key: cord-0919852-qi4nh9ix
authors: Wang, Zengmiao; Whittington, Jason; Yuan, Hsiang-Yu; Miao, Hui; Tian, Huaiyu; Stenseth, Nils Chr.
title: Evaluating the effectiveness of control measures in multiple regions during the early phase of the COVID-19 pandemic in 2020
date: 2021-09-13
journal: Biosaf Health
DOI: 10.1016/j.bsheal.2021.09.002
sha: cd36787da9204360810d6435073f3a5dff764092
doc_id: 919852
cord_uid: qi4nh9ix

The number of COVID-19 confirmed cases rapidly grew since the SARS-CoV-2 virus was identified in late 2019. Due to the high transmissibility of this virus, more countries are experiencing the repeated waves of the COVID-19 pandemic. However, with limited manufacturing and distribution of vaccines, control measures might still be the most critical measures to contain outbreaks worldwide. Therefore, evaluating the effectiveness of various control measures is necessary to inform policymakers and improve future preparedness. In addition, there is an ongoing need to enhance our understanding of the epidemiological parameters and the transmission patterns for a better response to the COVID-19 pandemic. This review focuses on how various models were applied to guide the COVID-19 response by estimating key epidemiologic parameters and evaluating the effectiveness of control measures. We also discuss the insights obtained from the prediction of COVID-19 trajectories under different control measures scenarios.

On 31 December 2019, a novel coronavirus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, although the name still in debate [1] ) was reported [2, 3] . However, due to the high human-to-human transmissibility (the basic reproductive number, R 0 2.2-2.7 [4] ), more than 175 million cases were reported, with 3 million deaths due to coronavirus disease 2019 worldwide by June 2021 [5] . In addition, the transmission also through asymptomatic cases [6] makes the disease control more challenging [7, 8] because many of them are not identified and isolated.

With the improvement in the case definition of COVID-19 [9, 10] and fast-growing accumulated cases, we gradually understood the transmissibility and severity of this virus in the first wave of China [11] ; many models are employed to provide timely and quantitative support. The choice of the models depends on the specific questions that need to be addressed. During the early stage of the pandemic, especially for a novel pathogen, the first question is to know the epidemiological parameters, e.g., the basic reproduction number (R 0 ) and serial interval. With these parameters, we will learn how fast an infectious disease will spread without any control and what will happen if the interventions are introduced. Transmission-dynamic models have been most widely used to estimate these parameters. Different differential equations are used to divide the population into separate compartments based on epidemiological status (for example, S for susceptible, E for exposed, I for infectious, and R for recovered in an S.E.I.R. model).

The following questions deal with how to treat infected patients and prevent others from getting infected. The development of drugs [12] [13] [14] [15] [16] is in progress, but for now, no drugs show significance in clinical outcomes [17] [18] [19] [20] . COVID-19 patients maintained SARS-CoV-2-specific IgG and neutralizing antibodies five or six months after infection [21, 22] , providing effective signs for prevention from reinfection and vaccination strategy [23] . Although the COVID-19 vaccines show safety and high protection against COVID-19 [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] , they will not be available widely soon due to limitations in manufacturing and distribution. Besides, it is still unknown how long the vaccines can protect us from the SARS-CoV-2 virus. Repeated outbreaks can occur if immunity can also maintain several months. Therefore, control measures are still significant measures to reduce transmission in the foreseeable future. The intensities of control measures vary greatly, from relatively flexible ones (for example, social distancing) to very stringent approaches (for example, lockdown). The effects of these control measures on COVID-19 transmission also vary correspondingly. It is important to quantify these effects quickly to inform the policy-makers to address the ongoing outbreaks. Therefore, various models are used to assess the effectiveness of different control measures [34] [35] [36] and evaluate the intervention strategies under different scenarios [37] [38] [39] [40] [41] .

To focus on how the models were applied to guide the COVID-19 pandemic response, we summarize the recent estimations of some key epidemiologic parameters and evaluate the effectiveness of control measures using various models in this review. We also discuss the insights obtained from the predictions of the trajectories of COVID-19 under different control measures scenarios, with the genomics and applications in the COVID-19 pandemic also summarized.

For the parameter estimation, the reproduction number (the average number of secondary cases each case generates) is of primary interest (see Table 1 for the definitions of epidemiological characteristics). It indicates that the disease continues to grow if the value is more than 1 and reduces in frequency but may die out if it is smaller than 1. Thus, this is the first important parameter usually assessed about the disease and directly indicates control measures.

