The effectiveness of public health interventions against COVID-19: Lessons from the Singapore experience.

BACKGROUND: In dealing with community spread of COVID-19, two active interventions have been attempted or advocated-containment, and mitigation. Given the extensive impact of COVID-19 globally, there is international interest to learn from best practices that have been shown to work in controlling community spread to inform future outbreaks. This study explores the trajectory of COVID-19 infection in Singapore had the government intervention not focused on containment, but rather on mitigation. In addition, we estimate the actual COVID-19 infection cases in Singapore, given that confirmed cases are publicly available. METHODS AND
FINDINGS: We developed a COVID-19 infection model, which is a modified SIR model that differentiate between detected (diagnosed) and undetected (undiagnosed) individuals and segments total population into seven health states: susceptible (S), infected asymptomatic undiagnosed (A), infected asymptomatic diagnosed (I), infected symptomatic undiagnosed (U), infected symptomatic diagnosed (E), recovered (R), and dead (D). To account for the infection stages of the asymptomatic and symptomatic infected individuals, the asymptomatic infected individuals were further disaggregated into three infection stages: (a) latent (b) infectious and (c) non-infectious; while the symptomatic infected were disaggregated into two stages: (a) infectious and (b) non-infectious. The simulation result shows that by the end of the current epidemic cycle without considering the possibility of a second wave, under the containment intervention implemented in Singapore, the confirmed number of Singaporeans infected with COVID-19 (diagnosed asymptomatic and symptomatic cases) is projected to be 52,053 (with 95% confidence range of 49,370-54,735) representing 0.87% (0.83%-0.92%) of the total population; while the actual number of Singaporeans infected with COVID-19 (diagnosed and undiagnosed asymptomatic and symptomatic infected cases) is projected to be 86,041 (81,097-90,986), which is 1.65 times the confirmed cases and represents 1.45% (1.36%-1.53%) of the total population. A peak in infected cases is projected to have occurred on around day 125 (27/05/2020) for the confirmed infected cases and around day 115 (17/05/2020) for the actual infected cases. The number of deaths is estimated to be 37 (34-39) among those infected with COVID-19 by the end of the epidemic cycle; consequently, the perceived case fatality rate is projected to be 0.07%, while the actual case fatality rate is estimated to be 0.043%. Importantly, our simulation model results suggest that there about 65% more COVID-19 infection cases in Singapore that have not been captured in the official reported numbers which could be uncovered via a serological study. Compared to the containment intervention, a mitigation intervention would have resulted in early peak infection, and increase both the cumulative confirmed and actual infection cases and deaths.
CONCLUSION: Early public health measures in the context of targeted, aggressive containment including swift and effective contact tracing and quarantine, was likely responsible for suppressing the number of COVID-19 infections in Singapore.

Introduction

In late December 2019, the coronavirus disease 2019 (COVID-19) was reported in Wuhan, China. This became the first epicenter of COVID-19, leading to a lockdown of Wuhan after human-to-human transmission was confirmed. The rapid increase in the number of infected persons in China and globally thereafter led the World Health Organization (WHO) to declare a public health emergency of international concern on January 30, 2020, and a pandemic on March 11, 2020, as it became increasingly evident that COVID-19 had spread globally [1].

According to the World Health Organization, as of June 11, 2020, 7,273,958 confirmed COVID-19 cases and 413,372 deaths have been reported globally [2]. The United States (USA) as of June 11, 2020, reports the highest number of confirmed cases of 1,968,331, and deaths (111,978). Human-to-human transmission will be difficult to suppress as infected individuals may be able to transmit the virus days before experiencing significant symptoms.

In dealing with community spread of COVID-19, two active interventions have been attempted or advocated. The first is “containment”, involving quarantine of specific individuals based on tracing from their contact to a known infected individual or their history of recent travel to a high prevalence country or region. “Mitigation” is a second strategy aiming to limit movement at the population level; social distancing ranges from limiting physical proximity between people to no less than one meter to community lockdown. At different points in the progression of COVID-19, many countries have implemented various policy strategies, with most applying a mixture of containment and mitigation to reduce disease burden, morbidity and mortality when faced with local exponential growth of infected cases, all whilst aiming to minimize social and economic disruption. An additional major consideration in policy discussions is how these interventions mitigates stress on healthcare systems so that essential medical care can be provided to non-COVID as well as COVID patients. This is the rationale for pursuing interventions that might not substantially reduce total numbers of infections but would rather “flatten the curve”.

The city-state of Singapore was one of the first countries to record a confirmed case of COVID-19 shortly after the outbreak in China. In response, the Singaporean government adopted an aggressive containment strategy focusing mainly on swift and effective contact tracing and quarantine of individuals in order to prevent small clusters of COVID-19 infection from amplifying in a chainlike fashion into widespread community transmission. The containment strategy implemented by Singapore has been associated with a more moderate rise in number of infections than otherwise expected; the moderate number has allowed the Singapore health system to meet the needs of the confirmed COVID-19 cases, resulting in very few (25) deaths as of June 7, 2020. Given the extensive impact of COVID-19 globally, there is international interest to learn from best practices that have shown to work in controlling community spread to inform future epidemic outbreaks. This research aims to explore what the trajectory of COVID-19 infection would have been in Singapore had the government intervention not focused on containment, but rather on mitigation to inform future epidemic outbreak interventions.

