Table 1. Reported items of methodology of the reviewed studies [part 1]
Study first author
|
Ahmadi [44]
|
Al-Qaness [52]
|
Ayyoubzadeh [84]
|
DELPHI [10]
|
Ghaffarzadegan [41]
|
Gu (YYG) [17]
|
Haghdoost [27]
|
Situation of study
|
Published paper
|
Published paper
|
Published paper
|
Web site
|
Published paper
|
Web site
|
Full report (Farsi)
|
Epidemic start date
|
20-02-19
|
20-01-22
|
20-02-11
|
N/M (a)
|
20-01-02
|
20-01-26
|
20-01-21
|
Inputs: Population
|
N/M (a)
|
N/M (a)
|
N/M (a)
|
Yes
|
Yes
|
Yes
|
Yes
|
Inputs: Cases
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
No
|
No
|
Inputs: Cases (source)
|
MOHME (b) official reports
|
World Health Organization
|
Worldometers website (c)
|
Johns Hopkins University (d)
|
MOHME (b) official reports; unofficial reports
|
Johns Hopkins University (d)
|
N/A (e)
|
Inputs: Deaths
|
Yes
|
No
|
No
|
Yes
|
Yes
|
Yes
|
No
|
Inputs: Deaths (source)
|
MOHME (b) official reports
|
N/A (e)
|
N/A (e)
|
Johns Hopkins University (d)
|
MOHME (b) official reports; unofficial reports
|
Johns Hopkins University (d)
|
N/A (e)
|
Other input data
|
Number of cured [recovered] cases
|
N/A (e)
|
N/A (e)
|
Nonpharmaceutical interventions
|
Number of tests, Detected infected travelers, Travel data
|
Case and hospitalization data (f)
|
Post-infection isolated persons, Hospitalized cases, Infected cases recovered without isolation or hospitalization
|
Output date range (number of days)
|
20-02-19 to 20-04-03 (45 days)
|
20-01-22 to 20-04-07 (77 days)
|
20-02-11 to 20-03-18 (37 days)
|
20-06-01 to 20-07-15 (45 days)
|
19-12-31 to 20-06-30 (183 days)
|
20-01-26 to 20-11-01 (281 days)
|
20-01-21 to 20-05-20 (121 days)
|
Place
|
Iran
|
4 countries
|
Iran
|
148 countries
|
Iran
|
70 countries
|
Iran and Tehran capital city
|
Compartmental model (g)
|
SIR (g)
|
None
|
None
|
SEIR+ (g) (h)
|
SEIR+ (g)
|
SEIR (g)
|
SEIR+ (g)
|
Statistical method: name
|
3 growth models (i)
|
6 time-series models (j)
|
2 Models (k)
|
Regression trees
|
Dynamic simulation model
|
Machine learning
|
Dynamic model
|
R0 estimation results
|
1.75
|
None
|
None
|
None
|
2.72 (before starting the interventions)
|
4 estimates (k)
|
3 estimates (l)
|
Scenarios /models: number
|
3 (m)
|
1
|
1
|
1
|
6 (n)
|
1
|
4 (o)
|
Other factors
|
No
|
No
|
No
|
Yes. Asymptomatic cases, under-reporting
|
Yes (p)
|
Yes. Asymptomatic cases, under-reporting
|
Yes (q)
|
Primary outcomes
|
Cumulative deaths, Cumulative cases
|
Cumulative cases
|
Normalized Daily cases
|
Cumulative and daily deaths and cases
|
Cumulative deaths, Cumulative cases, Current cases
|
Cumulative and daily deaths and cases, Daily prevalent cases
|
Cumulative and daily deaths and cases, Daily prevalent cases
|
Primary outcomes interval estimates
|
No
|
No
|
No
|
No
|
No
|
Yes
|
No
|
Other outcomes
|
None
|
None
|
None
|
Active, Active hospitalized, Cumulative hospitalized, Active ventilated
|
None
|
Reproduction Number
|
Needed hospital beds, ICU beds
|
Other outcomes interval estimates
|
N/A (e)
|
N/A (e)
|
N/A (e)
|
No
|
N/A (e)
|
Yes
|
No
|
Model validation
|
No
|
Yes (r)
|
Yes (s)
|
No (t)
|
Yes (u)
|
Yes (v)
|
No
|
Study limitations mentioned
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
No
|
Study limitations described
|
Yes
|
No
|
No
|
Yes
|
No
|
Yes
|
No
|
(a) N/M: Not Mentioned.
(b) MOHME: Ministry of Health and Medical Education, Iran.
(c) Worldometers Coronavirus [49].
(d) Johns Hopkins University, Coronavirus Resource Center [4, 5].
(e) N/A: Not applicable.
(f) “We do not use case-related data in our modeling. We do look at case and hospitalization data to help determine the bounds for our search grid, as changes in cases lead changes in deaths.” Gu (YYG) [17].
(g) Compartmental models: S: Susceptible, E: Exposed, I: Infected, R: Removed or Recovered, L: Latent. In any model with a + sign, there are other components for augmentation of model.
(h) DELPHI model: The model underlying the predictions is DELPHI (Differential Equations Leads to Predictions of Hospitalizations and Infections), that is based on SEIR with augmentations for under-detection and governmental response. DELPHI [10].
(i) Three growth models: M1: Gompertz Differential Equation, M2: Von Bertalanffy differential growth equation, and M3: Cubic polynomial least squared errors.
(j) Six time-series models: (1) Adaptive Neuro-Fuzzy Inference System (ANFIS) enhanced with Genetic Algorithm (GA), (2) Original Adaptive Neuro-Fuzzy Inference System (ANFIS), (3) Particle Swarm Optimizer (PSO), (4) Artificial Bee Colony (ABC), (5) hybridized of Flower Pollination Algorithm and SALP Swarm Algorithm (SSAFPA), (6) Sine-Cosine Algorithm (SCA).
(h) Two Models: Linear Regression, Long Short-Term Memory (LSTM).
(k) Four estimates: Initial R0 = 2.65. Reopen R = 1.17. Current R = 1.2. Post-mitigation R = 0.90.
(l) Three estimates: 7.24 (at the beginning). 2.58 (after interventions). 1.82 (conditional to isolation of 50% within 3 days).
(m) Three scenarios based on 3 growth models: S1: Gompertz Differential Equation, S2: Von Bertalanffy differential growth equation, and S3: Cubic polynomial least squared errors.
