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. 2021 Aug 19;16(8):e0256516. doi: 10.1371/journal.pone.0256516

Effects of government policies and the Nowruz holidays on confirmed COVID-19 cases in Iran: An intervention time series analysis

Ali Hadianfar 1,2, Razieh Yousefi 1,2, Milad Delavary 3, Vahid Fakoor 4,*, Mohammad Taghi Shakeri 5, Martin Lavallière 3
Editor: Siew Ann Cheong6
PMCID: PMC8376046  PMID: 34411182

Abstract

Background

Public health policies with varying degrees of restriction have been imposed around the world to prevent the spread of coronavirus disease 2019 (COVID-19). In this study, we aimed to evaluate the effects of the implementation of government policies and the Nowruz holidays on the containment of the COVID-19 pandemic in Iran, using an intervention time series analysis.

Methods

Daily data on COVID-19 cases registered between February 19 and May 2, 2020 were collected from the World Health Organization (WHO)’s website. Using an intervention time series modeling, the effect of two government policies on the number of confirmed cases were evaluated, namely the closing of schools and universities, and the implementation of social distancing measures. Furthermore, the effect of the Nowruz holidays as a non-intervention factor for the spread of COVID-19 was also analyzed.

Results

The results showed that, after the implementation of the first intervention, i.e., the closing of universities and schools, no statistically significant change was found in the number of new confirmed cases. The Nowruz holidays was followed by a significant increase in new cases (1,872.20; 95% CI, 1,257.60 to 2,476.79; p<0.001)), while the implementation of social distancing measures was followed by a significant decrease in such cases (2,182.80; 95% CI, 1,556.56 to 2,809.04; p<0.001).

Conclusion

The Nowruz holidays and the implementation of social distancing measures in Iran were related to a significant increase and decrease in COVID-19 cases, respectively. These results highlight the necessity of measuring the effect of health and social interventions for their future implementations.

Introduction

As a global pandemic, COVID-19 has resulted in 403,080 deaths [1, 2] and 7,028,020 confirmed cases as of June 7, 2020. The first confirmed COVID-19 cases in Iran were reported in Qom on February 19, 2020. Shortly after this, COVID-19 cases were reported in other Iranian cities, and the country is still heavily impacted by this pandemic as of June 8, 2020 with 175,927 confirmed cases and 8,351 deaths [1].

COVID-19 can lead to severe acute respiratory distress syndrome (ARDS), anemia, secondary infection, acute cardiac injury, fever, fatigue, dry cough, and ultimately, to death [3, 4]. Due to these serious symptoms, countries have implemented COVID-19-related policies to limit the spread of the disease and prevent the exhaustion of the national health system’s resources and capacities [5]. However, there are a few studies about the impact of such government policies on the number of COVID-19 cases, and questions remain about the impact of such measures on case numbers. Siedner et al. used intervention time series analysis to investigate whether the implementation of social distancing measures was associated with a reduction in the mean daily growth rate of COVID-19 cases in US states. Their results showed that social distancing measures were associated with a decrease in pandemic growth [6].

Time series analysis has been used to model trends in the prevalence and incidence of COVID-19 cases registered in the Johns Hopkins epidemiological database (https://coronavirus.jhu.edu/) [7]. Similarly, Soudeep et al. proposed a time series model to analyze the trend pattern of COVID-19 incidence [8], and Petropoulos et al. provided statistical forecasts for confirmed cases of COVID-19, using robust time series models [9]. In Iran, Moftakhar and Seif predicted the number of newly infected patients using the ARIMA model on March 20, 2020, anticipating 3,574 cases by April 20, 2020 [10]. Jamshidi et al. applied a model for COVID-19 prediction in Iran based on China’s parameters. According to their prediction, the expected cumulative number of confirmed cases in Iran could have reached 29 000 from March 25 to April 15, 2020 [11]. Time series analyses can also detect change points and assess the influence of interventions. Change points are abrupt changes that represent transitions occurring in time series data [12]. An interventional analysis is useful when the exact effect of interventions is of interest. In other words, the analysis aims to predict or identify an intervention and its related effects using data, by applying a time series analysis [13].

In Iran, the government implemented health policies for COVID-19 and applied social distancing rules to limit its transmission. Amid the pandemic, Iran celebrated the Nowruz holidays (the New Year in Iran, March 20 to May 2, 2020), a time when people usually visit elderly relatives. This elderly population represents the most at-risk population for severe disease and death if infected with COVID-19 [14]. It was expected that the outbreak of COVID-19 would encourage people to stay at home. Despite all warnings, Iranians started their Nowruz travels inside the country, leading to a high incidence of COVID-19 disease in the Northern provinces [15]. In their study, Heidari and Sayfouri showed that Persian Nowruz aggravated the COVID-19 crisis in Iran [16]. Besides social distancing measures, the effect of national holidays in countries affected by COVID-19 has not been well-researched.

Evidence suggests that multiple factors can influence the number of COVID-19 cases in Iran. We employed a time series analysis to analyze the trend of new COVID-19 cases in Iran following the establishment of social distancing directives and related government policies and the Nowruz holidays (Iranian New Year, also known as the Persian New Year that usually occurs on March 21st) to case numbers.

Materials and methods

The collected dataset included the new confirmed cases of COVID-19 in Iran from February 19 to May 2, 2020. The dataset was obtained from the daily reports of the Iranian Ministry of Health and Medical Education (https://behdasht.gov.ir/), which is identical to the COVID-19 data published on the WHO website (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/), which aggregates case data from national authorities. Summary of statistics including mean, standard deviation, minimum, maximum, skewness and kurtosis are presented in Table 1.

Table 1. Descriptive statistics of new confirmed cases per day of COVID-19.

