The Analysis of Dynamic Processes of Virus Transmission: How Could Diagnostic Tests and Vaccinations Can Stop COVID-19 Pandemic?

Objectives: COVID-19 reached its fourth year of pandemic since 2020. The repeated waves of infections have been driven by multiple factors such as pathological traits of variants, diagnostic accuracy, and vaccination conditions. This study revisits and analyzes the dynamic processes of viral transmission to generate new scientific knowledge. Method(s): A cascade model of viral transmission from one case to another was developed, and theoretically analyzed how the number of infected cases at time t, D+[t], can be changed at time t+1, D+[t+1], considering six parameters: 1) k:level of transmission, 2) Rt: effective reproduction number, 3) rho: capture rate of infected cases, 4) theta: immunity protection rate in individuals, 5) epsilon: evasion rate from vaccines, and 6) Sn: test sensitivity. Result(s): The formula which associates D+[t] with D+[t+1] was given as follows: D+[t+1] = K.D+[t], where K = {(1-Sn) + (1-rho) / rho}{1-Rtk (1-theta(1-epsilon))k} / {1-Rt (1-theta(1-epsilon))}. Also, assuming K be smaller than 1, the lower limit of test sensitivity to stop the viral transmission was formulated: Sn > {Rt (1-theta(1-epsilon))-Rtk(1-theta(1-epsilon))k} / {(1-Rtk(1-theta(1-epsilon))k)rho}. In example computations, the formula indicated that a one-off PCR test with the sensitivity of 85% would not be sufficient to contain highly contagious infections such as the Omicron variants, and that it would be practically impossible to control the situation with the immune-evasive sub-variants in circulation. Conclusion(s): The theory developed in this study broadens the science on evidence-based public health and will be useful for outcomes studies and informed decisions on public policy for pandemic control.Copyright © 2023

Monitoring the Covid-19 Immune Landscape in Japan

Intro: COVID-19 vaccination in Japan started on February 17, 2021. Because the timing of vaccination and the risk of severe COVID-19 greatly varied with age, the present study aimed to monitor the age-specific fractions of the population who were immune to SARS-CoV-2 infection after vaccination. Method(s): Natural infection remained extremely rare, accounting for less than 5% of the population by the end of 2021;thus, we ignored natural infection- induced immunity and focused on vaccine-induced immunity. We estimated the fraction of the population immune to infection by age group using vaccination registry data from February 17, 2021, to October 17, 2021. We accounted for two important sources of delay: (i) reporting delay and (ii) time from vaccination until immune protection develops. Finding(s): At the end of the observation period, the proportion of individuals still susceptible to SARS-CoV-2 infection substantially varied by age and was estimated to be >=90% among people aged 0-14 years, in contrast to approximately 20% among the population aged >=65 years. We also estimated the effective reproduction number over time using a next-generation matrix while accounting for differences in the proportion immune to infection by age. Discussion(s): The COVID-19 immune landscape greatly varied by age, and a substantial proportion of young adults remained susceptible. Vaccination contributed to a marked decrease in the reproduction number. Conclusion(s): The present study offers a novel approach to monitoring the age- related immune landscape over time in Japan.Copyright © 2023

Individual risk-aversion responses tune epidemics to critical transmissibility (R = 1).

Changes in human behaviour are a major determinant of epidemic dynamics. Collective activity can be modified through imposed control measures, but spontaneous changes can also arise as a result of uncoordinated individual responses to the perceived risk of contagion. Here, we introduce a stochastic epidemic model implementing population responses driven by individual time-varying risk aversion. The model reveals an emergent mechanism for the generation of multiple infection waves of decreasing amplitude that progressively tune the effective reproduction number to its critical value R = 1. In successive waves, individuals with gradually lower risk propensity are infected. The overall mechanism shapes well-defined risk-aversion profiles over the whole population as the epidemic progresses. We conclude that uncoordinated changes in human behaviour can by themselves explain major qualitative and quantitative features of the epidemic process, like the emergence of multiple waves and the tendency to remain around R = 1 observed worldwide after the first few waves of COVID-19.

Estimating the time-dependent effective reproduction number and vaccination rate for COVID-19 in the USA and India.