(n) Six scenarios based on combination of two factors: Seasonality (S), and Policy interventions (P). (1) S1P1: Seasonality conditions 1 (no effect or status quo) and Policy effect 1 (status quo contact rate). Estimates for 2020-03-19, the end of first month after the epidemic start date, are equal across the six scenarios. (2) S1P2: Seasonality conditions 1 (no effect or status quo) and Policy effect 2 (aggressive efforts to decrease contact rate by half of what it would be otherwise). (3) S2P1: Seasonality conditions 2 (moderate effect; infectivity of the virus decreases linearly from April 1st and halves by June 1st, then stays the same for the rest of the simulation) and Policy effect 1 (status quo contact rate). (4) S2P2: Seasonality conditions 2 (moderate effect; infectivity of the virus decreases linearly from April 1st and halves by June 1st, then stays the same for the rest of the simulation) and Policy effect 2 (aggressive efforts to decrease contact rate by half of what it would be otherwise). (5) S3P1: Seasonality conditions 3 (very strong mitigating effect; infectivity of the virus decreases from April 1st to a quarter of its base value by June 1st, then stays the same for the rest of the simulation) and Policy effect 1 (status quo contact rate). (6) S3P2: Seasonality conditions 3 (very strong mitigating effect; infectivity of the virus decreases from April 1st to a quarter of its base value by June 1st, then stays the same for the rest of the simulation) and Policy effect 2 (aggressive efforts to decrease contact rate by half of what it would be otherwise).
(o) Four scenarios: S0: Basic scenario (no intervention), only 10% isolation. S1: Worst scenario, minimum (25%) isolation. S2: Medium scenario, medium (32%) isolation. S3: Best scenario, maximum (40%) isolation.
(p) Seven other factors included: Asymptomatic cases, Under-reporting / Completeness of reporting cases and deaths to MOHME, Delays in reporting cases and deaths to MOHME, Testing availability, Number of tests performed, Social distancing / Quarantine interventions, Seasonality.
(q) Two other factors included: Seasonality, Social distancing / Quarantine interventions.
(r) Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination (R square).
(s) Root Mean Squared Error (RMSE).
(t) Friedman [31] assessed predictive performance of international COVID-19 mortality forecasting models, using median absolute percent error (MAPE) and Median absolute errors (MAE).
(u) Root Mean Squared Error (RMSE).
(v) Mean Square Error (MSE), Mean Absolute Error (MAE), and Ratio Error (RE). Did not mention the results.
Table 1. Reported items of methodology of the reviewed studies, continued [part 2]
Study first author
|
Hsiang [45]
|
IHME [12]
|
Imperial [13]
|
LANL [14]
|
Mashayekhi [28]
|
Moftakhar [87]
|
Moghadami [36]
|
Situation of study
|
Published paper
|
Web site [12] and published paper [30]
|
Web site [13] and published paper [34]
|
Web site
|
Summary report (Farsi)
|
Published paper
|
medRxiv preprint
|
Epidemic start date
|
N/M (a)
|
N/M (a)
|
20-01-03
|
N/M (a)
|
20-02-19 [?]
|
20-02-19
|
20-02-19
|
Inputs: Population
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
No
|
No
|
Inputs: Cases
|
Yes
|
Yes
|
Yes
|
Yes
|
No
|
Yes
|
Yes
|
Inputs: Cases (source)
|
Wikipedia (b)
|
Johns Hopkins University (c)
|
Johns Hopkins University (c)
|
Johns Hopkins University (c)
|
N/A (d)
|
MOHME (e) and Johns Hopkins (c)
|
MOHME (e)
|
Inputs: Deaths
|
Yes
|
Yes
|
Yes
|
Yes
|
No
|
No
|
Yes
|
Inputs: Deaths (source)
|
Wikipedia (b)
|
Johns Hopkins University (c)
|
Johns Hopkins University (c)
|
Johns Hopkins University (c)
|
N/A (d)
|
N/A (d)
|
MOHME (e)
|
Other input data
|
3 variables (f)
|
4 variables (g)
|
5 variables (h)
|
N/M (a)
|
N/M (a)
|
N/M (a)
|
None
|
Output date range (number of days)
|
~20-02-28 to 20-04-06 (~39 days)
|
20-02-04 to 21-02-01 (364 days)
|
20-01-06 to 20-11-24 (324 days)
|
20-03-14 to 20-11-07 (239 days)
|
N/M (a) (360 days)
|
20-03-21 to 20-04-20 (31 days)
|
20-03-21 to 20-04-20 (31 days)
|
Place
|
6 countries
|
165 countries
|
164 countries
|
157 countries
|
Iran
|
Iran
|
Iran and top 5 provinces
|
Compartmental model (i)
|
SIR+ (i)
|
SEIR (i)
|
SIR, SEIR , SEIR+ (i)
|
SEIR+ (i)
|
SLIR+ (i)
|
None
|
None
|
Statistical method: name
|
Multiple regression
|
Curve fitting (backcating) functional analysis (forecasting)
|
Regression trees
|
Dynamic growth parameter modeling
|
Dynamic model
|
Autoregressive Integrated Moving Average (ARIMA)
|
Exponential smoothing model
|
R0 estimation results
|
Not used
|
N/M (a)
|
N/M (a)
|
N/M (a)
|
Not used
|
Not used
|
Not used
|
Scenarios /models: number
|
2 (j)
|
3 (k)
|
6 (l)
|
1
|
3 (m)
|
1
|
1
|
Other factors
|
Yes. Under-reporting.
|
Yes (n)
|
Yes. Under-reporting.
|
Yes. Under-reporting.
|
Yes (o)
|
No
|
No
|
Primary outcomes
|
Cumulative cases
|
Cumulative and daily deaths and cases
|
Cumulative and daily deaths and cases
|
Cumulative and daily deaths and cases
|
Cumulative and daily deaths, Daily symptomatic and asymptomatic cases
|
Daily cases
|
Cumulative deaths, cases, recovered cases
|
Primary outcomes interval estimates
|
Yes
|
Yes
|
Yes
|
Yes
|
No
|
Yes
|
Yes
|
Other outcomes
|
None
|
Yes (p)
|
Yes (q)
|
None
|
None
|
None
|
None
|
Other outcomes interval estimates
|
N/A (d)
|
Yes
|
Yes
|
N/A (d)
|
N/A (d)
|
N/A (d)
|
N/A (d)
|
Model validation
|
(?)
|
Yes (r)
|
Yes (s)
|
Yes (t)
|
No
|
Yes (u)
|
Yes (v)
|
Study limitations mentioned
|
Yes
|
Yes
|
No
|
No
|
Yes
|
Yes
|
No
|
Study limitations described
|
Yes
|
Yes
|
No
|
No
|
No
|
Yes
|
No
|
(a) N/M: Not Mentioned.
(b) Wikipedia. COVID-19 pandemic in Iran [88].