Statistics Estimate
Mean 1303.4
Std. dev 845.2
Min 2.0
Max 3186.0
Skewness 0.472
Kurtosis 2.67
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Interventions

In Iran, different health policies have been implemented to control the spread of COVID-19 [5, 17]. The first intervention enacted in Iran was the closing of kindergartens, schools, and universities. Although emphasis was put on the importance of handwashing, wearing a mask, and staying at home, most of the population did not take these measures seriously. The second intervention that was comprised of new social distancing measures was launched on March 27, 2020, and the police were in charge of enforcing this. The entry of traffic into the cities was restricted to their residents, only. People were required to return to their homes, as many Iranians had traveled to other provinces during the Nowruz holidays. Also, all new travel outside of the cities was banned for non-essential purposes, and an automatic vehicle seizure of 23 days, and 5,000,000 IRR fines (approximately US$ 35) were imposed for travel ban offenders [17]. On April 17, 2020 Iran decreased social distancing measures and focused on the Smart Social Distancing Plan [18]. This plan was in line with social distancing and provided conditions for society to gradually return to normal [16]. After May 2nd, Iran reached the disease management phase, expanded its active case finding program, implemented contact tracing, and tested those in close contact with COVID-19 patients.

Statistical analysis

An autoregressive integrated moving average (ARIMA) is a powerful tool to forecast a time series model [19], and a seasonal auto-regressive integrated moving average (SARIMA) model is used when a seasonal component is involved [20]. SARIMA, firstly, proposed by Box and Jenkins in the 1970s [19]. It is presented as SARIMA (p, d, q)(P, D, Q)S, where p is the order of auto-regressive (AR), q is the order of moving average (MA), d is the order of the differences. The ACF and PACF are used for knowing the order of AR and MA. Also, based on the trend and season of time series, the order of differences will be recognized. Moreover, P, D, and Q are the corresponding seasonal orders, with S as the steps of the seasonal differences [19]. The effect of the intervention, a one-off event affecting the new confirmed cases variable, was analyzed by using intervention time series analysis. Introduced by Box and Tiao, intervention time series analysis is an approach for handling the effectiveness of interventions in a dynamic regression framework [21]. Although it is assumed that an intervention can only happen at a specific time, its effects can spread over time. Fig 1 indicates examples of these data points, over time. In this study, three types of interventions, including step, delayed (linear trend), and decayed (exponential trend) response, were considered to evaluate the impact of the Nowruz holidays and government policies (Fig 2).

Fig 1.

Fig 1

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Three type of interventions used in dynamic regression; step response (a), delayed response (b), and decayed response (c).

Fig 2. Timeline of COVID-19-related events in Iran, from February 19 to April 19, 2020.

Fig 2

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Red dots present events in the COVID-19 outbreak, blue dots present control measures implemented by the Iranian government.

Fig 3 shows the time series of newly confirmed COVID-19 cases in Iran from February 19 to May 2, 2020. Two government policies were considered as two separate interventions. The first policy was the closing of schools and universities (CSU), which was implemented on March 1, 2020. And on March 27, 2020, when new legislation came into force requiring the social distancing measures (SDM) to be implemented (second policy) and enforced by the police.

Fig 3. The times series of new confirmed COVID-19 cases in Iran from February 19, 2020 to May 2, 2020.

Fig 3

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Yellow, Red and Blue vertical lines indicate time of implementation of Closing schools and universities (CSU), Nowruz holidays and implementation of new social distancing measures (SDM), respectively.

For time series modeling, it is assumed that the intervention occurs at a time point, say ‘τ’, where a dummy variable can be considered 0 before the intervention and 1 after the intervention [22]. This is called a step intervention. Fig 3 shows the effect of the CSU and SDM on the series in x using these dummy variables with τ1 and τ2 corresponding to March 1, 2020 and March 27, 2020, respectively. The model is then,

Yt=β0+β1CSUt+β2Nowruzholidayst+β3SDMt+et (1)

, where Yt represents the outcome variable overtime point t, which is considered the number of confirmed COVID-19 cases. The value of β0 is the baseline level of the response variable (also the initial value at t = 0). β1 and β3 represent the effects of the drop in newly confirmed COVID-19 cases because of the CSU and SDM interventions. Also, β2 represents the effect of the Nowruz holidays in increasing the number of new confirmed COVID-19 cases. Furthermore, a SARIMA model was used for the error term et that must follow the white noise. We computed 95% confidence intervals based on Z test.

Plots of the autocorrelation function (ACF), partial autocorrelation function (PACF), and Ljung–Box test were proposed for determining uncorrelated residuals [23]. Furthermore, residual plots were used to assess the zero-mean assumption, and the normality of residuals was evaluated using the Kolmogorov-Smirnov test. All the model developments, computations, and comparisons were performed using the R forecast package, and the statistical significance level was set at P-value less than 0.05.

Ethical statement

The data was provided by the Iranian Ministry of Health and is publicly available online on the WHO website. Therefore, ethical approval was not required.

Results

After detecting the first COVID-19 case on February 19, 2020 in Iran, the daily number of new confirmed cases rose gradually to 1,046 until March 19, 2020. With the beginning of the Nowruz holidays on March 20, the number of new confirmed cases increased sharply with a three-day delay, exceeding 3,000 at the maximum point on March 30 (see Fig 3 above).

As shown in Table 2, the mean number of new confirmed cases in Iran was 645.47 cases per day (95% CI, 78.32 to 1,212.62; p = 0.03) before March 2, 2020 (before the interventions). A significant linear increment in new confirmed cases was observed, which was about 0 to 1,872.20 (95% CI, 1,257.603 to 2,486.79; p<0.001), after the Nowruz holidays. Furthermore, with a three-day delay, a linear increase in COVID-19 case numbers was observed for eight days after the beginning of the Nowruz holidays.

Table 2. The effects of the government policies and Nowruz holidays on the daily confirmed new COVID-19 cases, SARIMA models.

