Rapid review of COVID-19 epidemic estimation studies for Iran

DOI: https://doi.org/10.21203/rs.3.rs-31437/v1

Abstract

Background: To inform the researchers about the methodology and results of epidemic estimation studies performed for COVID-19 epidemic in Iran, we aimed to perform a rapid review.

Methods: We searched for and included published articles, preprint manuscripts and reports that estimated numbers of cumulative or daily deaths or cases of COVID-19 in Iran. We found 23 articles or reports and included eight articles (four published, four preprints) and three reports.

Results: The included studies provided outputs for a total of 45 study-scenarios. Seven studies used 3-4 compartmental disease models. At the end of month two of the epidemic (2020-04-19), the lowest (and highest) values of predictions were 3762 (97445) for cumulative deaths, 60720 (1489201) for cumulative cases, and at the end of month four (2020-06-20), were 86931 (3002721) for cumulative deaths, and 1602592 (2917927) for cumulative cases.

Conclusions: Meticulousness and degree of details reported for disease modeling and statistical methods used in the included studies varied widely. Greater heterogeneity was observed regarding the results of predicted outcomes. Consideration of minimum and preferred reporting items in epidemic estimation studies might better inform future revisions of the available models and new models to be developed. 

Background

 “On 31 December 2019, the World Health Organization (WHO) China Country Office was informed of cases of pneumonia unknown etiology (unknown cause) detected in Wuhan City, Hubei Province of China” [1]. The disease was officially designated as coronavirus disease 2019 or COVID-19 by WHO on 2020-02-11 [2]. Due to the rapid outbreak of the disease worldwide, WHO characterized the situation as a pandemic on 11 March [3]. The first two confirmed cases of COVID-19 in Iran were officially reported on 2020-02-19 in city of Qom, by the Ministry of Health and Medical Education (MOHME) [4]. Since then, MOHME has officially reported number of cumulative and new confirmed cases, deaths, and recovered cases in a daily basis on press conferences. Those numbers are available by date on different web pages of the web site of the MOHME but are not compiled in one page. To our best knowledge, the most straightforward route to access daily and cumulative cases and deaths is the compilation of WHO situation reports or more comprehensive sources such as Johns Hopkins University dashboard for COVID-19 [5].

Number of cases and deaths, in addition to other characteristics of epidemic are very important in decision-making and disease control. However, official reports suffer from undercounting in all countries. A relatively high percentage of patients with COVID-19 are asymptomatic or have a mild form of the disease which increases the chance of remaining undiagnosed. The emerging nature of COVID-19 has aggravated undercounting, as many countries are not prepared for conducting enough tests; as a result, many of suspected cases or deaths may not ever be confirmed by standard laboratory tests. Epidemiological studies of COVID-19 and model-based predictions and estimations are useful in assessing transmission rates, predicting epidemic trends and fatality rates with the inclusion of different intervention, environmental (seasonality), and virologic (mutations) scenarios, and thus can help policymakers for informed decision making in a timely way [6].

Despite the potential role of epidemic modeling and estimation studies in predicting outbreak size and trend, multiplicity of factors influencing viral disease transmission, relative uncertainty of data on model parameters, shifting disease dynamics in the setting of evolving epidemic, and suboptimality of model building methods and reporting, have been known to limit predictive models’ usefulness. A systematic review on prediction models for covid-19 concluded that many of the models suffer from poor reporting, high risk of bias, and optimistic reporting of performance [7].

Since the beginning of the outbreak in Iran, researchers inside and outside the country have used models to estimate or predict the size and trajectory of the epidemic of COVID-19 in Iran. Some of these studies have been published in scientific journals, or available in their not yet peer-reviewed form or are presented as official or unofficial reports. We aimed to perform a rapid review mainly describing currently available COVID-19 estimates for Iran. We did not intend to scrutinize or criticize the studies or models at this point. Our objective was to review methods and results of COVID-19 epidemic estimation studies for Iran. The ultimate goal is to inform the audience, policy makers and researchers, for better decisions, as well as potential updates of their prediction or estimation studies or new studies being designed and conducted currently and in future. 

Methods

(1) Study design and outcomes of interest

This a rapid review, not a systematic review. The main outcomes of interest were the predicted values (and calendar dates) of (1) cumulative deaths, (2) cumulative cases, (3) daily deaths, and (4) daily cases of COVID-19 in Iran. Deaths are less dependent on testing than cases. Cumulative estimates do not show daily fluctuations. Daily estimates help demonstrate epidemic waves and peaks.  

(2) Place and time scope of target studies

We included all studies which their target population was or included Iran, and we found them from 2020-03-19 to 2020-04-12. Some of the included studies have separate estimates for subnational regions as well. There were other studies [8–10] that met our inclusion criteria, but we preferred to keep them for the next update(s) of this review, for the sake of time, urgency and practicality of the results.

(3) Search strategy and selection criteria

There is not a customized protocol for the items that must be available in a report of epidemic estimation, prediction or epidemic model. Carrasco et. al. proposed a CCPV protocol (Characteristics, Construction, Parameterization and Validation aspects protocol) to standardize the reporting of Influenza pandemic models [11]. The items included in the “transparent reporting of a multivariable prediction model for individual prognosis or diagnosis” (TRIPOD) gives a general idea of the reporting items, however it is not specifically for the reason of epidemic prediction/ estimation models [12]. Wynants et al. review of prediction models for diagnosis and prognosis of covid-19 infection, even though focusing the individual-level modeling, can be considered in reporting epidemic estimation studies [7].  

