 |
F | 125 |
␣ | 118 |
C | 117 |
Infectious | 116 |
India | 109 |
Figure | 107 |
Table | 103 |
R | 102 |
June | 96 |
April | 96 |
los | 92 |
COVID | 91 |
max | 87 |
σ | 86 |
M | 84 |
Coronavirus | 84 |
A | 84 |
I | 83 |
CC | 82 |
≈ | 80 |
Monte | 80 |
Carlo | 80 |
Appendix | 80 |
λ | 79 |
nan | 79 |
Y | 78 |
Health | 78 |
Eqs | 77 |
South | 77 |
Z | 75 |
Germany | 71 |
¼ | 69 |
Model | 69 |
sha | 68 |
DOI | 68 |
BY | 67 |
−1 | 65 |
Wuhan | 65 |
Korea | 65 |
Hopkins | 63 |
CoV-2 | 62 |
UK | 61 |
ND | 59 |
US | 58 |
USA | 58 |
covid19.analytics | 58 |
McKendrick | 57 |
Disease | 57 |
i | 57 |
Spain | 57 |
Kermack | 56 |
las | 55 |
L | 55 |
PPE | 54 |
New | 54 |
∈ | 53 |
ρ | 53 |
R(t | 53 |
E | 53 |
Q | 52 |
Johns | 52 |
NC | 51 |
Infected | 51 |
del | 51 |
un | 50 |
United | 50 |
EnRenew | 50 |
Data | 50 |
infectados | 49 |
S(t | 49 |
para | 48 |
número | 48 |
covid-19 | 48 |
medRxiv | 46 |
Bangladesh | 46 |
Control | 45 |
Mathematical | 44 |
France | 44 |
SIRD | 44 |
U | 44 |
Hamiltonian | 43 |
R. | 43 |
α | 42 |
Markov | 42 |
Epidemic | 42 |
Diseases | 42 |
≤ | 41 |
University | 41 |
PGED | 40 |
Ebola | 40 |
Covid-19 | 39 |
CA | 39 |
Analysis | 39 |
DDFT | 39 |
States | 39 |
| | 38 |
K | 38 |
B | 38 |
Japan | 36 |
II | 36 |
es | 36 |
t. | 36 |
series | 35 |
dt | 35 |
W | 35 |
Models | 35 |
Modeling | 35 |
HCD | 35 |
Brazil | 35 |
ICU | 34 |
x(t | 34 |
Pandemic | 34 |
HMF | 33 |
H | 33 |
York | 33 |
World | 33 |
MERS | 32 |
MCMC | 32 |
Laplace | 32 |
January | 32 |
Hamilton | 32 |
Adomian | 32 |
una | 31 |
NHS | 31 |
Lyapunov | 31 |
CoViD19 | 31 |
Europe | 30 |
Planck | 30 |
Figs | 29 |
Kampen | 29 |
Dynamics | 29 |
quasiperiodic | 29 |
G | 29 |
min | 29 |
NY | 29 |
SIRsim | 29 |
Sec | 29 |
geo | 28 |
chr | 28 |
Susceptibles | 28 |
Poisson | 28 |
Canada | 28 |
= | 28 |
America | 27 |
TS | 27 |
van | 26 |
X | 26 |
July | 26 |
February | 26 |
Differential | 26 |
8) | 26 |
tasa | 26 |
Organization | 25 |
Gamma | 25 |
Hubei | 25 |
NJ | 25 |
.t/ | 25 |
S. | 25 |
loc | 25 |
tiempo | 25 |
ν | 25 |
Center | 24 |
DE | 24 |
Fokker | 24 |
Lemma | 24 |
Ref | 24 |
Texas | 24 |
Toronto | 24 |
datos | 24 |
observados | 24 |
∼ | 24 |
I. | 23 |
III | 23 |
Liu | 23 |
Method | 23 |
Optimal | 23 |
Phase | 23 |
23 | |
r(t | 23 |
ts | 23 |
c2 | 23 |
china | 23 |
δ | 22 |
× | 22 |
contagio | 22 |
c1 | 22 |
Plastic | 22 |
Padé | 22 |
IC | 22 |
Euler | 22 |
County | 22 |
Africa | 22 |
Ω | 22 |
LV | 21 |
Amazon | 21 |
Bernoulli | 21 |
December | 21 |
S(0 | 21 |
Networks | 21 |
Section | 21 |
preprint | 21 |
Province | 20 |
-covid19 | 20 |
AR | 20 |
Australia | 20 |
El | 20 |
H1N1 | 20 |
I(0 | 20 |
Kutta | 20 |
Lagrange | 20 |
R0 | 20 |
Research | 20 |
S.t/ | 20 |
SIRemp | 20 |
Science | 20 |
V | 20 |
valores | 20 |
γ(t | 20 |
Runge | 20 |
Sir | 19 |
ANFIS | 19 |
ESIR | 19 |
Estimation | 19 |
Impact | 19 |
Lst | 19 |
Methods | 19 |
ROC | 19 |
Sweden | 19 |
Theorem | 19 |
Time | 19 |
Transmission | 19 |
evolución | 19 |
máximo | 19 |
si | 19 |
∑ | 19 |
Smith | 18 |
Anderson | 18 |
Lotka | 18 |
Modelling | 18 |
Novel | 18 |
ODE | 18 |
Q(t | 18 |
data | 18 |
Systems | 18 |
se | 18 |
esta | 18 |
figura | 18 |
muestra | 18 |
sd | 18 |
Volterra | 18 |
Lancet | 17 |
Bayesian | 17 |
CSSE | 17 |
City | 17 |
Decomposition | 17 |
En | 17 |
Hand | 17 |
Kerala | 17 |
Information | 17 |
Michigan | 17 |
NN | 17 |
Recovered | 17 |
September | 17 |
Society | 17 |
Theory | 17 |
d(t | 17 |
en | 17 |
Global | 16 |
Lagrangian | 16 |
Institute | 16 |
ICA | 16 |
Austria | 16 |
Equations | 16 |
C. | 16 |
Brownian | 16 |
Ontario | 16 |
National | 16 |
Kingdom | 16 |
Public | 16 |
como | 16 |
https://doi.org/10.1101 | 16 |
epidemia | 16 |
eff | 16 |
i(t | 16 |
aislamiento | 16 |
Yasuni | 16 |
lo | 16 |
Statistical | 16 |
R.t/ | 16 |
SSIR | 15 |
Application | 15 |
British | 15 |
Cuba | 15 |
La | 15 |
Lagartococha | 15 |
Los | 15 |
NPIs | 15 |
PG | 15 |
SIRS | 15 |
Switzerland | 15 |
curvas | 15 |
dI | 15 |
por | 15 |
transcritical | 15 |
valor | 15 |
µ | 15 |
∂ | 15 |
Wang | 15 |
M. | 14 |
Prediction | 14 |
Number | 14 |
North | 14 |
N. | 14 |
