
index: 0


Prediction:

disease name: COVID-19 
location: South Africa 
date: - 
R0 value: 2.95 
%CI values: (95% credible interval [CrI] 2.83-3.33) 
method: mathematical model


Label:

disease name: COVID-19 
location: South Africa 
date: - 
R0 value: 2.95 
%CI values: (95% credible interval [CrI] 2.83-3.33) 
method: mathematical model
-------------------------------------------------
index: 1


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 2


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 3


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 4


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 5


Prediction:

disease name: COVID-19 
location: Japan 
date: from 6 January to 31 March 2020 
R0 value: 1.954 
%CI values: (95% confidence interval (CI) 1.851-2.025) 
method: -


Label:

disease name: COVID-19 
location: Japan 
date: 6 January to 31 March 2020 
R0 value: 1.954 
%CI values: 95% confidence interval (CI) 1.851-2.025 
method: estimating the current state of epidemic dissemination
-------------------------------------------------
index: 6


Prediction:

disease name: COVID-19 
location: Algeria 
date: March 01 to April 10, 2020 
R0 value: 2.1 
%CI values: - 
method: SEIR modelling


Label:

disease name: Covid-19 
location: Algeria 
date: March 01 to April 10, 2020 
R0 value: 2.1 
%CI values: - 
method: SEIR modelling
-------------------------------------------------
index: 7


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 8


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 9


Prediction:

disease name: COVID-19 
location: China 
date: before 8th February 2020 
R0 value: 2.33 
%CI values: (95% CI 1.96-3.69) 
method: mathematical model with multiple datasets


Label:

disease name: COVID-19 
location: China 
date: before 8th February 2020 
R0 value: 2.33 
%CI values: 95% CI 1.96-3.69 
method: Mathematical Model with Multiple Datasets
-------------------------------------------------
index: 10


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 11


Prediction:

disease name: SARS-CoV-2 
location: Paris 2020 
date: - 
R0 value: across the entire hospital (2.6) and in individual wards (from 3 to 15) 
%CI values: - 
method: a realistic epidemic model which accounts for progressive stages of infection, stochastic effects and a large proportion of asymptomatic infections


Label:

disease name: SARS-CoV-2 
location: a closely monitored hospital outbreak in Paris, entire hospital 
date: 2020 during the first wave 
R0 value: 2.6 
%CI values: - 
method: realistic epidemic model 
| 
disease name: SARS-CoV-2 
location: a closely monitored hospital outbreak in Paris, individual wards 
date: 2020 during the first wave 
R0 value: from 3 to 15 
%CI values: - 
method: realistic epidemic model 
| 
disease name: SARS-CoV-2 
location: a closely monitored hospital outbreak in Paris 
date: 2020 during the first wave, mask-wearing policy: before 
R0 value: 8.7 
%CI values: - 
method: realistic epidemic model 
| 
disease name: SARS-CoV-2 
location: a closely monitored hospital outbreak in Paris 
date: 2020 during the first wave, mask-wearing policy: after 
R0 value: 1.3 
%CI values: - 
method: realistic epidemic model
-------------------------------------------------
index: 12


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 13


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 14


Prediction:

disease name: COVID-19 
location: Philippines 
date: - 
R0 value: 1.5 to 4 
%CI values: - 
method: a conceptual mathematical model


Label:

unanswerable
-------------------------------------------------
index: 15


Prediction:

unanswerable


Label:

disease name: COVID-19 
location: Nigeria 
date: - 
R0 value: - 
%CI values: - 
method: Mathematical model
-------------------------------------------------
index: 16


Prediction:

unanswerable


Label:

disease name: COVID-19 
location: North Carolina and the Charlotte Metropolitan Region 
date: - 
R0 value: - 
%CI values: - 
method: susceptible-infected-removed (SIR) model
-------------------------------------------------
index: 17


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 18


Prediction:

disease name: COVID-19 
location: Wuhan, China 
date: January 1, 2020 and March 8, 2020 
R0 value: 3.54 
%CI values: (95% credible interval [CrI]: 3.40-3.67) 
method: modelling approach


Label:

disease name: COVID-19 
location: Wuhan, China 
date: January 1, 2020 and March 8, 2020 
R0 value: 3.54 
%CI values: 95% credible interval [CrI]: 3.40-3.67 
method: modelling approach to reconstruct the full-spectrum dynamics of COVID-19 
| 
disease name: COVID-19 
location: Wuhan, China 
date: as of March 8 
R0 value: 0.28 
%CI values: (0.23-0.33) 
method: modelling approach to reconstruct the full-spectrum dynamics of COVID-19
-------------------------------------------------
index: 19


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 20


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 21


Prediction:

disease name: COVID-19 
location: Culiac'an Sinaloa, Mexico 
date: - 
R0 value: 1.562 
%CI values: (95% confidence interval) 1.401,1.742 
method: -


Label:

disease name: COVID-19 
location: Culiacán Sinaloa, Mexico 
date: - 
R0 value: 1.562 
%CI values: (1.401,1.742) 
method: -
-------------------------------------------------
index: 22


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 23


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 24


Prediction:

unanswerable


Label:

disease name: novel SARS-CoV-2 (COVID-19) 
location: USA 
date: - 
R0 value: R0_1=1.0; R0_2=17.4; R0_3=1.0 
%CI values: - 
method: SEIR epidemiological compartmental model (susceptible-exposed-infected-recovered) with k phases 
| 
disease name: novel SARS-CoV-2 (COVID-19) 
location: Brazil 
date: - 
R0 value: R0_1=8.0; R0_2=1.3 
%CI values: - 
method: SEIR epidemiological compartmental model (susceptible-exposed-infected-recovered) with k phases 
| 
disease name: novel SARS-CoV-2 (COVID-19) 
location: France 
date: - 
R0 value: R0_1=4.3; R0_2=9.3; R0_3=0.5 
%CI values: - 
method: SEIR epidemiological compartmental model (susceptible-exposed-infected-recovered) with k phases
-------------------------------------------------
index: 25


Prediction:

disease name: COVID-19 
location: Tamil Nadu, India 
date: - 
R0 value: 1.88 (min = 1.09, max = 2.51) 
%CI values: - 
method: Log-linear model


Label:

disease name: COVID-19 
location: Tamil Nadu, India 
date: first 41 days of the outbreak started on March 7, 2020 
R0 value: 1.88 (min = 1.09, max = 2.51) and 0.76 (min = 0.56, max = 0.99) in the growth and decay phases 
%CI values: - 
method: log-linear regression approach
-------------------------------------------------
index: 26


Prediction:

disease name: 2009 Influenza 
location: South Africa 
date: - 
R0 value: 1.33 
%CI values: 1.31, 1.36 
method: novel extension of a network-based approach to estimating the reproductive number


Label:

disease name: Influenza 
location: South Africa 
date: - 
R0 value: 1.33 (1.31, 1.36) 
%CI values: - 
method: novel extension of a network-based approach, South Africa as a single entity with homogeneous transmission characteristics 
| 
disease name: Influenza 
location: South Africa 
date: - 
R0 value: (1.23-1.37) 
%CI values: - 
method: novel extension of a network-based approach, fitting a new model for each province with no inter-province connections 
| 
disease name: Influenza 
location: South Africa 
date: - 
R0 value: (1.33 to 1.34) overall 
%CI values: - 
method: novel extension of a network-based approach, proposed method with any of the four similarity measures
-------------------------------------------------
index: 27


Prediction:

disease name: COVID-19 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: a mathematical model incorporating age specific transmission dynamics of COVID-19


Label:

unanswerable
-------------------------------------------------
index: 28


Prediction:

disease name: SARS-CoV-2 
location: India 
date: - 
R0 value: - 
%CI values: - 
method: BDSIR package of BEAST2


Label:

unanswerable
-------------------------------------------------
index: 29


Prediction:

disease name: COVID-19 
location: New York City 
date: Phase 1 
R0 value: 4.60 
%CI values: - 
method: SEIR compartmental model


Label:

disease name: COVID-19 
location: New York City 
date: - 
R0 value: 4.60 in Phase 1 without any intervention 
%CI values: - 
method: SEIR compartmental model
-------------------------------------------------
index: 30


Prediction:

disease name: novel coronavirus (COVID-19) 
location: Shahroud, Iran 
date: in the early 14 days 
R0 value: 2.7 
%CI values: (95% confidence interval (CI): 2.1 to 3.4) 
method: serial interval distribution and the number of incidence cases 
| 
disease name: novel coronavirus (COVID-19) 
location: by the end of the day 41 
date: - 
R0 value: 1.13 
%CI values: (95% CI: 1.03 to 1.25) 
method: serial interval distribution and the number of incidence cases


Label:

disease name: COVID-19 
location: Shahroud, Northeast of Iran 
date: early 14 days 
R0 value: 2.7 
%CI values: 95% confidence interval (CI): 2.1 to 3.4 
method: the serial interval distribution and the number of incidence cases 
| 
disease name: COVID-19 
location: Shahroud, Northeast of Iran 
date: by the end of the day 41 
R0 value: 1.13 
%CI values: 95% CI: 1.03 to 1.25 
method: the serial interval distribution and the number of incidence cases
-------------------------------------------------
index: 31


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 32


Prediction:

disease name: primary pneumonic plague, caused by Yersinia pestis 
location: Mukden, China, and Madagascar 
date: - 
R0 value: 2.8 to 3.5 
%CI values: exceeded unity 
method: likelihood based approach


Label:

disease name: primary pneumonic plague 
location: Mukden, China 
date: 1946 
R0 value: 2.8 to 3.5 
%CI values: The lower 95% confidence intervals of R(0) exceeded unity 
method: statistical estimation of R(0) based on similar information 
| 
disease name: primary pneumonic plague 
location: Madagascar 
date: 1957 
R0 value: 2.8 to 3.5 
%CI values: The lower 95% confidence intervals of R(0) exceeded unity 
method: statistical estimation of R(0) based on similar information
-------------------------------------------------
index: 33


Prediction:

disease name: COVID-19 
location: Cameroon 
date: as of July 9, 2020 
R0 value: 3.41 
%CI values: (95 % CI : 2.2 - 4.4) 
method: An integer derivative model


Label:

disease name: COVID-19 
location: Cameroon 
date: - 
R0 value: 3.41 
%CI values: ( 95 % CI : 2.2 - 4.4 ) 
method: compartmental mathematical model
-------------------------------------------------
index: 34


Prediction:

disease name: COVID-19 
location: five countries 
date: - 
R0 value: - 
%CI values: - 
method: standard epidemic three-variable model, SIR (Susceptible, Infected and Removed) is extended into a five-variable model SCARE: Susceptible, Carrier, Affected (i.e. sick), Recovered and Eliminated (i.e. dead). Using WHO and Oxford data on cases and deaths, we rely on indirect inference techniques to estimate the parameters of SIR and SCARE.