Based on our understanding of the TRIPOD statement, epidemic modelling literature, and the studies we reviewed, we think an epidemic estimation / prediction model is expected to report at least following items, that we call them ‘preferred reporting items’: (1) Epidemic start date and rationale, (2) Epidemic (disease) model type and description, (3) Statistical model type, description, and equation(s), (4) Model assumptions and their verification, (5) Model scenarios’ detailed description, (6) Validation process and findings, (7) List and sources of model parameters and input data, and (8) Model outputs preferably with uncertainty intervals for scenarios. Some of the reports that we found in our searching process were not the final versions; we included any study if met all the following ‘minimum reporting items’: (1) Provided estimates for at least one of the COVID-19 four outcomes of interests (cumulative deaths, cumulative cases, daily deaths or daily cases) in Iran in any period of time, (2) Provided a list of input data and their sources, (3) An explanation on methods of using input data and generation of model outputs was available.

Exclusion criteria were: (1) Absence of all four main outcomes of interest, or (2) Absence of all the preferred reporting items, or (3) Elaboration on a previous modeling or estimation study without the aim (or content) of updating or improving the previous estimates.

By “report”, we mean studies results of which were not published as a journal article (or pre-print), but were released as short or long reports, available on the internet or shared with researchers. There are differences between epidemic modeling, prediction, and estimation; Modeling studies use explicit disease models and statistical models. Prediction studies do not use explicit disease models but predict (project) the number of cases and or deaths in future. Estimation studies provide estimates of cases for a recent point in time. The common feature of all these three study types is that they provide estimates of cases and or deaths in at least one point in calendar time. For pragmatic reasons, we call all of them as estimation studies.

Study revisions / updates: From the start date of our study, 2020-03-19, up to 2020-04-12, we actively searched and checked for revisions or updates of studies and their published formats (from pre-print, to journal pre-proof, to final published article). Stating from 2020-04-12, any of such revisions or updates were not included, but kept for a next revision of this rapid review. For studies (or reports) with ongoing or multiple updates, such as Saberi [13] study, we decided to fix on one updated version, and keep the inclusion of further updates for a next revision of this review.

We searched PubMed and used Google Scholar and plain Google for articles (or reports) matching our study inclusion criteria. The used keywords were Iran, COVID, COVID-19, COVID 19, Corona, SARS-CoV-2, epidemic, outbreak, pandemic, case*, death*, fatal*, mortalit*, model*, estimat*, and predict*. The search syntax used in PubMed is shown in the Appendix. We performed the same search with keywords in Farsi in Google Scholar, and Google We also used studies or reports provided to us by our researcher colleagues. We report all the found, included, excluded, and ‘kept for later’ studies using PRISMA 2009 flow diagram (Moher et al., 2009) [14] in Appendix Figure 1 and Appendix Table  1.

(4) Data abstraction methods

We developed a spreadsheet for abstracting the items of methods and results from included studies – the items not restricted to the minimum required ones. Each study was reviewed independently by at least two authors, and discrepancies were resolved with involvement of a third reviewer. Two reviewers (MML and LJ) finalized the abstracted items for methodology of the target studies.

For abstraction of the results of the studies, we selected a set of six fixed calendar dates, and found and recorded the estimated / predicted values of main outcomes (cumulative deaths, cumulative cases, daily deaths, or daily cases) for each of those dates. To start with, we fixed the presumed epidemic start time on the date on which the first two cases were officially reported dead, on 2020-02-19 (1398-11-30 Hijri solar), although later official reports indicated the actual start date of the epidemic to be earlier. Rationale for this was that most of the studies used the official reports to start with, and most of the studies’ predictions also started from that date (2020-02-19).

We decided not to include the estimations for the end of the first, second and third weeks of the epidemic in our set of fixed dates, since we were already in month two of the epidemic, for sake of brevity, and considering the less robust nature of the predictions as early as the first month when the numbers were much smaller. The set of six fixed calendar dates were designated as the end of each Hijri solar month after the epidemic presumed start date, since the start date coincided with the last day of month 11 of the Hijri solar calendar, and as such, targeting the end of each solar month would enhance cross-study comparisons and further use of administrative data. We did not report predictions beyond the month six, for the sake of brevity, and given the more uncertainty regarding such longer-term intervention scenarios and outcomes. However, we demonstrated all the time span of the available predictions in our graphs to provide a visual overview. The six fixed dates, the ends of months one to six, are used and shown in table 2.

Most of the reviewed studies had considered more than one scenario for the progression of the epidemic, based on different intervention options. Some studies considered different statistical models for prediction of epidemic progress, and we treated them as study-models. Altogether, such arrangement provided multiple study-scenarios/models for which we abstracted the estimated / predicted values of the main outcomes. Occasional studies provided confidence limits for point estimates which were also recorded.

Besides the six fixed dates, we also abstracted the data for the following items for each study-scenario/model: (a) predicted date of every peak in daily / new / active cases or deaths, and (b) predicted date of ‘epidemic control’ or equivalent (with the same study’s criteria or definition of control). Predicted values of main outcomes were recorded for these dates. We also recoded the methods used for assessment of each statistical model’s validity (or fitness) and their findings.

Two reviewers (FP and MRH) abstracted the estimates from the target studies. For abstraction of the main outcomes’ values at the designated dates, we periodized our sources and methods as, (1) mention in article text and tables, (2) digitization of article graphs. We used a web-based plot digitizer, “WebPlotDigitizer 4.2” [15]. Our error in digitizing data was less than 5%, as measured using the following formula: error in digitizing = ((digitized value – mentioned value) / (digitized value)), where the mentioned value means the value was mentioned in study text or tables. We used the reported COVID-19 cases and deaths complied by the Johns Hopkins University for each calendar date, as equal to Iran’s official reported data compiled in WHO situation reports. For developing our graphs, we chose the median scenario/models for cumulative cases from each study in order to demonstrate the main level of the predictions. Only three studies estimated cumulative deaths, so that outcome could not be used for identification of median scenarios across all studies. In studies with even number of scenario/models, we chose the one (of the middle two) with the higher values of estimates. The same selected median scenario/models for cumulative cases were used to graph the outcomes where the predictions were available. We recorded the text, table number, or the graph number for each study where we extracted every single number or date used in our tables and graphs. “Additional file 2 - Target studies’ abstracted data” includes all the detailed data we abstracted from the studies, as well as detailed findings from the studies’ methods.