Alemania | 14 |
Lloyd | 14 |
I(β | 14 |
B. | 14 |
Algorithm | 14 |
Reopening | 14 |
Prevention | 14 |
ij | 14 |
Reproduction | 14 |
exponencial | 14 |
SF | 14 |
sars | 14 |
p(t | 14 |
u(t | 14 |
países | 14 |
eSAIR | 14 |
Stochastic | 14 |
SSEs | 14 |
SSE | 14 |
SNA | 14 |
Foundation | 13 |
Lombardy | 13 |
John | 13 |
JHU | 13 |
Israel | 13 |
Iran | 13 |
IV | 13 |
Houston | 13 |
COVID19 | 13 |
Forecasting | 13 |
European | 13 |
Engineering | 13 |
East | 13 |
College | 13 |
Britton | 13 |
Association | 13 |
MLP | 13 |
Network | 13 |
P | 13 |
coronavirus | 13 |
−I | 13 |
Γ | 13 |
QMF | 13 |
w(t | 13 |
t+1 | 13 |
ecuaciones | 13 |
curva | 13 |
ðtÞ | 13 |
confinamiento | 13 |
Spread | 13 |
SIF | 13 |
SI | 13 |
Ro | 13 |
RRG | 13 |
R(0 | 13 |
casos | 13 |
England | 12 |
Pareto | 12 |
Next | 12 |
Humans | 12 |
GitHub | 12 |
CFR | 12 |
D. | 12 |
Simulation | 12 |
Computing | 12 |
August | 12 |
Asia | 12 |
Se | 12 |
þ | 12 |
Surgery | 12 |
s(t | 12 |
Susceptible | 12 |
√ | 12 |
−8 | 12 |
β(t | 12 |
≡ | 12 |
k(t | 12 |
doi | 12 |
YP | 12 |
Washington | 12 |
Epidemiology | 11 |
NAM | 11 |
Morocco | 11 |
Kendall | 11 |
Imperial | 11 |
Immunity | 11 |
Harris | 11 |
HJB | 11 |
Fourier | 11 |
Evolution | 11 |
Belgium | 11 |
EG | 11 |
E. | 11 |
D(t | 11 |
CDC | 11 |
Neural | 11 |
BMI | 11 |
ABC | 11 |
A. | 11 |
/γ | 11 |
NCBI | 11 |
LA | 11 |
Report | 11 |
eqns | 11 |
∞. | 11 |
ܶ | 11 |
Risk | 11 |
θ | 11 |
time | 11 |
tiempos | 11 |
solución | 11 |
sección | 11 |
lymphorrhea | 11 |
italy | 11 |
ξ | 11 |
doc_id | 11 |
activos | 11 |
Zhang | 11 |
Study | 11 |
Social | 11 |
Singapore | 11 |
Series | 11 |
S1 | 11 |
con | 11 |
Herd | 10 |
Group | 10 |
Heesterbeek | 10 |
Jersey | 10 |
IFR | 10 |
J. | 10 |
G. | 10 |
Keeling | 10 |
Gillespie | 10 |
Covid | 10 |
Ferguson | 10 |
De | 10 |
Beijing | 10 |
AUC | 10 |
1i | 10 |
/N | 10 |
! | 10 |
MC | 10 |
Li | 10 |
finales | 10 |
Newman | 10 |
dermoscopy | 10 |
Northern | 10 |
ℜ | 10 |
ܴ | 10 |
ε | 10 |
wuhan | 10 |
total | 10 |
este | 10 |
día | 10 |
π | 10 |
Uruguay | 10 |
TXL | 10 |
Scientific | 10 |
SPIR | 10 |
Russia | 10 |
Rohani | 10 |
U.S. | 10 |
Optimization | 10 |
Leone | 9 |
Journal | 9 |
LD | 9 |
Las | 9 |
Learning | 9 |
Legendre | 9 |
MATLAB | 9 |
Lobato | 9 |
London | 9 |
Maharashtra | 9 |
Middle | 9 |
Hong | 9 |
Human | 9 |
Armenia | 9 |
Ho | 9 |
Hastings | 9 |
Growth | 9 |
General | 9 |
DFE | 9 |
CoV | 9 |
Centre | 9 |
Boltzmann | 9 |
Bellman | 9 |
Approach | 9 |
American | 9 |
Allen | 9 |
Nishiura | 9 |
Ministry | 9 |
Country | 9 |
Princess | 9 |
disease | 9 |
Proof | 9 |
−→ | 9 |
β(i | 9 |
recuperados | 9 |
puede | 9 |
modelo | 9 |
model | 9 |
lim | 9 |
∝ | 9 |
k. | 9 |
https://doi.org/10 | 9 |
enfermos | 9 |
población | 9 |
condiciones | 9 |
Supplemental | 9 |
abril | 9 |
Respiratory | 9 |
SFN | 9 |
SIVRT | 9 |
Sierra | 9 |
Seattle | 9 |
Taylor | 9 |
Toda | 9 |
VoteRank | 9 |
WHO | 9 |
Watson | 9 |
Hospital | 8 |
Luxembourg | 8 |
IHME | 8 |
INFOcas | 8 |
Infection | 8 |
L. | 8 |
Lambert | 8 |
Liberia | 8 |
RRN | 8 |
Mathematics | 8 |
Measles | 8 |
Medical | 8 |
Peng | 8 |
Physics | 8 |
RCT | 8 |
Rate | 8 |
HIV | 8 |
He | 8 |
Borel | 8 |
Guinea | 8 |
DEs | 8 |
Applications | 8 |
BAPRAS | 8 |
Based | 8 |
Basic | 8 |
Robert | 8 |
Caputo | 8 |
Case | 8 |
Dashboard | 8 |
Government | 8 |
Diamond | 8 |
Diekmann | 8 |
Dirac | 8 |
Epidemics | 8 |
Eqn | 8 |
Gibbs | 8 |
8 | |
Remark | 8 |
Population | 8 |
Router | 8 |
dependencia | 8 |
exc | 8 |
h(q | 8 |
https | 8 |
https://doi.org/10.1101/2020.06.11.20128058 | 8 |
infección | 8 |
lag-1 | 8 |
medidas | 8 |
personas | 8 |
pico | 8 |
soluciones | 8 |
· | 8 |
ρ(t | 8 |
ܵ | 8 |
݀ | 8 |
→ | 8 |
S(∞ | 8 |
−γ | 8 |
donde | 8 |
∀t | 8 |
cual | 8 |
Threshold | 8 |
cases% | 8 |
SIHRD | 8 |
Song | 8 |
Spatial | 8 |
State | 8 |
Syndrome | 8 |
T00 | 8 |
SEIRS | 8 |
Times | 8 |
VM | 8 |
Z. | 8 |
Zika | 8 |
aproximadamente | 8 |
Trust | 8 |
b(a | 8 |
MMR | 7 |
Gabaix | 7 |
H(I(t | 7 |
Huang | 7 |
Jacobi | 7 |
Kong | 7 |
Language | 7 |
MH | 7 |
Mitarai | 7 |
May. | 7 |
Metropolis | 7 |
Nentries | 7 |
Numerical | 7 |