Label:

disease name: COVID-19 
location: Belgium 
date: - 
R0 value: - 
%CI values: - 
method: standard epidemic three-variable model, SIR (Susceptible, Infected and Removed) is extended into a five-variable model SCARE: Susceptible, Carrier, Affected (i.e. sick), Recovered and Eliminated (i.e. dead)
-------------------------------------------------
index: 35


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 36


Prediction:

disease name: COVID-19 
location: Shiyan city, Hubei province, China 
date: - 
R0 value: 1.81 
%CI values: - 
method: Epidemiological investigation


Label:

disease name: Coronavirus Disease-2019 
location: Shiyan City, Hubei Province, China 
date: - 
R0 value: 1.81 
%CI values: - 
method: Epidemiological investigation
-------------------------------------------------
index: 37


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 38


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 39


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 40


Prediction:

disease name: SARS-CoV-2 
location: Tuscany (Italy) 
date: February-June 2020 
R0 value: 6.055 
%CI values: - 
method: compartmental model


Label:

disease name: SARS-CoV-2 
location: Tuscany, Italy 
date: (February-June 2020) 
R0 value: 6.055 
%CI values: - 
method: compartmental model
-------------------------------------------------
index: 41


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 42


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 43


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 44


Prediction:

disease name: COVID-19 
location: Mexico 
date: - 
R0 value: - 
%CI values: - 
method: a comprehensive nonlinear ODE model


Label:

disease name: COVID-19 
location: Mexico 
date: - 
R0 value: - 
%CI values: - 
method: comprehensive nonlinear ODE model
-------------------------------------------------
index: 45


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 46


Prediction:

disease name: COVID-19 
location: Japan 
date: Jan 22 to Feb 25 (Period I) 
R0 value: 4.66 
%CI values: - 
method: SIRD model and applied the Monte Carlo Simulation 
| 
disease name: COVID-19 
location: Japan 
date: Feb 26 to Apr 6 (Period II) 
R0 value: 2.50 
%CI values: - 
method: SIRD model and applied the Monte Carlo Simulation 
| 
disease name: COVID-19 
location: Japan 
date: Apr 7 to May 14 (Period III) 
R0 value: 1.79 
%CI values: - 
method: SIRD model and applied the Monte Carlo Simulation


Label:

disease name: COVID-19 
location: Japan 
date: Jan 22 to Feb 25 (Period I) 
R0 value: 4.66 
%CI values: - 
method: SIRD model and the Monte Carlo Simulation 
| 
disease name: COVID-19 
location: Japan 
date: Feb 26 to Apr 6 (Period II) 
R0 value: 2.50 
%CI values: - 
method: SIRD model and the Monte Carlo Simulation 
| 
disease name: COVID-19 
location: Japan 
date: Apr 7 to May 14 (Period III) 
R0 value: 1.79 
%CI values: - 
method: SIRD model and the Monte Carlo Simulation
-------------------------------------------------
index: 47


Prediction:

disease name: COVID-19 
location: Czech Republic 
date: Over March and April 
R0 value: > 2.00 to  1.00 
%CI values: - 
method: simple epidemiological model


Label:

disease name: COVID-19 
location: Czech Republic 
date: March and April 2020 
R0 value: >2.00 to  1.00 
%CI values: - 
method: simple epidemiological model
-------------------------------------------------
index: 48


Prediction:

disease name: COVID-19 
location: Thailand 
date: - 
R0 value: 1.25 
%CI values: - 
method: Deterministic system dynamics and compartmental models


Label:

disease name: Coronavirus Disease 2019 (COVID-19) 
location: Thailand 
date: - 
R0 value: reproduction number (R) between Thais and migrants was estimated at 1.25 and 2.5, respectively 
%CI values: - 
method: Deterministic system dynamics and compartmental models
-------------------------------------------------
index: 49


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 50


Prediction:

unanswerable


Label:

disease name: Hantavirus 
location: Paraguay 
date: - 
R0 value: - 
%CI values: - 
method: System of ordinary differential equations (ODE) and a continuous-time Markov chain (CTMC) model
-------------------------------------------------
index: 51


Prediction:

disease name: bluetongue 
location: Kazakhstan 
date: - 
R0 value: - 
%CI values: - 
method: mathematical model


Label:

unanswerable
-------------------------------------------------
index: 52


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 53


Prediction:

disease name: COVID-19 
location: the United States 
date: - 
R0 value: r0 approx 0.29 per day 
%CI values: - 
method: The Distributed Logistic Model and the Adaptive Logistic Model


Label:

disease name: COVID-19 
location: United States, Italy, and the United Kingdom 
date: - 
R0 value: 0.29 per day for each country 
%CI values: - 
method: Distributed Logistic Model and the Adaptive Logistic Model of epidemics
-------------------------------------------------
index: 54


Prediction:

unanswerable


Label:

disease name: COVID-19 
location: India 
date: 1 August 2020 to 30 September 2020 
R0 value: 1 
%CI values: - 
method: SIR model and Bayesian approach combined with Monte Carlo Markov algorithms
-------------------------------------------------
index: 55


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 56


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 57


Prediction:

disease name: COVID-19 
location: Milan 
date: February to May 2020 
R0 value: 3.0 
%CI values: - 
method: a new information system was developed to record COVID-19 cases with positive nasopharyngeal swab


Label:

disease name: COVID-19 
location: Metropolitan Area of Milan 
date: February to May 2020 
R0 value: 3.0 
%CI values: - 
method: -
-------------------------------------------------
index: 58


Prediction:

disease name: COVID-19 
location: China 
date: - 
R0 value: 2.1 
%CI values: (95% UI=1.8-2.4) 
method: age-stratified mathematical model 
| 
disease name: COVID-19 
location: China 
date: - 
R0 value: 0.06 
%CI values: (95% UI=0.05-0.07) 
method: age-stratified mathematical model


Label:

disease name: Coronavirus Disease 2019 (COVID-2019) 
location: China 
date: late 2019 
R0 value: 2.1 
%CI values: (95% UI=1.8-2.4) 
method: age-stratified mathematical model 
| 
disease name: Coronavirus Disease 2019 (COVID-2019) 
location: China 
date: following onset of interventions 
R0 value: 0.06 
%CI values: (95% UI=0.05-0.07) 
method: age-stratified mathematical model
-------------------------------------------------
index: 59


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 60


Prediction:

disease name: COVID-19 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: a stochastic SIR model, specified as a system of stochastic differential equations


Label:

unanswerable
-------------------------------------------------
index: 61


Prediction:

disease name: SARS-2003 
location: - 
date: - 
R0 value: 2-5 
%CI values: - 
method: - 
| 
disease name: MERS-2012 
location: - 
date: - 
R0 value: 0.3-0.8 
%CI values: - 
method: - 
| 
disease name: COVID-19 
location: - 
date: - 
R0 value: 1.4- 5.7 
%CI values: - 
method: -


Label:

disease name: Coronavirus disease 2019 (COVID-19) 
location: Global 
date: 27th May, 2020 
R0 value: 1.4- 5.7 
%CI values: - 
method: -
-------------------------------------------------
index: 62


Prediction:

disease name: COVID-19 
location: - 
date: - 
R0 value: 1.5 
%CI values: - 
method: series of spreadsheet simulations using SEIS, SEIR, and SEIRS models 
| 
disease name: COVID-19 
location: - 
date: - 
R0 value: 2.3 
%CI values: - 
method: series of spreadsheet simulations using SEIR, SEIR, and SEIRS models 
| 
disease name: COVID-19 
location: - 
date: - 
R0 value: 3.0 
%CI values: - 
method: series of spreadsheet simulations using SEIR, SEIR, and SEIRS models


Label:

unanswerable
-------------------------------------------------
index: 63


Prediction:

disease name: COVID-19 
location: United States 
date: - 
R0 value: - 
%CI values: - 
method: a theoretical differential equation mathematical model


Label:

disease name: COVID-19 
location: United States 
date: As of January 19, 2021 
R0 value: - 
%CI values: - 
method: theoretical differential equation mathematical model, estimated model parameters through epidemiological data, obtained accurate mathematical models, and adopted global sensitivity analysis methods
-------------------------------------------------
index: 64


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 65


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 66


Prediction:

disease name: COVID-19 
location: 20 countries 
date: - 
R0 value: - 
%CI values: - 
method: a new estimator that is asymptotically consistent, has a smaller estimation variance than the least squares estimator, and achieves the semiparametric efficiency


Label:

disease name: COVID-19 
location: 20 countries 
date: - 
R0 value: - 
%CI values: - 
method: SEIR (Susceptible-Exposed-Infectious-Recovered) model with both deterministic and stochastic simulations
-------------------------------------------------
index: 67


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 68


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 69


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 70


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 71


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 72


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 73


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 74


Prediction:

disease name: COVID-19 
location: Nigeria 
date: - 
R0 value: less than unity (R_0 &lt; 1) 
%CI values: - 
method: next-generation method


Label:

disease name: COVID-19 
location: Nigeria 
date: - 
R0 value: less than unity 
%CI values: - 
method: next-generation method
-------------------------------------------------
index: 75


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 76


Prediction:

disease name: COVID-19 
location: - 
date: prior to lockdown 
R0 value: 3.18 
%CI values: (95% confidence interval) 
method: stochastic age-structured transmission model 
| 
disease name: COVID-19 
location: - 
date: during lockdown 
R0 value: 0.68 
%CI values: (0.66, 0.69) 
method: stochastic age-structured transmission model


Label:

disease name: COVID-19 
location: le-de-France 
date: prior to lockdown 
R0 value: 3.18 
%CI values: [3.09, 3.24] (95% confidence interval) 
method: stochastic age-structured transmission model 
| 
disease name: COVID-19 
location: le-de-France 
date: during lockdown 
R0 value: 0.68 
%CI values: [0.66, 0.69] 
method: stochastic age-structured transmission model
-------------------------------------------------
index: 77


Prediction:

disease name: COVID-19 
location: Canada 
date: - 
R0 value: - 
%CI values: - 
method: operator theory and spectral method


Label:

disease name: COVID-19 
location: across the globe 
date: - 
R0 value: - 
%CI values: - 
method: operator theory and spectral method
-------------------------------------------------
index: 78


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 79


Prediction:

disease name: COVID-19 
location: India 
date: - 
R0 value: - 
%CI values: - 
method: SEIRD epidemiological model


Label:

disease name: coronavirus pandemic (COVID-19) 
location: India 
date: - 
R0 value: - 
%CI values: - 
method: SEIRD epidemiological model
-------------------------------------------------
index: 80