Results

General characteristics of reviewed studies: We found 13 articles and 10 reports and included eight published articles / preprint manuscripts [16–23] and three reports [13, 24, 25]. From the 13 found articles, three articles were kept for a next version of this review[8–10], one article was excluded because it was not an epidemic estimation study, and one article was excluded because of absent minimum necessary details on methods. From the 10 found reports, three were included, four were excluded because they were not an independent epidemic modeling study, three excluded because of absent minimum necessary details on methods, and one was excluded because it was not an epidemic estimation study. Appendix Table 1 shows the articles and reports found, included, excluded, and exclusion reasoning. Appendix Figure 1 shows the PRISMA studies flow diagram. Four of the eight reviewed articles were published (Moradi et al., Tuite et al., Zareie et al., Zhuang et al.) [18, 20, 21, 23] and the other four were in preprint phase at the time that we reviewed them (Ahmadi et al., Ghaffarzadegan et al., Muniz-Rodriguez et al., Zhan et al.) [16, 17, 19, 22]. All articles were written in English, and all reports were written in Farsi.

 

We report our findings following the ‘preferred reporting items’ mentioned above.

 

(1) Epidemic start date and rationale:

Five studies used the presumed official start date of 2020-02-19 (Ahmadi [16], Muniz-Rodriguez [19], Saberi [13], Zareie [21], Zhan [22]). One study (Moradi [18]) reported their estimates starting from 2020-02-20 without mention of the rationale. Ghaffarzadegan reported most of their estimates starting from 2020-01-02, based on unofficial reporting of suspected cases [17]. Haghdoost et al. designated their “Day-zero” as 2020-01-21 [Hijri solar date 1398-11-01], that is 20 days before the presumed official epidemic start date of 2020-02-19 [24]. Many of the predicted outcome values are zero or close to zero in the graphs prior to day 20 of the graphs. However, some of the graphs do seem to show non-zero values for cases or deaths before their day. They maintain that their start date of the epidemic in Iran (2020-01-21) was designated based on “available documentations and epidemiologic analyses”. Mashayekhi et al. did not mention their epidemic start date, and prediction graphs’ time axis showed day zero to 120 or 360 [25]. We made an assumption that their start date was 2020-02-19. Tuite et al. [20] and Zhuang et al. [23] did not use epidemic start date in their estimations.

 

(2) Epidemic (disease) model type and description:

Most of the studies used sort of compartmental model, however some of them, (Ghaffarzadegan, Zhan, Haghdoost and Mashayekhi) and were more detailed [17, 22, 24, 25]. Three studies used SIR models (Ahmadi [16], Saberi [13], Zareie [21]); three studies used  SEIR+ models (Ghaffarzadegan [17], Haghdoost [24], Zhan [22]), and (Mashayekhi [25]) used SLIR+  models, where S denotes Susceptible, E is for Exposed, I is for Infected, R for Recovered, L for Latent, and in any model with a + sign, there are other components for augmentation of model. Four studies did not use explicit disease models (Moradi [18], Muniz-Rodriguez [19], Tuite [20], Zhuang [23]).         

 

(3) Statistical model type, description, and equation(s): 

Some of the studies did not mentioned enough details about their statistical methods and did not clearly differentiate between the disease model and the statistical model. Ghaffarzadegan at al. [17], Haghdoost et al. [24], and Mashayekhi et al. [25] used dynamic models. Few studies provided formal representation (equation) of the model.

 

(4) Model assumptions and their verification:

None of the reviewed studies did explicitly mention all the assumptions, their verification methods, and results of the verification. Most studies did report some details about their assumptions.

 

(5) Model scenarios’ detailed description:

There were 45 total study-scenario/models for the 11 included studies. Scenario/models per study ranged from 1 to 12 (median 5). All but one of the studies formulated their scenarios based on planned interventions or natural phenomena that could affect disease transmission (seasonality conditions in Ghaffarzadegan [17]). Ahmadi started with statistical models and reasoned backwards about what intervention scenarios could match each statistical model [16].

 

Ghaffarzadegan had two policy effect scenarios with different levels of efforts to decrease contact rate as well as three seasonality condition options, that amounted to six total scenarios [17]. Haghdoost had four final scenarios, each with levels of isolation for the infected and suspected patients, as they maintained that “to postpone the heavy wave of the disease, the most effective tool is isolation of patients, in a way that the infected and suspected patients would have the least contact with healthy people”. In the early stages of model building, they modeled “the effects of people’s behaviour change and seasonality on disease transmission”, to show the basic or worst model. Then three intervention scenarios with different levels of isolations were added. The people’s behaviour change and seasonality scenarios end only in the basic or worst scenario with no intervention [24]. Mashayekhi has three scenarios, each with different levels of social [physical] contacts and observation of sanitation cautions. As such, Mashayekhi was the only study that considered two modalities of non-pharmacologic interventions [25]. Details of studies’ scenarios are presented in the Appendix.

The following items were not incorporated in any of the scenarios of the included studies: potential vaccine(s), potential pharmacological treatments, changes in case definition, changes in cause of death definition, possibility of reinfection, possibility of mutations or any change in virulence, prevalence of comorbidities and age-stratification of mortality. There were other factors which were considered in some of the studies, such as: testing availability and number of tests performed (Ghaffarzadegan [17]), interventions on social distancing / quarantine (Ghaffarzadegan [17], Haghdoost [24], Mashayekhi [25]), asymptomatic cases (Ghaffarzadegan [17], Mashayekhi [25]), seasonality (Ghaffarzadegan [17], Haghdoost [24]), completeness of reporting cases and deaths to MOHME (Ghaffarzadegan [17], Saberi [13]) and delays in reporting cases and deaths to MOHME (Ghaffarzadegan [17]).

