Estimation of the basic reproduction number (𝑅0) of the COVID-19 epidemic in Iran

Basic Reproduction Number (R0), Doubling Time, and Daily Growth Rate of the COVID-19 Epidemic: An Echological Study

Introduction

In infectious diseases, there are essential indices used to describe the disease state. In this study, we estimated the basic reproduction number, R0, peak level, doubling time, and daily growth rate of COVID-19.

Methods

This ecological study was conducted in 5 provinces of Iran. The daily numbers of new COVID-19 cases from January 17 to February 8, 2020 were used to determine the basic reproduction number (R0), peak date, doubling time, and daily growth rates in all five provinces. A sensitivity analysis was conducted to estimate epidemiological parameters.

Result

The highest and lowest number of deaths were observed in Hamedan (657 deaths) and Chaharmahal and Bakhtiari (54 deaths) provinces, respectively. The doubling time of confirmed cases in Kermanshah and Hamedan ranged widely from 18.59 days (95% confidence interval (CI): 17.38, 20) to 76.66 days (95% CI: 56.36, 119.78). In addition, the highest daily growth rates of confirmed cases were observed in Kermanshah (0.037, 95% CI: 0.034, 0.039) and Sistan and Baluchestan (0.032, 95% CI: 0.030, 0.034) provinces.

Conclusion

In light of our findings, it is imperative to tailor containment strategies to the unique epidemiological profiles of each region in order to effectively mitigate the spread and impact of COVID-19. The wide variation in doubling times underscores the importance of flexibility in public health responses. By adapting measures to local conditions, we can better address the evolving dynamics of the pandemic and safeguard the well-being of communities.

Comparison of the Effective Reproduction Number (Rt) Estimation Methods of COVID-19 Using Simulation Data Based on Available Data from Iran, USA, UK, India, and Brazil

BACKGROUND

Accurate determination of the effective reproduction number (Rt) is a very important strategy in the epidemiology of contagious diseases, including coronavirus disease 2019 (COVID-19). This study compares different methods of estimating the Rt of susceptible population to identify the most accurate method for estimating Rt.

STUDY DESIGN

A secondary study.

METHODS

The value of Rt was estimated using attack rate (AR), exponential growth (EG), maximum likelihood (ML), time-dependent (TD), and sequential Bayesian (SB) methods, for Iran, the United States, the United Kingdom, India, and Brazil from June to October 2021. In order to accurately compare these methods, a simulation study was designed using forty scenarios.

RESULTS

The lowest mean square error (MSE) was observed for TD and ML methods, with 15 and 12 cases, respectively. Therefore, considering the estimated values of Rt based on the TD method, it was found that Rt values in the United Kingdom (1.33; 95% CI: 1.14-1.52) and the United States (1.25; 95% CI: 1.12-1.38) substantially have been more than those in other countries, such as Iran (1.07; 95% CI: 0.95-1.19), India (0.99; 95% CI: 0.89-1.08), and Brazil (0.98; 95% CI: 0.84-1.14) from June to October 2021.

CONCLUSION

The important result of this study is that TD and ML methods lead to a more accurate estimation of Rt of population than other methods. Therefore, in order to monitor and determine the epidemic situation and have a more accurate prediction of the incidence rate, as well as control COVID-19 and similar diseases, the use of these two methods is suggested to more accurately estimate Rt.

Determinants of observing health protocols related to preventing COVID-19 in adult women: A qualitative study in Iran

Background

The best way to prevent COVID-19 is to observe health protocols. Therefore, identifying the reasons of following these protocols in order to plan and make intervention seems necessary. Therefore, the purpose of this study was to identify the determinants of observing health protocols related to prevention of COVID-19 among the Iranian adult women with a qualitative approach.

Method

In this qualitative study, the conventional content analysis approach was used. saturation was obtained after face-to-face semi-structured interviews with 38 women from Kermanshah who were selected through purposeful sampling and snowball sampling. Guba and Lincoln criteria were used for the strength of the research and Graneheim and Lundman method was used for its analysis.

Results

After analyzing the interviews, 5 categories, 12 subcategories and 110 initial codes were obtained. Categories and sub-categories were: 1- Individual factors (personality traits, health literacy about COVID-19); 2- Perceived risk having underlying disease in oneself and family, history of getting COVID-19 and death in close relatives; 3- Fear of the destructive consequences of the disease (concern about the economic consequences of getting the disease, concern about the treatment process); 4- Social and cultural factors (social monitoring, religious insight, ability to properly manage social interactions, impressionability from important others); 5- Environmental factors (supportive living environment, access to health and anti-infective materials).

Conclusion

Increasing the adherence of adult women to health instructions related to COVID-19 requires interventions at different levels of individual, environmental and social, and without accurate knowledge of the customs and culture of a society effective interventions cannot be established.

Estimation of the Reproductive Number Trend of the Novel Coronavirus "COVID-19" in Southern Iran from July to November 2020

Background

Effective measures to control COVID-19 should be based on an understanding of its epidemiological pattern and the evaluation of its prevalence in the community. This study aimed to examine the reproductive number (R) of COVID-19 and its trend in Fars Province in southern Iran.