Prediction:

disease name: COVID-19 
location: Wuhan City, China 
date: January-February, 2020 
R0 value: 3.49 
%CI values: (95% CrI 3.39-3.62) 
method: statistical methods and publicly available epidemiological datasets 
| 
disease name: COVID-19 
location: Wuhan City, China 
date: after January 23 in 2020 
R0 value: 0.84 
%CI values: (95% CrI 0.81-0.88) 
method: statistical methods and publicly available epidemiological datasets


Label:

disease name: COVID-19 
location: Wuhan City, China 
date: 2019-2020 
R0 value: 3.49 
%CI values: (95% CrI 3.39-3.62) 
method: statistical methods 
| 
disease name: COVID-19 
location: Wuhan City, China 
date: after January 23 in 2020 
R0 value: 0.84 
%CI values: (95% CrI 0.81-0.88) 
method: statistical methods
-------------------------------------------------
index: 81


Prediction:

unanswerable


Label:

disease name: COVID-19 
location: KSA 
date: - 
R0 value: - 
%CI values: - 
method: SIR and machine learning
-------------------------------------------------
index: 82


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 83


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 84


Prediction:

unanswerable


Label:

disease name: COVID-19 
location: Turkey 
date: - 
R0 value: 3 
%CI values: - 
method: SEIR modeling 
| 
disease name: COVID-19 
location: Turkey 
date: - 
R0 value: 1.38 
%CI values: - 
method: SEIR modeling 2nd scenario 
| 
disease name: COVID-19 
location: Turkey 
date: - 
R0 value: 1.38 
%CI values: - 
method: SEIR modeling 3rd scenario 
| 
disease name: COVID-19 
location: Turkey 
date: - 
R0 value: 0.98 
%CI values: - 
method: SEIR modeling 4th scenario
-------------------------------------------------
index: 85


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 86


Prediction:

disease name: COVID-19 
location: India 
date: March 1 to September 27 in 2021 
R0 value: maximum 
%CI values: - 
method: dynamical model of two strains, namely native and mutant, transmission dynamics with mutation and imperfect vaccination


Label:

unanswerable
-------------------------------------------------
index: 87


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 88


Prediction:

disease name: COVID-19 
location: Saudi Arabia 
date: - 
R0 value: ranged between 0.6014 and 2.7860 with an average of 1.4904 and currently holds at 0.8952 
%CI values: - 
method: modified Susceptible-Exposed-Infectious-Recovered (SEIR) model


Label:

disease name: coronavirus disease 2019 (COVID-19) 
location: Saudi Arabia 
date: - 
R0 value: 0.6014 and 2.7860 
%CI values: - 
method: Modified Susceptible-Exposed-Infectious-Recovered (SEIR) Model
-------------------------------------------------
index: 89


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 90


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 91


Prediction:

disease name: COVID-19 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: a mathematical model


Label:

disease name: COVID-19 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: A nonlinear epidemiological model considering asymptotic and quarantine classes
-------------------------------------------------
index: 92


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 93


Prediction:

disease name: SARS 
location: Beijing 
date: - 
R0 value: 1.0698 to 3.2524 
%CI values: - 
method: mathematical model


Label:

disease name: SARS 
location: Beijing 
date: from April 27 and cases transferred from suspect class to probable class from May 2 
R0 value: 1.0698 to 3.2524 
%CI values: - 
method: two-compartment suspect-probable model and a single-compartment probable model
-------------------------------------------------
index: 94


Prediction:

disease name: 2019-nCoV 
location: Wuhan 
date: till February 12, 2020 
R0 value: 1.44 
%CI values: (interquartile range: 1.40-1.57) 
method: SEIR modeling method


Label:

disease name: 2019-nCoV 
location: Wuhan 
date: after the closure of Wuhan city till February 12, 2020 
R0 value: 1.44 
%CI values: interquartile range: 1.40-1.47 
method: SEIR modeling method
-------------------------------------------------
index: 95


Prediction:

disease name: COVID-19 
location: UK 
date: March 2020 and May 2020 
R0 value: 3.23 
%CI values: - 
method: (SIRD) epidemiological model


Label:

disease name: COVID-19 
location: UK 
date: March 2020 and May 2020 
R0 value: 3.23 
%CI values: - 
method: susceptible - infected - recovered - deceased (SIRD) epidemiological model
-------------------------------------------------
index: 96


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 97


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 98


Prediction:

disease name: COVID-19 
location: - 
date: - 
R0 value: 3.25 - 3.4 
%CI values: - 
method: Wallinga and Lipsitch framework 11 and a novel statistical time delay dynamic system


Label:

disease name: COVID-19 
location: - 
date: - 
R0 value: 3.25-3.4 
%CI values: - 
method: Wallinga and Lipsitch framework 11 and a novel statistical time delay dynamic system
-------------------------------------------------
index: 99


Prediction:

disease name: SARS-CoV-2 
location: Wuhan 
date: Dec 2, 2019, and April 18, 2020 
R0 value: 156% 
%CI values: (95% CI 15-2-1.0) 
method: statistical transmission model


Label:

unanswerable
-------------------------------------------------
index: 100


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 101


Prediction:

disease name: Covid-19 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: age and space structured SIR model


Label:

disease name: Covid-19 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: An age and space structured SIR model
-------------------------------------------------
index: 102


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 103


Prediction:

disease name: COVID-19 
location: United States 
date: 21-January-2020 to 21-June-2020 
R0 value: 7.1 for New Jersey to 2.3 for Wyoming 
%CI values: - 
method: compartmental models via the next-generation matrix approach


Label:

disease name: COVID-19 
location: New Jersey, United States 
date: 21-January-2020 to 21-June-2020 
R0 value: 7.1 
%CI values: - 
method: Bayesian inference 
| 
disease name: COVID-19 
location: Wyoming, United States 
date: 21-January-2020 to 21-June-2020 
R0 value: 2.3 
%CI values: - 
method: Bayesian inference
-------------------------------------------------
index: 104


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 105


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 106


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 107


Prediction:

disease name: 2009 A/H1N1 Influenza 
location: Lima metropolitan area 
date: May 1 to December 31, 2009 
R0 value: 1.6-2 
%CI values: - 
method: - 
| 
disease name: 2009 A/H1N1 Influenza 
location: rest of Peru 
date: May 1 to December 31, 2009 
R0 value: 1.3-1.5% 
%CI values: - 
method: -


Label:

disease name: 2009 A/H1N1 Influenza 
location: Peru, Lima metropolitan area 
date: May 1 to December 31, 2009 
R0 value: 1.6-2.2 
%CI values: - 
method: - 
| 
disease name: 2009 A/H1N1 Influenza 
location: Peru, rest of Peru 
date: May 1 to December 31, 2009 
R0 value: 1.3-1.5 
%CI values: - 
method: -
-------------------------------------------------
index: 108


Prediction:

disease name: COVID-19 
location: Wuhan, China 
date: - 
R0 value: from a pre-introduction rate of 4.0 to 2.0 
%CI values: - 
method: interrupted time series analysis


Label:

disease name: COVID-19 
location: Wuhan, China 
date: February 2020 
R0 value: 4.0 to 2.0 
%CI values: - 
method: interrupted time series analysis
-------------------------------------------------
index: 109


Prediction:

disease name: novel coronavirus (COVID-19) 
location: India 
date: - 
R0 value: 1.53 to 3.25 
%CI values: - 
method: two R packages - R0 and - earlyR


Label:

disease name: Novel Coronavirus (COVID-19) 
location: India 
date: - 
R0 value: 1.53 to 3.25 
%CI values: - 
method: R packages 'R0' and 'earlyR'
-------------------------------------------------
index: 110


Prediction:

disease name: COVID-19 
location: England 
date: 13 May 
R0 value: 0.75 
%CI values: - 
method: Bayesian model


Label:

disease name: COVID-19 
location: England 
date: 13 May 2020 
R0 value: 0.75 
%CI values: - 
method: Bayesian model
-------------------------------------------------
index: 111


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 112


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 113


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 114


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 115


Prediction:

disease name: COVID-19 
location: Chile 
date: March-October, 2020 
R0 value: 1.8 
%CI values: (95% CI: 1.6, 1.9) 
method: -


Label:

disease name: COVID-19 
location: Chile 
date: March-October, 2020 
R0 value: 1.8 
%CI values: (95% CI: 1.6, 1.9) 
method: short-term forecasts based on the early transmission dynamics of COVID-19 
| 
disease name: COVID-19 
location: Chile, Greater Santiago and other municipalities 
date: as of November 2(nd), 2020 
R0 value: 0.96 
%CI values: (95% CI: 0.95, 0.98) 
method: short-term forecasts based on the early transmission dynamics of COVID-19
-------------------------------------------------
index: 116


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 117


Prediction:

disease name: SARS-CoV-2 
location: Benin, Western Africa 
date: - 
R0 value: 4.4 
%CI values: (95% confidence interval: 2.0-7.3) 
method: SARS-CoV-2 genome-based analyses


Label:

unanswerable
-------------------------------------------------
index: 118


Prediction:

disease name: COVID-19 
location: Canada 
date: - 
R0 value: Closed to unity 
%CI values: - 
method: a novel hybrid approach, a combination of neural networks and inverse problem


Label:

disease name: COVID-19 
location: Canada 
date: from 100 to 365 days of the current pandemic in Canada 
R0 value: closed to unity over a wide range 
%CI values: - 
method: second order nonlinear differential equation for the total confirmed cases from a SIR-like model
-------------------------------------------------
index: 119


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 120


Prediction:

disease name: coronavirus disease 2019 (COVID-19) 
location: Wanzhou, China 
date: - 
R0 value: 1.64 
%CI values: (95% confidence interval: 1.16-2.40) 
method: Epidemiological data were analyzed for 183 confirmed COVID-19 cases and their close contacts from five generations of transmission of severe acute respiratory syndrome coronavirus 2 throughout the entire COVID-19 outbreak in Wanzhou 
| 
disease name: coronavirus disease 2019 (COVID-19) 
location: Wanzhou, China 
date: - 
R0 value: 0.31-0.39 
%CI values: - 
method: Epidemiological data were analyzed for 183 confirmed COVID-19 cases and their close contacts from five generations of transmission of severe acute respiratory syndrome coronavirus 2 throughout the entire COVID-19 outbreak in Wanzhou


Label:

disease name: coronavirus disease 2019 (COVID-19) 
location: Wanzhou, China 
date: - 
R0 value: 1.64 
%CI values: 95% confidence interval: 1.16-2.40 
method: -
-------------------------------------------------
index: 121