Singapore’s approach to COVID-19

Guided by the experience of Severe Acute Respiratory Syndrome (SARS) in 2003 which led to 228 cases and 33 deaths in Singapore, the city-state had since ramped up its infectious disease prevention measures in combating future epidemics [3, 4]. The relative success behind Singapore’s containment of the pandemic, leading to only 25 deaths thus far, can be attributed to the government’s quick response, immediate contact tracing, targeted quarantine measures, strict quarantine management, and abundant community communication. Following the identification of the outbreak in Wuhan, China, the Singaporean government acted immediately to ensure the safety of their citizens [5]. Fig 1 shows the timeline of actions taken in Singapore in response to the COVID-19 outbreak. Having learned from the experience of combating SARS, the Singaporean government developed physical and operational infrastructure to support rapid contact tracing, quarantine, and medical services for infected individuals; the revision of the Infectious Disease Act (IDA) ensures that all measures needed to control any future outbreaks could be implemented [3].

Fig 1

Timeline of coronavirus actions taken in Singapore.

The initial success in containing the spread of COVID-19 has been attributed to the efficient and immediate contact tracing of patients who had been diagnosed with the virus [6]. Although contact tracing is by no means a new innovation, Singapore’s aggressive and proactive approach, praised by the WHO, has led to the avoidance of a community-wide spread [7]. Upon receiving word of a newly diagnosed patient, contact tracers immediately embark on a labor-intensive attempt to identify people who have been in contact with infected individuals, thereby being able to find those who may themselves may be infected [6]. Because the incubation period of COVID-19 is relatively short, contact tracing must be swift in order to contain the outbreak. Security cameras, receipts, and work calendars are used to fill in the gaps in memory of those infected who are unable to recall their whereabouts. Launched on March 20, the Trace Together application was developed to facilitate contact tracing by monitoring users’ locations and alerting any user who has come into contact with any individual who has tested positive for COVID-19. A detailed description of Singapore’s approach to COVID-19 and COVID-19 cases is provided in the online only S1 and S2 Appendices in S1 File.

Methods

Several simulation models have been developed to predict the infection trajectory of the COVID-19 pandemic. Lin and colleagues developed a modified SEIR (susceptible, exposed, infectious, removed) model considering risk perception [8], while Casella developed a control-oriented SIR model that stresses the effects of delays [3], and Wu and colleagues used transmission dynamics to estimate the clinical severity of COVID [9]. Giordano and colleagues developed a simulation model referred to as SIDARTHE that disaggregated the total population into eight stages to explore the impact of population-wide interventions [10]. Stochastic transmission models have also been considered [11]. Flexman and colleagues [12] used the discrete renewal process approach—which is related to the SIR model, an approach that has a strong theoretical basis in stochastic individual-based counting process—to model the number of infections over time [13].

We developed a COVID-19 infection model, which is a modified SIR model [13] similar to the SIDARTHE model [10], that differentiate between detected (diagnosed) and undetected (undiagnosed) individuals and segments total population into seven health states: susceptible (S), infected asymptomatic undiagnosed (A), infected asymptomatic diagnosed (I), infected symptomatic undiagnosed (U), infected symptomatic diagnosed (E), recovered (R), and dead (D). To account for the infection stages of the asymptomatic and symptomatic infected individuals, the asymptomatic infected individuals were further disaggregated into three infection stages: (a) latent—the infection stage before becoming infectious; (b) infectious; and (c) non-infectious—when the virus is no longer viable [14]. Likewise, the symptomatic infected individuals were disaggregated into two infection stages: (a) infectious and (b) non-infectious. The main differences between the proposed modified SIR model presented herein and the SIDARTHE model are that: (a) our model has 7 health states compared to the eight-health state of the SIDARTHE model. (b) our modified SIR model further disaggregates the asymptomatic infected individuals into three infection stages, which is not the case for SIDARTHE model. (c) the modified SIR model disaggregates the symptomatic infected individuals into two infection stages, which is absent in the SIDARTHE model. We strongly believe that the amendment made compared to the SIDARTHE model will significantly improve the accuracy of the simulation model to predict the infection trajectory of COVID-19, and more importantly, help to estimate the actual COVID-19 infection cases. Briefly, the transmission dynamics as depicted in Figs 2 and 3 is as follows: through exposure to an infected individual, some of the exposed individuals become infected, and transition to the infected asymptomatic health state. At the asymptomatic health state, infected individuals go through three infection stages—latent, infectious and non-infectious [14]. All newly infected individuals move from asymptomatic latent state to asymptomatic infectious stage. During the asymptomatic infectious stage, some individuals will develop symptoms and transition from asymptomatic to symptomatic infectious state, while others will transition to non-infectious asymptomatic and eventually recover from the virus. Infected individuals in asymptomatic and symptomatic health state can be diagnosed through testing and move from undiagnosed to diagnosed health state. Non-infectious infected individuals (asymptomatic and symptomatic) will overtime recover and move to the recovered health state; infectious symptomatic infected individuals may either recover or die from the infection.

Fig 2

The simulation model structure representing the dynamic interactions of different health state of COVID-19.

Fig 3

Detailed model structure for asymptomatic and symptomatic undiagnosed and diagnosed infected health state and their interactions.

The immune response including duration of immunity for COVID-19 infection is not yet understood [15]. Duration of antibody responses against other human coronaviruses may be relevant in this context. For example, following infection with SARS-CoV-1 (the virus that caused SARS), concentrations of neutralizing antibodies (NAbs) remained high for approximately 4 to 5 months before subsequently declining slowly during the next 2 to 3 years [16]; while neutralizing antibodies (NAbs) following infection with MERS-CoV (the virus that caused Middle East respiratory syndrome) have persisted up to 34 months in recovered patients [17]. But it is not yet known whether similar immune protection will be observed for individuals infected with COVID-19. For the purpose of this study, we are interested in projections over a relatively short time horizon (365 days) within which temporary immunity is likely still to be in place. As a result, we assumed a zero probability of becoming susceptible again after recovering from the infection over the simulation time, although anecdotal evidence of re-infection is found in literature [18]. In addition, our simulation model specifically makes a distinction between diagnosed and undiagnosed infected individuals. Our model, assume that only undiagnosed infectious individuals create infection and that due to proper isolation and compliance to strict rules diagnosed infectious individuals do not create infection.