Methods

In this ecological study, the R trend was examined from July to November 2020. Data were collected from the daily reports of Shiraz University of Medical Sciences. R is the product of three components, namely the number of infection-producing contacts per unit time, the probability of transmission per contact, and the contagiousness period. Incidence and prevalence rates were used to calculate R. The R value was calculated in Microsoft Excel 2016.

Results

The first and second peaks of COVID-19 were observed on July 6^th and November 22^nd, respectively. The median and mean of R were 1.42 and 1.41, respectively. The highest and lowest values of R were observed on October 20^th (2.60) and September 1^st (0.46), respectively. The values of R had a slightly decreasing trend in the second half of July and November than the first half. In the second half of August and September, an increasing trend was observed than the first half. There was not much change in the second half of October.

Conclusion

The highest value of R was related to the dates when there was a higher probability of exposure to the virus, including public holidays and different occasions on which the probability of people's participation in ceremonies, communities, and gatherings was higher.

Estimation of the basic reproduction number of Alpha and Delta variants of COVID-19 pandemic in Iran

Estimating the basic reproduction number of a pandemic and the changes that appear on this value over time provide a good understanding of the contagious nature of the virus and efficiency of the controlling strategies. In this paper, we focus on studying the basic reproduction number (R0) for two important variants of COVID-19 pandemic in Iran: Alpha and Delta variants. We use four different methods, three statistical models and one mathematical model, to compute R0: Exponential Growth Rate (EGR), Maximum Likelihood (ML), Sequential Bayesian (SB), and time-dependent SIR model. Alpha variant of COVID-19 was active in Iran from March 10, 2021 until June 10, 2021. Our computations indicate that total R0 of this variant according to EGR, ML, SB, and SIR model is respectively 0.9999 (95% CI: 0.9994-1), 1.046 (95% CI: 1.044-1.049), 1.06 (95% CI: 1.03-1.08), and 2.79 (95% CI: 2.77-2.81) in the whole active time interval. Moreover, during the time interval from April 3, 2021 to April 9, 2021 in which this variant was in its exponential growth in Iran, R0 of Alpha variant in Iran according to SB, EGR, ML, and SIR model is respectively 2.26 (95% CI: 2.04-2.49), 2.64 (95% CI: 2.58-2.7), 11.38 (95% CI: 11.28-11.48), and 12.13 (95% CI: 12.12-12.14). Delta variant was active in Iran during the time interval from June 22, 2021 until September 22, 2021. Our computations show that during the time interval from July 3, 2021 to July 8, 2021 in which this variant was in its exponential growth in Iran, R0 of Delta variant in Iran according to SB, EGR, ML, and SIR model is respectively 3 (95% CI: 2.34-3.66), 3.1 (95% CI: 3.02-3.17), 12 (95% CI: 11.89-12.12), and 23.3 (95% CI: 23.19-23.41). Further, total R0 of Delta variant in Iran in the whole active time interval according to EGR, ML, SB, and SIR model is respectively 1.042 (95% CI: 1.04-1.043), 1.053 (95% CI: 1.051-1.055), 0.79 (95% CI: 0.63-0.95), and 5.65 (95% CI: 5.6-5.7). As the results show Delta variant was more severe than Alpha variant in Iran. Chasing the changes in R0 during each variant shows that the controlling strategies applied were effective in controlling the virus spread.

A comprehensive estimation of country-level basic reproduction numbers R0 for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data

In a compartmental epidemic model, the initial exponential phase reflects a fixed interaction between an infectious agent and a susceptible population in steady state, so it determines the basic reproduction number R₀ on its own. After the exponential phase, dynamic complexities like societal responses muddy the practical interpretation of many estimated parameters. The computer program ARRP, already available from sequence alignment applications, automatically estimated the end of the exponential phase in COVID-19 and extracted the exponential growth rate r for 160 countries. By positing a gamma-distributed generation time, the exponential growth method then yielded R₀ estimates for COVID-19 in 160 countries. The use of ARRP ensured that the R₀ estimates were largely freed from any dependency outside the exponential phase. The Prem matrices quantify rates of effective contact for infectious disease. Without using any age-stratified COVID-19 data, but under strong assumptions about the homogeneity of susceptibility, infectiousness, etc., across different age-groups, the Prem contact matrices also yielded theoretical R₀ estimates for COVID-19 in 152 countries, generally in quantitative conflict with the R₀ estimates derived from the exponential growth method. An exploratory analysis manipulating only the Prem contact matrices reduced the conflict, suggesting that age-groups under 20 years did not promote the initial exponential growth of COVID-19 as much as other age-groups. The analysis therefore supports tentatively and tardily, but independently of age-stratified COVID-19 data, the low priority given to vaccinating younger age groups. It also supports the judicious reopening of schools. The exploratory analysis also supports the possibility of suspecting differences in epidemic spread among different age-groups, even before substantial amounts of age-stratified data become available.