Prediction:

disease name: SARS-CoV-2 
location: New York State (NYS) 
date: - 
R0 value: 5.7 
%CI values: - 
method: Model


Label:

disease name: Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) 
location: New York State (NYS) 
date: - 
R0 value: 5.7 
%CI values: - 
method: -
-------------------------------------------------
index: 122


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 123


Prediction:

disease name: novel coronavirus (COVID-19) 
location: Shahroud, Iran 
date: in the early 14 days 
R0 value: 2.7 
%CI values: (95% confidence interval (CI): 2.1-3.4) 
method: serial interval distribution and the number of incidence cases 
| 
disease name: novel coronavirus (COVID-19) 
location: Shahroud, Iran 
date: by the end of day 42 
R0 value: 1.13 
%CI values: (95% CI 1.03-1.25) 
method: serial interval distribution and the number of incidence cases


Label:

disease name: COVID-19 
location: Shahroud in Northeastern Iran 
date: early 14 days 
R0 value: 2.7 
%CI values: 95% confidence interval (CI): 2.1-3.4 
method: the serial interval distribution and the number of incidence cases 
| 
disease name: COVID-19 
location: Shahroud in Northeastern Iran 
date: by the end of day 42. 
R0 value: 1.13 
%CI values: 95% CI 1.03-1.25 
method: the serial interval distribution and the number of incidence cases
-------------------------------------------------
index: 124


Prediction:

disease name: Covid-19 
location: Spain 
date: Mid March - Mid May 
R0 value: 2.5 
%CI values: (95% CI 2.3-2.7) 
method: least-mean-square fitting of daily cases


Label:

disease name: Covid-19 
location: Spain 
date: over the whole span 
R0 value: 2.5 
%CI values: 95% CI 2.3-2.7 
method: (SIR) model 
| 
disease name: Covid-19 
location: Germany 
date: over the whole span 
R0 value: - 
%CI values: - 
method: (SIR) model 
| 
disease name: Covid-19 
location: Italy 
date: over the whole span 
R0 value: - 
%CI values: - 
method: (SIR) model
-------------------------------------------------
index: 125


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 126


Prediction:

unanswerable


Label:

disease name: COVID-19 
location: countries around the world 
date: - 
R0 value: - 
%CI values: - 
method: established epidemiological model augmented with a time-varying disease transmission rate
-------------------------------------------------
index: 127


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 128


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 129


Prediction:

disease name: COVID-19 
location: Europe 
date: - 
R0 value: - 
%CI values: - 
method: -


Label:

unanswerable
-------------------------------------------------
index: 130


Prediction:

disease name: dengue 
location: Singapore 
date: - 
R0 value: 1.89-2.23 
%CI values: (95% confidence interval: 1.15-3.00) 
method: single-phase Richards model


Label:

disease name: Dengue 
location: Singapore 
date: - 
R0 value: 1.89-2.23 
%CI values: (95% confidence interval: 1.15-3.00) 
method: single-phase Richards model
-------------------------------------------------
index: 131


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 132


Prediction:

disease name: COVID-19 
location: India 
date: - 
R0 value: - 
%CI values: - 
method: age-structured SIR model with social contact matrices obtained from surveys and Bayesian imputation


Label:

disease name: COVID-19 
location: India 
date: - 
R0 value: - 
%CI values: - 
method: age-structured SIR model with social contact matrices obtained from surveys and Bayesian imputation
-------------------------------------------------
index: 133


Prediction:

disease name: COVID-19 
location: Zambia 
date: - 
R0 value: 1.31 
%CI values: - 
method: classical Susceptible - Infected - Recovered (SIR) model


Label:

disease name: COVID-19 
location: Zambia 
date: for the third wave of the pandemic obtained from the Zambia National Public Health Institute (ZNPHI) 
R0 value: 1.31 
%CI values: - 
method: classical Susceptible - Infected - Recovered (SIR) model
-------------------------------------------------
index: 134


Prediction:

disease name: 2019-nCoV (COVID-19) 
location: China 
date: - 
R0 value: 3.56 
%CI values: (95% CI: 2.31-4.81) 
method: Flow-SEHIR model


Label:

disease name: COVID-19 
location: China 
date: Since December 8, 2019 
R0 value: 3.56 
%CI values: 95% CI: 2.31-4.81 
method: Flow-SEHIR model
-------------------------------------------------
index: 135


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 136


Prediction:

disease name: COVID-19 
location: Bangladesh 
date: - 
R0 value: - 
%CI values: - 
method: four compartmental model


Label:

disease name: COVID-19 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: a four compartmental model
-------------------------------------------------
index: 137


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 138


Prediction:

disease name: COVID-19 
location: Italy 
date: - 
R0 value: 3.44 
%CI values: 3.35-3.54 
method: extended Susceptible-Exposed-Infectious-Removed (SEIR) model 
| 
disease name: COVID-19 
location: Spain 
date: - 
R0 value: 6.25 
%CI values: 5.97-6.55 
method: extended Susceptible-Exposed-Infectious-Removed (SEIR) model 
| 
disease name: COVID-19 
location: Germany 
date: - 
R0 value: 4.03 
%CI values: 3.84-4.23 
method: extended Susceptible-Exposed-Infectious-Removed (SEIR) model 
| 
disease name: COVID-19 
location: France 
date: - 
R0 value: 4.00 
%CI values: 3.82-4.19 
method: extended Susceptible-Exposed-Infectious-Removed (SEIR) model


Label:

disease name: Coronavirus Disease 2019 (COVID-19) 
location: Italy 
date: - 
R0 value: 3.44 
%CI values: 3.35-3.54 
method: extended Susceptible-Exposed-Infectious-Removed (SEIR) model 
| 
disease name: Coronavirus Disease 2019 (COVID-19) 
location: Spain 
date: - 
R0 value: 6.25 
%CI values: 5.97-6.55 
method: extended Susceptible-Exposed-Infectious-Removed (SEIR) model 
| 
disease name: Coronavirus Disease 2019 (COVID-19) 
location: Germany 
date: - 
R0 value: 4.03 
%CI values: 3.84-4.23 
method: extended Susceptible-Exposed-Infectious-Removed (SEIR) model 
| 
disease name: Coronavirus Disease 2019 (COVID-19) 
location: France 
date: - 
R0 value: 4 
%CI values: 3.82-4.19 
method: extended Susceptible-Exposed-Infectious-Removed (SEIR) model
-------------------------------------------------
index: 139


Prediction:

disease name: COVID-19 
location: Brazil 
date: February 26, 2020 to July 2, 2020 
R0 value: 1.3 
%CI values: - 
method: iterative method in the Gompertz model 
| 
disease name: COVID-19 
location: Sao Paulo 
date: February 26, 2020 to July 2, 2020 
R0 value: 15621 
%CI values: - 
method: iterative method in the Gompertz model 
| 
disease name: COVID-19 
location: Rio de Janeiro 
date: February 26 to July 2, 2020 
R0 value: 1.4 
%CI values: - 
method: iterative method in the Gompertz model


Label:

disease name: COVID-19 
location: Brazil 
date: February 26, 2020, to July 2, 2020 
R0 value: 1.3 
%CI values: - 
method: iterative method in the Gompertz model 
| 
disease name: COVID-19 
location: Sao Paulo 
date: February 26, 2020, to July 2, 2020 
R0 value: 1.3 
%CI values: - 
method: iterative method in the Gompertz model 
| 
disease name: COVID-19 
location: Rio de Janeiro 
date: February 26, 2020, to July 2, 2020 
R0 value: 1.4 
%CI values: - 
method: iterative method in the Gompertz model
-------------------------------------------------
index: 140


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 141


Prediction:

disease name: COVID-19 
location: Morocco 
date: - 
R0 value: 2.9949 
%CI values: - 
method: Susceptible-Asymptomatic-Infectious deterministic model


Label:

disease name: COVID-19 
location: Morocco 
date: - 
R0 value: 2.9949 
%CI values: - 
method: Susceptible-Asymptomatic-Infectious deterministic model
-------------------------------------------------
index: 142


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 143


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 144


Prediction:

disease name: SARS-CoV-2 
location: England 
date: 8 February to 1 March 2022-2 
R0 value: 0.94 
%CI values: (0.91-0.96) 
method: REal-time Assessment of Community Transmission-1 (REACT-1) study


Label:

disease name: SARS-CoV-2 
location: England 
date: 8 February to 1 March 2022 
R0 value: 0.94 (0.91-0.96) 
%CI values: - 
method: -
-------------------------------------------------
index: 145


Prediction:

disease name: novel coronavirus disease 2019 (COVID-19) 
location: Lombardy, Italy 
date: until March 8 
R0 value: - 
%CI values: - 
method: modified compartmental Susceptible/ Exposed/ Infectious Asymptomatic/ Infected Symptomatic/ Recovered/ Dead (SEIIRD) model


Label:

disease name: COVID-19 
location: Lombardy, Italy 
date: March 20 and March 21, 2020 
R0 value: 4.53 
%CI values: min-max range: 4.40- 4.65 
method: modified compartmental Susceptible/ Exposed/ Infectious Asymptomatic/ Infected Symptomatic/ Recovered/ Dead (SEIIRD) model
-------------------------------------------------
index: 146


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 147


Prediction:

disease name: novel coronavirus 2019 (COVID-19) 
location: Kuwait 
date: until 19 April 2020 
R0 value: 2.2 
%CI values: - 
method: Deterministic and stochastic modeling


Label:

disease name: COVID-19 
location: Kuwait 
date: until 19 April 2020 and before the repatriation plan 
R0 value: 2.2 
%CI values: - 
method: Deterministic and stochastic modeling
-------------------------------------------------
index: 148


Prediction:

disease name: novel viral pathogen 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: Multiple Shooting for Stochastic systems (MSS)


Label:

unanswerable
-------------------------------------------------
index: 149


Prediction:

disease name: COVID-19 
location: 11 major cities in China 
date: January 24, 2020 to February 13, 2020 
R0 value: 2.7 
%CI values: - 
method: equation derived from the Susceptible-Exposed-Infectious-Recovered (SEIR) model


Label:

disease name: new coronavirus disease COVID-19 
location: Wuhan 
date: January 24, 2020 to February 13, 2020 
R0 value: 2.7 
%CI values: - 
method: equation derived from the Susceptible-Exposed-Infectious-Recovered (SEIR) model 
| 
disease name: new coronavirus disease COVID-19 
location: other cities,china 
date: January 24, 2020 to February 13, 2020 
R0 value: ranging from 1.8 to 2.4 
%CI values: - 
method: equation derived from the Susceptible-Exposed-Infectious-Recovered (SEIR) model
-------------------------------------------------
index: 150