The effective reproduction number, $ R_t $, is a vital epidemic parameter utilized to judge whether an epidemic is shrinking, growing, or holding steady. The main goal of this paper is to estimate the combined $ R_t $ and time-dependent vaccination rate for COVID-19 in the USA and India after the vaccination campaign started. Accounting for the impact of vaccination into a discrete-time stochastic augmented SVEIR (Susceptible-Vaccinated-Exposed-Infectious-Recovered) model, we estimate the time-dependent effective reproduction number $ (R_t) $ and vaccination rate $ (\xi_t) $ for COVID-19 by using a low pass filter and the Extended Kalman Filter (EKF) approach for the period February 15, 2021 to August 22, 2022 in India and December 13, 2020 to August 16, 2022 in the USA. The estimated $ R_t $ and $ \xi_t $ show spikes and serrations with the data. Our forecasting scenario represents the situation by December 31, 2022 that the new daily cases and deaths are decreasing for the USA and India. We also noticed that for the current vaccination rate, $ R_t $ would remain greater than one by December 31, 2022. Our results are beneficial for the policymakers to track the status of the effective reproduction number, whether it is greater or less than one. As restrictions in these countries ease, it is still important to maintain safety and preventive measures.

Developing a Place–Time-Specific Transmissibility Index to Measure and Examine the Spatiotemporally Varying Transmissibility of COVID-19

The transmission rate of COVID-19 varies by location and time. A proper measure of the transmissibility of an infectious disease should be place- and time-specific, which is currently unavailable. This research aims to better understand the spatiotemporally changing transmissibility of COVID-19. It contributes to COVID-19 research in three ways. First, it presents a generally applicable modeling framework to estimate the transmissibility of COVID-19 in a specific place and time based on daily reported case data, called space-time effective reproduction number, denoted as (Formula presented.) Then, the developed model is used to create a spatiotemporal data set of (Formula presented.) values at the county level in the United States. Second, it investigates relationships between (Formula presented.) and dynamically changing context factors with multiple machine learning and spatial modeling techniques. The research examines the relationships from a cross-sectional perspective and a longitudinal perspective separately. The longitudinal view allows us to understand how local human dynamics and policy factors influence changes in (Formula presented.) over time in the place, whereas the cross-sectional view sheds light on the demographic, socioeconomic, and environmental factors behind spatial variations of (Formula presented.) at a specific time slice. Some general trends of the relationships are found, but the level of impact by each context factor varies geographically. Third, the best performing local longitudinal models have promising potential to simulate or forecast future transmissibility. The random forest and the exponential regression models based on time-series data gave the best performances. These models were further evaluated against ground truth data of county-level reported cases. Their good prediction accuracies in the case study prove that these machine learning models are promising in their ability to predict transmissibility in hypothetical or foreseeable scenarios. © 2023 by American Association of Geographers.

Modelling presymptomatic infectiousness in COVID-19

This paper considers SEPIR, an extension of the well-known SEIR continuous simulation compartment model. Both models can be fitted to real data as they include parameters that can be estimated from the data. SEPIR deploys an additional presymptomatic infectious compartment, not modelled in SEIR but known to exist in COVID-19. This stage can also be fitted to data. We focus on how to fit SEPIR to a first wave of COVID. Both SEIR and SEPIR and the existing SEIR models assume a homogeneous mixing population with parameters fixed. Moreover, neither includes dynamically varying control strategies deployed against the virus. If either model is to represent more than just a single wave of the epidemic, then the parameters of the model would have to be time dependent. In view of this, we also show how reproduction numbers can be calculated to investigate the long-term overall outcome of an epidemic. © 2023 The Operational Research Society.

Mathematical Model of COVID-19 Pandemic with Double Dose Vaccination.

This paper is concerned with the formulation and analysis of an epidemic model of COVID-19 governed by an eight-dimensional system of ordinary differential equations, by taking into account the first dose and the second dose of vaccinated individuals in the population. The developed model is analyzed and the threshold quantity known as the control reproduction number [Formula: see text] is obtained. We investigate the equilibrium stability of the system, and the COVID-free equilibrium is said to be locally asymptotically stable when the control reproduction number is less than unity, and unstable otherwise. Using the least-squares method, the model is calibrated based on the cumulative number of COVID-19 reported cases and available information about the mass vaccine administration in Malaysia between the 24th of February 2021 and February 2022. Following the model fitting and estimation of the parameter values, a global sensitivity analysis was performed by using the Partial Rank Correlation Coefficient (PRCC) to determine the most influential parameters on the threshold quantities. The result shows that the effective transmission rate [Formula: see text], the rate of first vaccine dose [Formula: see text], the second dose vaccination rate [Formula: see text] and the recovery rate due to the second dose of vaccination [Formula: see text] are the most influential of all the model parameters. We further investigate the impact of these parameters by performing a numerical simulation on the developed COVID-19 model. The result of the study shows that adhering to the preventive measures has a huge impact on reducing the spread of the disease in the population. Particularly, an increase in both the first and second dose vaccination rates reduces the number of infected individuals, thus reducing the disease burden in the population.