In the early stage of the COVID-19 pandemic, diverse models with different assumptions were used to estimate the R 0 of the SARS-CoV-2 virus across regions/countries. Insightful results were obtained based on the COVID-19 case surveillance data. Using the model formulated renewal equations, R 0 was about 2.2 by January 22, 2020, in Wuhan [42] . By modelling the transmission with a Poisson process, this parameter in Wuhan was reported to be 2.7-4.0 before December 31, 2019 [43] and above 3.0 before January 26, 2020 [44] . In Niger, it fluctuated between 0.4 and 3.0, with a wide 95% CI before March 17, 2020, using the same Poisson process transmission model [45] . Following the classic S.E.I.R. (susceptible-exposed-infectiousrecovered) model, R 0 was 2.68 in Wuhan by January 25, 2020 [46] and 3.15 before the introduction of the emergency response on January 23, 2020, at the province level in China [47] . With the extension of a classic S.E.I.R. model to seven compartments (adding pre-symptomatic infectious, unascertained infectious, and isolation in hospital compartments), R 0 was estimated to be 3.54 before control measures were taken in Wuhan [48] . The S.I.R. model (similar to the S.E.I.R. model, but without E compartment) stratified by age groups and contact patterns were also proposed, and R 0 was 1-4 before interventions for Wuhan and Shanghai, China [49] . With the spread of this virus to the world, the R 0 was estimated at an international scale. In Japan, it was reported to be 4.66, calculated from a S.I.R.D. (susceptible-infectious-recovered-deceased) model [50] . Based on the likelihood-based estimation procedure for severe acute respiratory syndrome (SARS) [51] , R 0 was 4.02 on March 17 in the United States [52] and about 2.0 on March 20, 2020, in Canada [53] . Case surveillance data suffers from the limited PCR testing capacity or the undocumented infection [54] [55] [56] , especially for the emergence of a novel pathogen. Considering that, Flaxman et al. [57] proposed a set of statistical models (death model and infection model) based on death data to estimate R 0 . They performed a comprehensive analysis by including the countries of Europe. R 0 was estimated to be 3.8 (95%CI: 2.4-5.6) on average across all European countries. Although the variance of R 0 exists from different models or regions, all of them were larger than 1 (see Table 2 for R 0 before and after control measures). This was a consistent indication that the outbreak of SARS-CoV-2 would worsen, and actions should be taken to prevent the spread of this virus.

Besides R 0 , other parameters are also of interest and estimated using statistical methods, including incubation time, serial interval, generation time, and attack rate. The mean incubation period for the time window from Dec 24, 2019, to Feb 17, 2020, was estimated to be 5.2 days (95%CI: 1.8-12.4), and the mean serial interval at 5.1 days (1.3-11.6) outside Hubei province, China [58] . The generation time was 4.6 (95% CI: 4.2-5.1) days based on the data from Jan 1, 2020, to Feb 11, 2020, in China [59] . The attack rate of health care workers in Wuhan was about 11.9% [60] , and the secondary attack rate among household contacts was 12.4% (95% CI 9.8-15.4) [61] . Some other characteristics of COVID-19 were also significant and estimated. For example, rates of symptomatic cases, medical consultations, hospitalizations, and deaths in Wuhan from December 1, 2019, to March 31, 2020, were estimated to be 796 (95% CI: 703-977), 489 (95% CI: 472-509), 370 (95% CI: 358-384), and 36.2 (95% CI: 35.0-37.3) per 100,000 persons, respectively [62] . By March 2, 2020, The overall estimated case-fatality rate was 3.6% (1.1-7.2) [63] . After the COVID-19 pandemic was under control, the overall hospitalized case fatality rate (C.F.R.) in China was estimated to be 4.6% (95% CI 4.5%-4.8%) [64] , and the overall symptomatic case fatality risk of COVID-19 in Wuhan became 1.4% (0.9-2.1%) [65] . Clinical characteristics were also reported [66] . These epidemiological parameters enable us to deepen our understanding of this disease that provided vital information to policymakers.

We used google scholar to search the literature related to the effectiveness of control measures published from Dec 01, 2019, to Dec 30, 2020, in each country. The following keywords were used: non-pharmaceutical interventions (or NPIs, control measures), name of the country, and COVID-19 (or SARS-CoV-2). To include all possible control measures, we did not specify the control measures for each country. Instead, titles and abstracts of papers published in English were screened, meeting that a model (either a statistical or a mathematical model) was used to evaluate the effectiveness of implemented control measures on COVID-19 for countries. We excluded studies that evaluated the effects of control measures related to economics, other diseases, or the optimal control measure strategies.