Output Estimate SE P–value 95%CIc Ljung–Box Kolmogorov–Smirnov
Intercept 645.47 289.36 0.03 (78.32, 1212.62)
Effect (CSU a ) 130.0 172.87 0.45 (-208.82, 468.83)
Nowruz holidays 1872.20 313.57 <0.001 (1257.603, 2486.79)
Effect (SDM b ) 2182.80 319.51 <0.001 (1556.56, 2809.04)
Noise (1,0,0)(1,0,0) 0.18 0.96
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a Closing schools and universities (CSU), March 1, 2020

b New social distancing measures (SDM), March 27, 2020

c Confidence interval (CI) were obtained using a Z test.

The results showed no significant change in the number of new confirmed cases after the implementation of the first intervention (CSU). Whereas, after implementing the second intervention (SDM), new daily confirmed cases decreased significantly (p<0.001) from 2,182.80 (95% CI, 1,556.56 to 2,809.04) to 1,343 during the intervention period.

The new confirmed COVID-19 cases model is as follows:

Yt=645.47+130CSUt+187.2Nowruzholidayst+218.8SDMt+et (2)

, where et~SARINA(1,0,0)(1,0,0)14 and indicates a seasonal variation in the new daily cases of COVID-19 in Iran. There is a recurrent pattern of changes in the number of new cases within the period series. This season was 14 days, which shows that a uniform pattern happens every 2 weeks. This can be due to the disease’s incubation period, which has been documented at a maximum of 14 days [24].

It seems that the effect of social distancing rules had been significant, considering the gradual downtrend in the daily number of new cases. As shown in Fig 3, the daily number of cases exponentially decreased after March 27 for 23 days. In detail, this reduction was observed from March 27 to April 19, 2020, when the government removed the social distancing restriction.

For model diagnostics, the residuals should be white noise. In this connection, there was no pattern in the plot of residuals, and they were randomly scattered around zero. Also, there were no spikes in the autocorrelation and partial autocorrelation functions, indicating that there was no remaining autocorrelation regarding the residuals (Fig 4). Furthermore, the Ljung-Box (LB) test was utilized to understand whether any of the groups of autocorrelations of a time series are different from zero. As shown in Table 2, uncorrelated residuals were confirmed at the 5% significance level (p>0.05). Moreover, the Kolmogorov-Smirnov test established the normality of residuals. The goodness of fit statistics means absolute percentage error obtained was 1.88, which is highly accurate forecasting, based on the research study by Lewis [25].

Fig 4. Autocorrelation and partial autocorrelation functions of SARIMA residuals.

Fig 4

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Table 3 shows the prediction of Covid-19 new confirmed cases, with 95% confidence interval, in 7 days. It should be noted that the prediction can be utilized if the observed spreading pattern, and the number and type of test for detection the COVID-19 cases continues as before and if policies and restrictions are not removed. Therefore, the number of daily new cases would be 813 and 814 for May 3rd 2020 and May 4th 2020, respectively.

Table 3. Forecasted number of daily Covid-19 new cases with 95% confidence intervals.

Days (of 2020) Prediction
SARIMA 95% CI for SARIMA
Lower Upper
3-May 813 516 1110
4-May 814 410 1217
5-May 829 354 1304
6-May 815 287 1343
7-May 769 200 1338
8-May 822 221 1423
9-May 818 190 1445
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Discussion

COVID-19 is an infectious disease spread through direct contact between individuals [26]. Outbreak control measures implemented to diminish the contacts within the population can reduce the height of the peak, the speed at which the virus spreads, and the final scope of the pandemic. In Iran, different policies and strategies have been implemented, based on the experience and recommendations of China and the WHO to control the outbreak of COVID-19 [5, 17]. In general, we found a correlation between the national Nowruz holidays, the new social distancing measures and the number of newly confirmed COVID-19 cases in Iran. However, the closing of kindergartens, schools, and universities was not followed by a reduction in new cases.

The first government policy implemented by Iran to combat the outbreak of COVID-19 was the closing of kindergartens, schools, and universities. The result of our study shows that this intervention did not contribute to control the pandemic. A review study, including 16 studies about the effect of school closures during coronavirus outbreaks, indicates that their impact on the spread of COVID-19 is very weak [27]. Therefore, policymakers need to be aware of the uncertainty of evidence about the efficacy of school closures to slow down COVID-19, and may need to consider combinations of various distancing measures, instead.

The Nowruz holidays (an Iranian national holidays) created a significant increase in the number of COVID-19 cases, three days after its start. Due to the lack of personal protective equipment such as masks and disinfectants, as well as a high level of contact between people, the Nowruz holidays was an occasion for easy transmission of the disease in Iran [28].

Prior to the implementation of social distancing measures, the transmission rates of the COVID-19 infection in Iran were increasing. New social distancing measures were implemented in Iranian Provinces to reduce the risk of expansion of the pandemic. The most important part of the social distancing rules was travel restrictions and car seizure as well as 5,000,000 IRR (US$ 35) fines implemented on March 27, 2020. Our findings indicated that the implementation of the social distancing measures in Iran were effective in controlling the spread of the outbreak, and that the number of new daily COVID-19 cases significantly decreased after adopting these measures. Our results regarding the impact of social distancing policies on the number of COVID-19 cases support earlier findings on the effectiveness of such measures [6, 29, 30]. These studies have shown a decrease in the average daily new COVID-19 cases, once sanitary measures were implemented.

There are some potential reasons for the ineffectiveness of government policies to reduce the number of new cases. In Iran, like some other countries such as China [31], the outbreak coincided with a national holiday. We found a negative relationship between the Nowruz holidays and the number of cases, which might have dwindled the effectiveness of disease control measures. The general population was requested to stay at home and self-quarantine during the Nowruz holidays, as well as refrain from visiting their families. Therefore, the degree of the outbreak was expected to be manageable. This somewhat contradictory result may be because millions of Iranians traveled around the country. With the beginning of the Nowruz holidays, the police reported heavy traffic toward northern cities, therefore traveling might have exacerbated the spread of the outbreak. This finding corroborates the results of Heidari and Sayfouri, who suggested that Nowruz aggravated the COVID-19 crisis in Iran [16]. It should be noted that, by increasing the number of tests, diagnoses of COVID-19 were increased. It is valuable to mention that the rate of test increment was non-stop, even after social distancing. Therefore, the decrease of COVID-19 patients after the enforcement of social distancing cannot be attributable to a lack of access to testing or to improper distribution.