A wastewater-based risk index for SARS-CoV-2 infections among three cities on the Canadian Prairie.

Wastewater surveillance (WWS) is useful to better understand the spreading of coronavirus disease 2019 (COVID-19) in communities, which can help design and implement suitable mitigation measures. The main objective of this study was to develop the Wastewater Viral Load Risk Index (WWVLRI) for three Saskatchewan cities to offer a simple metric to interpret WWS. The index was developed by considering relationships between reproduction number, clinical data, daily per capita concentrations of virus particles in wastewater, and weekly viral load change rate. Trends of daily per capita concentrations of SARS-CoV-2 in wastewater for Saskatoon, Prince Albert, and North Battleford were similar during the pandemic, suggesting that per capita viral load can be useful to quantitatively compare wastewater signals among cities and develop an effective and comprehensible WWVLRI. The effective reproduction number (Rt) and the daily per capita efficiency adjusted viral load thresholds of 85 × 106 and 200 × 106 N2 gene counts (gc)/population day (pd) were determined. These values with rates of change were used to categorize the potential for COVID-19 outbreaks and subsequent declines. The weekly average was considered 'low risk' when the per capita viral load was 85 × 106 N2 gc/pd. A 'medium risk' occurs when the per capita copies were between 85 × 106 and 200 × 106 N2 gc/pd. with a rate of change <100 %. The start of an outbreak is indicated by a 'medium-high' risk classification when the week-over-week rate of change was >100 %, and the absolute magnitude of concentrations of viral particles was >85 × 106 N2 gc/pd. Lastly, a 'high risk' occurs when the viral load exceeds 200 × 106 N2 gc/pd. This methodology provides a valuable resource for decision-makers and health authorities, specifically given the limitation of COVID-19 surveillance based on clinical data.

Evaluating the impact of multiple factors on the control of COVID-19 epidemic: A modelling analysis using India as a case study.

The currently ongoing COVID-19 outbreak remains a global health concern. Understanding the transmission modes of COVID-19 can help develop more effective prevention and control strategies. In this study, we devise a two-strain nonlinear dynamical model with the purpose to shed light on the effect of multiple factors on the outbreak of the epidemic. Our targeted model incorporates the simultaneous transmission of the mutant strain and wild strain, environmental transmission and the implementation of vaccination, in the context of shortage of essential medical resources. By using the nonlinear least-square method, the model is validated based on the daily case data of the second COVID-19 wave in India, which has triggered a heavy load of confirmed cases. We present the formula for the effective reproduction number and give an estimate of it over the time. By conducting Latin Hyperbolic Sampling (LHS), evaluating the partial rank correlation coefficients (PRCCs) and other sensitivity analysis, we have found that increasing the transmission probability in contact with the mutant strain, the proportion of infecteds with mutant strain, the ratio of probability of the vaccinated individuals being infected, or the indirect transmission rate, all could aggravate the outbreak by raising the total number of deaths. We also found that increasing the recovery rate of those infecteds with mutant strain while decreasing their disease-induced death rate, or raising the vaccination rate, both could alleviate the outbreak by reducing the deaths. Our results demonstrate that reducing the prevalence of the mutant strain, improving the clearance of the virus in the environment, and strengthening the ability to treat infected individuals are critical to mitigate and control the spread of COVID-19, especially in the resource-constrained regions.

SIR model for the spread of COVID-19: A case study

In this article, we study the spread pattern of the epidemic of COVID-19 disease from the point of view of mathematical modeling. Considering that this virus follows the basic rules of epidemic disease transmission, we use the SIR model to show the spread process of this disease in Iran. Then we estimate the primary reproduction number (R0) of COVID-19 in Iran by matching an epidemic model with the data of reported cases. © 2022

An SEQAIHR model to study COVID-19 transmission and optimal control strategies in Hong Kong, 2022.