On January 23, 2020, the first lockdown strategy in the world to prevent the dispersal of COVID-19 was implemented in Wuhan, China. At the same time, other types of control measures, such as case isolation, suspending public transport, closed down schools and entertainment venues, and banning public gatherings, were also applied. Additionally, the highest level of emergency for public health response was initialized nationally in China to prepare against outbreaks in other regions. In the pioneering analysis of evaluating the control measures' effectiveness in Wuhan, the Wuhan shutdown was related to the delayed arrival of COVID-19 in different cities in China by 2.91 days [47, 67] . Wuhan's 'travel restriction' strategy showed a more marked effect on the international scale, where case importations were reduced by nearly 80% until mid-February [68] . Most countries adopted similar interventions to contain the transmission of COVID-19, such as case-based self-isolation mandates, encouragement of social distancing, banning public events, and ordering school closures and lockdowns. During the 1918 influenza pandemic 100 years ago, similar control measures had also been employed [69, 70] . The various control measures [71] showed substantial effects on economics [72] and the physical and mental health of individuals [73] [74] [75] [76] [77] [78] [79] [80] . Therefore, evaluating the effectiveness of timely control measures is recognized as a crucial step to combat this virus.

The time-dependent reproduction number (R t ), also called effective reproduction number, was employed to evaluate the effects of combined control measures (see Table 2 ). For countries, this value was smaller than one after control measures, such as China in Asia [47, 48, 81] , and the 12 countries in Europe and North America [53, 57] . But for Japan [50] and the United States [52] , R t remained higher than 1. Other indicators were also used to illustrate the effect of control measures. Dehning et al. [82] focused on the spreading rate and found that it was 0.09 around 23 March 2020, showing the control measures were effective and new cases began to decline in Germany. Serial intervals were also used to inform the effectiveness of control measures. It was 7.8 days on average in mid-January 2020 and decreased to 2.2 days in early February 2020 in China after implementing control measures on Jan 23, 2020 [83] . The reduction of those indicators showed that the control measures were adequate to control COVID-19. However, the effectiveness of control measures appeared different in different countries, revealing differences in public response and cooperation with these measures.

Based on the Complexity Science Hub COVID-19 Control Strategies List (CCSL) [71] , the control measures are classified into a four-level hierarchical structure, from eight broad themes at level 1 to >2,000 group codes at level 4. With so many different control measures, it is essential to understand each type's effectiveness. A series of studies were conducted based on other models and criteria. In a comprehensive analysis across the 11 European countries, the researchers [57] found that lockdown had a significant identifiable effect on transmission (81% (75-87%) reduction) of the SARS-CoV-2 virus in terms of R t by a set of statistical models (death model and infection model). At the same time, public events bans, school closures, self-isolation, social distancing, etc., did not significantly reduce the transmission of the SARS-CoV-2 virus. Under a stochastic age-structured transmission model, the potential impact of four different control measures (school closures, physical distancing, shielding of people aged 70 years or older, and self-isolation of symptomatic cases) was assessed to mitigate the related burden of COVID-19 in the U.K. It was concluded that the four interventions were each likely to decrease R t , but not sufficiently to prevent I.C.U. demand from exceeding health service capacity, and only the lockdown periods were sufficient to bring R t below 1 [84] . The effectiveness of lockdown is consistent with a previous study of interventions [57] , and the same conclusion about the importance of lockdowns was also made in France [85, 86] . By extended S.E.I.R. model (two additional compartments: A, infectious and asymptomatic; F, dead) and variance analysis, Chiu et al. [87] found that the fraction of contacts traced was one of the most significant drivers of variation in R t in the U.S. and other most significant drivers including hygiene effectiveness relative to social distancing, the degree of mitigation during shelter-in-place, and maximum relative increase in contacts from shelter-in-place. They together contribute >50% of the variance in R t . The daily growth rate of COVID-19 cases was also used to evaluate the effectiveness of control measures. Lin et al. [88] studied the effect of the "stay-at-home" order from March 16, 2020, to April 10, 2020, at the state level in the United States using Poisson regression. They found that the growth rate had slowed down since March 29, 2020, with an average reduction of 12.8%. Courtemanche et al. [89] employed an event regression study and found that the shelter-in-place orders in the U.S. had led to statistically significant (p < 0.01) reductions in the COVID-19 case. The growth rates reduced by 3.0% after six to ten days, 4.5% after eleven to fifteen days, 5.9% after sixteen to twenty days, and 8.6% from twenty-one days onward. Their study also showed that closing restaurant dining rooms and bars or entertainment centers and gyms led to significant reductions in the growth rate of COVID-19 cases in all periods after introduction. At the same time, public school closure did not affect the daily growth rate. As for mandating face mask use in public, it was reported that this measure was associated with a decline in the daily COVID-19 growth rate by 0.9%, 1.1%, 1.4%, 1.7%, and 2.0% in 1-5, 6-10, 11-15, 16-20, and 21 or more days after state face mask orders were signed, respectively [90] .