The COVID-19 epidemic in Singapore can be separated into two separate outbreaks: one in the general community (herein referred to as local) and the other among the foreign workers living in dormitories (referred to as migrants). Hence, two simulation models with the same structure (described in Figs 2 and 3) but with slightly different model inputs are used, as indicated in the model input table. The distinction between the local and migrant workers is important to capture the infection dynamics in these two populations and the different policies implemented to control the outbreaks. The ordinary differential equations that describe the evolution of the population in each health state over time are described below:

The parameters are defined as follows:

b denote the proportion of undiagnosed infected individuals (asymptomatic and symptomatic) in the infectious stage. We assumed herein that diagnosed infected individuals (asymptomatic and symptomatic) do not create infection because in Singapore, all diagnosed infected COVID-19 patients are properly isolated or quarantined in hospitals or community isolation centers and comply to strict rules until they are determined recovered. c is contact frequency of susceptible individuals and y is the probability of infection given a contact with infected individual. Thus, b c y indicates the probability of disease transmission given contact with infected individuals. Contact frequency was estimated to be 8 close contacts per person per day at the start of the infection and reduced to 4 close contact per person per day during the nationwide lockdown (known in Singapore as Circuit Breaker) for the local community; contact frequency for the migrant workers were estimated to be 10 close contact per person per day. The contact frequency of migrant workers remained unchanged during the lockdown period due to their living arrangements.

o, z, x, p, w, ᴍ, ᴋ, and r, respectively, denote latent period (the time it takes to become infectious), duration of infectiousness (asymptomatic), onset to isolation delay, fraction quarantined, incubation time to develop symptoms, imported cases, fraction asymptomatic not developing symptoms and average recovery time (undiagnosed and diagnosed asymptomatic). Duration of infectiousness for asymptomatic infected individuals is shorter compared to symptomatic infected individuals. The rate of diagnosis via testing for asymptomatic infected individuals are expected to be lower compared to symptomatic infected individuals because the probability of testing individuals with symptoms is much higher compared to those without symptoms. Average recovery time is a reflection of criteria for discharge and denotes the time non-infectious infected individuals are declared recovered.

f,h,e, and n, denote respectively duration of infectiousness (symptomatic), symptoms development to care delay (symptomatic), mortality rate (undiagnosed and diagnosed symptomatic), and average recovery time (undiagnosed and diagnosed symptomatic). Mortality rate for undiagnosed and diagnosed symptomatic infected individuals, were assumed to be the same. The model assumes higher probability of diagnosis among symptomatic infected individuals compared to asymptomatic infected individuals.

Al, Ai, and Ani denote infected asymptomatic undiagnosed latent, infected asymptomatic undiagnosed infectious and infected asymptomatic undiagnosed not-infectious.

Il, Ii, and Ini denote infected asymptomatic diagnosed latent, infected asymptomatic diagnosed infectious and infected asymptomatic diagnosed not-infectious.

Ui, and Uni demote infected symptomatic undiagnosed infectious and infected symptomatic undiagnosed not-infectious.

Ei, and Eni demote infected symptomatic diagnosed infectious and infected symptomatic diagnosed not-infectious.

Model inputs

Table 1 shows the input parameters for the COVID-19 simulation model for the reference case, and sources of the input parameters. COVID-19 Singapore data from 23rd January to 7th June 2020 was used to estimate some of the model inputs including contact frequency, mortality rate, and imported cases. COVID-19 data used was fully anonymized before it was accessed. In addition, published evidence from Singapore and other countries were used for other parameters as shown in Table 1. The endogenously estimated reproduction number reflects the progressive introduction of policies to control the infection. At the start of the infection the reproduction number for the local infection was R0 = 2, while that for the migrant workers was R0 = 3, which resulted in increased number of cases. After the introduction of nationwide lockdown (circuit breaker) to enforce social distancing, enhanced contact tracing and isolation and compulsory wearing of face-mask, in addition to increased hygiene and behavioural change awareness, the estimated reproduction number for the local infection decreased to R0 = 0.73 while that for the migrant workers was R0 = 1.5. The endogenous parameter “proportion of undiagnosed infected” is defined as the sum of “infected asymptomatic undiagnosed infectious” and “infected symptomatic undiagnosed infectious”, divided by “total population. Likewise, the endogenous parameter “fraction quarantined” is defined as “total confirmed cases” divided by “total infected cases”.

Table 1

Model inputs (parameters with * were included in the sensitivity analysis and varied ±25%).

Parameter		Values (local)	Values (migrant)	Reference
Proportion of undiagnosed infected	b	endogenous	endogenous
Contact frequency per person	c	8–2 persons	10 persons	Ministry of Health, Singapore (2020) [19]
Probability of infection given contact*	y	0.039	0.039	Lei Luo (2020) [20]
Latent period *	o	2.3 days	2.3 days	Xi He et al, 2020 [21]
Duration of infectiousness asymptomatic*	z	4.5 days	4.5 days	Calibrated
Onset to isolation delay*	x	9–2 days	9–2 days	Ng, Y. et al (2020) [22]
Incubation time*	w	5 days	5 days	Pung et al (2020) [23]
Recovery time diagnosed asymptomatic*	r	10 days	10 days	Calibrated
Recovery time undiagnosed asymptomatic*	r	10 days	10 days	Calibrated
Duration of infectiousness symptomatic*	f	13 days	13 days	National Centre for Infectious Diseases (2020) [24]
Symptoms development to care delay*	h	5.5–2 days	5.5–2 days	Steven Sanche et al (2020) [25]
Mortality rate undiagnosed symptomatic*	e	0.004	0.004	Ministry of Health, Singapore (2020) [19]
Mortality rate diagnosed symptomatic*	e	0.004	0.004	Ministry of Health, Singapore (2020) [19]
Recovery time undiagnosed symptomatic*	n	10 days	10 days	Calibrated
Recovery time diagnosed symptomatic*	n	10 days	10 days	Calibrated
Fraction asymptomatic without symptoms*	ᴋ	0.7	0.7	Michael Day (2020) [26]
Imported cases	ᴍ	Time series	0	Ministry of Health Singapore (2020) [19]
Fraction quarantined	p	endogenous	endogenous