(c) Johns Hopkins University, Coronavirus Resource Center [4, 5].
(d) N/A: Not Applicable.
(e) MOHME: Ministry of Health and Medical Education, Iran.
(f) Four variables: Cumulative recoveries, Active cases, Any changes to domestic COVID-19-testing regimes, such as case definitions or testing methodology, and Non-pharmaceutical interventions.
(g) Three variables: Mobility, Testing, and Seroprevalence (the latter for 41 locations).
(h) Five variables: Interventions, Social contacts, Comorbidities, Hospital bed capacity, Intensive Care Unit bed capacity.
(i) Compartmental models: S: Susceptible, E: Exposed, I: Infected, R: Removed or Recovered, L: Latent. In any model with a + sign, there are other components for augmentation of model.
(j) Two scenarios: ‘No-policy scenario’ and ‘Actual policies’.
(k) Three scenarios: S1 Best (Masks): ‘Universal Masks’ scenario reflects 95% mask usage in public in every location. S2 Reference (Current): ‘Current projection’ scenario assumes social distancing mandates are re-imposed for 6 weeks whenever daily deaths reach 8 per million (0.8 per 100,000). S3 Worse (Easing): ‘Mandates easing’ scenario reflects continued easing of social distancing mandates, and mandates are not re-imposed.
(l) Six scenarios: S1: Additional 50% Reduction. S2: Maintain Status Quo. S3: Relax Interventions 50%. S4: Surged Additional 50% Reduction. S5: Surged Maintain Status Quo. S6: Surged Relax Interventions 50%.
(m) S1: Ideal scenario, serious distancing. People reduce their social [physical] contacts to 20% of regular level, voluntarily or on a forced basis, after number of cases and deaths have increased, plus close observation of sanitation cautions, so that transmission rate reduces by 65%. S2: Medium scenario, not serious distancing. People reduce their social [physical] contacts only to 20% of regular level, voluntarily, after number of cases and deaths have increased, and other settings are like scenario 1. S3: Worst scenario. People reduce their social [physical] contacts only to 50% of regular level, voluntarily, after number of cases and deaths have increased, plus inadequate observation of sanitation cautions, so that transmission rate reduces only by 40% (instead of 55%), and 60% of people do not observe the sanitation cautions.
(n) Five other factors included: Asymptomatic cases, Mobility, Testing, Seroprevalence, Seasonality.
(o) Two other factors included: Asymptomatic cases, Social distancing / Quarantine interventions.
(p) Six other outcomes: All beds needed, Intensive Care Unit beds needed, Invasive ventilators needed, Tests, Mobility, Seroprevalence.
(q) Five other outcomes: Hospital demand, Hospital incidence, Intensive Care Unit demand, Intensive Care Unit incidence, Rt (Effective Reproduction Number).
(r) IHME web site [12] refers to Friedman [31], who assessed predictive performance of international COVID-19 mortality forecasting models, using median absolute percent error (MAPE) and Median absolute errors (MAE).
(s) Mean Absolute Percentage Error (MAPE).
(t) They validated the model “by looking at the coverage of the forecasts, i.e. the proportion of times that the number of confirmed cases/deaths fell within a specified lower and upper bound, X min and X max. Coverage plots can help visualize how well the model is doing.”
(u) Graphical residual assessment of the model.
(v) Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MEA), Mean Absolute Percentage Error (MAPE), Akaike Information Criterion (AIC).
Table 1. Reported items of methodology of the reviewed studies, continued [part 3]
Study first author
|
Moradi [42]
|
Muniz-Rodriguez [37]
|
Pourghasemi (PLoS ONE) [38]
|
Pourghasemi (IJID) [39]
|
Rafieenasab [51]
|
Rahimi Rise [29]
|
Saberi (web site) [21]
|
Saberi (paper) [22]
|
Situation of study
|
Published paper
|
Published paper
|
Published paper
|
Published paper
|
Published paper
|
Published paper (Farsi)
|
Web site [21]
|
Published paper
|
Epidemic start date
|
20-02-20
|
20-02-19
|
20-02-25 [?]
|
20-02-25 [?]
|
20-02-19
|
20-02-01
|
20-02-19
|
20-02-19
|
Inputs: Population
|
No
|
N/M (a)
|
Yes
|
Yes
|
N/M (a)
|
Yes
|
N/M (a)
|
Yes
|
Inputs: Cases
|
No
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Yes
|
Inputs: Cases (source)
|
N/A (b)
|
MOHME (c) official reports
|
MOHME (c) official reports
|
MOHME (c) official reports
|
MOHME (c) official reports
|
Worldometers website (d)
|
MOHME (c) official reports, WHO, Worldometers(d)
|
MOHME (c) official reports, WHO
|
Inputs: Deaths
|
Yes
|
No
|
Yes
|
Yes
|
No
|
Yes
|
Yes
|
Yes
|
Inputs: Deaths (source)
|
MOHME (c) official reports
|
N/A (b)
|
MOHME (c) official reports
|
MOHME (c) official reports
|
N/A (b)
|
Worldometers website (d)
|
MOHME (c) official reports, WHO, Worldometers(d)
|
MOHME (c) official reports, WHO, Worldometers(d)
|
Other input data
|
None
|
Travel data
|
Environmental and meteorological conditions
|
Environmental and meteorological conditions
|
None
|
Public transportation variables
|
None
|
None
|
Output date range (number of days)
|
20-02-20 to 20-03-26 (36 days)
|
20-02-19 to 20-02-29 (11 days)
|
~20-02-25 to ~20-06-10 (~107 days) (e)
|
~20-02-25 to ~20-06-20 (~117 days) (f)
|
20-02-19 to 20-06-07 (110 days)
|
20-02-01 to 20-08-01 (183 days)
|
20-02-19 to 21-02-02 (350 days)
|
~20-03-19 to 20-10-26 (~222 days)
|
Place
|
Iran
|
Iran and 2 multi-province regions
|
Iran and Fars Province
|
Iran, 31 Provinces of Iran, World
|
Iran
|
Iran
|
Iran
|
Iran
|
Compartmental model (g)
|
None
|
None
|
None
|
None
|
SIR+ (g) (h)
|
SEIR (g)
|
SIR (g)
|
SEIR+ (g) (i)
|
Statistical method: name
|
Calculating number of cases based on different assumptions for case fatality rate (CFR)
|
Generalized growth mode; Based on the calculation of the epidemic doubling times
|
Autoregressive Integrated Moving Average (ARIMA) and polynomial regression
|
Fourth-degree polynomial regression
|
3-steps model based on the SIR model
|
Dynamic model
|
Classical SIR(g) mathematical model with homogenous mixing assumption
|
Ordinary least squares minimization
|
R0 estimation results
|
Not used
|
Two methods: 3.6 and 3.58
|
Not used
|
Not used
|
2.8-3.3 (range)
|
Not used
|
2.37 (for the last 7 days before 20-03-21)
|
1.73 (20-03-01) and 0.69 (20-04-15) (j)
|
Scenarios /models: number
|
4 (k)
|
2 (l)
|
1
|
1
|
1
|
2
|
12 (m)
|
3 (n)
|
Other factors
|
No
|
No
|
No
|
No
|
No
|
Yes. Asymptomatic cases
|
Yes (o)
|
Yes (p)
|
Primary outcomes
|
Cumulative cases
|
Daily cases
|
Cumulative and daily deaths and cases (q)
|
Cumulative and daily deaths and cases (r)
|
Cumulative and daily deaths, Daily cases
|
Daily deaths and cases
|
Cumulative cases, Daily active cases
|
Fractions of national population estimated to be confirmed and suspected cases (s)
|
Primary outcomes interval estimates
|
No
|
Yes
|
No
|
No
|
No
|
No
|
No
|
Yes
|
Other outcomes
|
Case Fatality Rate
|
None
|
None
|
None
|
None
|
None
|
None
|
Intensive Care Unit beds needed
|
Other outcomes interval estimates
|
No
|
N/A (b)
|
N/A (b)
|
N/A (b)
|
N/A (b)
|
N/A (b)
|
N/A (b)
|
Yes
|
Model validation
|
No
|
No
|
Yes (t)
|
Yes (u)
|
No
|
Yes (v)
|
No
|
Yes (w)
|
Study limitations mentioned
|
Yes
|
Yes
|
No
|
Yes
|
No
|
No
|
Yes
|
Yes
|
Study limitations described
|
No
|
Yes
|
No
|
No
|
No
|
No
|
No
|
Yes
|
(a) N/M: Not Mentioned.