Although different models and criteria were employed, the conclusion was reached that control measures are adequate to control the transmission of the SARS-CoV-2 (see Table 3 for the evaluated control measures). The lockdown was the most effective one in studies from Wuhan, China to Europe of the various control measures. The importance of early intervention and aggressive control [91] , contact tracing [92, 93] , and face masks [94] , and eye protection [95] in combatting the COVID-19 pandemic were also highlighted. In addition, the model also explained the sub-exponential growth of COVID-19 cases before February 2020 in China [96] and the impact of socioeconomic factors on the transmission of this virus [97, 98] . An interesting finding is that the control measures reduced not only the transmission of COVID-19 but also other respiratory infectious diseases [99] [100] [101] [102] .

Big data has been widely used to investigate the COVID-19 transmission in the current pandemic [103] [104] [105] [106] [107] [108] [109] [110] [111] . Successful examples include the human-mobility-based transmission-dynamic model. With the assistance of big data and powerful computational resources, a more detailed picture of SARS-CoV-2 dynamics will be available.

Human movement is responsible for spreading infectious diseases, and data on movement patterns have been successfully applied to understand the transmission of infectious diseases [112] [113] [114] [115] [116] . Previous studies have shown that the spread of COVID-19 from Hubei province to other provinces of China was driven primarily by human mobility [117] [118] [119] [120] [121] [122] . Multiple transmission-dynamic models are needed to integrate the mobility data for the areas of interest in general. The mobility from one place to another is incorporated into the transmissiondynamic models for these two regions and represents the compartments flow between these two regions. By taking all the transmission-dynamic models as a whole, related parameters can be estimated. At the exact time, related public health efforts can be quantified, and the control measures can be evaluated. Aggregating mobile phone data in Shenzhen, China to the S.E.I.R. model, Zhou et al. [104] found that if intra-city mobility were reduced by 20%, the epidemic peak would have been delayed for about two weeks, with peak incidence decreasing 33%. If the reduction increased to 40%, the peak would be delayed by four weeks and the peak daily incidence reduced by around 66%. A 60% reduction in mobility delayed the peak by 14 weeks and decreased the magnitude of the epidemic by 91% (see Table 4 ). A study in the U.K. showed that human mobility reduction significantly impacted reducing COVID-19-related deaths [123] . Using COVID-19 data and travel network information, Lai et al. [124] developed a stochastic S.E.I.R. framework to simulate different outbreaks and interventions (early detection, isolation of cases, travel restrictions, and contact reductions) across China. They found that the effectiveness of other control measures varied. Early detection and isolation of patients averted more cases than did travel restrictions and contact reductions. However, the combination of these control measures was found to achieve the most vigorous and most rapid effect. Schlosser et al. [125] found that COVID-19 lockdown induces structural changes in mobility networks in Germany while distancing and isolation can flatten the curve with a S.I.R. model integrated with the mobility network. Using data on high-speed train passengers from 19 December 2019 through 6 March 2020 in China, Hu et al. [126] found that COVID-19 has a high transmission risk among train passengers. Still, this risk showed significant differences with co-travel time and seat location. The transmission rate decreased with increasing distance traveled and increased with increasing co-travel time. Besides, smartphone applications through Bluetooth or G.P.S. site to track interaction between individuals are proposed to increase efficiency of contact tracing [127] [128] [129] [130] .

Information sharing and transmission have dramatically improved due to social media tools, such as Facebook, Twitter, YouTube, and WhatsApp [131] . With these platforms, public health guidelines about COVID-19 reached the world with incredible speed. Based on the penetration of social media, many helpful topics were investigated during the pandemic, such as public attention [132] [133] [134] , predicting the epidemic outbreak [135, 136] , and human sentiments [137] . The spatial transmission of COVID-19 in Wuhan, China was also studied using the geo-tagging Sina Weibo data [138] .

Irrespective of the specific approaches, the critical question is what we can learn from the current pandemic responses. The governments in each country adopted customized control measures strategies to contain the spread of COVID-19 cases based on their politics, economics, and culture, ranging from stringent lockdowns, the "Flatten the curve" approaches, to voluntary control measures. Although control measures vary, they can generally be classified into two groups (see Table 5 ): 1) Suppression; 2) Mitigation [139] ). Suppression aims to reduce R t to below one and eliminate human-to-human transmission gradually. Mitigation seeks to slow down the speed of the virus transmission to delay the arrival time of the pandemic peak to have more time to prepare medical supplies and avoid the over-extension of medical resources to minimize avoidable deaths. In terms of which strategies were employed by different countries, China, South Korea, and India opted to focus on suppression, while most other countries pursued mitigation. However, the results of the control measures strategies are still challenging to predict. India employed the Suppression strategy and locked down the whole country from March 25, 2020, to May 31, 2020. Yet, R t is still above 1 for most states in India, except Gujarat [140] . Sweden took a unique approach of not implementing strict closures instead of relying on personal responsibility. Despite such loose control measures, R t was strikingly below 1 [57] . A study [141] showed that mild mandates overlaid with voluntary standards could achieve results highly similar to late-onset strict orders in an individual-based model. The data from Google and Apple reported an 18%-33% decrease in the workplace during April [141, 142] in Sweden supports this conclusion. The phenomenon in India and Sweden may be caused by several factors, such as differences in population density, the degree of policy implementation, the willingness of public cooperation, and the quality of health services. If the public follows the guidelines well, lockdown may be unnecessary.