 

(6) Validation process and findings:

Only one study fully reported their validation process and findings (Ghaffarzadegan [17]): out of sample prediction test RMSPE (Root mean square percentage error) and RMSE (Root mean square error, as well as sensitivity analysis. The model performed better in replicating cumulative cases of infection and death than recovered. Zhan [22] assumed that once the epidemic spreading profile of a given city in Iran was “related” to that of a given city in the historical archive, that observation permitted by virtue of the validity of another study to formulate their optimization to predict the epidemic progression in any given city in Iran. Zhuang [23] mentioned that their estimates with consistent with similar estimates by Tuite [20].

 

(7) List and sources of model parameters and input data:

List and sources of model parameters are available in the Supplementary electronic material (“Studies’ Methods” tab). In Haghdoost’s study, for number of deaths and cases to start with, assumptions were made that on day-zero, there had been 1080 persons exposed to the virus In Iran (including 75 in Tehran), from which 90 persons had become infected in Iran (including 5 in Tehran) [24]. Four studies (Tuite, Zhuang Haghdoost and Mashayekhi,) [20, 23–25] did not report using number of confirmed cases or confirmed deaths as model input. Among other studies, Ghaffarzadegan used other sources of data, including unofficial reports for number of cases and death and number of performed tests [17].

 

(8) Model outputs preferably with uncertainty intervals for scenarios:

No study predicted all four outcomes. The most frequently predicted outcomes ranked as cumulative cases (7 studies), daily cases (6 studies), cumulative deaths (4 studies), and daily deaths (1 study). One study predicted two outcomes (cumulative cases and cumulative deaths). Three studies predicted three of the four outcomes. Nine studies provided time-series estimates for number of infected cases and six studies for the number of deaths; two studies (Tuite [20] and Zhuang [23]) reported estimates of cumulative cases for a single point in time.

Forms of outcomes: The intended outcomes and the terminology used in the included studies for the same outcomes, varied across the studies. For daily cases, two distinct groups could be recognized: daily incident cases, and daily prevalent cases. Our designation of daily incident cases included “new cases” reported daily by MOHME, “new cases” predicted by Haghdoost [24], and “daily cases” by Zareie [21]. Our designation of daily prevalent cases included “current cases” (Ghaffarzadegan [17]), “maximum number of cases per day” (Haghdoost [24]), “daily cases” by Mashayekhi [25], and “daily active cases” by Saberi [13]. Active cases are the difference between total cumulative cases with cumulative number of deceased and recovered cases.

Time period of coverage for estimations are very different among the studies; Haghdoost and Mashayekhi covered the longest time periods [24, 25]. Tuite and Zhuang studies are merely based on number of cases originated in Iran and detected in other countries, and each of them provides only one estimate for the number of cases, not across the time [20, 23]. Saberi updated (and updates) the model outputs in a weekly basis; in each round of running model, the most recent data were used to update previous estimates [13].

Table 1 summarizes the findings regarding the methodology used in the reviewed studies. Table 2 shows the estimates of cumulative deaths. Table 3 summarizes the outcomes at the end of month two (2020-04-19) and month four (2020-06-20) after the official epidemic start date.   Estimates of cumulative cases, daily deaths and daily cases are demonstrated in Appendix Tables 2, 3, and 4 respectively. Appendix Table 5 demonstrates predictions of peak dates and values of outcomes, and Appendix Table 6 shows predictions of epidemic control dates and values of outcomes.

Figures 1 to 5 demonstrate the reported and estimated outcomes in median scenarios. Figures 1 and 2 show the cumulative deaths and cumulative cases respectively. Figure 3 shows the daily deaths. Figures 4 and 5 show the estimated daily prevalent cases, with and without the estimate form Saberi [13]. That estimate by Saberi, even in the median scenario, had high values compared to other studies. Appendix Figure 2 demonstrates the officially reported cumulative confirmed cases, deaths, and recovered cases, and Appendix Figure 3 shows the daily equivalents. To visualize the quantitative diversity of the studies’ results, we also graphed the reported and worst-scenario estimated cumulative deaths in Appendix Figures 4 and 5, with and without the estimate form Mashayekhi [25]. That estimate by Mashayekhi, was the most extreme prediction among all the studies.

Table 1. Reported items of methodology of the reviewed studies

 

Ahmadi [16]

Ghaffarzadegan [17]

Haghdoost [24]

Moradi [18]

Muniz-Rodriguez [19]

Mashayekhi [25]

Saberi [13]

Tuite [20]

Zareie [21]

Zhan [22]

Zhuang [23]

Situation of study

Published paper

medRxiv preprint

Full report (Farsi)

Published paper

medRxiv preprint

Summary report (Farsi)

Full online report

Published paper

Preprint version

medRxiv preprint

medRxiv preprint

Epidemic start date

20-02-19

20-01-02

20-01-21

20-02-20

20-02-19

20-02-19 [?]

20-02-19

N/M (a)

20-02-19

20-02-19

N/M (a)

Inputs: Population

N/M (a)

Yes

Yes

No

N/M (a)

Yes

N/M (a)

N/M (a)

N/M (a)

N/M (a)

Yes

Inputs: Cases

Yes

Yes

No

No

Yes

No

Yes

No

Yes

Yes

No

Inputs: Cases (source)

MOHME official reports

MOHME official reports; unofficial reports

NA (b)

NA (b)

MOHME official reports

NA (b)

MOHME official reports, WHO, Worldometers

NA (b)

MOHME official reports

MOHME official reports

NA (b)

Inputs: Deaths

Yes

Yes

No

Yes

No

No

Yes

No

No

Yes

No

Inputs: Deaths (source)

MOHME official reports

MOHME official reports; unofficial reports

NA (b)

MOHME official reports

NA (b)

NA (b)

MOHME official reports, WHO, Worldometers

NA (b)

NA (b)

Mazandaran province deaths (20-02-19 to 20-03-06)

NA (b)

Other input data

Number of cured [recovered] cases

Number of tests; detected infected travelers and travel data

         