It is worth noting that, due to the rapidly increasing incidence trend of COVID-19, it is not only essential to design and implement rules but also to critically plan the moment of implementation of such measures. Late implementation of social distancing measures, such as in Italy, can lead to an exponential increase in the mortality rate in the population [32]. Previous studies have also indicated that earlier implementation of measures can be more productive. A recent study has shown that every one-day delay in the implementation of social distancing measures leads to a 2.41-day delay in containment of the pandemic [15]. The impact of delays may be particularly significant for communities that are prone to rapid disease transmission. For example, during the Nowruz holidays in Iran, people visited multiple relatives and many others used the two-week break to travel to tourist destinations across the country. Therefore, earlier implementation of restriction rules and prevention of non-essential travel could have made it easier to control the spread of the outbreak.

There are some limitations to the analysis conducted in this study. First, we only analyzed the available data related to the period from February 19 to May 2, 2020, as the Iranian Ministry of Health began its active case finding program during this period. Second, we did not have access to sub-groups and geographical data. These data could be valuable in determining the heterogeneity of the effect of social distancing in different subgroups and for different geographical areas. Moreover, there were also some variables, such as the level of access to the healthcare system, the changing diagnostic criteria of COVID-19, the people’s compliance to health rules, and preventive programs that could influence the effectiveness of social distancing.

Notwithstanding these limitations, the results of this study suggest that in general, government policies can have a significant influence on COVID-19 case numbers and can be used to control similar outbreaks. Our results also highlight the critical influence of national holidays on the spread of COVID-19 in Iran. Our findings should be considered for planning and implementing future measures. Future studies should also address the cost-benefit of these plans and other possible options when deciding on the implementation of such national measures.

Conclusion

This study evaluated the effects of two government policies and the Nowruz holidays on the number of new COVID-19 cases in Iran, using intervention time series analysis. The results indicated that the Nowruz holidays significantly increased case numbers. We also found that the implementation of social distancing measures as a non-pharmaceutical, and non-medical, intervention in Iran had a significant influence on reducing the new daily cases of COVID-19 and could effectively control the spread of the disease in Iran.

Supporting information

S1 Data

(XLSX)

Click here for additional data file. (22.5KB, xlsx)

Acknowledgments

The authors would like to thank Mr. Felix Siebert from the Technische Universität of Berlin for his substantial contributions in revising the manuscript.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

Cesar V Munayco

12 Feb 2021

PONE-D-20-30409

Effects of government policies and the Nowruz holidays on confirmed COVID-19 cases in Iran: An intervention time series analysis

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Cesar V Munayco, M.D, MSc, MPH, DrPH

Academic Editor

PLOS ONE

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4. Please amend either the abstract on the online submission form (via Edit Submission) or the abstract in the manuscript so that they are identical.

Additional Editor Comments:

This manuscript is interesting, but there are some issues in interpreting the results and their limitations. The authors must be sober and cautious when they value their findings.

It would be better if the authors would consider a counterfactual because there is no way to confirm that this epidemic trend will behave the same without these interventions. Most of the time, some areas do not fully accomplish all interventions, and these areas are perfect as a counterfactual.

The authors must give information about the preemptive measures' compliance to value them and evaluate their effect on the epidemic curve.

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

**********

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Reviewer #1: Yes

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Reviewer #1: The authors have conducting fairly straightforward analyses using highly aggregated surveillance data with known limitations to answer complex questions. Greater attention needs to be placed in the analytical approach and its appropriateness to the research question. Conceptually, authors need to better address the nature of epidemic growth and reduction, particularly taking into account the limitations of the data used. Finally, the authors make numerous statements attributing causality to their findings without carefully consideration of the causality principles and how their evidence supports them.

The ARIMA model intrinsically does not have growth restriction and in theory it could grow without limits, which does not correspond to reality, because during epidemics, even in large populations the number of susceptible individuals is eventually exhausted. This is conceptually erroneous when applied to the complete growth and reduction cycle of a pandemic, and it assumes that all the reduction cycle is due to the physical distancing measures, without taking into account the reduction of the susceptible individuals and its impact. It should be clarified if the autocorrelation is the only mechanism that accounts for the multiplicative growth pattern of the epidemic. Particularly because the use of pulse-like effects to estimate the potential effect of the control measures and the Norwuz holidays does not affect the autocorrelation or the slope of the curve.

Likewise, it should be clarified if the ARIMA model uses a Poisson or another distribution for count data such as cases, and what are the effects of that in the estimations.

Similarly, the authors do little to explore other potential explanations that could lead to the temporal correlations observed. Rarely a single measure takes place alone, and often there are multiple simultaneous efforts to mount a public health response that could also lead to the trends observed.

What is the precision of estimates, and is model fitness high enough that can serve to respond trend questions? No goodness of fit statistics are presented nor the observed and estimated results are presented, visually or in a tabulation.

Finally, no sensitivity or sub-group analyses are presented to document the robustness of the results and related conclusions. Most countries have shown highly heterogeneous sub-national epidemic expansions in the first wave of the pandemic, with the national pattern primarily reflecting trends in the capital or largest cities, and very different pattern in smaller or distant/less connected cities. Conclusions could greatly change when addressing these heterogeneity.

Conceptually, which of the three types of interventions better fits the interventions evaluated, and why were the others considered? Did the empirical results of the regression matched the expectation due to the conceptual type of intervention?