During the COVID-19 pandemic, one of the major concerns was a medical emergency in human society. Therefore it was necessary to control or restrict the disease spreading among populations in any fruitful way at that time. To frame out a proper policy for controlling COVID-19 spreading with limited medical facilities, here we propose an SEQAIHR model having saturated treatment. We check biological feasibility of model solutions and compute the basic reproduction number ( R 0 ). Moreover, the model exhibits transcritical, backward bifurcation and forward bifurcation with hysteresis with respect to different parameters under some restrictions. Further to validate the model, we fit it with real COVID-19 infected data of Hong Kong from 19th December, 2021 to 3rd April, 2022 and estimate model parameters. Applying sensitivity analysis, we find out the most sensitive parameters that have an effect on R 0 . We estimate R 0 using actual initial growth data of COVID-19 and calculate effective reproduction number for same period. Finally, an optimal control problem has been proposed considering effective vaccination and saturated treatment for hospitalized class to decrease density of the infected class and to minimize implemented cost.

The Effects of Time Window-Averaged Mobility on Effective Reproduction Number of COVID-19 Viral Variants in Urban Cities.

During epidemics, the estimation of the effective reproduction number (ERN) associated with infectious disease is a challenging topic for policy development and medical resource management. The emergence of new viral variants is common in widespread pandemics including the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A simple approach is required toward an appropriate and timely policy decision for understanding the potential ERN of new variants is required for policy revision. We investigated time-averaged mobility at transit stations as a surrogate to correlate with the ERN using the data from three urban prefectures in Japan. The optimal time windows, i.e., latency and duration, for the mobility to relate with the ERN were investigated. The optimal latency and duration were 5-6 and 8 days, respectively (the Spearman's ρ was 0.109-0.512 in Tokyo, 0.365-0.607 in Osaka, and 0.317-0.631 in Aichi). The same linear correlation was confirmed in Singapore and London. The mobility-adjusted ERN of the Alpha variant was 15-30%, which was 20-40% higher than the original Wuhan strain in Osaka, Aichi, and London. Similarly, the mobility-adjusted ERN of the Delta variant was 20%-40% higher than that of the Wuhan strain in Osaka and Aichi. The proposed metric would be useful for the proper evaluation of the infectivity of different SARS-CoV-2 variants in terms of ERN as well as the design of the forecasting system.

Hotspot analysis of COVID-19 infection using mobile-phone location data.

Restrictions on outdoor activities are required to suppress the COVID-19 pandemic. To monitor social risks and control the pandemic through sustainable restrictions, we focus on the relationship between the number of people going out and the effective reproduction number. The novelty of this study is that we have considered influx population instead of staying-population, as the data represent congestion. This enables us to apply our analysis method to all meshes because the influx population may always represent the congestion of specific areas, which include the residential areas as well. In this study, we report the correlation between the influx population in downtown areas and business districts in Tokyo during the pandemic considering the effective reproduction number and associated time delay. Moreover, we validate our method and the influx population data by confirming the consistency of the results with those of the previous research and epidemiological studies. As a result, it is confirmed that the social risk with regard to the spread of COVID-19 infection when people travel to downtown areas and business districts is high, and the risk when people visit only residential areas is low.

Infectivity of Omicron BA.5 Comparison with Original Strain and Other Mutated Strain of SARS-CoV-2 in Japan

Background: Earlier studies have indicated the BA.5 sublineage of Omicron variant strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as more infective than BA.2. Object: This study estimated BA.5 infectivity while controlling other factors possibly affecting BA.5 infectivity including vaccine effectiveness, waning effectiveness, other mutated strains, Olympic Games, and countermeasures. Method: The effective reproduction number R(t) was regressed on shares of BA.5 and vaccine coverage, vaccine coverage with some delay, temperature, humid-ity, mobility, shares of other mutated strains, counter-measures including the Go to Travel Campaign, and the Olympic Games and associated countermeasures. The study period was February 2020–July 22, 2022, using data available on August 12, 2022. Results: A 120 day lag was assumed to assess waning. Mobil-ity, some states of emergency, vaccine coverage and those with lag, and the Delta and Omicron BA.2 pro-portions were found to be significant. The omicron BA.1 proportion was significant, but with an unex-pected sign. The estimated coefficient of BA.5 was negative but not significant. The Go to Travel Campaign was significantly negative, indicating reduced infectiv-ity. The Olympic Games were negative but not sig-nificant, indicating that they did not raise infectivity. Discussion: The obtained estimated results show that BA.5 did not have higher infectivity than the original strain. It was lower than either Delta or Omicron BA.2 variant strains. That finding might be inconsis-tent with results obtained from earlier studies. This study controlled several factors potentially affecting R(t), though the earlier studies did not. Therefore, results from this study might be more reliable than those of earlier studies. © Fuji Technology Press Ltd.