(b) N/A: Not Applicable.
(c) MOHME: Ministry of Health and Medical Education, Iran.
(d) Worldometers Coronavirus [49].
(e) Start and end dates mentioned in manuscript text, mentioned in title of their Figure 14, and shown within their Figure 14 do not seem to be congruent.
(f) Start and end dates mentioned in manuscript text, mentioned in title of their Figure 15, and shown within their Figure 15 do not seem to be congruent.
(g) Compartmental models: S: Susceptible, E: Exposed, I: Infected, R: Removed or Recovered, L: Latent. In any model with a + sign, there are other components for augmentation of model.
(h) SIR with exact and approximated solutions, extrapolation based on least squares model with three functions.
(i) SEIR+ Distinguishing between fatal and recovered cases combined with an estimate of the percentage of symptomatic cases using delay-adjusted Case Fatality Rate.
(j) Estimated effective reproduction number that ranged from 0.66 to 1.73 between February and April 2020, with a median of 1.16. Estimated a reduction in the effective reproduction number during this period, from 1.73 (95% CI 1.60–1.87) on 1 March 2020 to 0.69 (95% CI 0.68–0.70) on 15 April 2020, due to various non-pharmaceutical interventions.
(k) Four scenarios based on different values of Case Fatality Rate. S1: 0.3%, S2: 0.5%, S3: 1%, and S4: 2%.
(l) Based on two different methods to estimate R0.
(m) (1) S1P10: Scenario 1 (Best scenario, based on official reports with correction factor of 1) with 10 million susceptible population.
(2) 12 scenarios based on combination of three options for number of cases and deaths to start with, and four options for the susceptible population size. (1) S1P10: Scenario 1 (Best scenario, based on official reports with correction factor of 1) with 10 million susceptible population. (2) S1P30:Scenario 1 (Best scenario, based on official reports with correction factor of 1) with 30 million susceptible population. (3) S1P50: Scenario 1 (Best scenario, based on official reports with correction factor of 1) with 50 million susceptible population. (4) S1P80: Scenario 1 (Best scenario, based on official reports with correction factor of 1) with 80 million susceptible population. (5) S2P10: Scenario 2 (Medium scenario, based on official reports with correction factor of 5 (after Dr. Rick Brennan, Director of Emergency Operations, World Health Organization [54]) with 10 million susceptible population. (6) S2P30: Scenario 2 (Medium scenario, based on official reports with correction factor of 5 (after Dr. Rick Brennan, Director of Emergency Operations, World Health Organization [54]) with 30 million susceptible population. (7) S2P50: Scenario 2 (Medium scenario, based on official reports with correction factor of 5 (after Dr. Rick Brennan, Director of Emergency Operations, World Health Organization [54]) with 50 million susceptible population. (8) S2P80: Scenario 2 (Medium scenario, based on official reports with correction factor of 5 (after Dr. Rick Brennan, Director of Emergency Operations, World Health Organization [54]) with 80 million susceptible population. (9) S3P10: Scenario 3 (Worst scenario, based on official reports with correction factor of 10 (after Russell [55]) with 80 million susceptible population. (10) S3P30: Scenario 3 (Worst scenario, based on official reports with correction factor of 10 (after Russell [55]) with 30 million susceptible population. (11) S3P50: Scenario 3 (Worst scenario, based on official reports with correction factor of 10 (after Russell [55]) with 50 million susceptible population. (12) S3P80: Scenario 3 (Worst scenario, based on official reports with correction factor of 10 (after Russell [55]) with 10 million susceptible population.
(n) Three scenarios: (1) maintaining the same level of control measures as of 12 April 2020, (2) reinforcing the control measures to increase physical distancing by a 20% increase in the reproduction number, and (3) partial lifting the restrictions to ease physical distancing by a 20% decrease in the reproduction number.
(o) Completeness of reporting cases and deaths to MOHME.
(p) Accounted for the under-reporting of the number of infected cases using delay-adjusted case fatality ratio (CFR) approach.
(q) Cumulative deaths and cases (for Iran and Fars Province), Daily deaths and cases (for Fars Province)
(r) Cumulative deaths and cases (for Iran and World), Daily or cumulative cases in 30 days after the first day of infected cases in the 31 Iranian provinces)
(s) We transformed their reported fractions of national population estimated to be confirmed and suspected cases to numbers of people estimated to be confirmed and suspected cases, using a total national population of 84297880 (used by IHME [12]).
(t) Area Under Curve (AUC).
(u) Area Under Curve (AUC).
(v) Root Mean Squared Error (RMSE).
(w) Root Mean Squared Error (RMSE).