Prediction:

disease name: SARS-CoV-2 
location: 10 countries and one cruise ship 
date: Prior to significant public health interventions 
R0 value: 1.4 and 2.8 
%CI values: - 
method: phylodynamic analyses of genomic data


Label:

disease name: SARS-CoV-2 
location: 10 countries and one cruise ship 
date: - 
R0 value: 1.4 and 2.8 
%CI values: - 
method: phylodynamic analyses of genomic data
-------------------------------------------------
index: 151


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 152


Prediction:

disease name: COVID-19 
location: Rohingya refugee camps of Bangladesh 
date: - 
R0 value: 0.7563 
%CI values: - 
method: modified Susceptible-Exposed-Infectious Recovered (SEIR) transmission model


Label:

disease name: COVID-19 
location: Rohingya Refugee Camp, Bangladesh 
date: - 
R0 value: 0.7563 
%CI values: - 
method: modified Susceptible-Exposed-Infectious Recovered (SEIR) transmission model
-------------------------------------------------
index: 153


Prediction:

disease name: COVID-19 
location: UK 
date: - 
R0 value: 1.85 
%CI values: - 
method: prospective case ascertained study design based on the World Health Organization FFX protocol


Label:

disease name: COVID-19 
location: United Kingdom 
date: Late January 2020 
R0 value: 1.85 and a household reproduction number of 2.33 
%CI values: - 
method: prospective case ascertained study design based on the World Health Organization FFX protocol
-------------------------------------------------
index: 154


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 155


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 156


Prediction:

disease name: COVID-19 
location: Wuhan, China 
date: - 
R0 value: 245 
%CI values: (95% CI: 116-487) 
method: combined SEIR framework model with data on cases of COVID-19 in China and International cases that originated in Wuhan 
| 
disease name: COVID-19 
location: Wuhan, China 
date: - 
R0 value: 1.05 
%CI values: (042-240) 
method: combined SEIR framework model with data on cases of COVID-19 in China and International cases that originated in Wuhan


Label:

disease name: COVID-19 
location: within and outside of Wuhan, China 
date: January and February 2020 (one week before travel restrictions on Jan 23rd) 
R0 value: 245 
%CI values: (95% CI: 116-487) 
method: SEIR framework model 
| 
disease name: COVID-19 
location: within and outside of Wuhan, China 
date: January and February 2020 (one week after travel restrictions on Jan 23rd) 
R0 value: 1.05 
%CI values: (042-240) 
method: SEIR framework model
-------------------------------------------------
index: 157


Prediction:

disease name: MRSA 
location: - 
date: - 
R0 value: 0.337 
%CI values: - 
method: Next Generation Matrix method 
| 
disease name: MRSA 
location: - 
date: - 
R0 value: 0.278 
%CI values: - 
method: Next Generation Matrix method


Label:

disease name: methicillin-resistant Staphylococcus aureus (MRSA) 
location: Intensive Care Unit (ICU) 
date: - 
R0 value: 0.337 
%CI values: - 
method: Next Generation Matrix method (single-staff model) 
| 
disease name: methicillin-resistant Staphylococcus aureus (MRSA) 
location: Intensive Care Unit (ICU) 
date: - 
R0 value: 0.278 
%CI values: - 
method: Next Generation Matrix method (other two models)
-------------------------------------------------
index: 158


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 159


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 160


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 161


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 162


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 163


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 164


Prediction:

disease name: novel coronavirus disease (COVID-2019) 
location: Iran 
date: 19 Feb to 15 March 
R0 value: 2.11 
%CI values: (95% CI, 1.87-2.50) 
method: mathematical model


Label:

disease name: COVID-19 
location: Iran 
date: 19 Feb-15 March, 2020 
R0 value: 2.11 
%CI values: (95% CI, 1.87-2.50) 
method: mathematical model
-------------------------------------------------
index: 165


Prediction:

unanswerable


Label:

disease name: COVID-19 
location: Taiwan 
date: 253 days between April and December 2020 
R0 value: voluntary population-based interventions, if used alone, were estimated to have reduced the reproduction number to 1.30 
%CI values: 95% CrI, 1.03-1.58 
method: stochastic branching process model
-------------------------------------------------
index: 166


Prediction:

unanswerable


Label:

disease name: SARS-CoV-2 
location: Ohio prison 
date: - 
R0 value: 14 and 3 
%CI values: - 
method: -
-------------------------------------------------
index: 167


Prediction:

disease name: COVID-19 
location: Kuwait 
date: - 
R0 value: - 
%CI values: - 
method: maximum likelihood framework


Label:

disease name: COVID-19 
location: Kuwait 
date: - 
R0 value: - 
%CI values: - 
method: maximum likelihood framework
-------------------------------------------------
index: 168


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 169


Prediction:

disease name: Severe Acute Respiratory Syndrome 
location: Greece 
date: Prior to measures were implemented 
R0 value: 2.38 
%CI values: (95% CI 2.01-2.80) 
method: susceptible-exposed-infectious-recovered model


Label:

disease name: severe acute respiratory syndrome coronavirus 2 
location: Greece 
date: Before measures were implemented 
R0 value: 2.38 
%CI values: (95% CI 2.01-2.80) 
method: susceptible-exposed-infectious-recovered model
-------------------------------------------------
index: 170


Prediction:

disease name: Covid-19 
location: USA 
date: - 
R0 value: 2.5 
%CI values: - 
method: Monte-Carlo method


Label:

unanswerable
-------------------------------------------------
index: 171


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 172


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 173


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 174


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 175


Prediction:

disease name: COVID-19 
location: Wuhan 
date: January 24, 2020 to February 13, 2020 
R0 value: 2.7011 
%CI values: - 
method: (SEIR) model


Label:

disease name: new coronavirus disease COVID-19 
location: Wuhan 
date: from January 24, 2020 to February 13, 2020 
R0 value: 2.7011 
%CI values: - 
method: Susceptibleâ€“Exposedâ€“Infectiousâ€“Recovered (SEIR) model 
| 
disease name: new coronavirus disease COVID-19 
location: other cities,china 
date: from January 24, 2020 to February 13, 2020 
R0 value: ranging from 1.7762 to 2.3700 
%CI values: - 
method: Susceptibleâ€“Exposedâ€“Infectiousâ€“Recovered (SEIR) model
-------------------------------------------------
index: 176


Prediction:

unanswerable


Label:

disease name: Covid-19 
location: ten countries 
date: anuary 22, 2020 - April 18, 2020 
R0 value: - 
%CI values: - 
method: Susceptible-Infected-Removed (SIR) model
-------------------------------------------------
index: 177


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 178


Prediction:

disease name: SARS 
location: Hong Kong 
date: - 
R0 value: 1.2 
%CI values: - 
method: simple mathematical model 
| 
disease name: SARS 
location: Toronto (Canada) 
date: - 
R0 value: 1.32 
%CI values: - 
method: simple mathematical model


Label:

disease name: SARS 
location: Hong Kong 
date: - 
R0 value: 1.2 
%CI values: - 
method: simple mathematical model 
| 
disease name: SARS 
location: Toronto (Canada) 
date: - 
R0 value: 1.32 
%CI values: - 
method: simple mathematical model
-------------------------------------------------
index: 179


Prediction:

disease name: SARS-CoV-2 
location: Israel 
date: - 
R0 value: initially around 2.5 
%CI values: - 
method: phylodynamic analysis


Label:

disease name: SARS-CoV-2 
location: Israel 
date: - 
R0 value: initially around 2.5 
%CI values: - 
method: phylodynamic analysis
-------------------------------------------------
index: 180


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 181


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 182


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 183


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 184


Prediction:

disease name: COVID-19 
location: Africa 
date: - 
R0 value: 1.98 (Sudan) and 9.66 (Mauritius), with a median of 3.67 
%CI values: (90% CrI: 3.31 - 4.2) 
method: simple SEIR model and a Bayesian inference framework


Label:

disease name: COVID-19 
location: Sudan 
date: - 
R0 value: 1.98 
%CI values: - 
method: SEIR model and a Bayesian inference framework 
| 
disease name: COVID-19 
location: Mauritius 
date: - 
R0 value: 9.66 
%CI values: - 
method: SEIR model and a Bayesian inference framework 
| 
disease name: COVID-19 
location: Africa 
date: - 
R0 value: a median of 3.67 
%CI values: 90% CrI: 3.31 - 4.12 
method: SEIR model and a Bayesian inference framework
-------------------------------------------------
index: 185


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 186


Prediction:

disease name: novel coronavirus disease (COVID-19) 
location: Bangladesh 
date: March 8 to May 11, 2020 
R0 value: 1.84 
%CI values: [95% CI: 1.82-1.86], [95% CI: 1.81-1.84], and [95% CI: 1.92-1.96], for MERS, COVID-19, and SARS SI, respectively, without adjusting reporting rate 
method: exponential growth model utilizing daily COVID-19 diagnosis data in Bangladesh from March 8 to May 11, 2020 
| 
disease name: novel coronavirus disease (COVID-19) 
location: Bangladesh 
date: March 8 to May 11, 2020 
R0 value: 1.82 
%CI values: [95% CI: 1.81-1.84], [95% CI: 1.91-1.96], and [95% CI: 1.71-1.73] for a five-fold increase 
method: exponential growth model utilizing daily COVID-19 diagnosis data in Bangladesh from March 8 to May 11, 2020


Label:

disease name: novel coronavirus disease (COVID-19) 
location: Bangladesh 
date: March 8 to May 11, 2020 
R0 value: 1.82 
%CI values: 95% CI: 1.81-1.84 
method: exponential growth model
-------------------------------------------------
index: 187


Prediction:

disease name: COVID-19 
location: China 
date: - 
R0 value: - 
%CI values: - 
method: a networked meta-population model


Label:

disease name: COVID-19 
location: China 
date: - 
R0 value: - 
%CI values: - 
method: networked meta-population model
-------------------------------------------------
index: 188


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 189


Prediction:

disease name: COVID-19 
location: India 
date: - 
R0 value: 1.73 to 1.08 
%CI values: - 
method: incidence and Optimum function in desolve package to the data of cumulative daily new confirmed cases for robustly estimating the reproduction number in the R software


Label:

disease name: Covid-19 
location: India 
date: June and July 
R0 value: 1.73 to 1.08 
%CI values: - 
method: Novel method implemented in the incidence and Optimum function in desolve package to the data of cumulative daily new confirmed cases for robustly estimating the reproduction number in the R software.
-------------------------------------------------
index: 190


Prediction:

disease name: SARS-CoV-2 B.1.617.2 (Delta) variant 
location: Guangzhou, China 
date: Late May 
R0 value: 3.60 
%CI values: (95% confidence interval: 2.50-5.2) 
method: -