OCP | 7 |
Outbreak | 7 |
Esto | 7 |
Para | 7 |
Example | 7 |
Clinical | 7 |
Estimating | 7 |
Dynamic | 7 |
Problems | 7 |
1:11142 | 7 |
< | 7 |
Bailey | 7 |
CRAN | 7 |
CRPS | 7 |
California | 7 |
Census | 7 |
Chen | 7 |
Chicago | 7 |
Compartmental | 7 |
Complex | 7 |
Condmat | 7 |
DDE | 7 |
Dakos | 7 |
Policy | 7 |
intervalo | 7 |
Proposition | 7 |
país | 7 |
enfermedad | 7 |
health | 7 |
log | 7 |
muy | 7 |
más | 7 |
o(1 | 7 |
orden | 7 |
plt | 7 |
después | 7 |
 | 7 |
ϕ(t | 7 |
−C | 7 |
≥ | 7 |
≪ | 7 |
Recovery | 7 |
� | 7 |
duración | 7 |
ܽ | 7 |
debe | 7 |
Tokyo | 7 |
Schorfheide | 7 |
dSPIR | 7 |
Size | 7 |
Surgeons | 7 |
T. | 7 |
TM | 7 |
Team | 7 |
Statistics | 7 |
Trauma | 7 |
autofluorescence | 7 |
considera | 7 |
UI | 7 |
Review | 7 |
Zhou | 7 |
Van | 7 |
I(x | 6 |
Function | 6 |
Gaussian | 6 |
Gelman | 6 |
Fermi | 6 |
H(Q | 6 |
HMC | 6 |
FISPO | 6 |
Hamster | 6 |
Harko | 6 |
Hungary | 6 |
Hybrid | 6 |
Grant | 6 |
Koch | 6 |
I2(t | 6 |
International | 6 |
J(t | 6 |
José | 6 |
KR08 | 6 |
Kim | 6 |
LVA | 6 |
LVSIR | 6 |
MLE | 6 |
Machine | 6 |
Materials | 6 |
Michael | 6 |
FALSE | 6 |
Moon | 6 |
FES | 6 |
Chain | 6 |
Explorer | 6 |
Chaos | 6 |
NUTS | 6 |
): | 6 |
AA | 6 |
AI | 6 |
Angeles | 6 |
Bayes | 6 |
Becker | 6 |
Bryson | 6 |
COSIR | 6 |
CV | 6 |
Callarú | 6 |
Care | 6 |
Community | 6 |
Evaluation | 6 |
Corea | 6 |
Countries | 6 |
DRC | 6 |
Day | 6 |
Dead | 6 |
Department | 6 |
Distribution | 6 |
Early | 6 |
Economic | 6 |
Environment | 6 |
Eq.(1 | 6 |
Este | 6 |
NP | 6 |
Corona | 6 |
Nadai | 6 |
cuarentena | 6 |
decaimiento | 6 |
dos | 6 |
días | 6 |
entre | 6 |
exponenciales | 6 |
g(z | 6 |
generate.SIR.model | 6 |
j/ | 6 |
log(1 | 6 |
mid | 6 |
pandemia | 6 |
pandemic | 6 |
peak | 6 |
período | 6 |
puntos | 6 |
son | 6 |
sus | 6 |
vec | 6 |
Λ | 6 |
γ=0.057 | 6 |
κ | 6 |
ω | 6 |
ϕ | 6 |
ܰ | 6 |
߮ | 6 |
ݎ | 6 |
November | 6 |
dR | 6 |
segundo | 6 |
covid19.genomic.data | 6 |
Storn | 6 |
PDE | 6 |
covid19 | 6 |
Percolation | 6 |
Pneumonia | 6 |
Por | 6 |
Price | 6 |
Python | 6 |
Royal | 6 |
SAIR | 6 |
SIRIT | 6 |
SIRP | 6 |
Scheffer | 6 |
Situation | 6 |
Regression | 6 |
Superspreading | 6 |
Watsons | 6 |
caso | 6 |
cantidad | 6 |
c. | 6 |
cov-2 | 6 |
Watts | 6 |
Working | 6 |
V. | 6 |
Turkey | 6 |
Thucydides | 6 |
Testing | 6 |
TH | 6 |
Non | 5 |
ML | 5 |
Logistic | 5 |
Leclerc | 5 |
Law | 5 |
L(Q | 5 |
KMH | 5 |
Healthcare | 5 |
Identification | 5 |
I.t/ | 5 |
I(∞ | 5 |
Hu | 5 |
Galton | 5 |
Handbook | 5 |
H(Z | 5 |
ORD | 5 |
Nϕ(t | 5 |
Republic | 5 |
Observability | 5 |
P(E | 5 |
Frasca | 5 |
Rt | 5 |
Rev | 5 |
Response | 5 |
Region | 5 |
RCTs | 5 |
Quality | 5 |
Protective | 5 |
Processes | 5 |
Personal | 5 |
Patient | 5 |
Panel | 5 |
PageRank | 5 |
PCR | 5 |
P. | 5 |
Free | 5 |
Agent | 5 |
Fractional | 5 |
Animals | 5 |
C.I. | 5 |
Building | 5 |
Brockmann | 5 |
Barton | 5 |
BSSH | 5 |
BRN | 5 |
B(x | 5 |
S19 | 5 |
Forecast | 5 |
Age | 5 |
AIDS | 5 |
A4 | 5 |
A2 | 5 |
A1 | 5 |
2a | 5 |
(= | 5 |
CLT | 5 |
Central | 5 |
Chung | 5 |
Como | 5 |
First | 5 |
Fig.3-(b | 5 |
Feehery | 5 |
Family | 5 |
FFP3 | 5 |
F(t | 5 |
Esta | 5 |
Es | 5 |
Equipment | 5 |
Deb | 5 |
Critical | 5 |
Covid19 | 5 |
Council | 5 |
Contribution | 5 |
Conference | 5 |
S.t/=N | 5 |
Inverse | 5 |
S2 | 5 |
máximos | 5 |
situación | 5 |
siendo | 5 |
ser | 5 |
propiedad | 5 |
primero | 5 |
parámetros | 5 |
panel | 5 |
meta | 5 |
iCR | 5 |
locn | 5 |
ln | 5 |
livemap | 5 |
live.map | 5 |
l(q | 5 |
journal | 5 |
importante | 5 |
stage-3 | 5 |
su | 5 |
tot | 5 |
t→∞ | 5 |
SEIS | 5 |
≝ | 5 |
∕N | 5 |
−i | 5 |
−b | 5 |
ι(t | 5 |
γ(e | 5 |
βt | 5 |
Φ | 5 |
Ǒ | 5 |
|C | 5 |
x. | 5 |
w(J | 5 |
von | 5 |
variación | 5 |
ill | 5 |
À | 5 |
iCD | 5 |
Stability | 5 |
Vespignani | 5 |
TX | 5 |
TSconfirmed | 5 |
System | 5 |
Sur | 5 |
Supplementary | 5 |
Strategies | 5 |
Square | 5 |
Wallinga | 5 |
Slovakia | 5 |
Schwartz | 5 |
School | 5 |
Scale | 5 |
Sarcoma | 5 |
https://doi.org/10.1101/2020.09.18.20197723 | 5 |
SRA | 5 |
Vista | 5 |
Si | 5 |
Welfare | 5 |
d. | 5 |
hk | 5 |
Wu | 5 |
https://doi.org/10.1101/2020.05.24.20112029 | 5 |