Simulated interventions

As a reference case, we simulated the spread of COVID-19 based on extant Singapore policy, denoted “Singapore containment intervention”. This was then compared to two counterfactual interventions to estimate the impact Singapore might have experienced under alternative interventions, denoted “mitigation: physical distancing with low quarantine rate”; and “mitigation: physical distancing with moderate quarantine rate”. For details of COVID-19 mitigation and containment interventions see the literature as cited [27, 28].

Singapore containment approach

As noted, the interventions implemented in Singapore to manage COVID-19 focused mainly on containment, emphasizing a swift and meticulous approach to contact tracing and isolate. In the model, onset to isolation delay decreased from 9 days early in the outbreak to 2 days as provided in the literature as cited [21]. In addition, social distancing interventions put in place that discourage large group gathering, separation in public, and the promotion of telecommuting decreased the number of close contacts per person from 8 to 4 for the local community and that for the migrant workers remain at 10. Due to the living arrangement of the migrant workers, confirmed cases were isolated from the rest of migrant workers living in the dormitories, thus contact frequency remained relatively unchanged during the lockdown (circuit breaker) period.

Social distancing with low isolation rate

Under this scenario, social distancing interventions were implemented on day 72 after the first confirmed case of COVID-19 and lasted for two months after which contact frequency increased gradually over the simulation time. As a consequence, contact per person is assumed to decrease from 8 close contacts per persons per day to 4 and begins to increase gradually after the lockdown; the contact frequency for the migrant workers is assumed to remain unchanged at 10 close contact per person per day. In addition, it was assumed that 20% of diagnosed asymptomatic and symptomatic individuals under the Singapore containment approach will be diagnosed and isolated under this scenario. The difference between the Singapore containment approach and the social distancing with low isolation rate is that, under the social distancing with low isolation rate, only 20% of diagnosed asymptomatic and symptomatic individuals are quarantined, while that for the Singapore containment approach is 100%.

Social distancing with moderate isolation rate

This counterfactual scenario is similar to the previous social distancing intervention, except that 40% of diagnosed asymptomatic and symptomatic individuals under the Singapore containment approach will be diagnosed and isolated under this scenario. The difference between the social distance with moderate isolation rate, the social distancing with low isolation rate and the Singapore containment approach are that, under the social distancing with moderate isolation rate, only 40% of diagnosed asymptomatic and symptomatic individuals are quarantined, while that for social distancing with low rate is 20% and Singapore containment approach is 100%.

Model validation and sensitivity analysis

We compared our simulated new cases and cumulative confirmed COVID-19 cases in Singapore for the local population and migrant workers with official data from the Ministry of Health, Singapore (see Fig 4) from the onset of the epidemic (January 23, 2020) to June 7, 2020. For sensitivity analysis, multivariate sensitivity analysis that varies selected parameters by ±25% was performed with random draws from uniform distributions over the designated range to explore how a change in these parameters influences the outcome variables namely cumulative infected cases, deaths, and fraction infection (see S5–S7 Appendices in S1 File).

Fig 4

Comparing simulated confirmed new cases and cumulative confirmed cases to data.

Results

The results of the simulation in presented in Table 2 and Fig 5. The reference case (Singapore Intervention), in which the model simulates containment interventions actually implemented in Singapore, closely tracked historical trends of COVID-19 infection from January 23, up to June 7, 2020 (see Fig 5). Under the Singapore Intervention, by the end of the current epidemic cycle without considering the possibility of a second wave, the confirmed number of Singaporeans infected with COVID-19 (diagnosed asymptomatic and symptomatic cases) is projected to be 52,053 (with 95% confidence range of 49,370–54,735) representing 0.87% (0.83%-0.92%) of the total population; the actual number of Singaporeans infected with COVID-19 (diagnosed and undiagnosed asymptomatic and symptomatic infected cases) is projected to be 86,041 (81,097–90,986), which is 1.65 times the confirmed cases and represents 1.45% (1.36%-1.53%) of the total population. A peak in infected cases is projected to have occurred on around day 125 (27/05/2020) for the confirmed infected cases and around day 115 (17/05/2020) for the actual infected cases. The number of deaths is estimated to be 37 (34–39) among those infected with COVID-19 by the end of the epidemic cycle; consequently, the perceived case fatality rate is projected to be 0.07%, while the actual case fatality rate is estimated to be 0.043%.

Table 2

Projected time to peak infection, duration of infection, cumulative infection, proportion infected and total deaths.

Interventions	Cumulative infected Cases (person)		% of population infected		Total deaths (person)
	Confirmed	Actual	Confirmed	Actual
Singapore Intervention	52,053 [49,370–54,735]	86,041 [81,097–90,986]	0.87% [0.83%-0.92%]	1.45% [1.36%-1.53%]	37 [34–39]
Mitigation with Low Isolation Rate	67,539 [64,245–70,832]	260,420 [249,985–270,855]	1.14% [1.08%-1.19%]	4.38% [4.20%-4.55%]	137 [129–145]
Mitigation with Moderate Isolation Rate	65,266 [62,122–68,409]	179,104 [170,065–188,143]	1.10% [1.04%-1.15%]	3.01% [2.86%-3.16%]	90 [85–95]

Fig 5

Projected actual and confirmed cases of COVID-19; as well as the projected actual and confirmed percentage of the population infected in Singapore under current containment intervention and alternative mitigation interventions.