Label:

disease name: SARS-CoV-2 B.1.617.2 (Delta) variant 
location: Guangzhou, China 
date: late May 
R0 value: 3.6 
%CI values: 95% confidence interval: 2.50-5.30 
method: Transmission characteristics of Delta variant were analysed for 153 confirmed cases and two complete transmission chains with seven generations were fully presented.
-------------------------------------------------
index: 191


Prediction:

unanswerable


Label:

disease name: H2N2 viruses 
location: Rochester, New York 
date: - 
R0 value: 1.2 
%CI values: - 
method: Hemagglutinin inhibition (HAI) assays against historical human and recent avian influenza A(H2N2) viruses 
| 
disease name: H2N2 viruses 
location: Hong Kong, China 
date: - 
R0 value: 1.2 
%CI values: - 
method: Hemagglutinin inhibition (HAI) assays against historical human and recent avian influenza A(H2N2) viruses
-------------------------------------------------
index: 192


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 193


Prediction:

disease name: COVID-19 
location: Fars Province, Iran 
date: February 18th to September 30th, 2020 
R0 value: 2.8 
%CI values: - 
method: cross-sectional study


Label:

disease name: Coronavirus Disease 2019 (COVID-19) 
location: Fars Province, Iran 
date: February 18th to September 30th, 2020 
R0 value: 2.8 
%CI values: - 
method: Cross-sectional study
-------------------------------------------------
index: 194


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 195


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 196


Prediction:

disease name: COVID-19 
location: Libya 
date: - 
R0 value: 7.6 
%CI values: - 
method: SEIR model or a variation of it


Label:

disease name: COVID-19 
location: Libya 
date: May 5th 
R0 value: 7.6 
%CI values: - 
method: SEIR model
-------------------------------------------------
index: 197


Prediction:

disease name: COVID-19 
location: Wuhan, China 
date: - 
R0 value: 5.6 
%CI values: - 
method: rigorous epidemiological analysis


Label:

disease name: COVID-19 
location: - 
date: - 
R0 value: 5.6 
%CI values: - 
method: rigorous epidemiological analysis
-------------------------------------------------
index: 198


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 199


Prediction:

unanswerable


Label:

disease name: COVID-19 
location: 12 diverse world regions 
date: February and December of 2020. 
R0 value: - 
%CI values: - 
method: -
-------------------------------------------------
index: 200


Prediction:

disease name: HIV-1 
location: humanized mice 
date: - 
R0 value: 0.61 and 0.61 
%CI values: - 
method: the reduced quasi-steady state (RQS) model 
| 
disease name: HIV-1 
location: humanized mice 
date: - 
R0 value: 0.76 and 0.69 
%CI values: - 
method: the piece-wise regression (PWR) model 
| 
disease name: HIV-1 
location: humanized mice 
date: - 
R0 value: 2.38 and 2.30 
%CI values: - 
method: the piece-wise regression (PWR) model


Label:

disease name: HIV-1 
location: - 
date: - 
R0 value: 2.30 
%CI values: - 
method: Reduced quasi-steady state (RQS) model 
| 
disease name: HIV-1 
location: - 
date: - 
R0 value: 2.38 
%CI values: - 
method: the piece-wise regression (PWR) model
-------------------------------------------------
index: 201


Prediction:

disease name: 2009 A/H1N1 influenza 
location: 8th Hospital of Xi'an in Shaanxi Province of China 
date: - 
R0 value: - 
%CI values: - 
method: compartmental model by including a new compartment of the intensity of the media reports


Label:

disease name: 2009 A/H1N1 influenza 
location: Shaanxi Province of China 
date: - 
R0 value: - 
%CI values: - 
method: compartmental model by including a new compartment of the intensity of the media reports
-------------------------------------------------
index: 202


Prediction:

unanswerable


Label:

disease name: SARS-CoV-2/COVID-19 
location: Germany 
date: - 
R0 value: 2.4 and 3.4 
%CI values: - 
method: comprehensible nonparametric methods including time-delay correlation analyses
-------------------------------------------------
index: 203


Prediction:

disease name: COVID-19 
location: Gansu 
date: As of 25 February 2020 
R0 value: 2.61 in imported case stage to 0.66 in indigenous case stage 
%CI values: - 
method: Epidemiological investigation


Label:

disease name: COVID-19 
location: Gansu province 
date: As of 25 February 2020 
R0 value: decreased from 2.61 in imported case stage to 0.66 in indigenous case stage 
%CI values: - 
method: Epidemiological investigation
-------------------------------------------------
index: 204


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 205


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 206


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 207


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 208


Prediction:

disease name: COVID-19 
location: China (excluding Hubei province) 
date: - 
R0 value: 2.5 
%CI values: - 
method: mathematical model based on epidemiology of COVID-19 
| 
disease name: COVID-19 
location: Hubei province 
date: - 
R0 value: 2.9 
%CI values: - 
method: mathematical model based on epidemiology of COVID-19


Label:

disease name: Corona Virus Disease 2019 (COVID-19) 
location: China (excluding Hubei province) 
date: January 30 
R0 value: 2.5 
%CI values: - 
method: mathematical model based on epidemiology of COVID-19, incorporating the isolation of healthy people, confirmed cases and close contacts 
| 
disease name: Corona Virus Disease 2019 (COVID-19) 
location: Hubei province 
date: January 30 
R0 value: 2.9 
%CI values: - 
method: mathematical model based on epidemiology of COVID-19, incorporating the isolation of healthy people, confirmed cases and close contacts
-------------------------------------------------
index: 209


Prediction:

disease name: Chikungunya 
location: Chad 
date: - 
R0 value: 1.8519 
%CI values: - 
method: non-linear least-square method


Label:

disease name: Chikungunya 
location: Chad 
date: - 
R0 value: 1.8519 
%CI values: - 
method: non-linear least-square method
-------------------------------------------------
index: 210


Prediction:

disease name: COVID-19 
location: Chongqing 
date: - 
R0 value: 3.8 
%CI values: - 
method: retrospective survey


Label:

disease name: COVID-19 
location: Chongqing 
date: - 
R0 value: 3.8 
%CI values: - 
method: Field and telephone interviews
-------------------------------------------------
index: 211


Prediction:

disease name: SARS-CoV-2 
location: Japan 
date: January 14 through July 31, 2020 
R0 value: 0.0415 
%CI values: (95% CI, 0.0138- 0.0691) 
method: - 
| 
disease name: SARS-CoV-2 
location: Japan 
date: January 14 through July 31, 2020 
R0 value: 1.11 
%CI values: (95% CI, 0.9171-1.3226) 
method: - 
| 
disease name: SARS-CoV-2 
location: Japan 
date: January 14 through July 31, 2020 
R0 value: 0.2811 
%CI values: (95% CI, 0.2074-0.3687) 
method: -


Label:

disease name: SARS-CoV-2 
location: Japan 
date: during January 14 through July 31, 2020 
R0 value: underage infection sources directed to underage, adult, and elderly people were estimated respectively as 0.0415 (95% CI, 0.0138- 0.0691), 1.11 (95% CI, 0.9171-1.3226), and 0.2811 (95% CI, 0.2074-0.3687) 
%CI values: - 
method: - 
| 
disease name: SARS-CoV-2 
location: Japan 
date: during January 14 through July 31, 2020 
R0 value: adult infection source directed to underage, adult, and elderly people were estimated respectively as 0.0140 (95% CI, 0.0120-0.0162), 0.5392 (95% CI, 0.5236-0.5550), and 0.1135 (95% CI, 0.1074-0.1197) 
%CI values: - 
method: - 
| 
disease name: SARS-CoV-2 
location: Japan 
date: during January 14 through July 31, 2020 
R0 value: elderly infection source directed to underage, adult, and elderly people were estimated as 0.065 (95% CI, 0.0039-0.0091), 0.3264 (95% CI, 0.3059-0.3474), and 0.3991 (95% CI, 0.3757-0.4229) 
%CI values: - 
method: -
-------------------------------------------------
index: 212


Prediction:

unanswerable


Label:

disease name: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 
location: U.S. 
date: - 
R0 value: 2.5 
%CI values: - 
method: computational model of the U.S. simulating the spread of SARS-CoV-2 and the potential clinical and economic impact of reducing the infectious period duration
-------------------------------------------------
index: 213


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 214


Prediction:

disease name: 2009 H1N1 Pandemic Influenza 
location: Hong Kong 
date: - 
R0 value: - 
%CI values: - 
method: extended model


Label:

unanswerable
-------------------------------------------------
index: 215


Prediction:

disease name: SARS-CoV-2 
location: Iran 
date: - 
R0 value: 2.58 
%CI values: - 
method: -


Label:

disease name: novel coronavirus 
location: iranian community 
date: - 
R0 value: 2.58 
%CI values: - 
method: SPSS V21.0 and R software
-------------------------------------------------
index: 216


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 217


Prediction:

disease name: COVID-19 
location: New Jersey 
date: 21-January-2020 to 21-June-2020 
R0 value: 7.1 
%CI values: - 
method: compartmental models via the next-generation matrix approach 
| 
disease name: COVID-19 
location: Wyoming 
date: 21-January-2020 to 21-June-2020 
R0 value: 2.3 
%CI values: - 
method: compartmental models via the next-generation matrix approach


Label:

disease name: COVID-19 
location: New Jersey 
date: 21-January-2020 to 21-June-2020 
R0 value: 7.1 
%CI values: - 
method: Bayesian inference 
| 
disease name: COVID-19 
location: Wyoming 
date: 21-January-2020 to 21-June-2020 
R0 value: 2.3 
%CI values: - 
method: Bayesian inference
-------------------------------------------------
index: 218


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 219


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 220


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 221


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 222


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 223


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 224


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 225


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 226


Prediction:

disease name: COVID-19 
location: India 
date: from 30 February to 14 April 2020 
R0 value: 3.3 
%CI values: (95%CI, 3.1 to 3.5) 
method: polynomial regression


Label:

disease name: coronavirus disease 2019 (COVID-19) 
location: India 
date: from 30 February to 14 April 2020 
R0 value: 3.3 
%CI values: 95%CI, 3.1 to 3.5 
method: epidemic compartmental model SEIR
-------------------------------------------------
index: 227


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 228


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 229


Prediction:

disease name: MERS-CoV 
location: South Korea 
date: - 
R0 value: - 
%CI values: - 
method: a mathematical model for MERS-CoV transmission dynamics


Label:

disease name: MERS 
location: South Korea 
date: - 
R0 value: - 
%CI values: - 
method: mathematical mode
-------------------------------------------------
index: 230