Table 1. Reported items of methodology of the reviewed studies, continued [part 4]
Study first author
|
Shen [43]
|
Singh [89]
|
Srivastava [15]
|
Thu [48]
|
Tuite [46]
|
Zhan [40]
|
Zhuang [47]
|
Situation of study
|
Published paper
|
Published paper
|
Web site [15] and preprint [35]
|
Published paper
|
Published paper
|
Published paper
|
Published paper
|
Epidemic start date
|
20-02-20
|
N/M (a)
|
N/M (a)
|
N/M (a)
|
N/M (a)
|
20-02-19
|
N/M (a)
|
Inputs: Population
|
No
|
No
|
N/M (a)
|
No
|
N/M (a)
|
N/M (a)
|
Yes
|
Inputs: Cases
|
Yes
|
Yes
|
Yes
|
Yes
|
No
|
Yes
|
No
|
Inputs: Cases (source)
|
“WIND DATA” (b)
|
Worldometers (c)
|
Johns Hopkins University (d)
|
WHO
|
N/A (b)
|
MOHME (e) official reports
|
N/A (f)
|
Inputs: Deaths
|
No
|
No
|
Yes
|
Yes
|
No
|
Yes
|
No
|
Inputs: Deaths (source)
|
N/M (a)
|
N/M (a)
|
Johns Hopkins University (d)
|
WHO
|
N/A (f)
|
WHO
|
N/A (f)
|
Other input data
|
None
|
None
|
None
|
Social distancing
|
Exported cases from Iran to other countries; Travel data
|
COVID-19 spreading profiles of 367 cities in China
|
Exported cases from Iran to other countries, Travel data
|
Output date range (number of days)
|
20-02-20 to 20-04-20 (61 days)
|
20-04-24 to 20-07-07 (75 days)
|
20-09-19 to 20-12-19 (every 7th day, 14 dates, 92 days duration)
|
20-03-30 to 20-05-02 (34 days)
|
20-01-01 to N/M (a)
|
20-02-22 to 20-06-24 (124 days)
|
20-02-01 to 20-02-24 (24 days)
|
Place
|
9 countries and 11 provinces / municipalities in China
|
15 countries
|
184 countries
|
10 countries
|
Iran
|
Iran and 12 provinces
|
Iran
|
Compartmental model (g)
|
None
|
None
|
SIR+ (g) (h)
|
None
|
None
|
SEIR+ (g)
|
None
|
Statistical method: name
|
Logistic growth
|
Autoregressive Integrated Moving Average (ARIMA)
|
Hyper-parametric learning
|
Linear growth rates (i)
|
N/M (a)
|
Data-driven prediction algorithm (k)
|
Binomial distributed likelihood framework
|
R0 estimation results
|
Not used
|
Not used
|
1.44 (20-03-21), 1.46 (20-03-28)
|
Not used
|
Not used
|
Not used
|
Not used
|
Scenarios /models: number
|
1
|
1
|
3 (l)
|
1
|
6 (m)
|
1
|
5 (n)
|
Other factors
|
No
|
No
|
Asymptomatic cases, under-reporting
|
No
|
No
|
No
|
No
|
Primary outcomes
|
Cumulative cases
|
Cumulative cases
|
Cumulative deaths and cases
|
Daily cases
|
Cumulative cases
|
Cumulative and daily cases
|
Cumulative cases
|
Primary outcomes interval estimates
|
No
|
Yes
|
No
|
No
|
Yes
|
Yes
|
Yes
|
Other outcomes
|
None
|
None
|
None
|
None
|
None
|
None
|
None
|
Other outcomes interval estimates
|
N/A (f)
|
N/A (f)
|
N/A (f)
|
N/A (f)
|
N/A (f)
|
N/A (f)
|
N/A (f)
|
Model validation
|
Yes (o)
|
Yes (p)
|
Yes (q)
|
No
|
No
|
Yes (j)
|
No
|
Study limitations mentioned
|
Yes
|
Yes
|
Yes
|
Yes
|
No
|
Yes
|
Yes
|
Study limitations described
|
No
|
Yes
|
Yes
|
Yes
|
No
|
Yes
|
No
|
(a) N/M: Not mentioned.
(b) Mentioned: “WIND DATA, a leading financial data services provider in China”.
(c) Worldometers Coronavirus. [49].
(d) Johns Hopkins University, Coronavirus Resource Center [4, 5].
(e) MOHME: Ministry of Health and Medical Education, Iran.
(f) N/A: Not Applicable.
(g) SEIR+ Distinguishing between fatal and recovered cases combined with an estimate of the percentage of symptomatic cases using delay-adjusted Case Fatality Rate.
(h) SI-kJ alpha model: S: Susceptible. I: Infected. k: k sub-states of infection. J: J is a hyperparameter introduced for a smoothing effect to deal with noisy data. Alpha: an additional hyperparameter to minimizes the Root Mean Squared Error.
(i) They have not named their method. It could be names as linear growth rates, according to their Equation (1) and Equation (2).
(j) Another study by Zhan and colleagues was cited for validity of their models.
(k) A data-driven prediction algorithm to find the most resembling growth curve from the historical profiles in China.
(l) Three scenarios: Current, Released, Restricted, each with 6 levels of putative under-ascertainment parameter.
(m) Six scenarios based on six sets of international travel destinations.
(n) Five scenarios based on selected combinations of (1) Effective catchment population, (2) Detection window 10 or 8 days, and (3) 90% or 70% load factors.
(o) R Square.
(p) Akaike Information Criterion (AIC).
(q) Root Mean Squared Error (RMSE).