In comparison, a mitigation intervention with 20% isolation rate was projected to increase the confirmed infected cases by 29.7% (23.4%-36.0%), increase the actual infected cases by 202% (190.5%-214.7%), move the time to peak infection for confirmed infected cases by 27 days earlier (30/04/2020) and that for the actual infected cases by 28 days earlier (19/04/2020) compared to the Singapore Intervention. In addition, the number of Singaporeans estimated to die from COVID-19 will increase 3.7 (3.4–3.9) fold relative to the Singapore Intervention by the end of the epidemic. As a result, under this intervention, the perceived case fatality rate is estimated to be 0.20%, while the actual case fatality rate is projected to be 0.05%. Under a mitigation intervention with 20% isolation rate, the actual infected cases are projected to be 3.0 (2.9–3.1) fold relative to what it would have been under the Singapore intervention.

Likewise, a mitigation intervention with 40% quarantine rate, compared to the Singapore specific containment intervention could increase the confirmed cases by 25.3% (19.3%-31.4%), increase actual cases by 108% (97.6%-118.6%), move the time to peak infection by 19 days earlier (9/05/2020 for confirmed cases and 29/04/2020 for actual infected cases) for both the confirmed and actual infected cases, and increase the number of deaths 2.45-folds. As a result, the perceived case fatality rate is estimated to be 0.13% whereas the actual case fatality rate is estimated to be 0.05%. Equally, the actual infected cases are projected to be 2.0 (1.9–2.2) fold of the cases estimated under the Singapore intervention.

In addition to the counterfactual analysis, we explored the impact of: (a) immunity on the scenarios explored (see S8 Appendix in S1 File for how the immunity assumptions were implemented in the model and the simulation results), and (b) what would have happened if Singapore allowed the virus to take its natural course without intervention (i.e., uninhibited spread/herd immunity) approach (see S9 Appendix in S1 File for how the uninhibited spread assumptions were implemented in the model and the simulation results). The results from our simulation model when the possibility of reinfection (using 6 months’ immunity duration) is implemented suggest that the projected numbers remain relatively unchanged and a likelihood of second wave under the mitigation with low isolation intervention at the end of the simulation time; as expected, the only difference is that the recovered population decreases as individuals’ transition from recovered to susceptible health state. This result supports our assumption that reinfection will have limited impact within the context of our modelling study. Further modelling exploration shows that had Singapore implemented a strategy of no active intervention (uninhibited spread leading to herd immunity), the number of Singaporeans infected with COVID-19 is projected to be 4.69 (4.64–4.74) million, representing 78.84% (77.99%-79.70%) of the total population. A peak in infected cases is projected to occur around day 154 (25/06/2020) and the number of deaths is estimated to be 2,466 (2,385–2,547) among those infected with COVID-19 by the end of the epidemic cycle assuming the current death rate remains unchanged.

Discussion

In this study, we developed a COVID-19 infection model to explore what the trajectory of COVID-19 infection might have been in Singapore had the government intervention not focused on containment, but rather on mitigation. Compared to projections of a model calibrated to actual Singapore data based on a prompt and aggressive containment strategy, the simulation results indicate that a mitigation approach would have resulted in early peak infection, and increased both the cumulative confirmed and actual infection cases and deaths. Importantly, our simulation model result suggests that there about 65% more COVID-19 infection cases in Singapore that have not been captured in the official reported numbers which could possibly be uncovered via a serological study. In addition, further modelling exploration suggests that: (a) assuming a possibility of reinfection in individuals will have limited impact on the simulation results; (b) a strategy that focuses on uninhibited spread of COVID-19 would delay the peak of infection and increase both the cumulative cases and deaths by orders of magnitude.

In this counterfactual modelling exercise, we found that what seems to work to significantly decrease infected COVID-19 cases is the early implementation of containment interventions that focuses on meticulous and swift contact tracing and individual-level quarantine, in addition to standard health advice on hand washing, wearing of face mask, and social distancing. What our model suggests is that implementation of social distancing without contact tracing and individual-level quarantine does not work well. The policy implication based on insight from our simulation model is that general public health measures have to be applied together with targeted, aggressive and rapid containment in order to expect to substantially reduce the number of people infected with COVID-19 and consequent mortality, and should be the preferred intervention for managing COVID-19 and future epidemic outbreaks.

It is important to note that Singapore’s implementation of contact tracing and quarantine to stop the spread of the virus has not been easy. Given that some individual transmission may occur before development of significant symptoms, the Singapore quarantine policy expended substantial effort to identify all exposed individuals deemed to have close contact with a confirmed infected individual, not only symptomatic individuals. In addition, contract tracing has to be swift to reduce the delay time from onset to isolation. It is important to note that Singapore was able to show early success in containment.

However, a recent outbreak in crowded foreign worker dormitories in Singapore has rapidly escalated the number of cases. Massive efforts are currently ongoing to isolate, test, sort, re-house and treat patients on-site at these dormitories. Most cases are being managed at community isolation facilities. As this population is relatively young with little co-morbidity, it is expected that the actual numbers of cases needing intensive care will be low and mortality also correspondingly low. This recent turn of events suggests that due to the ability of COVID-19 to transmit in pre-symptomatic or even asymptomatic individuals, contact tracing and quarantine also has limitations and requires application combined with more general social distancing measures.

The simulation model used for this study has several limitations. First, the epidemiology of COVID-19 is still not fully understood in terms of transmission and infectivity of the virus. Thus, we had to calibrate important parameters such as duration of infectiousness for asymptomatic individuals. Second, to reduce the complexity of the model, migration dynamics of the Singapore population were not included in the model, though migration plays an important role in the spread of COVID-19. We note that individuals traveling into Singapore can be easily targeted for containment in comparison with larger countries with less easily controlled borders. Lastly, contact frequency and pattern are highly dynamic across different segments of the population; however, an average contact frequency was used in the model to represent all individuals.