Could the closure of schools have a relatively smaller effect than expected and was not detectable with the methods used? What if this measure slowed down the decrease observed in mid-March 2020?

The abstract should present actual results to support the authors’ statements and conclusions, as p-values are insufficient for that purpose.

The case definition needs to be clearly described stating how testing availability and distribution countrywide affects the validity and proper interpretation of findings

How many days are expected to pass for interventions to start being effective? How was this lag introduced in the analyses?

**********

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PLoS One. 2021 Aug 19;16(8):e0256516. doi: 10.1371/journal.pone.0256516.r002

Author response to Decision Letter 0

14 Apr 2021

Professor. Cesar V Munayco

Academic Editor

PLOS ONE

SUBJECT: Resubmission of manuscript PONE-D-20-30409

Re: Effects of government policies and the Nowruz holiday on confirmed COVID-19 cases in Iran: An intervention time series analysis

Dear Editor,

We would like to thank you the attention brought to our manuscript. Also, we would like to thank the Associate Editor and the Reviewer for their careful reading of our manuscript and their insightful comments. We have reviewed our manuscript accordingly. The comments have been noted and we have done our best to revise the paper accordingly. Below is a point-by-point response to each comment raised by the Associate Editor and the Reviewer (original comments from reviewers appear in red, responses in black). It should be noted that the modifications in the manuscript are highlighted using track changes in Word. In addition, a final version of the manuscript, including all types of corrections, is attached.

We hope that the revised version of the manuscript will now meet the requirements of PLOS ONE.

Yours sincerely,

Vahid Fakoor

Associate Professor, Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad-Iran,

P.O.Box, 9177943369

Mobile: 0989151038414

E-mail: fakoor@um.ac.ir

Academic Editor:

We would like to thank you for your comments. Below please see our response to the comments and suggestions for which we have revised our manuscript.

Academic Editor’s Comments to the Author

Point-by-point responses to the issues raised by the Academic Editor:

1) raises questions about

• Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf

and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf.

ANSWER: The manuscript has been carefully modified in accordance with the PLOS ONE’s style requirements.

• We suggest you thoroughly copyedit your manuscript for language usage, spelling, and grammar. If you do not know anyone who can help you do this, you may wish to consider employing a professional scientific editing service.

Upon resubmission, please provide the following:

The name of the colleague or the details of the professional service that edited your manuscript

A copy of your manuscript showing your changes by either highlighting them or using track changes (uploaded as a *supporting information* file)

A clean copy of the edited manuscript (uploaded as the new *manuscript* file).

ANSWER: Thanks for your suggestion. We have sought the help of a professional editing service to review our manuscript and the documents requested are attached.

The editing service was provided by:

Micheline Harvey

Translator

Traduction – révision – correction – transcription/Translation – revision – correction – transcription

418-806-6794

www.secretairevirtuelle.com

Membre du Réseau des professionnels en soutien administratif virtuel RPSAV

• We note that there is a discrepancy within the manuscript regarding the dates from which data were collected, for instance between the abstract and the methods section. Please amend as necessary.

ANSWER: Thank you for your comment. We amended it in line 21, as required.

• Please amend either the abstract on the online submission form (via Edit Submission) or the abstract in the manuscript so that they are identical

ANSWER: Thank you for your comment. We amended it, as required.

Additional Editor Comments:

• This manuscript is interesting, but there are some issues in interpreting the results and their limitations. The authors must be sober and cautious when they value their findings.

ANSWER: Thank you for your constructive suggestion. We have modified some parts of the results section in lines 168-173, as follows:

As shown in Table 1, the mean number of new confirmed cases in Iran was 645.47 cases per day (95% CI, 78.32 to 1,212.62; p=0.03) before March 2, 2020 (before interventions). A significant linear increment in new confirmed cases was observed, which was about 0 to 1,872.20 (95% CI, 1,257.603 to 2,486.79; p<0.001), after the Nowruz holiday. Furthermore, with a three-day delay, a linear increase in COVID-19 case numbers was observed for eight days after the beginning of the Nowruz holiday.

The results showed no significant change in the number of new confirmed cases after the implementation of the first intervention (CSU). Whereas, after implementing the second intervention (SDM), new daily confirmed cases decreased significantly (p<0.001) from 2,182.80 (95% CI, 1,556.56 to 2,809.04) to 1,343 during the intervention period.

We also modified some parts of the discussion section in lines 220-223, as follows:

The Nowruz holiday (an Iranian national holiday) created a significant increase in the number of COVID-19 cases, three days after its start. Due to the lack of personal protective equipment such as masks and disinfectants, as well as a high level of contact between people, the Nowruz holiday was an occasion for easy transmission of the disease in Iran [28].

We also modified our limitations as follow in lines 262 to 269:

There are some limitations to the analysis conducted in this study. First, we only analyzed the available data related to the period from February 19 to May 2, 2020, as the Iranian Ministry of Health began its active case finding program during this period. Second, we did not have access to sub-groups and geographical-level data. These data could be valuable in determining the heterogeneity of the effect of social distancing in different subgroups and for different geographical areas. Moreover, there were also some variables, such as the level of access to the healthcare system, the changing diagnostic criteria for COVID-19, the people’s compliance to health rules, and preventive programs that could influence the effectiveness of social distancing.

• It would be better if the authors would consider a counterfactual because there is no way to confirm that this epidemic trend will behave the same without these interventions. Most of the time, some areas do not fully accomplish all interventions, and these areas are perfect as a counterfactual.

ANSWER: We agree with the editor’s comment. Although this is interesting, these interventions have been used simultaneously and uniformly everywhere in Iran, so that schools and universities were all closed simultaneously. Nowruz holiday is a national holiday throughout the country, as well as social distancing measures was as they were enforced by the police, and were applied in all the cities of Iran, so it is practically impossible to study the pandemic’s process without considering these interventions. Therefore, a counterfactual cannot be used as a comparison in this case.

