key: cord-1049884-tubled69 authors: Chen, Yi-Hsuan; Fang, Chi-Tai; Huang, Yu-Ling title: Effect of Non-lockdown Social Distancing and Testing-Contact Tracing During a COVID-19 Outbreak in Daegu, South Korea, February to April 2020: A Modeling Study date: 2021-07-29 journal: Int J Infect Dis DOI: 10.1016/j.ijid.2021.07.058 sha: e9f6512ae1e111889f9eaf1e4ed616c641269533 doc_id: 1049884 cord_uid: tubled69 OBJECTIVE: In Spring 2020, South Korea applied non-lockdown social distancing (avoiding mass gathering and non-essential social engagement, without restricting movement of people who were not patients or contacts), testing-and-isolation (testing), and tracing-and-quarantine the contacts (contact tracing), to successfully control the first large-scale COVID-19 outbreak outside China. However, the relative contributions of these two interventions remain uncertain. METHODS: We constructed an SEIR model of SARS-CoV-2 transmission (disproportionately through superspreading events), and fit the model to outbreak data in Daegu, South Korea, February to April 2020. We assessed effect of non-lockdown social distancing (population-wide control measures) and/or testing-contact tracing (individual-specific control measures), alone or combined, in term of basic reproductive number (R0) and the trajectory of epidemic. RESULTS: The point estimate for baseline R0, is 3.6 (sensitivity analyses range: 2.3 to 5.6). Combined interventions of non-lockdown social distancing and testing-contact tracing can suppress R0 to less than 1, and rapidly contain the epidemic, even under the worst scenario with a high baseline R0 of 5.6. In contrast, either intervention alone will fail to suppress R0. Non-lockdown social distancing alone just postpones the peak of the epidemic, while testing-contact tracing alone only flattens the curve but does not contain the outbreak. CONCLUSIONS: To successfully control a large-scale COVID-19 outbreak, both non-lockdown social distancing and testing-contact tracing must be implemented. The two interventions are synergistic. By July 19, 2021 , there are more than 188 million confirmed cases of coronavirus disease 2019 (COVID-19) and 4 million deaths globally [1] . Strict lockdown (confining people at home or shelter) has been widely enforced to limit the spread of its etiological agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [2] , but remains highly controversial due to the profound negative impacts on the society [3] . There is an urgent need for alternative strategies to control this pandemic. On February 18, 2020, the authority detected a cluster of COVID-19 cases in Daegu, South Korea, which rapidly escalated to the first large-scale outbreak outside of China [4] . Instead of lockdown, South Korea used non-lockdown social distancing (avoiding mass gathering and non-essential social engagement, without restricting movement of people who are not patients or contacts) combined with quarantining infectious individuals through testing-andisolation (testing) and tracing-and-quarantine the contacts (contact tracing), and controlled the outbreak rapidly within 4 weeks [5] [6] [7] . However, non-lockdown social distancing yielded mixed results elsewhere [1] , for which the causes remain unsettled. We hypothesize that prompt quarantining infectious persons is a pre-requisite for the success of a less strict policy on social activities. In this modeling study, we aimed to examine this hypothesis by assessing effects of non-lockdown social distancing and testing-contact tracing, alone or combined, on the basic reproductive number (R0) of SARS-CoV-2 and the trajectory of the COVID-19 outbreak in Daegu, South Korea, February to April 2020. This work was initiated, upon request, on February 25, 2020, to help South Korea to control the COVID-19 outbreak in Daegu. Preliminary results had been presented in special meetings with Korean Mission in Taipei, on February 25 and March 6, 2020, and the meeting report had been delivered to the South Korean government. This work had been presented as oral abstract INT4622 at the International AIDS Society COVID-19 Conference: Prevention (Virtual) on February 2, 2021. This is a modeling study based on Korean Centers for Disease Control (KCDC) daily statistics without identifiable individual data, and therefore, does not require ethical review and approval. We constructed a dynamic model of SARS-CoV-2 transmission disproportionately through superspreading events (SSEs) [8, 9] , and fit the model to outbreak data in Daegu, South Korea, February to April 2020. An earlier version of our model, specifically adapted for informing Taiwan national policymaking, had been published [10] . The SEIR model ( Figure 1 ) includes key features in natural history of SARS-CoV-2 infection, including an incubation period of 3.0 days [11] , a pre-symptomatic infectious period of 2.5 days [12] [13] [14] , and an infectious duration of 8.5 days (up to 6.0 days after illness onset) [15] (all values are mean duration). The model considers a very high heterogeneity in SARS-CoV-2 transmission [8, 9, 16] , with 14.7% of infectious individuals as superspreaders who caused 80% of new secondary