h(z | 5 |
está | 5 |
elem | 5 |
decir | 5 |
fld | 5 |
considerando | 5 |
Zhong | 5 |
Yang | 5 |
bin | 5 |
aproximación | 5 |
c=0.3 | 5 |
andĪ | 5 |
Italian | 4 |
Ji | 4 |
Johnson | 4 |
Kucharski | 4 |
Levin | 4 |
Linton | 4 |
Long | 4 |
Luxembourgish | 4 |
Lymphorrhea | 4 |
Introduction | 4 |
MF | 4 |
MCS | 4 |
Ising | 4 |
Ibragimov | 4 |
Interaction | 4 |
Intensive | 4 |
Infections | 4 |
Individual | 4 |
Indicators | 4 |
Illinois | 4 |
ISRO | 4 |
IR | 4 |
IEDCR | 4 |
Hunan | 4 |
Hubert | 4 |
Makse | 4 |
Hethcote | 4 |
MTC | 4 |
Nature | 4 |
Malaria | 4 |
Malaysia | 4 |
Hall | 4 |
Power | 4 |
Pontryagin | 4 |
Phys | 4 |
Parameter | 4 |
POSAS | 4 |
PHE | 4 |
Osthus | 4 |
Orce | 4 |
Office | 4 |
Nikolaou | 4 |
Nadini | 4 |
nan | 4 |
N,0 | 4 |
Muraoka,1997 | 4 |
MolecuLight | 4 |
Mobility | 4 |
Milan | 4 |
Micross | 4 |
Mexico | 4 |
Metrics | 4 |
Mechanism | 4 |
Matlab | 4 |
Markovian | 4 |
Management | 4 |
Hamiltonians | 4 |
CI | 4 |
HM | 4 |
Assessment | 4 |
Centers | 4 |
Cases | 4 |
Carpenter | 4 |
Cao | 4 |
Cancer | 4 |
Cameroon | 4 |
Boettiger | 4 |
Behavior | 4 |
Bartlett | 4 |
Baltimore | 4 |
BSDE | 4 |
Assumption | 4 |
Apache | 4 |
H. | 4 |
Antibody | 4 |
Amaro | 4 |
Althaus | 4 |
Administration | 4 |
AWT | 4 |
ARIMA | 4 |
AR(1 | 4 |
A.1 | 4 |
16.04.20 | 4 |
/b | 4 |
-p | 4 |
Predictions | 4 |
Chile | 4 |
CoSIR | 4 |
CoV2 | 4 |
Cochrane | 4 |
GITHUB | 4 |
G(σ | 4 |
Future | 4 |
Foundations | 4 |
FRA | 4 |
FP20 | 4 |
F(D | 4 |
Extended | 4 |
Exp(λ | 4 |
Exact | 4 |
Equation | 4 |
Epidemy | 4 |
Epidemiological | 4 |
Ellison | 4 |
Effect | 4 |
Economics | 4 |
Delhi | 4 |
David | 4 |
DT | 4 |
DGP | 4 |
Cvitanić | 4 |
Cross | 4 |
Cooper | 4 |
Colizza | 4 |
Coefficient | 4 |
Prado | 4 |
Shen | 4 |
Probability | 4 |
mayores | 4 |
relevante | 4 |
región | 4 |
rbind | 4 |
r. | 4 |
problema | 4 |
pq | 4 |
orange | 4 |
números | 4 |
nuestra | 4 |
mkdir | 4 |
millones | 4 |
medio | 4 |
mayo | 4 |
ese | 4 |
map | 4 |
j | 4 |
india | 4 |
https://doi.org/10.1101/2020.06.16.20133330 | 4 |
https://doi.org/10.1101/2020.05.22.20110171 | 4 |
hki | 4 |
gráfico | 4 |
función | 4 |
france | 4 |
fila | 4 |
f.t/ | 4 |
estudio | 4 |
rep | 4 |
retweet | 4 |
sistema | 4 |
sudo | 4 |
Problem | 4 |
−α | 4 |
−U | 4 |
← | 4 |
ψ | 4 |
χ | 4 |
κ(x | 4 |
ζ(t | 4 |
δ(t | 4 |
βy | 4 |
Σ | 4 |
Ξ. | 4 |
Ô | 4 |
À1 | 4 |
ya | 4 |
y0,Z | 4 |
y(t | 4 |
varios | 4 |
tweet | 4 |
ti | 4 |
th | 4 |
temporal | 4 |
t1 | 4 |
t/ | 4 |
summary | 4 |
estimada | 4 |
φ | 4 |
esa | 4 |
VSSC | 4 |
Treatment | 4 |
Teunis | 4 |
Tang | 4 |
Sustainable | 4 |
Structure | 4 |
Street | 4 |
Strategy | 4 |
Steffen | 4 |
Solitons | 4 |
Simple | 4 |
−τ | 4 |
Severe | 4 |
Seroprevalence | 4 |
Scotland | 4 |
Sciences | 4 |
Sci | 4 |
Schematic | 4 |
SQIR | 4 |
S0 | 4 |
Ross | 4 |
Roda | 4 |
R2(t | 4 |
PubMed | 4 |
Protection | 4 |
eqn | 4 |
Turinici | 4 |
RNA | 4 |
Virus | 4 |
debido | 4 |
Warin | 4 |
empleando | 4 |
d−1 | 4 |
dx | 4 |
dv | 4 |
disminución | 4 |
disassortative | 4 |
dinámica | 4 |
df | 4 |
detección | 4 |
describir | 4 |
efecto | 4 |
dado | 4 |
arXiv | 4 |
West | 4 |
continua | 4 |
White | 4 |
Zealand | 4 |
antes | 4 |
entonces | 4 |
c=0.8 | 4 |
azules | 4 |
b. | 4 |
cada | 4 |
col | 4 |
MSEIR | 3 |
LPE | 3 |
LLT | 3 |
LLE | 3 |
L(a | 3 |
LHR | 3 |
Knowledge | 3 |
Kent | 3 |
K. | 3 |
K(f | 3 |
Jan. | 3 |
Laboratory | 3 |
Lawniczak | 3 |
Lat | 3 |
Lee | 3 |
Lenhart | 3 |
Lerman | 3 |
Library | 3 |
Life | 3 |
Lim | 3 |
Linear | 3 |
Lopez | 3 |
JH | 3 |
MD | 3 |
Lewis | 3 |
Horn | 3 |
JCST | 3 |
Heinsberg | 3 |
GA | 3 |
Man | 3 |
Gaeta | 3 |
Geweke | 3 |
Giordano | 3 |
Glasgow | 3 |
Greater | 3 |
Gruhl | 3 |
Gu | 3 |
Hamer | 3 |
Hermite | 3 |
Ireland | 3 |
High | 3 |
IND | 3 |
IQM | 3 |
Incubation | 3 |
Indigenous | 3 |
Inference | 3 |
Inferring | 3 |
Initial | 3 |
Internet | 3 |
Interval | 3 |
Mainland | 3 |
Oxford | 3 |
Mathematica | 3 |
Matter | 3 |
Pastor | 3 |
Paulo | 3 |
Peak | 3 |
People | 3 |
Perc | 3 |
Perspective | 3 |
Peru | 3 |
Plague | 3 |
Polynomial | 3 |
Populations | 3 |
Portugal | 3 |
Possamaï | 3 |