Another essential feature from modeling is that we can predict the trajectory of COVID-19 under different control measures scenarios and appropriately inform policy-makers (see Table 6 ). According to the stochastic S.E.I.R. model from a previous study [124] , if population contact were to resume to normal levels, the lifting of travel restrictions might lead to another wave of infections. Thus, maintaining social distancing, Chang et al. [143] proposed a metapopulation susceptible-exposed-infectious-removed (S.E.I.R.) model that integrates fine-grained, dynamic mobility networks in ten of the largest U.S.metropolitan areas. According to their model, a small minority of 'super-spreaders points of interest (such as restaurants and religious establishments) account for a large majority of the infections, and restricting the maximum occupancy at each end of interest is more effective than uniformly reducing mobility. The IHME COVID-19 Forecasting Team [144] uses a deterministic S.E.I.R. (susceptible, exposed, infectious, and recovered) model to predict possible trajectories of COVID-19 in the United States from 22 September 2020 through 28 February 2021. They found that the universal mask use (95% mask use in public) could be sufficient to avert the worst effects of epidemic resurgences in many states. This strategy could save an additional 129,574 (85, 867 ) lives from September 22, 2020, through the end of February 2021, or another 95,814 (60,731-133,077) lives, assuming 85% of mask-wearing, when compared to the current social distancing mandates. Using mobility and case data, Ruktanonchai et al. [145] indicated that appropriate coordination could significantly improve the likelihood of eliminating community transmission throughout Europe.

With the development of next-generation sequencing technology, the SARS-CoV-2 genome was quickly released and shared [146] . The work to trace the zoonotic origin of SARS-CoV-2 was also performed using the methods from the genomics field. Although the genome sequence showed that this novel virus has high similarity to the bat coronavirus RaTG13 with an overall genome sequence identity of 96.2% [147] , there is a possibility of decades of evolutionary divergence between them [148] . The SARS-CoV-2 virus was closely related to SARS-like coronaviruses [146] . SARS-CoV-2 affects more regions within a shorter time than SARS-CoV, infecting older people with a higher probability [149] . However, the pathway of transmission from zoonotic reservoirs to humans is still unknown. Therefore, multi-source discipline methods (for example, geography and molecular epidemiology) and multi-source data (for example, human mobility and sequences from wild animals) are needed.

By now, there have been over 250,000 SARS-Cov-2 genome sequences deposited worldwide [150] . The most common SARS-Cov-2 virus clusters/clades in a region/country can be identified from a phylogenetic analysis of these sequences. The mutation diversity of the SARS-CoV-2 virus can be monitored [151] to identify the virus with greater transmissibility or pathogenicity (see Table 7 ). For example, the SARS-CoV-2 D614G variant is prevalent globally and transmits significantly faster, and displays increase competitive fitness than the wild-type virus in hamsters [152] . During the early stage of the COVID-19 pandemic in Washington state in the United States, researchers found that 84% of viral cases analyzed were grouped into a clade with SNPs of C8782T, C17747T, A17858G, C18060T, and T28144C, 9% were grouped into a smaller clade, and the rest of the viral cases were distributed across the phylogenetic tree [153] . Using the sequences from May 20, 2020, Yang et al. [154] found that the clade, featuring SNPs 241T, 3037T, 14408T, 23404G, 25563T, and 1059T, was dominant (∼51.07%) in most states of the United States. In Brazil, 490 sequences collected from December 24, 2019, to April 30, 2020, were classified into three clades [155] . The first clade (38% of Brazilian strains) was featured with a nucleotide substitution (G25088T) in the spike protein in São Paulo state. The second clade (34%) had two SNPs in ORF6 (T27299C) and nucleoprotein (T29148C), and the third clade (4%) was especially prevalent in the Ceará state. It showed that 80% of secondary infections traced back to 15% of SARS-CoV-2 primary infections, indicating the heterogeneity in the transmission of COVID-19 [156] . Similarly, the super-spreading events in Boston also revealed high heterogeneity according to the phylogenetic analysis [157] . A long-term projection was also obtained from a deterministic model of multiyear interactions between existing coronaviruses for the United States [158] . Emerging new COVID-19 lineages, such as B.1.1.7 [160] and P.1 [161] , have shown higher transmissibility than the existing ones. A recent study in France shows that current vaccination rates may not be sufficient to compensate for the increased transmissibility of B.1.1.7 [159] , which indicates that the control measures are still needed. Although the vaccines are being rolled out, a disturbing question is whether the vaccines are still effective against new lineages. For example, B.1.351 may be associated with immune escape [160, 161] . If this is true, it seems that we need rely on control measures. Considering the rapid evolution of SARS-CoV-2, suitable control measures will be proposed based on the prediction and the current status of SARS-CoV-2 clades [159, [162] [163] [164] [165] .