• The authors must give information about the pre-emptive measures' compliance to value them and evaluate their effect on the pandemic curve.

ANSWER: Thank you for your comment. Regarding the use of masks as an important pre-emptive measure, during the first months of the pandemic, not only was the use of masks not mandatory, but the number of available masks did not even cover the needs of medical staff. Masks produced by the Ministry of Health were thus collected, rationed, and distributed to medical centers throughout Iran (1).

As the government rejected plans to quarantine entire cities and only urged people to stay at home, this rule was not followed in the early months of the pandemic, and offices and companies remained open, leaving only children, students, and a small number of people at home (2). On April 4th, Iranian officials expressed concerns that many had ignored the rules to stay indoors and to cancel travel plans (3).

Regarding hand washing, it was initially recommended to wear gloves at the beginning of the pandemic, but was later announced that gloves can also lead to transmission, and that it is better to wash hands with soap and water for 20 seconds instead of wearing gloves (4,5). However, most of the population of Iran did not take pre-emptive measures such as wearing a mask, washing hands, and staying at home seriously. We have added a summary of this in the manuscript at lines 95-96 and 221-223.

1. Raoofi A, Takian A, Sari AA, Olyaeemanesh A, Haghighi H, Aarabi M. COVID-19 pandemic and comparative health policy learning in Iran. Arch Iran Med. 2020;23(4):220–34.

2. "Coronavirus: Iran has no plans to quarantine cities, Rouhani says". BBC. 26 February 2020. Archived from the original on 26 February 2020. Retrieved 26 February 2020.

3. "Iranians defy coronavirus rules as death toll reaches 3,452". Arab News. 4 April 2020.

4. https://www.tehrantimes.com/news/446498/COVID-19-crisis-washing-hands-for-20-seconds-not-wasting-water

5. https://www.who.int/gpsc/5may/Hand_Hygiene_Why_How_and_When_Brochure.pdf

1) Reviewer #1: The authors have conducting fairly straightforward analyses using highly aggregated surveillance data with known limitations to answer complex questions. Greater attention needs to be placed in the analytical approach and its appropriateness to the research question. Conceptually, authors need to better address the nature of epidemic growth and reduction, particularly taking into account the limitations of the data used. Finally, the authors make numerous statements attributing causality to their findings without carefully consideration of the causality principles and how their evidence supports them.

ANSWER: We appreciate the reviewer's comments. We have tried to enhance the manuscript by carefully addressing these points. As mentioned above, we have modified the results, discussion, and mentioned data limitations (please see lines 254-259). As for the goal of the interrupted time series, i.e., analyzing the effectiveness of interventions in a dynamic regression framework, the current article’s research question is: To find the effect of three interventions on the daily number of COVID-19 cases. Thus, we have not sought to examine the causal relationship between interventions and the daily number of COVID-19 cases. Therefore, some statements about causality have been corrected in the manuscript in lines 168-182, 191-192 and 237-238.

2) The ARIMA model intrinsically does not have growth restriction and in theory it could grow without limits, which does not correspond to reality, because during epidemics, even in large populations the number of susceptible individuals is eventually exhausted. This is conceptually erroneous when applied to the complete growth and reduction cycle of a pandemic, and it assumes that all the reduction cycle is due to the physical distancing measures, without taking into account the reduction of the susceptible individuals and its impact. It should be clarified if the autocorrelation is the only mechanism that accounts for the multiplicative growth pattern of the epidemic. Particularly because the use of pulse-like effects to estimate the potential effect of the control measures and the Nowruz holidays does not affect the autocorrelation or the slope of the curve.

ANSWER: Thank you for your comment. First, the data used in this study covers the initial 74 days from the beginning of the pandemic. The reduction of susceptible cases is not significant for this period, based on the population of Iran. Also, it should be mentioned that according to the statistics, the number of COVID-19 cases that appeared after the study period were higher, which reinforces our previous argument. Based on this explanation, it is valuable to say that SARIMA can be a good model to measure the pandemic’s growth pattern. It should be noted that the error component in SARIMA modeling explains that time points have an autocorrelation besides pulse-like effects. This is good when there are unknown factors involving the response variable that cannot evaluated, based on limited data and information.

3) Likewise, it should be clarified if the ARIMA model uses a Poisson or another distribution for count data such as cases, and what are the effects of that in the estimations.

ANSWER: Thank you for your comment. In the ARIMA model, white noise (i.e. errors) suggests a normal distribution which is conventionally assumed in times series. Having normally distributed errors is equivalent to having normally distributed observations for any linear time series model. Although it is not necessary to assume normality of errors, maximum likelihood is often used to estimate the model’s parameters, followed by a Gaussian likelihood, which gives good results even with non-normal data. Normality of errors is often assumed when using the AIC for order selection, and when computing prediction intervals.

4) What is the precision of estimates, and is model fitness high enough that can serve to respond trend questions? No goodness of fit statistics are presented nor are the observed and estimated results presented, visually or in a tabulation.

ANSWER: Thank you for your constructive suggestion. The precision of estimates and the model fitness have been added to the manuscript, as suggested. In this case, the mean absolute percentage error (MAPE) is 1.88, which is a highly accurate forecasting based on the research study by Lewis (1982, p.40). We added to at the end of the results section, in lines 201 to 203.

5) Finally, no sensitivity or sub-group analyses are presented to document the robustness of the results and related conclusions. Most countries have shown highly heterogeneous sub-national epidemic expansions in the first wave of the pandemic, with the national pattern primarily reflecting trends in the capital or largest cities, and very different pattern in smaller or distant/less connected cities. Conclusions could greatly change when addressing these heterogeneities.