In addition, modelling studies are needed to examine the impact of health systems response to COVID-19 on vulnerable non-COVID-19 patients; this will allow us to better balance of the needs of the entire population in response to future outbreaks.

Conclusion

This study demonstrates that contact tracing, testing and aggressive containment has likely been the key to suppressing the number of COVID-19 infections in Singapore. These interventions should be combined with social distancing in the intervention packages currently being implemented across all countries and in future epidemic. Social distancing, though vital in slowing the growth of COVID-19, will be much less effective alone unless complemented with aggressive containment.

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17 Nov 2020

PONE-D-20-19492

The Effectiveness of Public Health Interventions Against COVID-19: Lessons from Singapore Experience.

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Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors study the effectiveness of different interventions in the context of COVID-19 spread in Singapore. They distinguish between containment and mitigation, and use a modified SIR model to show the difference in total infections, peak load and number of deaths. One of the main contributions of the study is also an estimate of total COVID-19 infections (diagnosed and undiagnosed).

In addition to modeling contributions, the article also summarizes the early COVID-19 response in Singapore (e.g., Figure 1), and is a useful historical record. Though dated, the work in this paper is quite useful in highlighting the importance of timely and appropriate response to the pandemic.

Clarifications/Suggestions:

- One of the modifications to SIR model involves differentiating between the detected and undetected individuals. They also account for different infection stages. In terms of description, stating that 'symptomatic infected individuals were disaggregated into two infection stages' seems ambiguous. I think it is cleaner to state that the latent stage is shared by asymptomatic and symptomatic branches.

- Since a lot of the article hinges on the distinction between containment and mitigation, it would be good to add citations to align with the literature. For instance (https://jamanetwork.com/journals/jama/fullarticle/2764956) and (https://jamanetwork.com/journals/jama/fullarticle/2763187) are useful to mention.

- The authors report projected cases by end of current epidemic cycle, however no specific date is mentioned. Likewise, the peak timings are reported in terms of simulation days, than calendar time. It would be helpful if they could use actual months/dates.

- perceived and actual case fatality rate -> case fatality rate and infection fatality rate?

- In Background of Abstract, should it be 'we estimate the actual COVID-19 infections', given that confirmed case counts are publicly available.

- pg. 7 line 82 - Should be Figures 2 and 3.

- It would be good to provide an equation for \\beta given it implicitly captures the infectious population (is the proportion of total population?).

- The authors state the models were validated using case data until June 7th 2020. Would be useful to show these curves in Figure 5, along with the simulated curves (these are in S3, but useful in main paper to show the model fit).

- Though there are multiple parameters listed for the undiagnosed to diagnosed transition, none of them capture the detection probability. Is the fraction of detected infections just a function of the transition rates, or are there any assumptions about the testing protocol?

- How are the uncertainty bounds for the results obtained? Are these same as the +/-25% for selected parameters (the bounds in S8 seem to be much higher)? Which parameters were varied this way?

- The authors state that uninhibited spread would have lead to ~80% of the population infected. This is much higher than the compared scenarios, where the highest is around 4.5%. Is this based on R0=2 and remaining parameters unchanged? What about some of the diagnosis and isolation parameters?

Reviewer #2: 

General comment

The paper "The Effectiveness of Public Health Interventions Against COVID-19: Lessons from Singapore Experience" aims at carring out a counterfactual analysis on the spread of COVID-19 infections in Singapore under different scenarios.

The paper is interesting, well organised and well written for the most part, however several methodological details should be produced and clarified in order to make the anayisis transparent and reproducible, and ultimately, make the reader able to judge on its validity. To this end, data and source code that generated estimates and simulations should be shared with reviewers at least. In the rest of this document, I enumerate the main issues that should be tackled. In the last section, some typos are reported, however a general revision of the manuscript is needed.

In conclusion, I think that the paper is worth of publication in PLOS ONE once the following major questions and issues are addressed.

Major issues

1. [sect. "Methods"] It should be clearly stated what amendments authors made to the SIR or the SIDARTHE model, they should be clearly motivated, and their implications discussed.

2. [pp. 8-9] All equations should be carefully revised. Unlike reference models such as SIR or SIDARTHE, and unlike what stated in the paper (line 111), none of equations (1)-(14) is a differential equation. Moreover, I suggest authors to revise the mathematical notation by including only latin and greek characters: this would improve the readability of the paper. In any case, superscript-like characters such as the superscript turned m and the superscript delta (which are phonetic symbols) should be definitely avoided, as they may be confused with exponents.

3. The meaning and the role of parameters yat (Ѣ) and phi (Φ) in the model is not clear to me. For example, why only a portion yat of diagnosed people is quarantined? Moreover, parameter phi is meant to be "onset to isolation delay" throughout all the paper (except on page 9 where it is defined, and the definitions of phi and yet are switched), however it is not clear to me what term "onset" refers to, expecially if I examine the diagram in Figure 3.

4. [p. 11] It should be clearly explained how "endogenous" parameters are determined, and how calibration is performed.

5. [sect. "Simulated Interventions"] Differences and shared aspects of the scenarios should emerge more clearly, so as to make them more comparable. If choices on the parameters and conditions which distinguish each scenario are justified (when possible), this would definitely improve the validity of the analysis.

6. Authors state that they performed sensitivity analysis, however no comments to the results are provided neither in the manuscript not in the supplementary material.

7. It is not clear how confidence intervals in section "Results" are computed, as the model authors describe is deterministic.