Table 2. Predictions of cumulative deaths for the end of months one to six after the official epidemic start date (2020-02-19) and the latest date available in 2020
|
Date1(a)
|
20-03-19
|
20-04-19
|
20-05-20
|
20-06-20
|
20-07-21
|
20-08-21
|
Latest date
|
|
Date 2 (b)
|
98-12-29
|
99-01-31
|
99-02-31
|
99-03-31
|
99-04-31
|
99-05-31
|
in 2020 (c)
|
- First Author, Outcome
|
S/M (d)
|
Value
|
Value
|
Value
|
Value
|
Value
|
Value
|
Value
|
- MOHME official via [4, 5]
|
|
|
|
|
|
|
|
|
Cumulative deaths
|
N/A (e)
|
1284
|
5118
|
7183
|
9507
|
14634
|
20376
|
30712
|
- Ahmadi [44]
|
|
|
|
|
|
|
|
|
Cumulative deaths
|
M1 (f)
|
1264
|
··
|
··
|
··
|
··
|
··
|
··
|
Cumulative deaths
|
M2 (g)
|
1322
|
··
|
··
|
··
|
··
|
··
|
··
|
Cumulative deaths
|
M3 (h)
|
1263
|
··
|
··
|
··
|
··
|
··
|
··
|
- DELPHI [10]
|
|
|
|
|
|
|
|
|
Total detected deaths
|
S1(i)
|
··
|
··
|
··
|
8426
|
··
|
··
|
··
|
- Ghaffarzadegan [41]
|
|
|
|
|
|
|
|
|
Cumulative deaths
|
S1P1 (j)
|
15317
|
44078
|
70462
|
95658
|
··
|
··
|
··
|
Cumulative deaths
|
S1P2 (k)
|
15317
|
41702
|
52937
|
66549
|
··
|
··
|
··
|
Cumulative deaths
|
S2P1 (l)
|
15317
|
44078
|
68383
|
85262
|
··
|
··
|
··
|
Cumulative deaths
|
S2P2 (m)
|
15317
|
41702
|
52937
|
60015
|
··
|
··
|
··
|
Cumulative deaths
|
S3P1 (n)
|
15317
|
44078
|
68383
|
80213
|
··
|
··
|
··
|
Cumulative deaths
|
S3P2 (o)
|
15317
|
41702
|
52937
|
57341
|
··
|
··
|
··
|
- Gu (YYG) [17]
|
|
|
|
|
|
|
|
|
Cumulative deaths, mean
|
S1(i)
|
··
|
··
|
··
|
··
|
··
|
31955
|
··
|
Cumulative deaths, lower
|
S1(i)
|
··
|
··
|
··
|
··
|
··
|
29231
|
··
|
Cumulative deaths, upper
|
S1(i)
|
··
|
··
|
··
|
··
|
··
|
36014
|
··
|
- Haghdoost [27]
|
|
|
|
|
|
|
|
|
Cumulative deaths
|
S0 (p)
|
··
|
··
|
30700
|
··
|
··
|
··
|
··
|
Cumulative deaths
|
S1 (q)
|
3824
|
9107
|
13450
|
··
|
··
|
··
|
··
|
Cumulative deaths
|
S2 (r)
|
2796
|
6231
|
8632
|
··
|
··
|
··
|
··
|
Cumulative deaths
|
S3 (s)
|
··
|
··
|
6030
|
··
|
··
|
··
|
··
|
- IHME [12]
|
|
|
|
|
|
|
|
|
Cumulative deaths, mean (t)
|
S1 (u)
|
1215
|
5150
|
7183
|
9495
|
14642
|
20369
|
44087
|
Cumulative deaths, lower (t)
|
S1 (u)
|
1215
|
5150
|
7183
|
9495
|
14642
|
20369
|
38031
|
Cumulative deaths, upper (t)
|
S1 (u)
|
1215
|
5150
|
7183
|
9495
|
14642
|
20369
|
51027
|
Cumulative deaths, mean (t)
|
S2 (v)
|
1215
|
5150
|
7183
|
9495
|
14642
|
20369
|
67186
|
Cumulative deaths, lower (t)
|
S2 (v)
|
1215
|
5150
|
7183
|
9495
|
14642
|
20369
|
57913
|
Cumulative deaths, upper (t)
|
S2 (v)
|
1215
|
5150
|
7183
|
9495
|
14642
|
20369
|
72170
|
Cumulative deaths, mean (t)
|
S3 (w)
|
1215
|
5150
|
7183
|
9495
|
14642
|
20369
|
70877
|
Cumulative deaths, lower (t)
|
S3 (w)
|
1215
|
5150
|
7183
|
9495
|
14642
|
20369
|
57956
|
Cumulative deaths, upper (t)
|
S3 (w)
|
1215
|
5150
|
7183
|
9495
|
14642
|
20369
|
86235
|
- Imperial [13]
|
|
|
|
|
|
|
|
|
Cumulative deaths, mean
|
S1 (x)
|
763
|
3743
|
5276
|
7303
|
11537
|
16538
|
27195
|
Cumulative deaths, lower
|
S1 (x)
|
434
|
2095
|
3067
|
4203
|
6537
|
9895
|
17638
|
Cumulative deaths, upper
|
S1 (x)
|
1254
|
6096
|
8462
|
11620
|
17058
|
23543
|
36103
|
Cumulative deaths, mean
|
S2 (y)
|
763
|
3743
|
5276
|
7303
|
11537
|
16538
|
32372
|
Cumulative deaths, lower
|
S2 (y)
|
434
|
2095
|
3067
|
4203
|
6537
|
9895
|
19989
|
Cumulative deaths, upper
|
S2 (y)
|
1254
|
6096
|
8462
|
11620
|
17058
|
23543
|
45124
|
Cumulative deaths, mean
|
S3 (z)
|
763
|
3743
|
5276
|
7303
|
11537
|
16538
|
121960
|
Cumulative deaths, lower
|
S3 (z)
|
434
|
2095
|
3067
|
4203
|
6537
|
9895
|
42697
|
Cumulative deaths, upper
|
S3 (z)
|
1254
|
6096
|
8462
|
11620
|
17058
|
23543
|
252429
|
Cumulative deaths, mean
|
S4 (aa)
|
744
|
3616
|
5089
|
7053
|
11102
|
15908
|
26738
|
Cumulative deaths, lower
|
S4 (aa)
|
388
|
1777
|
2572
|
3590
|
5960
|
9019
|
16176
|
Cumulative deaths, upper
|
S4 (aa)
|
1127
|
5712
|
8112
|
10778
|
16171
|
22470
|
35621
|
Cumulative deaths, mean
|
S5 (bb)
|
744
|
3616
|
5089
|
7053
|
11102
|
15908
|
31916
|
Cumulative deaths, lower
|
S5 (bb)
|
388
|
1777
|
2572
|
3590
|
5960
|
9019
|
18504
|
Cumulative deaths, upper
|
S5 (bb)
|
1127
|
5712
|
8112
|
10778
|
16171
|
22470
|
48300
|
Cumulative deaths, mean
|
S6 (cc)
|
744
|
3616
|
5089
|
7053
|
11102
|
15908
|
85087
|
Cumulative deaths, lower
|
S6 (cc)
|
388
|
1777
|
2572
|
3590
|
5960
|
9019
|
40819
|
Cumulative deaths, upper
|
S6 (cc)
|
1127
|
5712
|
8112
|
10778
|
16171
|
22470
|
158299
|
- LANL [14]
|
|
|
|
|
|
|
|
|
Cumulative deaths, median
|
S1(i)
|
1284
|
5118
|
7183
|
9507
|
14634
|
20376
|
34263
|
Cumulative deaths, lower
|
S1(i)
|
1284
|
5118
|
7183
|
9507
|
14634
|
20376
|
30762
|
Cumulative deaths, upper
|
S1(i)
|
1284
|
5118
|
7183
|
9507
|
14634
|