ANSWER: Thank you for your comment. There are some limitations to the analysis conducted in this study. First, we only analyzed the data related to the period between February 19 and May 2, 2020, as this is when the Iranian Ministry of Health began its active case detection program. Second, we did not have access to sub-groups and geographical-level data. These data could have been valuable to determine the heterogeneity of the effect of social distancing with different sub-groups and in different geographical areas. Moreover, there were also other variables, such as the level of access to healthcare, changing diagnostic criteria, the people’s compliance to health rules, and preventive programs that could influence the effectiveness of social distancing.

We do understand the reviewer’s concern regarding the possible heterogeneity of the results. However, and as suggested in the limitations section of the manuscript, such a precise analysis cannot be conducted due to the nature of the data, and its availability in Iran.

6) Conceptually, which of the three types of interventions better fits the interventions evaluated, and why were the others considered? Did the empirical results of the regression matched the expectation due to the conceptual type of intervention?

ANSWER: Thank you for your comment. The three types of interventions were evaluated to find the best fit on the data and, based on the nature of each law that is enforced, the type of intervention will be different. So, there is no single answer to the question of what the best type of intervention is. However, in this study, we evaluated the three types of interventions, as explained in the manuscript, to determine which one best fit the data. For instance, the delayed response (linear), and decayed response (exponential) were the best fits for the second and third interventions in the current study. For the second question, it was expected that tCOVID-19 would not have an impact immediately, without delay. Results therefore show that conceived interventions are the same as those hypothesized in the study, based on the nature of COVID-19.

7) Could the closure of schools have a relatively smaller effect than expected and was not detectable with the methods used? What if this measure slowed down the decrease observed in mid-March 2020?

ANSWER:

It seems that closing schools has no impact on the number of new COVID-19 cases. This is because the trend of time series before March 20th was not decreasing, it was even showing a growing trend. A review study shows that the school closures during coronavirus outbreaks had a very weak impact on the spread of COVID-19 (1). In other words, the implementation of this law did not reduce the incidence of new coronavirus cases. However, if we assume that without this intervention, the trend may increase more than the current increased rate, it is therefore valuable to say that this method has limitations. So, it is complicated to determine how the trend of time series may change without this intervention and it depends on several variables, especially at the start of the pandemic in Iran, because at this time, it was very difficult to assess all the data and variables surrounding the new COVID-19 cases.

1-Viner RM, Russell SJ, Croker H, et al. School closure and management practices during coronavirus outbreaks including COVID-19: a rapid systematic review. Lancet Child Adolesc Health. 2020;4(5):397-404. doi:10.1016/S2352-4642(20)30095-X

8) The abstract should present actual results to support the authors’ statements and conclusions, as p-values are insufficient for that purpose.

ANSWER: Thank you for your constructive suggestion. The results section of the abstract was modified in lines 27-32 as follows:

The results showed that, after the implementation of the first intervention, i.e., the closing of universities and schools, no statistically significant change was found in the number of confirmed new cases. The Nowruz holiday was followed by a significant increase in new cases (1,872.20; 95% CI, 1,257.60 to 2,476.79; p<0.001)), while the implementation of social distancing measures was followed by a significant decrease in such cases (2,182.80; 95% CI, 1,556.56 to 2,809.04; p<0.001).

9) The case definition needs to be clearly described stating how testing availability and distribution countrywide affects the validity and proper interpretation of findings

ANSWER:

In Iran, the first cases of infection were reported in Qom and later spread to other parts of Iran. It should be noted that the following programs were implemented simultaneously with the spread of the pandemic in Iran:

- Launching more than 20 COVID-19 diagnostic laboratories across the country on February 26, 2020. Also, an Iranian Health Ministry spokesman says the Islamic Republic is preparing for the possibility of “tens of thousands” of people showing up to get tested for the new coronavirus on February 29, 2020.

- Increasing the number of coronavirus diagnostic laboratories to 50 centers in Iran on March 5, 2020.

- Launching a self-reporting system to identify suspicious cases and control infected cases on March 11, 2020.

- Launching a new coronavirus detection lab at the Pasteur Institute of Iran and increasing its coronavirus diagnostic capacity to up to 1,800 tests per day on March 14, 2020.(1)

By increasing the number of tests, COVID-19 diagnoses have been increased. It is valuable to mention that the rate of test increment was non-stop, even after social distancing. So, the decrease in COVID-19 patients after this enforced distancing cannot be due to lack of access to testing, or improper distribution. We have added this clarification to the manuscript, in lines 245-249.

1. Raoofi A, Takian A, Sari AA, Olyaeemanesh A, Haghighi H, Aarabi M. COVID-19 pandemic and comparative health policy learning in Iran. Arch Iran Med. 2020;23(4):220–34.

10) How many days are expected to pass for interventions to start being effective? How was this lag introduced in the analyses?

ANSWER: Regarding this issue, and for the first question, authors believe that there is no explicit answer concerning the number of expected days to pass for interventions to start being effective. Based on this study, the best lag was found according to the results of statistical modeling. It should be noted that in some cases, there won’t be any significant lags. However, in this paper, the first intervention related to school closings had no effect on new COVID-19 cases, while the Nowruz holiday did have an effect, albeit with a three-day delay. This is because COVID-19 symptoms can take up to 14 days to develop. In addition, for the second question, we analyzed the time series point by point, exactly after each intervention to see whether the intervention had a significant effect on the time series or not.

Attachment

Submitted filename: REVMH-Response to Reviewer-Plos one-covid19 2021-03-19.docx

Click here for additional data file. (48.4KB, docx)

Decision Letter 1

Siew Ann Cheong

16 Jul 2021

PONE-D-20-30409R1

Effects of government policies and the Nowruz holidays on confirmed COVID-19 cases in Iran: An intervention time series analysis

PLOS ONE

Dear Dr. Fakoor,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Specifically, please address the minor comments of Reviewer 3.