Statistical methods used to combine the effective reproduction number, [Formula: see text], and other related measures of COVID-19 in the UK.

In the recent COVID-19 pandemic, a wide range of epidemiological modelling approaches were used to predict the effective reproduction number, R(t), and other COVID-19-related measures such as the daily rate of exponential growth, r(t). These candidate models use different modelling approaches or differing assumptions about spatial or age-mixing, and some capture genuine uncertainty in scientific understanding of disease dynamics. Combining estimates using appropriate statistical methodology from multiple candidate models is important to better understand the variation of these outcome measures to help inform decision-making. In this paper, we combine estimates for specific UK nations/regions using random-effects meta-analyses techniques, utilising the restricted maximum-likelihood (REML) method to estimate the heterogeneity variance parameter, and two approaches to calculate the confidence interval for the combined estimate: the standard Wald-type and the Knapp and Hartung (KNHA) method. As estimates in this setting are derived using model predictions, each with varying degrees of uncertainty, equal-weighting is favoured over the standard inverse-variance weighting to avoid potential up-weighting of models providing estimates with lower levels of uncertainty that are not fully accounting for inherent uncertainties. Both equally-weighted models using REML alone and REML+KNHA approaches were found to provide similar variation for R(t) and r(t), with both approaches providing wider, and therefore more conservative, confidence intervals around the combined estimate compared to the standard inverse-variance weighting approach. Utilising these meta-analysis techniques has allowed for statistically robust combined estimates to be calculated for key COVID-19 outcome measures. This in turn allows timely and informed decision-making based on all available information.

Impact of COVID-19 asymptomatic individuals on effective regenerative math by multi-agent simulation based on the SEAIR model

Various simulations are currently being conducted in response to the spread of the novel coronavirus infection. However, few multi-agent simulations have been conducted using a model that considers asymptomatic persons, who are one of the factors contributing to the spread of infection. In this study, we extended the SEAIR model, which considers asymptomatic persons, to multi-agent simulations to investigate the effect of the proportion of asymptomatic persons on the effective number of reproductions. The results indicate that asymptomatic persons may influence the number of positive groups at the peak of the spread of infection and the convergence period. © 2022 IEEE.

Challenges In Assessing The Efficacy Of Non-Pharmacological Measures In The Context Of The COVID-19 Pandemic

Introduction. The outbreak of the COVID-19 global pandemic in 2020 has been a major challenge for the world's population and governments. The lack of vaccines, the saturation of health systems, and its rapid spread forced governments to take non-pharmacological interventions (NPI) that had a high impact on the population. Assessing the efficacy of these measures is a challenge for health technology assessment bodies. Methods. The main NPIs for which assessment was required were: mobility restrictions, social distancing, cancellation of events or reduction of seating capacity, closure or reduction of seating capacity in non-essential businesses, closure or limitation of seating capacity in educational establishments, and promotion of teleworking in potential jobs. The implementation of these measures at a global level provides a large population for the study of the impact of these measures. However, the challenges for their evaluation are numerous: * The joint implementation of these measures makes it difficult to evaluate them in an isolated manner. * The heterogeneity between countries and regions of the pandemic situation at the time when these measures are initiated and terminated. * The different accuracy in the application of the measures. * Heterogeneity in the quality and accessibility of public health services for citizens. Results. Outcome variables to assess the effectiveness of these measures should include parameters related to: * Incidence variables: the number of new or accumulated cases in a given time range, the variation in the number of cases in a given time range and the proportion of positive tests. * Transmission variables: the basic reproductive number (R0) and the effective reproductive number (Rt). * Severity and mortality variables: the number or variation of hospitalizations, the number or variation of intensive care unit (ICU) hospitalizations and the number or variation of deaths. Conclusions. The large number of available data, the heterogeneity of the measures, the differences between populations, the numerous outcome variables and the possible inclusion of mathematical modelling studies, are a methodological challenge for the HTA bodies.