20376
|
43022
|
- Mashayekhi [28]
|
|
|
|
|
|
|
|
|
Cumulative deaths
|
S1 (dd)
|
759
|
10316
|
11751
|
11857
|
··
|
··
|
··
|
Cumulative deaths
|
S2 (ee)
|
1285
|
33349
|
61322
|
77302
|
86931
|
92620
|
··
|
Cumulative deaths
|
S3 (ff)
|
11752
|
97445
|
612953
|
1819392
|
3002721
|
3562136
|
··
|
- Moghadami [36]
|
|
|
|
|
|
|
|
|
Cumulative deaths, mean(gg)
|
S1(i)
|
1144
|
5378
|
··
|
··
|
··
|
··
|
··
|
Cumulative deaths, lower(gg)
|
S1(i)
|
1104
|
3929
|
··
|
··
|
··
|
··
|
··
|
Cumulative deaths, upper(gg)
|
S1(i)
|
1166
|
7003
|
··
|
··
|
··
|
··
|
··
|
- Rafieenasab [51]
|
|
|
|
|
|
|
|
|
Cumulative deaths
|
S2 (hh)
|
32101
|
39026
|
··
|
··
|
··
|
··
|
··
|
Cumulative deaths
|
S3 (ii)
|
69583
|
388951
|
402569
|
··
|
··
|
··
|
··
|
- Srivastava [15]
|
|
|
|
|
|
|
|
|
Cumulative deaths
|
S1P1 (jj)
|
··
|
··
|
··
|
··
|
··
|
··
|
43631
|
Cumulative deaths
|
S1P2 (kk)
|
··
|
··
|
··
|
··
|
··
|
··
|
43282
|
Cumulative deaths
|
S1P5 (ll)
|
··
|
··
|
··
|
··
|
··
|
··
|
42289
|
Cumulative deaths
|
S1P10 (mm)
|
··
|
··
|
··
|
··
|
··
|
··
|
40802
|
Cumulative deaths
|
S1P20 (nn)
|
··
|
··
|
··
|
··
|
··
|
··
|
38324
|
Cumulative deaths
|
S1P40 (oo)
|
··
|
··
|
··
|
··
|
··
|
··
|
34721
|
Cumulative deaths
|
S2P1 (pp)
|
··
|
··
|
··
|
··
|
··
|
··
|
418834
|
Cumulative deaths
|
S2P2 (qq)
|
··
|
··
|
··
|
··
|
··
|
··
|
354756
|
Cumulative deaths
|
S2P5 (rr)
|
··
|
··
|
··
|
··
|
··
|
··
|
241214
|
Cumulative deaths
|
S2P10 (ss)
|
··
|
··
|
··
|
··
|
··
|
··
|
154826
|
Cumulative deaths
|
S2P20 (tt)
|
··
|
··
|
··
|
··
|
··
|
··
|
87664
|
Cumulative deaths
|
S2P40 (uu)
|
··
|
··
|
··
|
··
|
··
|
··
|
45995
|
Cumulative deaths
|
S3P1 (vv)
|
··
|
··
|
··
|
··
|
··
|
··
|
27959
|
Cumulative deaths
|
S3P2 (ww)
|
··
|
··
|
··
|
··
|
··
|
··
|
27786
|
Cumulative deaths
|
S3P5 (xx)
|
··
|
··
|
··
|
··
|
··
|
··
|
27327
|
Cumulative deaths
|
S3P10 (yy)
|
··
|
··
|
··
|
··
|
··
|
··
|
26724
|
Cumulative deaths
|
S3P20 (zz)
|
··
|
··
|
··
|
··
|
··
|
··
|
25909
|
Cumulative deaths
|
S3P40 (aaa)
|
··
|
··
|
··
|
··
|
··
|
··
|
25043
|
(a) Date 1: Gregorian.
(b) Date 2: Hijri.
(c) Latest date in 2020: As of 2020-10-19 for MOHME official via [4, 5], 2020-11-01 for Gu (YYG) [17], 2020-12-31 for IHME [12] and Imperial [13] [2020-11-28 for LANL [14], and 2020-12-19 for Srivastava [15].
(d) S/M: Scenario / Model.
(e) N/A: Not Applicable.
(f) M1: Gompertz.
(g) M2: Von Bertalanffy growth.
(h) M3: Cubic Polynomial.
(i) S1: Single scenario.
(j) S1P1: Seasonality conditions 1 (no effect or status quo) and Policy effect 1 (status quo contact rate). Estimates for 2020-03-19, the end of first month after the epidemic start date, are equal across the six scenarios.
(k) S1P2: Seasonality conditions 1 (no effect or status quo) and Policy effect 2 (aggressive efforts to decrease contact rate by half of what it would be otherwise).
(l) S2P1: Seasonality conditions 2 (moderate effect; infectivity of the virus decreases linearly from April 1st and halves by June 1st, then stays the same for the rest of the simulation) and Policy effect 1 (status quo contact rate).
(m) S2P2: Seasonality conditions 2 (moderate effect; infectivity of the virus decreases linearly from April 1st and halves by June 1st, then stays the same for the rest of the simulation) and Policy effect 2 (aggressive efforts to decrease contact rate by half of what it would be otherwise).
(n) S3P1: Seasonality conditions 3 (very strong mitigating effect; infectivity of the virus decreases from April 1st to a quarter of its base value by June 1st, then stays the same for the rest of the simulation) and Policy effect 1 (status quo contact rate).
(o) S3P2: Seasonality conditions 3 (very strong mitigating effect; infectivity of the virus decreases from April 1st to a quarter of its base value by June 1st, then stays the same for the rest of the simulation) and Policy effect 2 (aggressive efforts to decrease contact rate by half of what it would be otherwise).
(p) S0: Basic scenario (no intervention), only 10% isolation.
(q) S1: Worst scenario, minimum (25%) isolation.
(r) S2: Medium scenario, medium (32%) isolation.
(s) S3: Best scenario, maximum (40%) isolation.
(t) Smoothed estimates.
(u) S1 Best (Masks): ‘Universal Masks’ scenario reflects 95% mask usage in public in every location.
(v) S2 Reference (Current): ‘Current projection’ scenario assumes social distancing mandates are re-imposed for 6 weeks whenever daily deaths reach 8 per million (0.8 per 100,000).
(w) S3 Worse (Easing): ‘Mandates easing’ scenario reflects continued easing of social distancing mandates, and mandates are not re-imposed.
(x) S1: Additional 50% Reduction.
(y) S2: Maintain Status Quo.
(z) S3: Relax Interventions 50%.