infections [16] . Infectious individuals, superspreaders or non-superspreaders, can be asymptomatic throughout the course (10%) [17] [18] [19] . Infectiousness of asymptomatic infection is 30% lower than that of symptomatic infections [20, 21] . Model parameters are listed in Table 1 . Model equations are available in Supplementary Material 1. Since the epidemic at Daegu, South Korea, was initiated by COVID-19 cases imported from Wuhan, China, we used publicly available epidemiological data from Wuhan to estimate the baseline (before intervention) R0 (the average number of secondary cases generated by an infectious individual in a non-immune, all susceptible population) of the SARS-CoV-2 strain in this outbreak. China Centers for Disease Control and Prevention released the COVID-19 epidemic curve in Wuhan, by illness onset date, in an epidemic situation report on January 28, 2020 [22] . An exponential growth of COVID-19 epidemic occurred during the period from January 6, 2020 to January 22, 2020, with a linear R-square reaching 98% in regressing natural logarithm of numbers of incident cases (including both laboratory-confirmed and suspected cases, since numbers of the former were limited by diagnostic capacity) on calendar date. We estimated the transmission probability per contact, b, in the following formula of baseline R0 [23] , by fitting our model to the slope of exponential phase (January Table 1 . for referenced details) We used the numbers of Shincheonji church members and their close contacts in Daegu, the predominantly affected group [24] , to estimate the effective population size of this outbreak. We After validation of the model, we examined the effect of non-lockdown social distancing and testing-contact tracing, alone or combined, on R0 and trajectory of the COVID-19 epidemic: Non-lockdown Social Distancing. Non-lockdown social distancing decreases transmission through rendering a fraction (50% to 75%) of contact rate between susceptible and infectious individuals, or − , less effective due to physical distancing between people. For this fraction , we assume that the probability of transmission per contact, , decreases by a proportion ( ) of 90% [27] . We model the overall effect of non-lockdown social distancing on the average probably of transmission by the following formula: Testing-Contact Tracing. Testing-and-isolation detects and quarantines infectious individuals who are symptomatic by a rate of ( Figure 1 ). For Shincheonji religious group members in the Daegu outbreak, it took an average of 5.3 days from the onset of symptom to laboratory confirmation [24] before quarantine, equivalent to = 1/7.8 days (after adding the 2.5 day pre-symptomatic infectious period). Tracing contacts of detected infectious individuals allows early quarantine of persons who are infectious ( Figure 1 ) by a proportion of , ranging from 0% to 90%. The overall effect of test-contact tracing on R0 is given by Here, ℎ is proportion of symptomatic infectious individuals whose diagnosis is delayed till hospitalization due to severe/critical COVID-19. We conduct two-way sensitivity analyses on the estimate baseline for R0 across uncertainty ranges of latency period and infectious period (Table 1 for details). We assess the impact of interventions on R0 and epidemic trajectory across different levels of social distancing or testing-contact tracing, alone or combined. The point estimate for baseline R0 is 3.6 (95% confidence interval: 3.4-3.9). The two-way sensitivity analyses (sensitivity analyses range: 2.3 to 5.6) shows that the maximum estimate at the worst-case scenario is 5.6, when the latent period is 5 days and the infectious period is 11.6 days (Supplementary Figure 1) . We examined effect of single intervention on R0. Either non-lockdown social distancing or testing-contact tracing decreases R0 ( Figure 3 ). However, in the absence of testing-contact tracing, non-lockdown (50% to 75%) social distancing measures alone would not suppress R0 to less than 1 when baseline R0 is 3.6 (point estimate) or higher (Figure 3, Panel A) . On the other hand, without social distancing measures, feasible testing-contact tracing (with a mean interval from to illness onset to isolation of four days, and tracing 50% to 75% contacts) alone would also fail to suppress R0 to less than 1 when baseline R0 is 3.6 (point estimate) or In contrast to single interventions, combined interventions with non-lockdown (50%) social distancing and test-contact tracing (50%) decrease the R0 to less than 1 when the baseline R0 To our knowledge, this is the first study to show that the presence of testing-contact tracing is Previous studies showed that non-pharmacological interventions (testing-contact tracing and social distancing) were effective in reducing SARS-CoV-2 transmission in South Korea [5, 7, 28] , but it is difficult for empirical studies to separately measure respective effects of these two almost simultaneously implemented interventions. Using a mathematical modeling approach to examine hypothetical scenarios, our study provided new insight on the essential conditions for an effective SARS-CoV-2 epidemic