Estimation of the basic and the effective reproduction numbers for the 2009 influenza A (H1N1) in Greece

OBJECTIVE To estimate the R0 and Rt of influenza A (H1N1) 2009 pandemic in Greece and to compare these estimates with those reported in the literature for the influenza A (H1N1) 2009 pandemic in other countries, and with those of past influenza pandemics, seasonal influenza and the COVID-19 pandemic. METHOD We used data on the number of laboratory-confirmed influenza A (H1N1) 2009 cases per week for the period from mid-September 2009 to February 2010 in Greece that were collected by the Hellenic Centre for Diseases Control and Prevention. The daily number of cases was obtained, using linear and cubic spline interpolation to estimate the Rt. R0 was estimated apply-ing the maximum likelihood method on the daily number of cases obtained from cubic spline interpolation. Finally, sensitivity analysis was performed to assess the robustness of the R0 estimate. RESULTS By the end of October 2009, the number of laboratory-confirmed cases of influenza A (H1N1) 2009 increased exponentially, reaching a peak at the end of November. The estimated basic reproduction number R0 was 1.35 (95% confidence interval: 1.16-1.57). Rt was close to 1 in mid-October 2009;it increased subsequently and reached close to 1.4 in early November, then in December 2009 it declined below 1 and remained at low levels until the end of February 2010. CONCLUSIONS The estimation of the basic reproduction number of pandemic influenza A (H1N1) 2009 in Greece is in line with estimates provided by other countries, and places R0 at lower levels compared to R0 estimates for both previous influenza pandemics and the COVID-19 pandemic. In addition, the estimated 2009 R0 is similar to the higher estimates for R0 of seasonal influenza. Copyright © Athens Medical Society.

Risk Assessment of Importation and Local Transmission of COVID-19 in South Korea: Statistical Modeling Approach.

BACKGROUND: Despite recent achievements in vaccines, antiviral drugs, and medical infrastructure, the emergence of COVID-19 has posed a serious threat to humans worldwide. Most countries are well connected on a global scale, making it nearly impossible to implement perfect and prompt mitigation strategies for infectious disease outbreaks. In particular, due to the explosive growth of international travel, the complex network of human mobility enabled the rapid spread of COVID-19 globally. OBJECTIVE: South Korea was one of the earliest countries to be affected by COVID-19. In the absence of vaccines and treatments, South Korea has implemented and maintained stringent interventions, such as large-scale epidemiological investigations, rapid diagnosis, social distancing, and prompt clinical classification of severely ill patients with appropriate medical measures. In particular, South Korea has implemented effective airport screenings and quarantine measures. In this study, we aimed to assess the country-specific importation risk of COVID-19 and investigate its impact on the local transmission of COVID-19. METHODS: The country-specific importation risk of COVID-19 in South Korea was assessed. We investigated the relationships between country-specific imported cases, passenger numbers, and the severity of country-specific COVID-19 prevalence from January to October 2020. We assessed the country-specific risk by incorporating country-specific information. A renewal mathematical model was employed, considering both imported and local cases of COVID-19 in South Korea. Furthermore, we estimated the basic and effective reproduction numbers. RESULTS: The risk of importation from China was highest between January and February 2020, while that from North America (the United States and Canada) was high from April to October 2020. The R0 was estimated at 1.87 (95% CI 1.47-2.34), using the rate of α=0.07 for secondary transmission caused by imported cases. The Rt was estimated in South Korea and in both Seoul and Gyeonggi. CONCLUSIONS: A statistical model accounting for imported and locally transmitted cases was employed to estimate R0 and Rt. Our results indicated that the prompt implementation of airport screening measures (contact tracing with case isolation and quarantine) successfully reduced local transmission caused by imported cases despite passengers arriving from high-risk countries throughout the year. Moreover, various mitigation interventions, including social distancing and travel restrictions within South Korea, have been effectively implemented to reduce the spread of local cases in South Korea.

SIR model for the spread of COVID-19: A case study

In this article, we study the spread pattern of the epidemic of COVID-19 disease from the point of view of mathematical modeling. Considering that this virus follows the basic rules of epidemic disease transmission, we use the SIR model to show the spread process of this disease in Iran. Then we estimate the primary reproduction number (R0) of COVID-19 in Iran by matching an epidemic model with the data of reported cases.