(aa) S4: Surged Additional 50% Reduction.
(bb) S5: Surged Maintain Status Quo.
(cc) S6: Surged Relax Interventions 50%.
(dd) S1: Serious distancing.
(ee) S2: Not serious distancing.
(ff) S3: Worse than Scenario 2.
(gg) Dates for Moghadami [36] are 2020-03-21 and 2020-04-18, instead of 2020-03-19 and 2020-04-19 respectively.
(hh) S2: Based on SIR model.
(ii) S3: Approximation calculation.
(jj) S1P1: Scenario Current, Parameter 1.
(kk) S1P2: Scenario Current, Parameter 2.
(ll) S1P5: Scenario Current, Parameter 5.
(mm) S1P10: Scenario Current, Parameter 10.
(nn) S1P20: Scenario Current, Parameter 20.
(oo) S1P40: Scenario Current, Parameter 40.
(pp) S2P1: Scenario Released, Parameter 1.
(qq) S2P2: Scenario Released, Parameter 2.
(rr) S2P5: Scenario Released, Parameter 5.
(ss) S2P10: Scenario Released, Parameter 10.
(tt) S2P20: Scenario Released, Parameter 20.
(uu) S2P40: Scenario Released, Parameter 40.
(vv) S3P1: Scenario Restricted, Parameter 1.
(ww) S3P2: Scenario Restricted, Parameter 2.
(xx) S3P5: Scenario Restricted, Parameter 5.
(yy) S3P10: Scenario Restricted, Parameter 10.
(zz) S3P20: Scenario Restricted, Parameter 20.
(aaa) S3P40: Scenario Restricted, Parameter 40.
Table 3. Lowest and highest predictions at the end of month 2 (2020-04-19), month 4 (2020-06-20) after the official epidemic start date (2020-02-19), and the latest dates available in 2020 and 2021
- End of month 2 (20-04-19)
|
|
|
|
|
|
Outcomes:
|
Lowest
|
Study
|
MOHME
|
Highest
|
Study
|
Cumulative deaths
|
1777
|
Imperial (a)
|
5118
|
388951
|
Rafieenasab (b)
|
Daily deaths
|
30
|
Imperial (a)
|
87
|
11289
|
Rahimi Rise (c)
|
Cumulative cases
|
20588
|
Al-Qaness (d)
|
82211
|
2310161
|
IHME (e)
|
Incident daily cases
|
93
|
Thu (f)
|
1343
|
216262
|
Rahimi Rise (c)
|
Incident daily total cases (g k)
|
72950
|
Saberi (paper) (h)
|
..
|
1616385
|
Saberi (paper) (i)
|
- End of month 4 (20-06-20)
|
|
|
|
|
|
Cumulative deaths
|
3590
|
Imperial (a)
|
9507
|
1819392
|
Mashayekhi (j)
|
Daily deaths
|
5
|
Mashayekhi (k)
|
115
|
44934
|
Mashayekhi (j)
|
Cumulative cases
|
144305
|
DELPHI (l)
|
202584
|
4266964
|
IHME (e)
|
Incident daily cases
|
211
|
DELPHI (l)
|
2322
|
138892
|
Gu (YYG) (m)
|
Incident daily total cases (g)
|
9625
|
Saberi (paper) (h)
|
..
|
1255012
|
Saberi (paper) (i)
|
- Latest date available in 2020
|
|
|
|
|
|
Cumulative deaths
|
16176
|
Imperial (n)
|
30712 (o)
|
418834
|
Srivastava (p)
|
Daily deaths
|
0
|
Imperial (a)
|
373 (o)
|
3984
|
Imperial (q)
|
Cumulative cases
|
3588293
|
Imperial (n)
|
534631(o)
|
41475792
|
Imperial (q)
|
Incident daily cases
|
0
|
Imperial (a)
|
4251(o)
|
486745
|
Imperial (e)
|
Incident daily total cases (g)
|
9625
|
Saberi (paper) (h)
|
..
|
169110
|
Saberi (paper) (i)
|
- Latest date available in 2021
|
|
|
|
|
|
Cumulative deaths
|
40151
|
IHME (r)
|
..
|
125690
|
IHME (s)
|
Daily deaths
|
55
|
IHME (r)
|
..
|
1093
|
IHME (s)
|
Cumulative cases
|
19799934
|
IHME (r)
|
..
|
34417912
|
IHME (s)
|
Incident daily cases
|
14818
|
IHME (r)
|
..
|
236781
|
IHME (s)
|
(a) Imperial, S4: Surged Additional 50% Reduction. Lower 95% uncertainty limit [13].
(b) Rafieenasab, S3: Approximation calculation. Mean estimate [51].
(c) Rahimi Rise, S2: No interventions. Mean estimate [29].
(d) Al-Qaness, M1: Adaptive Neuro-Fuzzy Inference System (ANFIS) enhanced with Genetic Algorithm (GA). Mean estimate [52].
(e) IHME, S2 Reference (Current): ‘Current projection’ scenario assumes social distancing mandates are re-imposed for 6 weeks whenever daily deaths reach 8 per million (0.8 per 100,000). Upper 95% uncertainty limit [12].
(f) Thu, M1: Linear growth rate, equation 1. Mean estimate [48].
(g) Saberi (paper), Incident daily total cases (confirmed and suspected) [22].
(h) Saberi (paper), S1: 20% more distancing. Mean estimate [22].
(i) Saberi (paper), S3: 20% less distancing. Upper 95% uncertainty limit [22].
(j) Mashayekhi, S3: Worse than Scenario 2 (S2: Not serious distancing). Mean estimate[28].
(k) Mashayekhi, S1: S1: Serious distancing. Mean estimate [28].
(l) DELPHI, S1: Single scenario. Mean estimate [10].
(m) Gu (YYG) S1, Single scenario. Upper 95% uncertainty limit [17].
(n) Imperial, S4: Surged Additional 50% Reduction. Lower 95% uncertainty limit. For 2020-12-31 [13].
(o) MOHME official via [4, 5], as of 2020-10-19.
(p) Srivastava, S2P1: Scenario Released, Parameter 1. mean estimate For 2020-12-19 [15].
(q) Imperia, S3: Relax Interventions 50%. Upper 95% uncertainty limit. For 2020-12-31 [13].
(r) IHME, S1Best (Masks): ‘Universal Masks’ scenario reflects 95% mask usage in public in every location. Lower 95% uncertainty limit. For 2021-01-31 [12].
(s) IHME, S3 Worse (Easing): ‘Mandates easing’ scenario reflects continued easing of social distancing mandates, and mandates are not re-imposed. Upper 95% uncertainty limit. For 2021-01-31 [12].