key: cord-0877802-4ii1b12q authors: Rahman, Bootan; Sadraddin, Evar; Porreca, Annamaria title: The basic reproduction number of SARS‐CoV‐2 in Wuhan is about to die out, how about the rest of the World? date: 2020-05-19 journal: Rev Med Virol DOI: 10.1002/rmv.2111 sha: 60746dcbe0c2f3e1205cce6179529cefe0004fcb doc_id: 877802 cord_uid: 4ii1b12q The virologically confirmed cases of a new coronavirus disease (COVID‐19) in the world are rapidly increasing, leading epidemiologists and mathematicians to construct transmission models that aim to predict the future course of the current pandemic. The transmissibility of a virus is measured by the basic reproduction number (R(0)), which measures the average number of new cases generated per typical infectious case. This review highlights the articles reporting rigorous estimates and determinants of COVID‐19 R(0) for the most affected areas. Moreover, the mean of all estimated R(0) with median and interquartile range is calculated. According to these articles, the basic reproduction number of the virus epicentre Wuhan has now declined below the important threshold value of 1.0 since the disease emerged. Ongoing modelling will inform the transmission rates seen in the new epicentres outside of China, including Italy, Iran and South Korea. The appearance of a new infectious disease is always a complex phenomenon, especially if it becomes pandemic. Globally, infections by SARS-CoV-2 that causes COVID-19 are rapidly growing, and they extended very fast with transmission chains throughout the world since the first case was detected in the Chinese city of Wuhan in December 2019. Imported cases and secondary cases have been reported in more than 1 436 198 confirmed cases globally. 1 On 11 March 2020, the World Health Organization (WHO) declared COVID-19 a pandemic and called for governments to take urgent actions to change the course of the outbreak. 2 An infectious disease outbreak can be characterised by its basic reproductive number, known as R 0 , which represents the average number of secondary infections generated by each infected person. If R 0 is equal to 1 or less, this indicates that the number of secondary cases will decrease over time and, eventually, the outbreak will peter out. If it is higher than one, the outbreak is expected to increasingly transmit infection to secondary cases, indicating the need to use control measures to limit its extension. As governments and WHO work together to treat infected people and control the spread of the hitherto unknown SARS-CoV-2, several mathematical modelling groups in the China, United Kingdom, Europe and United States have rushed to estimate the basic reproduction number and predict the spread of SARS-CoV-2 infections and cases of COVID-19 disease. These groups used different approaches as illustrated in Table 1 with estimates hovering between 0.32 and 6.47 in Tables 2 and 3 . These differences are not surprising, as there is uncertainty about many of the factors go into estimating R 0 , such as different methods for modelling, different variables considered, and various estimation procedures. In this review, we summarise the basic reproduction number R 0 of multiple published articles for pandemic COVID-19. Screening from 1 January 2020 to 6 April 2020, yielded 50 articles which estimated the basic reproduction number for COVID- 19 . Most of these studies concern China, some of them are from Italy, Iran, South Korea, Singapore, Japan, Israel and Brazil. Initially, the WHO estimated the basic reproduction number for COVID-19 between 1.4 and 2.5, as declared in the statement regarding the outbreak of SARS-CoV-2, dated 23 January 2020. 52 Additionally, several articles aimed to more precisely estimate the COVID-19 R 0 . A review written by Liu et al 53 Description of R 0 estimation methods with list of used abbreviations ID Methods Method description with its abbreviation 1 SIR model [3] [4] [5] [6] [7] [8] [9] It is a compartmental model in epidemiology that divides an infectious disease into three parts: Susceptible-Infectious-Removed (SIR), which is represented as a dynamical system in mathematics. 2 SEIR model [10] [11] [12] [13] [14] [15] Susceptible-Exposed-Infectious-Removed (SEIR) model which is another type of compartmental model which differs from SIR model by adding exposed part that represents the delay time of infected by virus and apparing symptoms (latency period). 3 MSIR model 16 Maternally derived immunity-Susceptible-Infectious-Removed (MSIR) compartmental model that babies got protection from maternal antibodies. MSEIR model 16 It is the same as the model MSIR by joining Exposed component and becoming Maternally derived immunity-Susceptible-Exposed-Infectious-Removed (MSEIR). SEIHR model 17, 18 Entering the Hospitalized class to SEIR model to obtain: Susceptible-Exposed-Infectious-Hospitalized-Removed (SEIHR). 24 It is a mathematical model focusing on the effects of medical resourceson transmission of COVID-19, stands for susceptible S (t), pre-stage exposed E 1 (t), post-stage exposed E 2 (t), infected with mild symptoms I 1 (t), infected with serious symptoms I 2 (t), hospitalized H(t) and recovered R(t) individuals. It is a mathematical model that designed to show transsimssion between different stages in infectious disease. The abbrevation refers to: Susceptible-Infected -Diagnosed-Ailing-Recognised-Threatened-Healed-Extinct (SIDARTHE) model. In this model, being infected is dividing into 5 types as: undetected asymptomatic infected, detected asymptomatic infected, undetected symptomatic infected, detected symptomatic infected, and infected with detected life-threatening symptoms; whereas the removed class in compartmental model is classfied into recovered and dead. Exponential growth 9,26-31 It is a model that varies exponentially with the time by a specific rate. 14 Generalized growth model 32 It is the growth model with two parameters: (r) represents the growth rate parameter with (p) that is the scaling growth rate parameter. Whenever P = 1, the generalized growth model returns to exponential growth and if 0 < P < 1, then it is sub-exponential (polynomial) growth. Logistic growth model 33 