Practice | 3 |
Pre | 3 |
Proc | 3 |
Procedures | 3 |
P{X | 3 |
QALY | 3 |
Quarantine | 3 |
Quebec | 3 |
R1(t | 3 |
RE | 3 |
Fuks | 3 |
PDC | 3 |
Outbreaks | 3 |
Order | 3 |
Nest | 3 |
Mauritius | 3 |
Mean | 3 |
Ministerio | 3 |
Moleculight | 3 |
Moreno | 3 |
Multi | 3 |
Multilayer | 3 |
Murray | 3 |
N,1 | 3 |
Natl | 3 |
NetLogo | 3 |
O. | 3 |
Netherlands | 3 |
Neumann | 3 |
Nicolás | 3 |
Nonequilibrium | 3 |
Nonlinear | 3 |
Normal(Nϕ(t | 3 |
Nowcasting | 3 |
Nutz | 3 |
Nσ | 3 |
O(n | 3 |
G(Q | 3 |
COVID-2019 | 3 |
Framework | 3 |
Cauchy | 3 |
Automatic | 3 |
B(r | 3 |
BC18 | 3 |
Bauch | 3 |
Baumgartner | 3 |
Beare | 3 |
Beauchemin | 3 |
Bergamo | 3 |
Bernstein | 3 |
Bilge | 3 |
Biology | 3 |
Biswas | 3 |
Body | 3 |
Book | 3 |
Breast | 3 |
Brock | 3 |
Burn | 3 |
Burns | 3 |
Buscarino | 3 |
CCSE | 3 |
Ramakrishnan | 3 |
COVID‐19 | 3 |
CR | 3 |
Athens | 3 |
Athenians | 3 |
Associated | 3 |
ASPS | 3 |
-2 | 3 |
-dates | 3 |
-which | 3 |
.t | 3 |
0:05 | 3 |
8d | 3 |
A(x(t | 3 |
A.2.2 | 3 |
AGGREGATED | 3 |
ANN | 3 |
Abel | 3 |
Artstein | 3 |
Accession | 3 |
Acemoglu | 3 |
Advanced | 3 |
Advances | 3 |
Agency | 3 |
Algorithms | 3 |
Andersson | 3 |
Andrew | 3 |
ArXiv | 3 |
Argentina | 3 |
Castillo | 3 |
Chapman | 3 |
Final | 3 |
Chemical | 3 |
E9 | 3 |
Ecology | 3 |
Effort | 3 |
Egypt | 3 |
Element | 3 |
English | 3 |
Enrique | 3 |
Entropy | 3 |
Eq.(2 | 3 |
Eq.(3 | 3 |
Ergonul | 3 |
Estas | 3 |
Estos | 3 |
Eubank | 3 |
Evolutionary | 3 |
Exp(d | 3 |
F. | 3 |
FBSDE | 3 |
3 | |
Fatality | 3 |
Fehelberg | 3 |
Feng | 3 |
Fig.10 | 3 |
E8 | 3 |
E10 | 3 |
Dynamical | 3 |
D.M. | 3 |
Chikungunya | 3 |
CoVID-19 | 3 |
CoVstat | 3 |
Compute | 3 |
Congo | 3 |
Consideremos | 3 |
Cotta | 3 |
Curve | 3 |
Czechia | 3 |
D.C. | 3 |
DALY | 3 |
Dudouet | 3 |
DC | 3 |
Daily | 3 |
Daniel | 3 |
Dear | 3 |
Death | 3 |
Deep | 3 |
Definition | 3 |
Deviation | 3 |
Director | 3 |
District | 3 |
R_0 | 3 |
8a | 3 |
ReLU | 3 |
lineal | 3 |
logrado | 3 |
línea | 3 |
march | 3 |
mean | 3 |
medRχiv.org | 3 |
mismo | 3 |
mn | 3 |
mtrends | 3 |
mucho | 3 |
muestran | 3 |
myLocn | 3 |
n+m | 3 |
n. | 3 |
naranja | 3 |
new | 3 |
observada | 3 |
obtenidos | 3 |
operatorQ | 3 |
oracle | 3 |
parámetro | 3 |
pp | 3 |
predicción | 3 |
predichos | 3 |
logra | 3 |
limit1 | 3 |
g. | 3 |
latency | 3 |
h= | 3 |
hoy | 3 |
http | 3 |
httpd | 3 |
https://doi.org/10.1101/2020.04.26.20080960 | 3 |
i(0 | 3 |
i,1 | 3 |
i. | 3 |
i.e | 3 |
i=1 | 3 |
implantación | 3 |
infectives | 3 |
iniciales | 3 |
interés | 3 |
iso | 3 |
j.bjps.2020.02 | 3 |
j=1 | 3 |
ji | 3 |
k(x | 3 |
k+1 | 3 |
kSI | 3 |
kλ | 3 |
l(τ | 3 |
presenta | 3 |
pudiera | 3 |
q(β | 3 |
r(0 | 3 |
xg | 3 |
xt | 3 |
z(x | 3 |
± | 3 |
Élie | 3 |
Þ | 3 |
Δ | 3 |
Δt | 3 |
Π | 3 |
β(1 | 3 |
ι | 3 |
λe(I | 3 |
ϕ(x | 3 |
ܴሺݐሻ | 3 |
݊ | 3 |
ܫሺ0ሻ | 3 |
ܫሺݐሻ/ܰ | 3 |
−10 | 3 |
−Z | 3 |
−t | 3 |
∕dt | 3 |
⊂ | 3 |
Reconstruction | 3 |
vec3 | 3 |
variable | 3 |
usr | 3 |
shiny | 3 |
rand/1 | 3 |
red | 3 |
reducingĪ | 3 |
reportados | 3 |
resultados | 3 |
rjags | 3 |
s.u | 3 |
scattergram | 3 |
seq | 3 |
shell | 3 |
siguiente | 3 |
t−1 | 3 |
single.trend | 3 |
spain | 3 |
squared | 3 |
sra | 3 |
también | 3 |
tor | 3 |
totales | 3 |
trabajo | 3 |
transmisibilidad | 3 |
t|s | 3 |
growth.rate | 3 |
segunda | 3 |
frame | 3 |
Walker | 3 |
Shiny | 3 |
Solution | 3 |
Springer | 3 |
Surgical | 3 |
Systematic | 3 |
TMC | 3 |
Tables | 3 |
Temporal | 3 |
Therapy | 3 |
Thieme | 3 |
Touzi | 3 |
Transitions | 3 |
Transmissibility | 3 |
Trend | 3 |
Trends | 3 |
Una | 3 |
Unit | 3 |
VI | 3 |
VINCE | 3 |
Vaccine | 3 |
Veneto | 3 |
Vo | 3 |
WALANT | 3 |
Service | 3 |
Semiparametric | 3 |
Select | 3 |
S(β)dβ | 3 |
Reconstructive | 3 |
fixedĪ | 3 |
Regional | 3 |
Repository | 3 |
Resource | 3 |
Robertson | 3 |
Root | 3 |
Rubin | 3 |
S!I | 3 |
S(x | 3 |
S.t | 3 |
SciPy | 3 |
S3 | 3 |
S4 | 3 |
SDE | 3 |
SEIRD | 3 |
SG | 3 |
SIDARTHE | 3 |
SSRN | 3 |
Salud | 3 |
Sampling | 3 |
Scaling | 3 |
Wales | 3 |
Satorras | 3 |
Wall | 3 |
corresponden | 3 |
crece | 3 |
crecimiento | 3 |
dI(t | 3 |
dI.t/=dt | 3 |
dL | 3 |
da | 3 |
dadas | 3 |