Prediction:

disease name: COVID-19 
location: Brunei 
date: March 9 to April 5, 2020 
R0 value: between 3.9 and 6.0 
%CI values: - 
method: Epidemiological and clinical information was obtained for all confirmed COVID-19 cases, whose symptom onset was from March 9 to April 5, 2020


Label:

disease name: COVID-19 
location: Brunei 
date: from March 9 to April 5, 2020 
R0 value: between 3.9 and 6.0 
%CI values: - 
method: Time-varying R
-------------------------------------------------
index: 231


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 232


Prediction:

unanswerable


Label:

disease name: A/H1N1 influenza 
location: Chile, southern region 
date: 01-May to 31-December, 2009 
R0 value: 1.6-2.0 
%CI values: - 
method: growth rate of the exponential pandemic phase by date of symptoms onset 
| 
disease name: A/H1N1 influenza 
location: Chile, central region 
date: 01-May to 31-December, 2009 
R0 value: 1.3-1.5 
%CI values: - 
method: growth rate of the exponential pandemic phase by date of symptoms onset 
| 
disease name: A/H1N1 influenza 
location: Chile, northern regions 
date: 01-May to 31-December, 2009 
R0 value: 1.2-1.3 
%CI values: - 
method: growth rate of the exponential pandemic phase by date of symptoms onset
-------------------------------------------------
index: 233


Prediction:

disease name: seasonal influenza 
location: England 
date: first 12 weeks of 2018 
R0 value: - 
%CI values: - 
method: stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP)


Label:

unanswerable
-------------------------------------------------
index: 234


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 235


Prediction:

disease name: COVID-19 
location: Israel 
date: - 
R0 value: approximately 2.2 
%CI values: - 
method: -


Label:

disease name: COVID-19 
location: Israel 
date: 2021 
R0 value: 2.2 
%CI values: - 
method: -
-------------------------------------------------
index: 236


Prediction:

disease name: novel coronavirus disease 2019 (COVID-19) 
location: China 
date: - 
R0 value: 5.78 
%CI values: (95%CI: 5.71-5.89) 
method: dynamical transmission model with contact trace and quarantine


Label:

disease name: novel coronavirus disease 2019 (COVID-19) 
location: Wuhan, China 
date: - 
R0 value: 5.78 
%CI values: 95%CI: 5.71-5.89 
method: Markov Chain Monte Carlo algorithm
-------------------------------------------------
index: 237


Prediction:

disease name: novel coronavirus disease (COVID-19) 
location: Lombardy Region, Italy 
date: - 
R0 value: 3.1 
%CI values: (95% CI, 2.9 to 3.2) 
method: -


Label:

disease name: COVID-19 
location: Lombardy, Italy 
date: - 
R0 value: 3.1 
%CI values: (95% CI, 2.9 to 3.2) 
method: -
-------------------------------------------------
index: 238


Prediction:

disease name: COVID-19 
location: China 
date: 2019-2020 
R0 value: 0.24 
%CI values: (95% CrI: 0.01-1.38) 
method: quantitative modeling analyses 
| 
disease name: COVID-19 
location: China 
date: 2019-2020 
R0 value: 2.37 
%CI values: (95% CrI: 2.08-2.71) 
method: quantitative modeling analyses


Label:

disease name: COVID-19 
location: China 
date: 2019-2020 
R0 value: 0.24 for market-to-human transmission 
%CI values: (95% CrI: 0.01-1.38) 
method: quantitative modeling analyses 
| 
disease name: COVID-19 
location: China 
date: 2019-2020 
R0 value: 2.37 for human-to-human transmission 
%CI values: (95% CrI: 2.08-2.71) 
method: quantitative modeling analyses
-------------------------------------------------
index: 239


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 240


Prediction:

disease name: COVID-19 
location: South East Asia 
date: - 
R0 value: - 
%CI values: - 
method: compartment model called SEIR (Susceptible, Exposed, Infected, Recovered).


Label:

disease name: COVID-19 
location: a region in South East Asia 
date: - 
R0 value: 3 
%CI values: - 
method: compartment model (SEIR)
-------------------------------------------------
index: 241


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 242


Prediction:

unanswerable


Label:

disease name: COVID-19, Delta variant 
location: United States 
date: End of November 2021 to date 
R0 value: 0.28 
%CI values: - 
method: Model fitted using daily case data for the COVID-19 pandemic in the United States 
| 
disease name: COVID-19, Omicron 
location: United States 
date: End of November 2021 to date 
R0 value: 0.96 
%CI values: - 
method: Model fitted using daily case data for the COVID-19 pandemic in the United States
-------------------------------------------------
index: 243


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 244


Prediction:

disease name: SARS-CoV-2 
location: Weifang, People's Republic of China 
date: - 
R0 value: 3.4 
%CI values: (95% highest posterior density interval: 2.1-5.2) 
method: Bayesian model-based phylodynamic methods


Label:

disease name: COVID-19 
location: Weifang, People's Republic of China 
date: 4 February 
R0 value: 3.4 
%CI values: 95% highest posterior density interval: 2.1-5.2 
method: Bayesian model-based phylodynamic methods
-------------------------------------------------
index: 245


Prediction:

disease name: COVID-19 
location: Tokyo 
date: - 
R0 value: - 
%CI values: - 
method: Susceptible-Infectious-Recovered (SIR) model


Label:

disease name: coronavirus disease 2019 (COVID-19) 
location: Tokyo 
date: - 
R0 value: - 
%CI values: - 
method: Susceptible-Infectious-Recovered (SIR) model
-------------------------------------------------
index: 246


Prediction:

disease name: COVID-19 
location: Sao Paulo State (Brazil) 
date: - 
R0 value: 9.24 
%CI values: - 
method: mathematical model considering young and elder subpopulations 
| 
disease name: COVID-19 
location: Spain 
date: - 
R0 value: 8 
%CI values: - 
method: mathematical model considering young and elder subpopulations


Label:

disease name: covid-19 
location: Sao Paulo State (Brazil) 
date: - 
R0 value: 9.24 
%CI values: - 
method: mathematical model considering young and elder subpopulation 
| 
disease name: covid-19 
location: Spain 
date: - 
R0 value: 8 
%CI values: - 
method: mathematical model considering young and elder subpopulation
-------------------------------------------------
index: 247


Prediction:

disease name: SARS-CoV-2 
location: China 
date: - 
R0 value: 2.1 
%CI values: (95% UI = 1.8-2.4) 
method: age-stratified mathematical model 
| 
disease name: SARS-CoV-2 
location: China 
date: - 
R0 value: 0.06 
%CI values: (95% UI = 0.05-0.07) 
method: age-stratified mathematical model


Label:

disease name: SARS-CoV-2 
location: China 
date: - 
R0 value: 2.1 
%CI values: (95% UI = 1.8-2.4) 
method: age-stratified mathematical model 
| 
disease name: SARS-CoV-2 
location: China 
date: following interventions' onset 
R0 value: 0.06 
%CI values: (95% UI = 0.05-0.07) 
method: age-stratified mathematical model
-------------------------------------------------
index: 248


Prediction:

disease name: COVID-19 
location: Nigeria 
date: - 
R0 value: less than unity( R_01) 
%CI values: - 
method: community-based transmission model for COVID-19 in Nigeria


Label:

disease name: COVID-19 
location: Nigeria 
date: - 
R0 value: R_01 
%CI values: - 
method: community-based transmission model
-------------------------------------------------
index: 249


Prediction:

disease name: SARS-CoV-2 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: epidemiological models with inbuilt distributions of susceptibility or exposure to SARS-CoV-2 outbreaks


Label:

unanswerable
-------------------------------------------------
index: 250


Prediction:

disease name: COVID-19 
location: South Kalimantan Province 
date: - 
R0 value: - 
%CI values: - 
method: SIR Model, which is accommodates the death cases in the data, supported by several methods, namely Runge Kutta Method and Nonlinear Least Squares Method


Label:

unanswerable
-------------------------------------------------
index: 251


Prediction:

disease name: COVID-19 
location: India 
date: 30th January 2020 to 30th March 2020 
R0 value: 2.02 
%CI values: - 
method: SEIR model and Regression model


Label:

disease name: COVID-19 
location: India 
date: 30th January 2020 to 30th March 2020 
R0 value: 2.02 
%CI values: - 
method: SEIR model and Regression model
-------------------------------------------------
index: 252


Prediction:

disease name: COVID-19 
location: Wuhan 
date: between 1 January and 8 March 2020 
R0 value: 3.54 
%CI values: (95% credible interval 3.40-3.67) 
method: modelling approach


Label:

disease name: COVID-19 
location: Wuhan (China) 
date: 1 January and 8 March 2020 
R0 value: 3.54 
%CI values: (95% credible interval 3.40-3.67) 
method: modelling approach 
| 
disease name: COVID-19 
location: Wuhan (China) 
date: as of 8 March 2022 
R0 value: 0.28 
%CI values: (95% credible interval 0.23-0.33) 
method: modelling approach
-------------------------------------------------
index: 253


Prediction:

disease name: coronavirus disease 2019 (covid-19) 
location: Sao Paulo State (Brazil) 
date: - 
R0 value: 1.5 to 3.0 
%CI values: - 
method: SIR model


Label:

disease name: Coronavirus Disease 2019 (COVID-19) 
location: Sao Paulo State (Brazil) 
date: - 
R0 value: 9.24 
%CI values: - 
method: more elaborate model 
| 
disease name: Coronavirus Disease 2019 (COVID-19) 
location: Spain 
date: - 
R0 value: 8.0 
%CI values: - 
method: more elaborate model 
| 
disease name: Coronavirus Disease 2019 (COVID-19) 
location: - 
date: - 
R0 value: 1.5 to 3.0 
%CI values: - 
method: Simple mathematical model, the SIR model
-------------------------------------------------
index: 254


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 255


Prediction:

disease name: COVID-19 
location: Singapore 
date: Jan.19-Feb.26 
R0 value: 1.97 
%CI values: 1.45, 2.48 
method: - 
| 
disease name: COVID-19 
location: Tianjin 
date: Jan.21-Feb.27 
R0 value: 1.87 
%CI values: 1.65,2.09 
method: -


Label:

disease name: COVID-19 
location: Singapore 
date: Jan.19-Feb.26 
R0 value: 1.97 (1.45, 2.48) 
%CI values: - 
method: - 
| 
disease name: COVID-19 
location: Tianjin, China 
date: Jan.21-Feb.27 
R0 value: 1.87 (1.65,2.09) 
%CI values: - 
method: -
-------------------------------------------------
index: 256


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 257


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 258


Prediction:

disease name: novel influenza 
location: 55 international metropolitan areas 
date: February 2009 
R0 value: 1.53, 1.70, and 1.90 
%CI values: - 
method: SEIR model