This review discusses how the models (such as S.I.S., S.I.R., and S.E.I.R.) can be applied to guide COVID-19 response [47, 50] . Based on these models, we summarize the estimations of some key epidemiology parameters and evaluate the effectiveness of control measures. Using the available data type and the experience of the modelers, the statistical model is another option [52, 53] . In terms of evaluating the effectiveness of control measures, most researchers adopt the use of transmission-dynamic models. Conclusions from models are consistent that the control measures were effective [166, 167] , and lockdown is the most effective way to mitigate the spread of the virus [168] . However, the effectiveness of control measures was evaluated in only a small proportion of countries. We suggest that more studies may be needed for more countries to complete the control measures, especially for low-income countries. With the complete characterization of the SARS-CoV-2 virus, more effective strategies can be applied to combat this virus.

We also discuss the insights obtained from predictions of the trajectories of COVID-19 under different control measures scenarios. By a phylogenetic tree analysis, we can get the complete picture of SARS-CoV-2 clades worldwide and monitor the mutation diversity to guard against the emergence of new strains using the massive genome sequence data. The transmission dynamics of COVID-19 are complicated, which is influenced by the nature of the virus [169] [170] [171] , human behavior [172] , and meteorological factors [173] [174] [175] . Due to the difficulty in theory and computation, models have to simplify this process and focus on specific elements. Better data is needed [67] to get a more realistic model. More attention should be paid to inappropriate assumptions and the misinterpretation of data or results, especially for emerging infectious diseases with limited prior knowledge. Based on the study, we proposed some suggestions for policymakers to contain the COVID-19 outbreak. First, if the COVID-19 situation is complicated, a lockdown policy may be employed to cut the transmission chain and bring R t under one as soon as possible. And supplemented actions should be taken to curtail the spreading of the virus indeed. Second, the measures should be taken as quickly as possible in case of missing the control window. Since more contagious COVID-19 lineages are emerging, it seems that not much time is left if the more contagious COVID-19 clans seed in a region. Third, improving the public understanding of COVID-19 may increase general compliance with the control strategy. The individuals may be self-isolated when they realized the danger of the invisible enemy.

Although we tried to normalize and summarize the results from previous research, due to the variation of the data sources and models, the comparison of results from the literature may have left some unavoidable bias in this review. Furthermore, the evaluation of the effectiveness of control measures may be affected by other factors (e.g., the political environment and citizen consciousness) that were not considered in most of the previous research. Finally, whether the outcomes of control measures can be replicated in other parts of the world still needs further investigation. The duration of time between onset of symptoms in a primary case and a secondary case. *same with above one 

Reducing case numbers to low levels and maintaining that situation indefinitely.

Most of other country (e.g., Sweden, US) [139, 141, 142] * Although the intervention strategies are summarized into two categories, it is hard to decide the category for each country, since most of the control measures are the combination of mitigation and suppression strategies. 

A distinct name is needed for the new coronavirus

A Novel Coronavirus from Patients with Pneumonia in China

Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding

High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2, Emerg

An interactive web-based dashboard to track COVID-19 in real time

Effective control of SARS-CoV-2 transmission in Wanzhou, China

Importing coronavirus disease 2019 (COVID-19) into China after international air travel

Transmission of 2019-nCoV Infection from an Asymptomatic Contact in Germany

Effect of changing case definitions for COVID-19 on the epidemic curve and transmission parameters in mainland China: a modelling study

Estimating effects of intervention measures on COVID-19 outbreak in Wuhan taking account of improving diagnostic capabilities using a modelling approach

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Anti-C5a antibody IFX-1 (vilobelimab) treatment versus best supportive care for patients with severe COVID-19

Tocilizumab among patients with COVID-19 in the intensive care unit: a multicentre observational study

Remdesivir in adults with severe COVID-19: a randomised, double-blind

Rapid repurposing of drugs for COVID-19

No evidence of clinical efficacy of hydroxychloroquine in patients hospitalized for COVID-19 infection with oxygen requirement: results of a study using routinely collected data to emulate a target trial