8. On page 14, authors claim that they “explored the impact of (a) immunity on the scenarios explored, and (b) what would have happened if Singapore allowed the virus to dake it natural course without intevention approach”, however no results are provided either in the manuscript of in the supplementary material. Moreover they do not explain how they modified the model (and set the parameters) in order to relax the immunisation hypothesis.

Minor issues

1. Figures S1 and S2 of the Supplementary Material are missing. All the supplementary file should be revised, as there are several figures which are not completely readable, wherease captions and title graphs do not adequately clarify what information is plotted.

2. Second and third items on page 9 would be more easy-to-read if parameters were reported (also ?) along with definitions.

Some typos and other minor details

1. [p. 3, sect. "Why this study was done?", point 1, 3rd line] A quotation mark is missing for keyword "2019 nCoV".

2. [p. 3, sect. "Why this study was done?", point 3] Last sentence says "The list of 30 papers included are provided in the appendix". If authors refer to Appendix S10 of the supplementary material, they could explicitly cite the reference label "S10". Moreover, Appendix S10 actually consists of 31 instead of 30 references.

3. [line 8] Please, specify the date adverb "currently" refers to.

4. [line 82] I think that figures authors refer to are those with numbers 2 and 3.

5. [page 8] Equation number (11) has not been used.

6. [equation 13] Right parenthesis is missing.

7. [line 146] Remove comma after "alpha".

8. [line 154] "denotes" should be "denote", and a space is missing before "kappa".

9. [line 163] "deMotes" should be "denote".

10. [line 213] "will diagnosed" should be "will be diagnosed".

11. [line 220] "...these parameters influence..." should be "...these parameters influences...".

12. [line 260] "it natural course" should be "its natural course".

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

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16 Feb 2021

Response to Reviewers

REVIEWER #1

The authors study the effectiveness of different interventions in the context of COVID-19 spread in Singapore. They distinguish between containment and mitigation, and use a modified SIR model to show the difference in total infections, peak load and number of deaths. One of the main contributions of the study is also an estimate of total COVID-19 infections (diagnosed and undiagnosed).

In addition to modelling contributions, the article also summarizes the early COVID-19 response in Singapore (e.g., Figure 1), and is a useful historical record. Though dated, the work in this paper is quite useful in highlighting the importance of timely and appropriate response to the pandemic.

Clarifications/Suggestions:

Comment #1: One of the modifications to SIR model involves differentiating between the detected and undetected individuals. They also account for different infection stages. In terms of description, stating that 'symptomatic infected individuals were disaggregated into two infection stages' seems ambiguous. I think it is cleaner to state that the latent stage is shared by asymptomatic and symptomatic branches.

Response: Thanks for the suggestion. We appreciate and understand your suggestion, however, upon critical examination, we felt that our current description will improve understanding of the mechanics of the model given the different stages for each health state. Hence, we decided to keep the description as it is.

Comments #2: Since a lot of the article hinges on the distinction between containment and mitigation, it would be good to add citations to align with the literature. For instance (https://jamanetwork.com/journals/jama/fullarticle/2764956) and (https://jamanetwork.com/journals/jama/fullarticle/2763187) are useful to mention.

Response: Thanks for the suggestion. We have included the citations in the list of references and simulated interventions section of the manuscript.

Comments #3: The authors report projected cases by end of current epidemic cycle, however no specific date is mentioned. Likewise, the peak timings are reported in terms of simulation days, than calendar time. It would be helpful if they could use actual months/dates.

Response: Thanks for the suggestion. We have included calendar time to the reported peak times.

Comments #4: Perceived and actual case fatality rate -> case fatality rate and infection fatality rate?

Response: Thanks for the comment. The perceived case fatality rate is the number of deaths divided by confirmed cases; while the actual case fatality is the number of deaths divided by the actual cases (confirmed and unconfirmed infections).

Comments #5: In Background of Abstract, should it be 'we estimate the actual COVID-19 infections', given that confirmed case counts are publicly available.

Response: Thanks for the suggestion. We have made the changes as suggested as follows:

“In addition, we estimate the actual COVID-19 infection cases in Singapore, given that confirmed cases are publicly available.”

Comments #6: pg. 7 line 82 - Should be Figures 2 and 3.

Response: Thanks for the correction. We have made the changes to figure 2 and 3.

Comments #7: It would be good to provide an equation for \\beta given it implicitly captures the infectious population (is the proportion of total population?).

Response: Thanks for the suggestion. The equation for beta (β which is now "b") is “infected asymptomatic undiagnosed infectious” and “infected symptomatic undiagnosed infectious”, divided by “total population”.

Comments #8: The authors state the models were validated using case data until June 7th 2020. Would be useful to show these curves in Figure 5, along with the simulated curves (these are in S3, but useful in main paper to show the model fit).

Response: Thanks for the suggestion. We have moved the validation graphs to the main paper as suggested.

Comments #9: Though there are multiple parameters listed for the undiagnosed to diagnosed transition, none of them capture the detection probability. Is the fraction of detected infections just a function of the transition rates, or are there any assumptions about the testing protocol?

Response: Thanks for the comment. Yes, the detected infections are just a function of the transition rates—as infected individuals move from undiagnosed infected to diagnosed infected. Thus, the detection probability is reflected in the “onset to isolation delay” parameter since as clearly indicated in the manuscript, in Singapore, all confirmed COVID-19 cases are isolated.

Comments #10: How are the uncertainty bounds for the results obtained? Are these same as the +/-25% for selected parameters (the bounds in S8 seem to be much higher)? Which parameters were varied this way?