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

Start day of output

20-02-19

19-12-31

20-01-21

20-02-20

20-02-19

N/M (a)

20-02-19

20-01-01

20-02-19

N/M (a)

20-02-01

End day of output

20-04-03

20-06-30

20-05-20

20-03-26

20-02-29

N/M (a)

21-02-02

N/M (a)

20-04-15

N/M (a)

20-02-24

Output length (days)

45

183

121

36

11

360

350

N/A (b)

57

N/A (b)

24

Place

Iran

Iran

Iran and Tehran capital city

Iran

Iran and 2 multi-province regions

Iran

Iran

Iran

Iran

Iran and some of the provinces

Iran

Compartmental model (c)

SIR (c)

SEIR+ (c)

SEIR+ (c)

No

No

SLIR+ (c)

SIR (c)

No

SIR (c)

SEIR+ (c)

No

Statistical method: name

Gompertz Differential Equation, VBDGE (d), Cubic polynomial least squared errors

Dynamic simulation model

Dynamic model

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

Dynamic model

Classical SIR (C) mathematical model in epidemiology with homogenous mixing assumption

N/M (a) (Fraser 2009 study was cited for methods)

3-steps model based on the SIR model

A data-driven prediction algorithm to find the most resembling growth curve from the historical profiles in China

Binomial distributed likelihood framework

R0 estimation results

1.75

2.72 (before starting the interventions)

7.24 (at the beginning), 2.58 (after interventions), 1.82 (conditional to isolation of 50% within 3 days)

Not used

Two methods: 3.6 and 3.58

Not used

2.37 (for the last 7 days before 20-03-21)

Not used

Not used

Not used

Not used

Scenarios: number

3 (e)

6 (f)

4 (g)

4 (h)

2 (i)

3 (j)

12 (k)

6 (l)

1

1

5 (m)

Other factors

No

Yes (n)

Yes (o)

No

No

Yes (p)

Yes (q)

No

No

No

No

Outputs: Cases, deaths, both

Both

Both

Both

Cases

Cases

Both

Both

Cases

Both

Cases

Cases

Model validation

No

Yes

No

No

No

No

No

No

No

Mentioned but not explained

Yes

 

 

Table 2. Predictions of cumulative deaths for the end of months one to six after the official epidemic start date (2020-02-19)

   

Date, Gregorian

20-03-19

20-04-19

20-05-20

20-06-20

20-07-21

20-08-21

   

Date, Hijri

98-12-29

99-01-31

99-02-31

99-03-31

99-04-31

99-05-31

Study 1st author

Scenario / model

Outcome

Value

Value

Value

Value

Value

Value

MOHME official [5]

N/A (a)

Cumulative deaths

1284

5118

··

··

··

··

Ahmadi [16]

M1 (b)

Cumulative deaths

1264

··

··

··

··

··

Ahmadi [16]

M2 (c)

Cumulative deaths

1322

··

··

··

··

··

Ahmadi [16]

M3 (d)

Cumulative deaths

1263

··

··

··

··

··

Ghaffarzadegan[17]

S1P1 (e)

Cumulative deaths

15317

44078

70462

95658

··

··

Ghaffarzadegan[17]

S1P2 (f)

Cumulative deaths

15317

41702

52937

66549

··

··

Ghaffarzadegan[17]

S2P1 (g)

Cumulative deaths

15317

44078

68383

85262

··

··

Ghaffarzadegan[17]

S2P2 (h)

Cumulative deaths

15317

41702

52937

60015

··

··

Ghaffarzadegan[17]

S3P1 (i)

Cumulative deaths

15317

44078

68383

80213

··

··

Ghaffarzadegan[17]

S3P2 (j)

Cumulative deaths

15317

41702

52937

57341

··

··

Haghdoost [24]

S0 (k)

Cumulative deaths

··

··

30700

··

··

··

Haghdoost [24]

S1 (l)

Cumulative deaths

3824

9107

13450

··

··

··

Haghdoost [24]

S2 (m)

Cumulative deaths

2796

6231

8632

··

··

··

Haghdoost [24]

S3 (n)

Cumulative deaths

··

··

6030

··

··

··

Mashayekhi [25]

S1 (o)

Cumulative deaths

759

10,316

11751

11857

··

··

Mashayekhi [25]

S2 (p)

Cumulative deaths

1285

33349

61322

77302

86931

92620

Mashayekhi [25]

S3 (q)

Cumulative deaths

11752

97445

612953

1819392

3002721

3562136

 

Table 3. Lowest and highest predictions at the end of month 2 (2020-04-19) and month 4 (2020-06-20) after the official epidemic start date (2020-02-19)

 

End of month 2 (20-04-19)

End of month 4 (20-06-20)

Outcome

Lowest

value

Study-Scenario

Highest

value

Study-Scenario

Lowest

value

Study-Scenario

Highest

value

Study-Scenario

Daily deaths

125

Mashayekhi[25] S1 (a)

7839

Mashayekhi [25] S3 (b)

5

Mashayekhi[25] S1 (c)

44934

Mashayekhi[25] S3 (d)

Cumulative deaths

3762

Ahmadi [16] M5 (e)

97445

Mashayekhi [25] S3 (f)

86931

Mashayekhi[25] S2 (g)

3002721

Mashayekhi[25] S3 (h)

Incident daily cases

6934

Haghdoost [24] S3 (i)

13460

Haghdoost [24] S1 (j)

··

No study (k)

··

No study (l)

Prevalent daily cases

370

Mashayekhi[25]  S1 (m)

1472165

Saberi [13] S3P50 (n)

655

Mashayekhi[25] S1 (o)

17479235

Saberi [13] S2P80 (p)

Cumulative cases

60720

Ahmadi [16] M4 (q)

1489201

Ghaffarzadegan [17] S2P1 (r)

1602592

Ghaffarzadegan [17] S3P2 (s)

2917927

Ghaffarzadegan [17] S1P1 (t)

 

MOHME: Official reports of MOHME for cumulative deaths and cases at 2020 05 05 were 6418 and 101970 respectively, with highest peaks with 158 daily deaths and 3186 daily cases [5].