Outcomes of Hydroxychloroquine Usage in United States Veterans Hospitalized with COVID-19

No evidence of rapid antiviral clearance or clinical benefit with the combination of hydroxychloroquine and azithromycin in patients with severe COVID-19 infection

Lopinavirritonavir in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, openlabel, platform trial

Robust neutralizing antibodies to SARS-CoV-2 infection persist for months

Robust Antibody and T Cell Responses in COVID-19 Patients Six Months After Infection

A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2

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Rapid COVID-19 vaccine development

Monte Carlo simulation of surveillance strategies for scrapie in Norwegian sheep

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Results of epidemic simulation modeling to evaluate strategies to control an outbreak of foot-and-mouth disease

An evaluation of alternate control strategies for foot-and-mouth disease in Australia: a regional approach

Development of the Dutch Johne's disease control program supported by a simulation model

A state-transition model to simulate the economics of bovine virus diarrhoea control

Simulation analyses to evaluate alternative control strategies for the 2002 foot-and-mouth disease outbreak in the Republic of Korea

CLEC) Group, Transmission Dynamics of COVID-19 in Mainland China: Impact of Public Health Control Measures

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An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China

Reconstruction of the full transmission dynamics of COVID-19 in Wuhan

Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

The effect of non-pharmaceutical interventions (NPIs) on the spread of COVID-19 pandemic in Japan: A modeling study

Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures

Time-Varying COVID-19 Reproduction Number in the United States

Assessing the progression of the COVID-19 pandemic in Canada using testing data and time-dependent reproduction numbers

Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)

Using influenza surveillance networks to estimate statespecific prevalence of SARS-CoV-2 in the United States

Underdetection of COVID-19 cases in France threatens epidemic control

Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study

Epidemiological parameters of COVID-19 and its implication for infectivity among patients in China

Epidemiology of and Risk Factors for COVID-19 Infection among Health Care Workers: A Multi-Centre Comparative Study

Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study

Disease burden and clinical severity of the first pandemic wave of COVID-19 in Wuhan, China

Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: A systematic review and meta-analysis

The differential demographic pattern of coronavirus disease 2019 fatality outside Hubei and from six hospitals in Hubei, China: a descriptive analysis

Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China

Clinical features of patients infected with 2019 novel coronavirus in

The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak

Nonpharmaceutical interventions implemented by U.S. cities during the 1918-1919 influenza pandemic

Nonpharmaceutical interventions for pandemic influenza, national and community measures

A structured open dataset of government interventions in response to COVID-19

COVID-19 outbreak: Migration, effects on society, global environment and prevention

Mental Health and the Covid-19 Pandemic

The mental health impact of the COVID-19 pandemic on people with and without depressive, anxiety, or obsessive-compulsive disorders: a longitudinal study of three Dutch casecontrol cohorts

Bidirectional associations between COVID-19 and psychiatric disorder: retrospective cohort studies of 62 354 COVID-19 cases in the

Diagnosis of physical and mental health conditions in primary care during the COVID-19 pandemic: a retrospective cohort study

Association between mental illness and COVID-19 susceptibility and clinical outcomes in South Korea: a nationwide cohort study

Immediate impact of stay-at-home orders to control COVID-19 transmission on socioeconomic conditions, food insecurity, mental health, and intimate partner violence in Bangladeshi women and their families: an interrupted time series

Mental health before and during the COVID-19 pandemic: a longitudinal probability sample survey of the U.K. population

The unfolding COVID-19 pandemic: A probability-based, nationally representative study of mental health in the United States

The importance of the timing of quarantine measures before symptom onset to prevent COVID-19 outbreaks -illustrated by Hong Kong's intervention model

Inferring change points in the COVID-19 spreading reveals the effectiveness of interventions

Serial interval of SARS-CoV-2 was shortened over time by nonpharmaceutical interventions

Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study

Estimating the burden of SARS-CoV-2 in France

Evaluating the impact of curfews and other measures on SARS-CoV-2 transmission in French Guiana

State-level needs for social distancing and contact tracing to contain COVID-19 in the United States

Statewide Stay-At-Home Directives on the Spread of COVID-19 in Metropolitan and Nonmetropolitan Counties in the United States

Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate

Community Use Of Face Masks And COVID-19: Evidence From A Natural Experiment Of State Mandates In The US, Health Aff

Differential effects of intervention timing on COVID-19 spread in the United States

Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time

Mask-wearing and control of SARS-CoV-2 transmission in the USA: a cross-sectional study

Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis

Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China

The impact of COVID-19 and strategies for mitigation and suppression in low-And middleincome countries

Epidemiology and transmission dynamics of COVID-19 in two Indian states

Nonpharmaceutical Interventions Used to Control COVID-19 Reduced Seasonal Influenza Transmission in China