Label:

unanswerable
-------------------------------------------------
index: 259


Prediction:

disease name: COVID-19 
location: India 
date: - 
R0 value: 0.8454 and 2.0317 
%CI values: - 
method: HPM (homotopy perturbation method)


Label:

disease name: COVID-19 and Omicron Outbreak 
location: India 
date: January 2022 
R0 value: 0.8454 - 2.0317 
%CI values: - 
method: HPM (homotopy perturbation method) and used Mathematica 12 software)
-------------------------------------------------
index: 260


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 261


Prediction:

disease name: COVID-19 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: $SHEIQRD (Susceptible-Stay at home-Exposed-Infected-Quarantine-Recovery-Death) coronavirus pandemic spread model


Label:

disease name: COVID-19 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: Susceptible-Stay at home-Exposed-Infected-Quarantine-Recovery-Death) coronavirus pandemic spread model
-------------------------------------------------
index: 262


Prediction:

disease name: COVID-19 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: exponential growth rate model


Label:

unanswerable
-------------------------------------------------
index: 263


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 264


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 265


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 266


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 267


Prediction:

disease name: Covid-19 
location: Italy 
date: - 
R0 value: - 
%CI values: - 
method: fitting the exponential growth rate of the infection across a 1month period 
| 
disease name: Covid-19 
location: Italy 
date: - 
R0 value: - 
%CI values: - 
method: day by day assessment, based on single observations 
| 
disease name: Covid-19 
location: Italy 
date: - 
R0 value: - 
%CI values: - 
method: day by day assessment, based on single observations


Label:

disease name: Covid-19 
location: Lombardy, Italy 
date: across a 1month period 
R0 value: 4.94 
%CI values: the susceptible exposed infected removed (SEIR) compartment model, by fitting the exponential growth rate of the infection across a 1month period and also by using day by day assessment, based on single observations 
method: (SEIR) compartment model 
| 
disease name: Covid-19 
location: Tuscany, Italy 
date: across a 1month period 
R0 value: 4 
%CI values: the susceptible exposed infected removed (SEIR) compartment model, by fitting the exponential growth rate of the infection across a 1month period and also by using day by day assessment, based on single observations 
method: (SEIR) compartment model 
| 
disease name: Covid-19 
location: Marche, Italy 
date: across a 1month period 
R0 value: 4 
%CI values: the susceptible exposed infected removed (SEIR) compartment model, by fitting the exponential growth rate of the infection across a 1month period and also by using day by day assessment, based on single observations 
method: (SEIR) compartment model 
| 
disease name: Covid-19 
location: Lazio, Italy 
date: across a 1month period 
R0 value: 3.7 
%CI values: the susceptible exposed infected removed (SEIR) compartment model, by fitting the exponential growth rate of the infection across a 1month period and also by using day by day assessment, based on single observations 
method: (SEIR) compartment model 
| 
disease name: Covid-19 
location: Campania, Italy 
date: across a 1month period 
R0 value: 3.6 
%CI values: the susceptible exposed infected removed (SEIR) compartment model, by fitting the exponential growth rate of the infection across a 1month period and also by using day by day assessment, based on single observations 
method: (SEIR) compartment model 
| 
disease name: Covid-19 
location: Apulia, Italy 
date: across a 1month period 
R0 value: 3.5 
%CI values: the susceptible exposed infected removed (SEIR) compartment model, by fitting the exponential growth rate of the infection across a 1month period and also by using day by day assessment, based on single observations 
method: (SEIR) compartment model 
| 
disease name: Covid-19 
location: Sicily, Italy 
date: across a 1month period 
R0 value: 3.4 
%CI values: the susceptible exposed infected removed (SEIR) compartment model, by fitting the exponential growth rate of the infection across a 1month period and also by using day by day assessment, based on single observations 
method: (SEIR) compartment model 
| 
disease name: Covid-19 
location: Abruzzo, Italy 
date: across a 1month period 
R0 value: 3.4 
%CI values: the susceptible exposed infected removed (SEIR) compartment model, by fitting the exponential growth rate of the infection across a 1month period and also by using day by day assessment, based on single observations 
method: (SEIR) compartment model 
| 
disease name: Covid-19 
location: Calabria, Italy 
date: across a 1month period 
R0 value: 3.1 
%CI values: the susceptible exposed infected removed (SEIR) compartment model, by fitting the exponential growth rate of the infection across a 1month period and also by using day by day assessment, based on single observations 
method: (SEIR) compartment model 
| 
disease name: Covid-19 
location: Basilicata, Italy 
date: across a 1month period 
R0 value: 2.5 
%CI values: the susceptible exposed infected removed (SEIR) compartment model, by fitting the exponential growth rate of the infection across a 1month period and also by using day by day assessment, based on single observations 
method: (SEIR) compartment model 
| 
disease name: Covid-19 
location: Molise, Italy 
date: across a 1month period 
R0 value: 2.4 
%CI values: the susceptible exposed infected removed (SEIR) compartment model, by fitting the exponential growth rate of the infection across a 1month period and also by using day by day assessment, based on single observations 
method: (SEIR) compartment model
-------------------------------------------------
index: 268


Prediction:

disease name: SARS 
location: Hong Kong 
date: - 
R0 value: - 
%CI values: - 
method: susceptible-exposed-infected-recovered (SEIR) type epidemic model


Label:

unanswerable
-------------------------------------------------
index: 269


Prediction:

disease name: COVID-19 
location: - 
date: - 
R0 value: up to around 9 
%CI values: - 
method: compartmental SEIAR model of epidemic spread


Label:

disease name: COVID-19 
location: European countries 
date: - 
R0 value: up to around 9 
%CI values: - 
method: compartmental SEIAR model of epidemic spread as a generalization of the SEIR model
-------------------------------------------------
index: 270


Prediction:

disease name: COVID-19 
location: Rohingya refugee camps of Bangladesh 
date: - 
R0 value: - 
%CI values: - 
method: modified Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model


Label:

disease name: COVID-19 
location: Rohingya Refugee Camp, Bangladesh 
date: end of December 2020 
R0 value: 0.7563 
%CI values: - 
method: modified Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model
-------------------------------------------------
index: 271


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 272


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 273


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 274


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 275


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 276


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 277


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 278


Prediction:

disease name: COVID-19 
location: - 
date: - 
R0 value: an initial value of 2.5 
%CI values: - 
method: susceptible-exposed-infectious-removed model with a nonlinear incidence rate


Label:

unanswerable
-------------------------------------------------
index: 279


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 280


Prediction:

disease name: COVID-19 
location: India 
date: by early September 2021 
R0 value: below, but close to, unity 
%CI values: - 
method: base mathematical model


Label:

unanswerable
-------------------------------------------------
index: 281


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 282


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 283


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 284


Prediction:

disease name: 2019-nCoV 
location: Wuhan 
date: - 
R0 value: - 
%CI values: - 
method: Reservoir-People transmission network model


Label:

disease name: 2019-nCoV 
location: - 
date: - 
R0 value: - 
%CI values: - 
method: Reservoir-People transmission network model.
-------------------------------------------------
index: 285


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 286


Prediction:

disease name: COVID-19 
location: India 
date: - 
R0 value: - 
%CI values: - 
method: compartmental model


Label:

unanswerable
-------------------------------------------------
index: 287


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 288


Prediction:

disease name: COVID-19 
location: India 
date: - 
R0 value: 2.56 
%CI values: - 
method: exponential growth model


Label:

disease name: COVID-19 
location: India 
date: until the end of April 
R0 value: 2.56 
%CI values: - 
method: mathematical model
-------------------------------------------------
index: 289


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 290


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 291


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 292


Prediction:

disease name: COVID-19 
location: - 
date: 21 February 2020 
R0 value: 1.9 to 6.5 
%CI values: - 
method: systematic review of the published literature and preprints


Label:

unanswerable
-------------------------------------------------
index: 293


Prediction:

disease name: COVID-19 
location: Qom, Iran 
date: - 
R0 value: 2 and 3 
%CI values: - 
method: time-varying method


Label:

disease name: COVID-19 
location: Qom, Iran 
date: early phase of the outbreak 
R0 value: 2 and 3 
%CI values: - 
method: time-varying method 
| 
disease name: COVID-19 
location: South Korea 
date: early phase of the outbreak 
R0 value: 1.5-2 
%CI values: - 
method: time-varying method 
| 
disease name: COVID-19 
location: Iran 
date: early phase of the outbreak 
R0 value: 4-5 
%CI values: - 
method: time-varying method 
| 
disease name: COVID-19 
location: China 
date: early phase of the outbreak 
R0 value: - 
%CI values: - 
method: time-varying method 
| 
disease name: COVID-19 
location: Italy 
date: early phase of the outbreak 
R0 value: - 
%CI values: - 
method: time-varying method
-------------------------------------------------
index: 294


Prediction:

unanswerable


Label:

unanswerable
-------------------------------------------------
index: 295


Prediction:

disease name: COVID-19 
location: Large Penitentiary Complex, April-June 2020, Brazil 
date: April 1, 2020 - June 12 
R0 value: 1.0-2.3 
%CI values: - 
method: -


Label:

disease name: coronavirus disease 
location: large penitentiary complex in Brazil 
date: on April 1, 2020. By June 12 
R0 value: 1.0-2.3 
%CI values: - 
method: -
-------------------------------------------------
index: 296


Prediction:

disease name: SARS-CoV-2 
location: - 
date: up to May 10, 2020 
R0 value: 3.14 
%CI values: (95% CI: 2.69-3.59) 
method: continuous random-effect model (DerSimonian-Laird (inverse variance) method


Label:

unanswerable
-------------------------------------------------
index: 297


Prediction:

disease name: COVID-19 
location: American countries 
date: - 
R0 value: - 
%CI values: - 
method: maximum likelihood method


Label:

disease name: COVID-19 
location: American countries 
date: - 
R0 value: - 
%CI values: - 
method: maximum likelihood method
-------------------------------------------------
index: 298


Prediction:

disease name: COVID-19 
location: the United States (U.S.) 
date: - 
R0 value: R(0)1 
%CI values: - 
method: SARS-COV-2 daily infection entropy


Label:

disease name: COVID-19 
location: USA 
date: - 
R0 value: 1 
%CI values: - 
method: -
-------------------------------------------------
index: 299


Prediction:

disease name: COVID-19 
location: Bangladesh 
date: - 
R0 value: 6.924 
%CI values: - 
method: SIR mathematical model


Label:

disease name: COVID-19 
location: Bangladesh 
date: until 31 May 2020 
R0 value: 6.924 
%CI values: - 
method: SIR mathematical model