Cumulative deaths: Lowest and highest predicted cumulative deaths for the end of the second months were 3762 and 97445, and at the end of month four were 11857 and 1819392 respectively.

Cumulative cases: Lowest and highest predicted cumulative cases for the end of the second month were 60720 and 1489201, and at the end of month four were 1602592 and 2917927 respectively

Daily deaths: Lowest and highest values of predicted highest peak of daily deaths (and their dates) were 443 (27-03-2020) and 44,934 (2020-06-20). Only Mashayekhi showed daily deaths predictions [25].

Daily cases: Lowest and highest predicted prevalent daily cases for the end of months 2 were 370 and 10125068, and at the end of month 4 were 5020 and 17146193 respectively. For months 1, 2, and 3, the highest number of predicted incident daily cases was in best scenario of Haghdoost at the end of month 2 (13460 new cases) [24], whereas the MOHME reported 1343 new cases for that date. The lowest was their worst scenario at the end of month 2 (2,272 new cases).

The highest number of predicted prevalent daily cases was in Saberi’s scenario S2P80, with about 17.5 million cases (17479235) as of end of month 4 [13].  Sabri’s estimates had the highest values across all studies within each month: S3P50 for month 1 (1472165), S3P80 for month 2 (10125068) and month 3 (17115184), and S1P80 for months 5 and 6 (17146193 and 7887213) [13]. The highest number of predicted prevalent daily cases was in Mashayekhi best scenario, month 2, with 370 cases. The highest number of predicted prevalent daily cases predicted by Mashayekhi was in worst scenario of Mashayekhi with about 3.5 million cases (3506023) as of end of month 4. It is notable how their numbers of symptomatic and asymptomatic cases compare across scenarios and across months. Lowest and highest predicted prevalent daily cases for the end of months two were 370 (Mashayekhi best scenario) and 10125068 (Saberi worst scenario), and at the end of month 4 were 5020 (Mashayekhi second best scenario) and 17146193 (Saberi’s S1P80 scenario) [13, 25].

Peak dates and control dates: Predicted highest peak value (and date) was 44934 (2020-06-20) for daily deaths, 15239 (2020-04-11) for incident daily cases, 17930000 (2020-06-16) for prevalent daily, and 70711000 (2020-06-25) for cumulative cases synchronous with predicted highest peak of prevalent daily cases. In Haghdoost [24] study, the three peak dates where the same in three scenarios. Values of predicted incident daily cases were similar for the first and second peaks, but for the third peak, the value decrease sizably from scenario 1 (15172) to scenario 3 (9223). Mashayekhi [25] and Ghaffarzadegan [17] also predicted more than one peak.

Three studies predicted the epidemic control (or end) dates and outcome’s values. Two studies predicted the potential date for epidemic to be controlled in April; Ahmadi et al. predicted the “end of the epidemic” on 2020-05-13 with 87000 cumulative cases or on 2020-06-01 with 4900 cumulative deaths (using Von Bertalanffy model) or 11000 cumulative deaths (using Gompertz model) [16]. Haghdoost predicted that with their either medium or best scenarios, the epidemic would be well controlled in month 2 of Hijri solar year 1399 (2020-04-20 to 2020-05-20). Their ‘maximum number of infected people in day’ would be 92100 in middle scenario and 9150 in best scenario [24]. Zhan et al. predicted that if the “authorities continue to impose strict control measures, the epidemic will come under control by the end of April and is expected to end before June 2020, and as the quality of treatment improves, more rapid recovery will be expected” [22]. Beyond the correspondent values of the predicted outcomes, no further criteria or definition of epidemic end or control was provided.

Discussion

There were lots of heterogeneity in methods and findings of the COVID-19 prediction models and estimation studies for Iran. After the presumed official start date of the COVID-19 epidemic in Iran, i.e. 2020-02-19, and at the end of month two (2020-04-19), the lowest (and highest) values of predictions were 3762 (97445) for cumulative deaths, 6934 and 60720 (1489201) for cumulative cases, and at the end of month four (2020-06-20), they were 86931 (3002721) for cumulative deaths, and 1602592 (2917927) for cumulative cases.

The epidemic start date and the reported number of cases and dates are the most important starting points for epidemic estimation studies. The presumed official start date of the COVID-19 epidemic in Iran was 2020-02-19, when the first two tandem cases were reported as dead. Report of the first case or cases as dead on the same date they were diagnosed is not the most frequent type of reporting in this pandemic. Haghdoost et al. study, dated 2020-03-15 [1398-12-25 Hijri solar], maintains that their start date of the epidemic in Iran for their modeling purpose was designated as 2020-01-21 [1398-11-01 Hijri solar] based on “available documentations and epidemiologic analyses” [24]. No description or references were provided for their “available documentations and epidemiologic analyses”. Two days later, MOHME announced in 2020-03-27 that the epidemic had probably started in month 11 of Hijri solar year 1398 (2020-01-21 to 2020-02-19) [26]. As such, the models that use the official start date of 2020-02-19 start with an inaccurate start date of the epidemic to begin with.

WHO Country Support Mission to Iran (2-11 March 2020) reported the following: “On 20 February, the Islamic Republic of Iran IHR [International Health Regulations] National Focal Point (IHR-NFP) notified WHO of five cases, including two deaths, of laboratory-confirmed COVID-19 cases. Three of the cases were from Qom City, and the fourth had a travel history to Qom. In the following days, the investigation concluded that the virus was probably circulating in Qom for several weeks, based on the following observations: Among 186 patients with severe acute severe acute respiratory infection (SARI) hospitalized during February, 8 deaths were observed (0 deaths for the same month last year). Samples taken in February in patients with influenza-like illness (ILi) symptoms that tested negative for Influenza were also tested for COVID-19. Among workers of the Salafchegan free zone located 50 km from Qom city centre, 5 tests were positive for COVID-19; their onset of symptoms was 10 February. In late February, of 17 Chinese workers who had not traveled back to China for the Chinese New Year, 5 tested positive.” [27]. As such, most of the models start with an inaccurate start date of the epidemic to begin with, and most of the studies rely on the officially reported numbers of cases and deaths.