deathsD | 3 |
decrecer | 3 |
deltaT | 3 |
derivada | 3 |
dw | 3 |
ea20 | 3 |
elúnico | 3 |
escenario | 3 |
esos | 3 |
estimado | 3 |
exp(λt | 3 |
experimentales | 3 |
ext | 3 |
Webinar | 3 |
fY | 3 |
fecha | 3 |
covid19.data | 3 |
diariamente | 3 |
corresponde | 3 |
avg | 3 |
Zhao | 3 |
considerado | 3 |
Woods | 3 |
Ziff | 3 |
Zino | 3 |
a(t | 3 |
acuerdo | 3 |
ambos | 3 |
apareciendo | 3 |
aunque | 3 |
alcanzaría | 3 |
azul | 3 |
brinda | 3 |
conf.d | 3 |
condición | 3 |
comienza | 3 |
cierta | 3 |
cero | 3 |
X(t | 3 |
beta^*$ | 3 |
b(t | 3 |
aún | 3 |
Interpretation | 2 |
JPRAS | 2 |
Interventions | 2 |
Islam | 2 |
Islands | 2 |
Isolation | 2 |
Issue | 2 |
Jeffrey | 2 |
Jin | 2 |
Jenkins | 2 |
Jens | 2 |
Jowett | 2 |
Jiang | 2 |
Jumping | 2 |
In-1 | 2 |
Integrity | 2 |
Integrated | 2 |
Institutes | 2 |
Institut | 2 |
Infrastructure | 2 |
Infectives | 2 |
Index | 2 |
Incidence | 2 |
Inc. | 2 |
Implications | 2 |
Ignition | 2 |
Ienca | 2 |
Iceland | 2 |
Jérémie | 2 |
Identifiability | 2 |
Junta | 2 |
Lattice | 2 |
KM | 2 |
KPP | 2 |
Level | 2 |
I.t | 2 |
Lesniewski | 2 |
Leskovec | 2 |
Lenton | 2 |
Leffler | 2 |
Lebesgue | 2 |
Lauer | 2 |
Latin | 2 |
Large | 2 |
Laplacian | 2 |
Landau | 2 |
Lagrangians | 2 |
LR | 2 |
LLB | 2 |
L/2 | 2 |
L(w(J | 2 |
Kuri | 2 |
Kuehn | 2 |
Krueger | 2 |
Krapivsky | 2 |
Krankheit-2019 | 2 |
Kolmogorov | 2 |
King | 2 |
Kinetic | 2 |
Karnataka | 2 |
Karmisholt | 2 |
Kandhway | 2 |
Kaggle | 2 |
Ian | 2 |
Gao | 2 |
I!R | 2 |
Greenberg | 2 |
Greedy | 2 |
Govt | 2 |
Gomes | 2 |
Gollier | 2 |
Glover | 2 |
Giuseppe | 2 |
GitHUB | 2 |
Ginzburg | 2 |
Gilbert | 2 |
Ghosh | 2 |
Geo | 2 |
Geneva | 2 |
Genetics | 2 |
Gauss | 2 |
Gardiner | 2 |
Gaffney | 2 |
GOLDD | 2 |
GG20 | 2 |
GDP | 2 |
GC | 2 |
G)-(I | 2 |
G(z | 2 |
G(Z | 2 |
FðtÞ | 2 |
Física | 2 |
Fynbo | 2 |
Fundação | 2 |
Functions | 2 |
Liam | 2 |
Green | 2 |
Guan | 2 |
Hückel | 2 |
Guanajuato | 2 |
Hébert | 2 |
HÞ | 2 |
Hsu | 2 |
Hossain | 2 |
Holmes | 2 |
Hill | 2 |
Hilbert | 2 |
Higgins | 2 |
Heterogeneous | 2 |
Heterogeneity | 2 |
Het00 | 2 |
Hern | 2 |
Hence | 2 |
Helsinki | 2 |
Hellewell | 2 |
Helbing | 2 |
Heidelberger | 2 |
Head | 2 |
Hans | 2 |
Hamsterster | 2 |
Halton | 2 |
HTML | 2 |
HP | 2 |
HIFLD | 2 |
Gustavo | 2 |
Gurney | 2 |
Guo | 2 |
Guide | 2 |
Guardian | 2 |
Li18 | 2 |
Pavlos | 2 |
License | 2 |
PRISMA | 2 |
PEK | 2 |
Overall | 2 |
Otunuga | 2 |
Ortiz | 2 |
Organisation | 2 |
Options | 2 |
Opportunities | 2 |
Oliveira | 2 |
Oli20 | 2 |
Occurrence | 2 |
OIC | 2 |
Nvar | 2 |
Notes | 2 |
Note | 2 |
Norovirus | 2 |
Normal(0 | 2 |
Node | 2 |
No | 2 |
Nisbet | 2 |
Nil | 2 |
Newton | 2 |
Neufeld | 2 |
Neto | 2 |
Nekovee | 2 |
Neilan | 2 |
Neiderud | 2 |
Nadu | 2 |
Nadaraya | 2 |
NSF | 2 |
PLoS | 2 |
Palladino | 2 |
NIPS | 2 |
Pan | 2 |
Puede | 2 |
Protopapas | 2 |
Protezione | 2 |
Propagation | 2 |
Prof. | 2 |
Processing | 2 |
Process | 2 |
Proceedings | 2 |
Principle | 2 |
Princeton | 2 |
Preliminary | 2 |
Predicting | 2 |
Practical | 2 |
Plummer | 2 |
Plotly | 2 |
Pires | 2 |
Phenomena | 2 |
Pesco | 2 |
Pescarini | 2 |
Peripheral | 2 |
Periodicity | 2 |
Period | 2 |
Peoples | 2 |
Peker | 2 |
Pearson | 2 |
Friday | 2 |
Pattern | 2 |
Parulian | 2 |
Paraíba | 2 |
NRF | 2 |
NB | 2 |
Lie | 2 |
Maryland | 2 |
Marco | 2 |
Mapping | 2 |
Maple | 2 |
Mak | 2 |
Main | 2 |
Maier | 2 |
Mahmud | 2 |
Maheshwari | 2 |
Madagascar | 2 |
Macroeconomics | 2 |
Ma | 2 |
MPL | 2 |
MOP | 2 |
MLT | 2 |
MCP | 2 |
MAH | 2 |
Lévy | 2 |
Lubik | 2 |
Lower | 2 |
Longstaff | 2 |
Lockdowns | 2 |
Lockdown | 2 |
Local | 2 |
Lithuania | 2 |
Lipsitch | 2 |
Lipschitz | 2 |
Lippi | 2 |
Likert | 2 |
Likelihood | 2 |
Martens | 2 |
Marzo | 2 |
N95 | 2 |
Mass | 2 |
N(t | 2 |
Mummert | 2 |
Ms. | 2 |
Moving | 2 |
Mosaic | 2 |
Mori | 2 |
Moore | 2 |
Modern | 2 |
Modellierung | 2 |
Mittag | 2 |
Mining | 2 |
Miller | 2 |
Microsoft | 2 |
Microbiology | 2 |
Mental | 2 |
Medicine | 2 |
MedSupplyDrive | 2 |
Mechanics | 2 |
Measures | 2 |
Measurement | 2 |
Measurability | 2 |
MeV | 2 |
McKendric | 2 |
Matz | 2 |
Matthes | 2 |
Math | 2 |
Material | 2 |