Please submit your revised manuscript by Aug 30 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Siew Ann Cheong, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

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Reviewer #2: Ref. PONE-D-20-30409R1

Title: Effects of government policies and the Nowruz holidays on confirmed 2 COVID-19 cases in Iran: An intervention time series analysis

The research paper on “Effects of government policies and the Nowruz holidays on confirmed COVID-19 cases in Iran: An intervention time series analysis” is very interesting and well written and used various statistical techniques, I find very useful and appropriate to the general readerships of the journal. The authors have carefully revised the manuscript which was raised by reviewers/ editor. Therefore, I strongly recommend (accepted) this article for publication in the present form.

Reviewer #3: 1. Before proceeding to the rigorous analyses of time series data, the authors need to conduct the basic statistical analysis of the data mentioning the minimum and maximum values, mean, skewness, kurtosis, etc. A diagrammatic representation of the features of the data be made (If possible).

2. The authors should provide autocorrelation function (ACF) and partial autocorrelation function (PACF) plots for the time series under study.

3. ARIMA (Or SARIMA) modeling methodology be briefly explained in the methods.

4. In the Table1, the test-statistic (e.g. t-test or Chi-square test) used need to clearly stated to find the 95% confidence intervals.

5. Could there be any future predictions using the proposed model? It would be best if the proposed model had been capable of drawing future predictions.

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Reviewer #2: No

Reviewer #3: No

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PLoS One. 2021 Aug 19;16(8):e0256516. doi: 10.1371/journal.pone.0256516.r004

Author response to Decision Letter 1

7 Aug 2021

Professor. Siew Ann Cheong

Academic Editor

PLOS ONE

SUBJECT: Resubmission of manuscript PONE-D-20-30409R1

Re: Effects of government policies and the Nowruz holiday on confirmed COVID-19 cases in Iran: An intervention time series analysis

Dear Editor,

We would like to thank you for the attention brought to our manuscript for this second review. Also, we would like to thank the academic editor and the reviewers for their careful reading of our manuscript and their insightful comments. We have reviewed the comments and, then, reviewed the manuscript accordingly. The comments have been noted and we have done our best to revise the paper. Below is a point-by-point response to each comment raised by the academic editor and the reviewers, especially the third reviewer (original comments from reviewers appear in red, responses in black). It should be noted that the modifications in the manuscript are highlighted using track changes in Word.

We hope that the revised version of our manuscript will now meet the PLOS ONE’s publication criteria.

Yours sincerely,

Vahid Fakoor

Associate Professor, Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad-Iran,

P.O.Box, 9177943369

Mobile: 0989151038414

E-mail:

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

ANSWER: Thanks for your comment. We have checked and modified some references (for example 2, 3, 4, 5, 6, 11, 15, 30). Also as you mentioned a paper, that is retracted, is replace with another relevant reference as bellow (in lines 60-63):

11. Zareie B, Roshani A, Mansournia MA, Rasouli MA, Moradi G. A Model for COVID-19 Prediction in Iran Based on China Parameters. Arch Iran Med. 2020 Apr 1;23(4):244-248. doi: 10.34172/aim.2020.05.

1) Reviewer #3: 1. Before proceeding to the rigorous analyses of time series data, the authors need to conduct the basic statistical analysis of the data mentioning the minimum and maximum values, mean, skewness, kurtosis, etc. A diagrammatic representation of the features of the data be made (If possible).

ANSWER: We appreciate the reviewer's comments. We amended them in table 1 in the section method in page 5.

2) The authors should provide autocorrelation function (ACF) and partial autocorrelation function (PACF) plots for the time series under study.

ANSWER: Thank you for your comment. We amended autocorrelation function (ACF) and partial autocorrelation function (PACF) for the residuals of model in page 13, as required.

3) ARIMA (Or SARIMA) modeling methodology be briefly explained in the methods.

ANSWER: As you suggest, a paragraph explaining these method have been added on page 6 for more clarification on SARIMA modeling as bellow:

1. SARIMA, firstly, proposed by Box and Jenkins in the 1970s. It is presented as SARIMA (p, d, q)(P, D, Q)S, where p is the order of auto-regressive (AR), q is the order of moving average (MA), d is the order of the differences. The ACF and PACF are used for knowing the order of AR and MA. Also, based on the trend and season of time series, the order of differences will be recognized. Moreover, P, D, and Q are the corresponding seasonal orders, with S as the steps of the seasonal differences (E.P. George, G.M. Jenkins Time series analysis forecasting and control Holden-Day (1976)).

4) In the Table1, the test-statistic (e.g. t-test or Chi-square test) used need to clearly stated to find the 95% confidence intervals.

ANSWER: It should be mentioned that the statistic for obtaining 95% confidence intervals is based on Z test on page 10 and a sentence was added in this regard. A note as also been added under Table 2 (the table number is now 2 with the addition of a new table in the manuscript).

5) Could there be any future predictions using the proposed model? It would be best if the proposed model had been capable of drawing future predictions.

ANSWER: The capability of SARIMA modeling is in both forecasting and interventional analysis. So, the bellow sentences and table 3 are put in page 13.

“Table 3 shows the prediction of Covid-19 new confirmed cases, with 95 % confidence interval, in 7 days. It should be noted that the prediction can be utilized if the observed spreading pattern, and the number and type of test for detection the COVID-19 cases continues as before and if policies and restrictions are not removed. Therefore, the number of daily new cases would be 813 and 814 for 3 May 2020 and 4 May 2020, respectively.”

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Decision Letter 2

Siew Ann Cheong

10 Aug 2021

Effects of government policies and the Nowruz holidays on confirmed COVID-19 cases in Iran: An intervention time series analysis

PONE-D-20-30409R2

Dear Dr. Fakoor,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Siew Ann Cheong, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Siew Ann Cheong

12 Aug 2021

PONE-D-20-30409R2

Effects of government policies and the Nowruz holidays on confirmed COVID-19 cases in Iran: An intervention time series analysis

Dear Dr. Fakoor:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Siew Ann Cheong

Academic Editor

PLOS ONE

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