In addition to Ghaffarzadegan [17] that used both official and unofficial data as input, one study (Saberi [13]), also used correction factors of 5 and 10 taken from other sources or studies applied to the officially reported numbers of cases and deaths [27]. A correction factor of 20 has been recently proposed for the epidemic in Iran [28]. We do not know when or where were the results of the ‘investigation’ referred to in the above quoted “In the following days, the investigation concluded that …”, were announced or published. We do not know whether or how the reports of acute severe acute respiratory infection (SARI) or influenza-like illness (ILi) cases and deaths are publicly available for researchers. We made a few tries to look at such reports but were unsuccessful.

Undercounting is a known issue with the number of official confirmed cases and deaths, almost in all countries. Factors such as health system capacity for performing tests, access of people to testing services, on-time availability of test results, precision of diagnostic or screening tests, performance of surveillance systems, and transparency of health systems affect number of cases and deaths in official reports. In addition to such factors, SARS-CoV-2 itself has characteristics that might aggravate undercounting. A study on Santa Clara county in the United States revealed that prevalence, based on testing antibodies to SARS-CoV-2, is 50-85-fold more than the confirmed cases [29]. This pattern is different from other viruses of the Coronaviridae family, such as Middle East Respiratory Syndrome (MERS-CoV), with an estimated 25-50% asymptomatic to mild cases [30]. Some of the reviewed studies have estimated number of infected cases without excluding or mentioning asymptomatic cases. An implicit conclusion is that their numbers mainly refer to symptomatic cases, similar to the case mix of their input data. Different approaches have been used in studies to fix the issue; some of them have not used number of confirmed cases as an input (i.e. Haghdoost [24], Mashayekhi [25], Tuite [20] and Zhuang [23]), or have included asymptomatic cases or correction factors in their analysis (i.e. Ghaffarzadegan [17] and Saberi [13]). This might have a practical impact on epidemic control, because asymptomatic or undetected mild cases do have a role in disease transmission in one hand, as well as a potential role in achieving herd immunity.

Only a few studies (Haghdoost [24], Muniz-Rodriguez [19] and Zhan [22]) provided some sort of subnational estimates as well as national. Access to COVID-19 data at provincial and subnational level has obviously been an important limitation for most researchers. This threatens the usability of models. Naturally, all the provinces are not at the same stage of epidemic growth, they have different conditions that affect disease transmission and their capacities to respond to the epidemic are different. This means that centralized strategies for estimation of the epidemic extent and intervention options might not fit all the needs of the subnational levels. Spatial heterogeneity in propagation of epidemics should be taken into account [31]. MOHME has a subnational level defined between the national and the provincial levels; some of the studies or operational plans have used these conglomerates of provinces in Iran, labeled as “climes”, which share relatively homogenous epidemiologic profiles within the climes before the COVID-19 era. Such conglomerates of provinces in Iran, or newly designed conglomerates, might be considered usable in estimation of the epidemic propagation in Iran with a smaller number of subnational geographic units (i.e. climes), compared to studying all the provinces, which is more resource-intensive.

One of the models (Saberi [13]) was updated weekly by including new input data; this means that all estimations (from the start date of the epidemic) change in each version of running model. The usual explanation for this approach is to feed model with more input data that can improve prediction and provide an opportunity for improving methods as well. The Institute for Health Metrics and Evaluation (IHME) uses the same approach for updating model outputs, although their statistical approach is different [32].

Among the models that have used number of confirmed cases or deaths as input, only one study (Ghaffarzadegan [17]) has considered delayed diagnosis in their calculations; this might be quite important. The Wuhan municipal headquarters for COVID-19 epidemic prevention and control released a notification and revised the total number of fatalities up by around 50% to 3869 after reviewing all available sources of data [33]. There is a usual practice in many of death registries that physicians use more general terms as the final diagnosis or cause of death when they cannot or do not have time to match patients’ characteristics with exact definitions. It is more common in situations like epidemics that all health care workers are overwhelmed with number of patients and preoccupied with treating patients. Also, field hospitals and COVID-19 specific hospices might not be linked properly to health information systems to share data. Some cases that were initially classified under more general terms (such as pneumonia or acute respiratory syndrome) or even more specified but incorrect diagnoses (such as seasonal flu) might be re-classified to COVID-19 after reviewing all clinical data, test results, or autopsies. Only one study (Ghaffarzadegan [17]) used training and testing data for development and calibration of their models. Only three studies (Ghaffarzadegan [17], Haghdoost [24] and Mashayekhi [25]) estimated more than one peak for the epidemic. Prediction of time and magnitude of future waves of the epidemic is an important aspects of modeling studies. Also, there is an implicit assumption in all studies or models, that the socio-economic response capacity will remain constant over the timespan of the epidemic and the calendar time period for which estimations are performed; this is not necessarily correct.

As some of the studies have not mentioned enough details on their epidemic model and statistical methods, the largest gap was related to not mentioning the methods used to assess model validity, accuracy, or fitness, and the findings of these methods. Most of the studies have not provided any method used to verify model assumptions. Absence of reporting uncertainty intervals for model output was another downside in statistical methods. Poor reporting has been one of the common issues in many of the COVID-19 prediction and estimation studies [7]. Although guidelines such as TRIPOD are not specifically designed for this type of studies, but can be used as a base to remind researchers about the standards of reporting [12]. There have been a few systematic reviews on COVID-19 epidemiological studies. None of the studies included in this research were among the included studies in Park’s systematic review [34].