Different transmission dynamics of COVID-19 and influenza suggest the relative efficiency of isolation/quarantine and social distancing against COVID-19 in China

Isolation/Quarantine Combined with Social Distancing Contained the Coronavirus Disease 2019 Outbreak in China

Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study

Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study

Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: a modelling study using mobile phone data

Rapid implementation of mobile technology for real-time epidemiology of COVID-19

Pre-symptomatic detection of COVID-19 from smartwatch data

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

Digital technologies in the public-health response to COVID-19

Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle

The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology

Quantifying the online news media coverage of the COVID-19 pandemic: Text Mining Study and Resource

Quantifying the impact of human mobility on malaria

Impact of human mobility on the emergence of dengue epidemics in Pakistan

Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics

Aggregated mobility data could help fight COVID-19

Human mobility, networks and disease dynamics on a global scale

Preliminary risk analysis of 2019 novel coronavirus spread within and beyond China

The effect of human mobility and control measures on the COVID-19 epidemic in China

Quantifying the association between domestic travel and the exportation of novel coronavirus (2019-nCoV) cases from Wuhan, China in 2020: A correlational analysis

Assessing spread risk of Wuhan novel coronavirus within and beyond China

Impact of population movement on the spread of 2019-nCoV in China

Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study

The impact of government measures and human mobility trend on COVID-19 related deaths in the UK

Effect of non-pharmaceutical interventions to contain COVID-19 in China

COVID-19 lockdown induces disease-mitigating structural changes in mobility networks

Risk of Coronavirus Disease 2019 Transmission in Train Passengers: an Epidemiological and Modeling Study

NHS COVID-19 app -NHSX

COVIDSafe app | Australian Government Department of Health

Social Media and Emergency Preparedness in Response to Novel Coronavirus

Causal Modeling of Twitter Activity During COVID-19

Characterizing the Propagation of Situational Information in Social Media during COVID-19 Epidemic: A Case Study on Weibo

Chinese public's attention to the COVID-19 epidemic on social media: Observational descriptive study

Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study

Using twitter and web news mining to predict COVID-19 outbreak, Asian Pac

The impact of covid-19 epidemic declaration on psychological consequences: A study on active weibo users

Exploring Urban Spatial Features of COVID-19 Transmission in Wuhan Based on Social Media Data

Impact of nonpharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand

A Comprehensive Public Health Evaluation of Lockdown as a Non-pharmaceutical Intervention

Spread in India: National Trends Masking State Level Variations

COVID-19 -Mobility Trends Reports -Apple

Mobility network models of COVID-19 explain inequities and inform reopening

Modeling COVID-19 scenarios for the United States

Assessing the impact of coordinated COVID-19 exit strategies across Europe

A new coronavirus associated with human respiratory disease in China

A pneumonia outbreak associated with a new coronavirus of probable bat origin

Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic

Outbreak of COVID-19 and SARS in mainland China: A comparative study based on national surveillance data

GISAID: Global initiative on sharing all influenza data -from vision to reality

Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2

SARS-CoV-2 D614G variant exhibits efficient replication ex vivo and transmission in vivo

Cryptic transmission of SARS-CoV-2 in Washington State

Uncovering two phases of early intercontinental COVID-19 transmission dynamics

Evolution and epidemic spread of SARS-CoV-2 in Brazil

Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2

Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period

A race between SARS-CoV-2 variants and vaccination: The case of the B.1.1.7 variant in France

Will SARS-CoV-2 variants of concern affect the promise of vaccines?

Sensitivity of infectious SARS-CoV-2 B.1.1.7 and B.1.351 variants to neutralizing antibodies

Short and medium-term challenges for COVID-19 vaccination: from prioritisation to the relaxation of measures

Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England

Age groups that sustain resurging COVID-19 epidemics in the United States

Inferring the effectiveness of government interventions against COVID-19

The temporal association of introducing and lifting non-pharmaceutical interventions with the time-varying reproduction number (R) of SARS-CoV-2: a modelling study across 131 countries

Association of tiered restrictions and a second lockdown with COVID-19 deaths and hospital admissions in England: a modelling study

Temporal dynamics in viral shedding and transmissibility of COVID-19

SARS-CoV-2, SARS-CoV, and MERS-CoV viral load dynamics, duration of viral shedding, and infectiousness: a systematic review and metaanalysis

Viral presence and immunopathology in patients with lethal COVID-19: a prospective autopsy cohort study

Cold-chain food contamination as the possible origin of COVID-19 resurgence in Beijing

Impacts of transportation and meteorological factors on the transmission of COVID-19

Meteorological conditions and nonpharmaceutical interventions jointly determined local transmissibility of COVID-19 in 41 Chinese cities: A retrospective observational study

Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic

The authors declare that there are no conflicts of interest.