Mastrolia | 2 |
Massaro | 2 |
Friendster | 2 |
Cp;j | 2 |
Frequency | 2 |
Broglie | 2 |
Brauer | 2 |
Brasil | 2 |
Brasche | 2 |
Brand24 | 2 |
Boundary | 2 |
Bossert | 2 |
Bone | 2 |
Bischof | 2 |
Biotechnology | 2 |
BioSample | 2 |
Bickmann | 2 |
Berlin | 2 |
Belarus | 2 |
Beispielszenarien | 2 |
Beatson | 2 |
Barlow | 2 |
Barabasi | 2 |
Bank | 2 |
BCG | 2 |
Ayse | 2 |
Avery | 2 |
Average | 2 |
Auto | 2 |
Attribution | 2 |
Atkeson | 2 |
Associations | 2 |
Artificial | 2 |
Art | 2 |
Army | 2 |
Brazilian | 2 |
Brown | 2 |
Ardabili | 2 |
Buccellato | 2 |
Chloropleth | 2 |
Chinazzi | 2 |
Chief | 2 |
Chib | 2 |
Chavez | 2 |
Charles | 2 |
Charity | 2 |
Chap | 2 |
Changes | 2 |
Chang | 2 |
Chan | 2 |
Ch | 2 |
Carlin | 2 |
Canniesburn | 2 |
Camilli | 2 |
Calibration | 2 |
Calculus | 2 |
Cadoni | 2 |
Cabo | 2 |
CRC | 2 |
COVID−19 | 2 |
CMMID | 2 |
CI(λ | 2 |
C(t | 2 |
C(Q | 2 |
Butcher | 2 |
Burks | 2 |
Bulirsch | 2 |
Bulgaria | 2 |
Area | 2 |
Araceli | 2 |
Fractals | 2 |
AMEs | 2 |
ADAM | 2 |
ACE2 | 2 |
A8 | 2 |
A3 | 2 |
A)-(C | 2 |
9a | 2 |
7vdz661d | 2 |
7b | 2 |
6d | 2 |
3a | 2 |
3B | 2 |
2λ | 2 |
2F | 2 |
2E | 2 |
2D | 2 |
2C | 2 |
1Þ | 2 |
1|M | 2 |
0:1 | 2 |
0.3333 | 2 |
/w | 2 |
/t | 2 |
/k | 2 |
/g | 2 |
/2 | 2 |
-server | 2 |
-1 | 2 |
# | 2 |
PðqÞ | 2 |
AME | 2 |
Acad | 2 |
Appl | 2 |
Academy | 2 |
Appendices | 2 |
Antonopoulos | 2 |
Antonio | 2 |
Angelopoulos | 2 |
Android | 2 |
Andalucía | 2 |
Anand | 2 |
Analytical | 2 |
Americans | 2 |
Alzheimer | 2 |
Alvarez | 2 |
Alonso | 2 |
All | 2 |
Alirol | 2 |
Alessandro | 2 |
Alejandro | 2 |
Albert | 2 |
Al | 2 |
Ahmetolan | 2 |
Ahmed | 2 |
African | 2 |
Advisory | 2 |
Additionaly | 2 |
Adaptive | 2 |
Adamu | 2 |
Adam | 2 |
Accuracy | 2 |
Accounting | 2 |
Access | 2 |
Choi | 2 |
Chris | 2 |
Civile | 2 |
Ethicon | 2 |
Empirical | 2 |
Emmanuel | 2 |
Emma | 2 |
Emerging | 2 |
Emergency | 2 |
Eichenbaum | 2 |
Effectiveness | 2 |
Edwin | 2 |
Education | 2 |
Editor | 2 |
Ecuador | 2 |
Economía | 2 |
Earth | 2 |
Earn | 2 |
E[Y | 2 |
EVD | 2 |
ERP021740 | 2 |
EM | 2 |
ED | 2 |
ECDC | 2 |
Dufresne | 2 |
Drug | 2 |
Drake | 2 |
Dr. | 2 |
Download | 2 |
Douglas | 2 |
Dong | 2 |
Dobie | 2 |
Dn | 2 |
Environmental | 2 |
Euganeo | 2 |
Clark | 2 |
Event | 2 |
Fractal | 2 |
Fowler | 2 |
Fourth | 2 |
Forgoston | 2 |
Flickr | 2 |
Flaxman | 2 |
Fitzpatrick | 2 |
Fisher | 2 |
Finite | 2 |
Fine | 2 |
Financial | 2 |
Fig.8 | 2 |
Fig.2 | 2 |
Feedback | 2 |
Falcone | 2 |
FW | 2 |
FQM-225 | 2 |
FIS2017 | 2 |
FIGURE | 2 |
FIG | 2 |
FEDER | 2 |
FACTS | 2 |
Extinct | 2 |
External | 2 |
Exposed | 2 |
Exponential | 2 |
Experimental | 2 |
Exchange | 2 |
Excel | 2 |
Diver | 2 |
Distributions | 2 |
Discussion | 2 |
Discovery | 2 |
Cruise | 2 |
Credit | 2 |
Creative | 2 |
Cq | 2 |
Coxian | 2 |
Cov(β | 2 |
Cori | 2 |
Convergence | 2 |
Controller | 2 |
Contract | 2 |
Continuum | 2 |
Contacts | 2 |
Contact | 2 |
Consistency | 2 |
Concern | 2 |
Computation | 2 |
Comprehensive | 2 |
Competitividad | 2 |
Compartment | 2 |
Comparative | 2 |
Commons | 2 |
Committee | 2 |
Colaneri | 2 |
Cohen | 2 |
Cob20 | 2 |
CoV-2-Epidemie | 2 |
Clément | 2 |
Clin | 2 |
Classical | 2 |
Cuomo | 2 |
D.1b | 2 |
D/2 | 2 |
Derivatives | 2 |
Disability | 2 |
Dinh | 2 |
Digg | 2 |
Diffusion | 2 |
Dietz | 2 |
Didier | 2 |
Diagnosis | 2 |
Di | 2 |
Development | 2 |
Deutschland | 2 |
Detection | 2 |
Dermoscopy | 2 |
Dermatologists | 2 |
Departamento | 2 |
DIC | 2 |
Demographic | 2 |
Democratic | 2 |
Demirci | 2 |
Delay | 2 |
DegreeDiscount | 2 |
Degree | 2 |
Declaration | 2 |
Debye | 2 |
Deaths | 2 |
Dataset | 2 |
Database | 2 |
Dalian | 2 |
Dado | 2 |
PðkÞ | 2 |
Itrend | 2 |
QSIR | 2 |
monte | 2 |
modelos | 2 |
miles | 2 |
mencionamos | 2 |
mayor | 2 |
maxima | 2 |
mas | 2 |
marco | 2 |
magnitud | 2 |
m. | 2 |
lógico | 2 |
log(R | 2 |
log(I | 2 |
linear | 2 |
limit2 | 2 |
ligne | 2 |
laúltima | 2 |
l. | 2 |
l,0 | 2 |
k≥1 | 2 |
k−1 | 2 |
kλ/(1 | 2 |
kth | 2 |
kmer | 2 |
ki | 2 |
k1 | 2 |
jy | 2 |
japan | 2 |
j. | 2 |
izquierdo | 2 |
modulation | 2 |
n(c | 2 |
itrends | 2 |
n+ | 2 |
pearson | 2 |
pathometry.-Part | 2 |