It is a mathematical model that starts exponentially but it gets stabilized due to the capacity of population. Bayesian estimation method 34 It is a paramter estimation method that deals with paramters as random variables in a statistical model. Fudan-CCDC model 12 Developed model for the growth rate and CCDC stands for Chinese Center for Disease Control. Least square based method 35 It is a procedure to best fit data in statistics. In order to understand a measure of transmissibility of the new disease, a lot of preprints and papers were published in the last months (Table 3) 52 shown by a vertical blue line in Figure 1 , the R 0 started dropping down, based on the data in Table 1 . The dot chart in Figures 2 and 3 53 However, the average R 0 between 2 and 3 seems to have stabilised in recent articles shown in Table 2 . As more results to mention, there are various methods utilised in estimating R 0 as listed in Table 1 In the globalised world of today, the evolution of the outbreak and information on COVID-19 have become available at an unprecedented pace. Still, R 0 is not easy to calculate, especially there is much more to know about this new infection. The articles in Table 3 WHO. Coronavirus disease 2019 (COVID-19), situation report-80, Accessed 9th WHO. Emergency Committee regarding theoutbreak of Coronavirus disease 2019 (COVID-19) Modelling the Epidemic 2019-nCoV Event in Italy: a preliminary note The effects of border control and quarantine measures on global spread of COVID-19 Preliminary evaluation of voluntary event cancellation as a countermeasure against the COVID-19 outbreak in Japan as of 11 Estimating the reproduction number of COVID-19 in Iran using epidemic modeling. medRxiv Epidemiological benchmarks of the COVID-19 outbreak control in China after Wuhan's lockdown: a modelling study with an empirical approach The Emergence of COVID-19 in China Epidemiological development of novel coronavirus pneumonia in China and its forecast Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions. medRxiv The reproductive number R0 of COVID-19 based on estimate of a statistical time delay dynamical system. medRxiv Analysis of potential risk of COVID-19 infections in China based on a pairwise epidemic model COVID-19 outbreak on the Diamond princess cruise ship: estimating the epidemic potential and effectiveness of public health countermeasures Modelling the epidemic trend of the 2019-nCOV outbreak in Hubei Province Infection dynamics of coronavirus disease 2019 (Covid-19) modeled with the integration of the Eyring rate process theory and free volume concept Estimating the Reproductive Number and the Outbreak Size of Novel Coronavirus Disease (COVID-19) in South Korea Stochastic discrete epidemic modeling of COVID-19 transmission in the province of Shaanxi incorporating public health intervention and case importation. medRxiv Impact of Wuhan's Epidemic Prevention Policy on the Outbreak of COVID-19 in Wuhan Modeling analysis of COVID-19 based on morbidity data in Anhui, China Data-based analysis, modelling and forecasting of the COVID-19 outbreak Modeling the epidemic dynamics and control of COVID-19 outbreak in China Data analysis and modeling of the evolution of COVID-19 in Brazil Modelling and assessing the effects of medical resources on transmission of novel coronavirus (COVID-19) in Wuhan, China A SIDARTHE Model of COVID-19 Epidemic in Italy Preliminary estimating the reproduction number of the coronavirus disease (COVID-19) outbreak in Republic of Korea and Italy by 5 Health policy COVID-19 and Italy: what next? The Lancet A single holiday was the turning point of the COVID-19 policy of Israel. medRxiv Reporting, epidemic growth, and reproduction numbers for the 2019 novel coronavirus (2019-nCoV) epidemic Transmission dynamics of 2019 novel coronavirus (2019-nCoV) Pattern of early human-to-human transmission of Wuhan Transmission potential of COVID-19 in Iran. medRxiv Early transmissibility assessment of a novel coronavirus in Wuhan Estimating the generation interval for COVID-19 based on symptom onset data. medRxiv Prediction of the epidemic peak of coronavirus disease in Japan, 2020 Real-time estimation of the risk of death from novel coronavirus (COVID-19) infection: inference using exported cases Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond princess cruise ship: a data-driven analysis Report 3: transmissibility of 2019-nCoV Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia COVID-19: lessons from SARS and MERS Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study The timing of one-shot interventions for epidemic control Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts Modeling and forecasting trend of COVID-19 epidemic in Iran. medRxiv Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): the epidemic and the challenges Transmission potential of COVID-19 in South Korea. medRxiv Estimating the basic reproduction number of COVID-19 in Wuhan Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China. medRxiv Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: a data-driven analysis in the early phase of the outbreak COVID-19 outbreak in Algeria: a mathematical model to predict cumulative cases. medRxiv WHO. Emergency Committee regarding theoutbreak of novel coronavirus 2019 (n-CoV) The reproductive number of COVID-19 is higher compared to SARS coronavirus Early dynamics of transmission and control of COVID-19: a mathematical modelling study. medRxiv Local regression models Coronavirus latest: Italy death toll overtakes China's, Retrieved on COVID-19 R0: magic number vidual affected by a transmittable disease is or conundrum? The basic reproduction number of SARS-CoV-2 in Wuhan is about to die out, how about the rest of the World Along with new pandemic control measures introducing and treating procedures more mathematically desiged models are required to take account of all factors, in this point of view, the mathematical models are more recommended to be used. All in all, still R 0 is not easy to calculate especially there is much more to know about this novel virus. https://orcid.org/0000-0002-6695-155XAnnamaria Porreca https://orcid.org/0000-0003-3278-1561