Three models (Ghaffarzadegan [17], Haghdoost [24] and Mashayekhi [25]) have considered scenarios to assess the effects of social distancing policies; this increases the usability of these models, however social distancing policies have a wide range of methods and effectiveness. They need to be clarified with more details in scenarios to increase practical usability of models for decision making. We expect to see postponement in the first peak date or reduction of its height (i.e. flattening the curve) in scenarios based on appropriate interventions [35]. Most of the studies have ignored availability and numbers of performed tests to detect COVID-19 cases; such data were not publicly available in the first few weeks after start of the epidemic in Iran. Only Ghaffarzadegan study [17] has included test coverage in the model. Evidence on potential seasonality effects is not conclusive yet, but it has been proposed by some researchers [36, 37]. This factor has been considered in two of the models [17, 24].

Non-COVID causes of mortality and morbidity are also important in epidemic modeling and intervention planning. Increase in non-COVID all-cause mortality and morbidity is a tandem phenomenon running alongside the COVID epidemic, that goes on with less drawn attention compared to the epidemic. European mortality monitoring (EuroMOMO) Network has assessed excess all-cause mortality overall for the participating European countries and estimated a marked increase [38]. As the cold season will come, taking into account the influenza season in estimations, and in particular regarding the caseload burden to be imposed on the health care system. Some COVID cases or deaths might be misclassified as non-COVID Acute Respiratory Distress Syndrome (ARDS), influenza, or pneumonia, and analyses of expected levels of such cases deaths could illuminate and improve COVID estimates. Some countries have expedited release of reports of provisional counts of death and excess deaths in January to March 2019 and January to March 2020, e.g. Iran [39]. Excess deaths in provinces of Iran have been assessed in non-peer-reviewed report [40].

Models differ in their mathematical configuration, designated start date of the epidemic for a given population, use of parameter values as input (e.g. Basic reproduction number, R0; or Case Fatality Ratio, CFR), use of data with varying time lengths to calibrate the model, and interventions formulated in scenarios. As Panovska-Griffiths explains, the ultimate questions of “can mathematical modelling solve the current Covid-19 crisis” or “which model is correct” evolve to realization that “no one model can give all the answers” and that “we need more models that answer complementary subquestions that can piece together the jigsaw and halt COVID- 19 spread” [41]. Estimation and use of a single correction factor for both the cases and deaths all across the time for any given country assumes invariance of diagnosis, detection and reporting completeness for infections and mortality during the epidemic; which is not necessarily true.

We believe that increasing public access to data on number of confirmed or suspected cases (and their outcomes of death and recovery), healthcare utilization by patients with COVID-19 (such as hospital admission, intensive care utilization and performed tests) both at national and province levels may improve accuracy and usability of models, and eventually lead to prevention of more cases and deaths. Estimates of cumulative deaths are less dependent on testing compared to cumulative cases. Estimates of daily deaths and daily cases look at possible waves and peak heights. Estimation of all these four outcomes can depict a better trajectory and extent of the epidemic. We suggest researcher to consider subnational estimations, as well as factors such as non-pharmacological interventions, test availability, age-stratification, and delayed diagnosis for updating their models. Some of the reports do not have enough details on methods and results that reduces their usage in disease control. We recommend researchers to consider standard items for reporting their models to increase practical use of the findings.

Study limitations

As mentioned, we did not include at this time 3 studies due to time constraints, as well as three set of subnational predictions. Some of the studies or reports may have had versions or updates beyond 2020-04-12, the date we were obliged to stop active searching for target studies (or reports) and any updated or finalized version of them in order to be able to report on first round of our findings here. We did not assess other outcomes such as utilization of intensive care unit (ICU) beds. We will try to update this rapid review by including all eligible studies and using the final versions of COVID-19 model. We are aware that some of the reports or pre-print versions of the manuscript might change in the future; the version of reports that have been used for this rapid review have been cited. We did not have access to the full report of one of the studies (Mashayekhi [25]). In the abridged study report we found, we noticed some discrepancies in outcome prediction values in different graphs [25]. None of our digitized outcome predictions reported here are 100% accurate. All of them are wrong in terms of having non-prefect accuracy. However, the error range is mostly around 1 or 2 percent points and below 5% for almost all instances.

Conclusions

We believe that COVID-19 models which consider scenarios for policy options, include key influencing factors such as testing availability and delayed diagnosis, and provide estimates for subnational regions are more useful for epidemic control. Increasing public access to COVID-19 related data is very important for improving quality of models and enhancing evidence-informed decisions to prevent more deaths. To increase the usability of reports, researchers should consider requirements of reporting a prediction or estimation model.

Abbreviations

CCPV

Characteristics, Construction, Parameterization and Validation

CFR

Case Fatality Ratio

COVID-19

Coronavirus Disease 2019

ICU

Intensive Care Unit

IHME

Institute for Health Metrics and Evaluation

IHR-NFP

International Health Regulations - National Focal Point

Ili

Influenza-like illness

MERS-CoV

Middle East Respiratory Syndrome - CoronaVirus

MOHME

Ministry of Health and Medical Education (of Iran)

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

R0

Basic reproduction number

SARI

Acute Severe acute Respiratory Infection

SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2

TRIPOD

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis

WHO

World Health Organization

Declarations

Ethics approval and consent to participate

All data are publicly available on an aggregate basis. Consequently, no ethics approval was necessary.

Consent for publication

Authors have full authority to publish the findings of this study.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

Competing interests

The authors declare that they have no competing interests.

Funding

No funding.

Authors' contributions

MML and FP conceived the study. MML and LJ produced Table 1. FP developed the data abstraction spreadsheet, reviewed and digitized the studies’ graphs and produced Table 2-3 and Appendix Tables. FP and MML drafted the manuscript. MRH reviewed and digitized the studies’ graphs. All authors contributed to finding the studies, reviewed three to all of the articles, and contributed substantial input to finalization of results and the manuscript. All authors read and approved the manuscript. 

Acknowledgements

Not applicable.

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