params | 2 |
p(Y | 2 |
origin | 2 |
optimista | 2 |
ont | 2 |
obtuvieron | 2 |
obtiene | 2 |
obtenida | 2 |
obtener | 2 |
observado | 2 |
obs | 2 |
n≥0 | 2 |
n−i−1 | 2 |
n−i | 2 |
nx | 2 |
nucx | 2 |
npis | 2 |
nonstationary | 2 |
nivel | 2 |
ningún | 2 |
negativos | 2 |
nd | 2 |
national | 2 |
nasomaxillary | 2 |
n.l.t | 2 |
n-1 | 2 |
n+α | 2 |
izquierda | 2 |
intercell | 2 |
permiten | 2 |
github | 2 |
forecast | 2 |
fn | 2 |
fluctuaciones | 2 |
final | 2 |
figuras | 2 |
expf | 2 |
exp(0.5 | 2 |
ex | 2 |
europe | 2 |
euro | 2 |
etapa | 2 |
están | 2 |
estricto | 2 |
estricta | 2 |
estimar | 2 |
estima | 2 |
escenarios | 2 |
epicanthal | 2 |
enfermo | 2 |
enferman | 2 |
encuentra | 2 |
ello | 2 |
ellas | 2 |
efectiva | 2 |
ef | 2 |
ebola | 2 |
ea97 | 2 |
e(t | 2 |
dω | 2 |
g(x | 2 |
grant | 2 |
integro | 2 |
gray | 2 |
int | 2 |
institute | 2 |
instante | 2 |
instantaneamente | 2 |
infectadas | 2 |
infectada | 2 |
inf | 2 |
independiente | 2 |
indefectiblemente | 2 |
imponga | 2 |
ikx | 2 |
iii | 2 |
ii | 2 |
idea | 2 |
ia | 2 |
https://github.com/mponce0/covid19analytics.datasets | 2 |
https://github.com/mponce0/covid19.analytics | 2 |
https://github.com/ANRGUSC/ | 2 |
https://doi.org/10.1101/2020.11.10.20211995 | 2 |
https://doi.org/10.1101/2020.09.11.20192229 | 2 |
https://doi.org/10.1101/2020.05.22.20110254 | 2 |
https://data.covid.umd.edu/ | 2 |
hipotética | 2 |
hasta | 2 |
han | 2 |
hace | 2 |
habitantes | 2 |
h4 | 2 |
green | 2 |
pequeña | 2 |
pero | 2 |
dz | 2 |
Ä | 2 |
Àc | 2 |
|λ | 2 |
|A| | 2 |
zur | 2 |
zona | 2 |
z(t | 2 |
york | 2 |
x(0 | 2 |
withS | 2 |
whereα | 2 |
w0 | 2 |
w. | 2 |
vez | 2 |
ver | 2 |
variar | 2 |
valueĪ | 2 |
uno | 2 |
united | 2 |
t− | 2 |
típico | 2 |
t|t | 2 |
2 | |
tori | 2 |
todos | 2 |
tn | 2 |
thatR | 2 |
te | 2 |
tanto | 2 |
tabla | 2 |
à | 2 |
È | 2 |
t0 | 2 |
Ë | 2 |
≥0 | 2 |
R.t/. | 2 |
∪ | 2 |
−β | 2 |
−j | 2 |
−[g(q | 2 |
−L/2 | 2 |
−J | 2 |
−9 | 2 |
∇ϕ | 2 |
∇ | 2 |
∆ | 2 |
∀j | 2 |
ܮ | 2 |
ൌ | 2 |
ܶ. | 2 |
π(λ | 2 |
κ(t | 2 |
η | 2 |
δ(r | 2 |
γI(t | 2 |
γ(z | 2 |
γ(R | 2 |
βS(t)/γ | 2 |
β)N | 2 |
β)(t | 2 |
Ξ | 2 |
Λ. | 2 |
Ī | 2 |
t2 | 2 |
t)I(u)du | 2 |
personsĪ | 2 |
rnorm | 2 |
resulta | 2 |
respecto | 2 |
respectivamente | 2 |
reportan | 2 |
report.summary | 2 |
regiónR | 2 |
reflejar | 2 |
referencia | 2 |
recuperación | 2 |
realistas | 2 |
rango | 2 |
radii | 2 |
r.c | 2 |
r(y | 2 |
quasirandom | 2 |
q=0.7 | 2 |
q=0.55 | 2 |
q/. | 2 |
q/ | 2 |
pyoverdine | 2 |
punto | 2 |
pues | 2 |
public | 2 |
prov | 2 |
presentan | 2 |
predecir | 2 |
positiva | 2 |
population | 2 |
poblacion | 2 |
resultar | 2 |
rondarían | 2 |
t)/N | 2 |
s. | 2 |
t)/I(t | 2 |
sólo | 2 |
synthSIR | 2 |
susceptiblesS | 2 |
susceptibles | 2 |
suponer | 2 |
sugieren | 2 |
suceder | 2 |
subsección | 2 |
states-"healthy | 2 |
states | 2 |
stage-2 | 2 |
stage-1 | 2 |
src='repo | 2 |
src | 2 |
south | 2 |
solo | 2 |
softmax | 2 |
sigue | 2 |
signo | 2 |
siempre | 2 |
sería | 2 |
septiembre | 2 |
sep | 2 |
sel | 2 |
seis | 2 |
sdĥd | 2 |
sciences | 2 |
sapiens | 2 |
débuts | 2 |
κβ | 2 |
dr)(t | 2 |
Supply | 2 |
Sun | 2 |
Studies | 2 |
Student | 2 |
Stoer | 2 |
Status | 2 |
Standing | 2 |
Standard | 2 |
St | 2 |
Squillante | 2 |
Squares | 2 |
Spreading | 2 |
Spiegelhalter | 2 |
Special | 2 |
Southeast | 2 |
Source | 2 |
Sn-1 | 2 |
Slovak | 2 |
Skin | 2 |
Sizes | 2 |
Singh | 2 |
Simon | 2 |
Silva | 2 |
Shu | 2 |
Shin | 2 |
Shi | 2 |
Sheikh | 2 |
Shahid | 2 |
Setting | 2 |
Sequential | 2 |
Super | 2 |
Suppression | 2 |
Sec.2 | 2 |
Survey | 2 |
Unobserved | 2 |
Units | 2 |
Un | 2 |
USC | 2 |
U.S.A. | 2 |
U.S | 2 |
U(t | 2 |
Type | 2 |
Tutorial | 2 |
Turn | 2 |
Transport | 2 |
Training | 2 |
Tourniquet | 2 |
Tornatore | 2 |
Tor | 2 |
Tools | 2 |
Tomba | 2 |
Ticuna | 2 |
Thompson | 2 |
Telle | 2 |
Tegel | 2 |
Techniques | 2 |
Tartari | 2 |
Tamil | 2 |
También | 2 |
TakaraBio | 2 |
São | 2 |
Systrom | 2 |
Synchronization | 2 |
SensorFlow | 2 |
Seasonal | 2 |
Usage | 2 |
Rt_eff | 2 |
Roger | 2 |
Rio | 2 |
Riccardo | 2 |
Ribeiro | 2 |
Rg | 2 |
Reλ | 2 |
RevMan | 2 |