control. SARS-CoV-2 spreads predominantly through SSEs, in which a small number of infectious individuals are associated with the vast majority of secondary infections [16, [29] [30] [31] [32] [33] . This suggests that early quarantine of a few superspreaders may yield an out-of-proportion effect to control the spread of SARS-CoV-2 [8, 34, 35] . Althouse et al. suggested that eliminate SSEs through extensive testing and contact tracing might make it possible to contain the epidemic without strict lockdown [34] . Based on the empirical data from the Daegu outbreak in 2020, the present work is the first study to demonstrate that testing-contact tracing does have an essential role in South Korea's successful control of a large-scale COVID-19 outbreak using non-lockdown approach. An important unsettled question is whether targeting the control effort in "problem places", where SSEs occur, can suppress SARS-CoV-2 transmission to allow the lifting of restrictive public health measures such as physical distancing and mask mandate elsewhere [34, 35] . Our modeling results show that, theoretically, this "target control" approach is indeed capable of suppressing R0 to less than 1 when simultaneously implemented with testing-contact tracing (50% to 75%) (Supplementary Figure 2) . Nevertheless, practically, it might be challenging to preemptively identify all such places before SSEs occur. Previous modeling studies show that, when superspreading is the predominant transmission pattern, individual-specific control measures (testing-contact tracing) outperform populationwide control measures (social distancing) [8] . In keeping with this general rule, the present modeling work in analyzing the Daegu outbreak show that testing-contact tracing alone will outperform social distancing alone in suppressing the R0 of SARTS-CoV-2 ( Figure 5 ) and controlling the COVID-19 outbreak ( Figure 6 ). However, delay in laboratory confirmation is the Achilles' heel of SARS-CoV-2 testing [34] [35] [36] . Studies showed that it took up to 3 days (average) from symptom onset to quarantine in Seoul, Korea [36] , because initial symptoms are often non-specific [37] . For less cooperative Shincheonji religious group members who avoided epidemiological investigation, there was an even longer delay (5.3 days) between illness onset and laboratory confirmation [24] . Furthermore, transmission frequently occurs before the onset of illness [12-14, 17, 18, 20, 21] . Therefore, population-wide control measures (social distancing or mask), which universally decrease the probability of transmission for all individuals, including undetected superspreaders, are essential for combined interventions to successfully control a COVID-19 outbreak. To avoid unnecessary model complexity, we did not consider a separate category for imported cases in outbreak scenarios. Korea Disease Control and Prevention Agency official statistics showed that, by April 6, 2020, only 12 (0.18%) of the 6,794 COVID-19 cases diagnosed in Daegu were imported cases. While the local outbreak was initiated by imported cases [4, 25, 38] , the number of imported cases become negligible soon after a dramatically increase in locally acquired cases. In non-outbreak scenarios, however, imported cases carry the risk of initiating new outbreaks. Even though testing-contact tracing and social distancing can keep local transmission levels from escalating to large-scale outbreaks, burdens on medical/public health systems will soon exceed the capacity limit in the absence of a strict border control [10] . Our previous modeling work showed that, when the number of escaped imported cases (not detected and quarantined at entry, due to false negative testing results, and not adhered to 14-days quarantine after entry) increases from one to ten per day, the 90-day cumulative number needed to hospitalize and quarantine will jump from 349 and 4,092 to 3,483 and 40,810, respectively [10] . Once medical and public health systems are overburdened and collapsing, testing-contact tracing will not be timely performed, with risk of losing epidemic control. Therefore, strict restriction of travel from high-risk areas is needed to prevent new outbreaks. Our finding has an important implication--adopting a non-lockdown approach to control a large-scale COVID-19 outbreak is feasible but needs to be accompanied by a strong testingcontact tracing mechanism to promptly identify and break chains of transmission. The lack of effective contact tracing thus may explain the unprecedentedly massive surge of COVID-19 epidemic in Europe after lifting lockdown in 2020 autumn/winter [1] . Allowing people freely go outside to receive testing and care, non-lockdown social distancing facilitates early detection/isolation of infectious persons and subsequent contact tracing which in turn ensures the feasibility of a non-lockdown social policy to control outbreak. Therefore, non-lockdown social distancing and testing-contact tracing not only have an epidemiological synergism in breaking chains of SARS-CoV-2 transmission, but also reinforce each other in practical implementation. 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