Response: Thanks for the comment. On the parameters included in the sensitivity analysis, table 1 clearly indicates the parameters included in the sensitivity analysis with (*). These parameters are:

Probability of infection given contact

Latent period

Duration of infectiousness asymptomatic

Onset to isolation delay

Incubation time

Recovery time diagnosed asymptomatic

Recovery time undiagnosed asymptomatic

Duration of infectiousness symptomatic

Symptomatic development to care delay

Mortality rate undiagnosed symptomatic

Mortality rate diagnosed symptomatic

Recovery time undiagnosed symptomatic

Recovery time diagnosed symptomatic

Fraction asymptomatic without symptoms

The uncertainty bounds were obtained as follows:

First, the sensitivity analysis was performed by varying the list of parameters included in the sensitivity analysis by ±25%; and the simulation model was run 500 times.

Second, the results were exported to excel and basic statistical analysis was performed to obtain 95% confidence interval around the mean values. The bounds in S7, S8 and S9 seems much higher because it includes the outliers. The reported uncertainty bounds in table 2 is the 95% confidence interval from the statistical analysis of the sensitivity analysis.

Comments #11: The authors state that uninhibited spread would have led to ~80% of the population infected. This is much higher than the compared scenarios, where the highest is around 4.5%. Is this based on R0=2 and remaining parameters unchanged? What about some of the diagnosis and isolation parameters?

Response: Thanks for the comment. Under the uninhibited spread strategy, diagnosis and isolation is assumed to be non-existent; and where diagnosis is assumed to be present, individuals diagnosed with COVID-19 are not isolated and R0=2. Thus, it is assumed that isolation of infected individuals is assumed to be absent.

REVIEWER #2

The paper "The Effectiveness of Public Health Interventions Against COVID-19: Lessons from Singapore Experience" aims at carrying out a counterfactual analysis on the spread of COVID-19 infections in Singapore under different scenarios.

The paper is interesting, well organised and well written for the most part, however several methodological details should be produced and clarified in order to make the analysis transparent and reproducible, and ultimately, make the reader able to judge on its validity. To this end, data and source code that generated estimates and simulations should be shared with reviewers at least. In the rest of this document, I enumerate the main issues that should be tackled. In the last section, some typos are reported, however a general revision of the manuscript is needed.

In conclusion, I think that the paper is worth of publication in PLOS ONE once the following major questions and issues are addressed.

Major issues

Comments #1: [sect. "Methods"] It should be clearly stated what amendments authors made to the SIR or the SIDARTHE model, they should be clearly motivated, and their implications discussed.

Response: Thanks for the comment. We have added the following statement in the methods section to address the difference between the modified SIR model used and the SIDARTHE model.

“The main differences between the proposed modified SIR model presented herein and the SIDARTHE model are that: (a) our model has 7 health states compared to the eight-health state of the SIDARTHE model. (b) our modified SIR model further disaggregates the asymptomatic infected individuals into three infection stages, which is not part of the SIDARTHE model. (c) the modified SIR model disaggregates the symptomatic infected individuals into two infection stages, which is absent in the SIDARTHE model. We strongly believe that the amended made compared to the SIDARTHE model will significantly improve the accuracy of the simulation model to predict the infection trajectory of COVID-19, and more importantly, help to estimate the actual COVID-19 infection cases.”

Comments #2: [pp. 8-9] All equations should be carefully revised. Unlike reference models such as SIR or SIDARTHE, and unlike what stated in the paper (line 111), none of equations (1)-(14) is a differential equation. Moreover, I suggest authors to revise the mathematical notation by including only Latin and Greek characters: this would improve the readability of the paper. In any case, superscript-like characters such as the superscript turned m and the superscript delta (which are phonetic symbols) should be definitely avoided, as they may be confused with exponents.

Response: Thanks for the comments. We have changed the equations to differential equations and also changed notations as suggested by the reviewer.

Comments #3: The meaning and the role of parameters yat (Ѣ) and phi (Φ) in the model is not clear to me. For example, why only a portion yat of diagnosed people is quarantined? Moreover, parameter phi is meant to be "onset to isolation delay" throughout all the paper (except on page 9 where it is defined, and the definitions of phi and yet are switched), however it is not clear to me what term "onset" refers to, especially if I examine the diagram in Figure 3.

Response: Thanks for the comment. yat (Ѣ, which is now “p”) is the fraction quarantined and is defined as “total confirmed cases” divided by “total infected cases”. This parameter yat (Ѣ, which is now “p”) estimates the fraction of total infected cases confirmed, this is important because there is a delay between getting infected and being diagnosed (confirmed) which is captured by the onset to isolation delay phi (Φ, which is now “x”). Thus, phi (Φ, which is “x”) is onset to isolation delay, which is the time it takes for someone infected with COVID-19 to be diagnosed and isolated. As indicated in reference [21], this parameter decreased from 9 days early in the outbreak to 2 days.

On page 9, the definition for yat (Ѣ, which is now “p”) and phi (Φ, which is now “x”) was not switched. The definitions are consistent with what we have in the manuscript. On page 9, yat (Ѣ, which is now “p”) is fraction quarantined and phi (Φ, which is now “x”) is onset to isolation delay.

Comments #4: [p. 11] It should be clearly explained how "endogenous" parameters are determined, and how calibration is performed.

Response: Thanks for the comment. We have added the definition of the endogenous parameters in the manuscript under the model input section. The definitions are:

Proportion of undiagnosed infected (b) = (Infected symptomatic undiagnosed infectious + Infected

Asymptomatic undiagnosed infectious)/Total population

Fraction quarantined (p) = Total confirmed cases / Total infected cases

Comments #5: [sect. "Simulated Interventions"] Differences and shared aspects of the scenarios should emerge more clearly, so as to make them more comparable. If choices on the parameters and conditions which distinguish each scenario are justified (when possible), this would definitely improve the validity of the analysis.

Response: Thanks for the comments, we have added few sentences, to clarify the differences between the simulated interventions.

Comments #6: Authors state that they performed sensitivity analysis, however no comments to the results are provided neither in the manuscript not in the supplementary material.

Response: Thanks for the comment. Table 2 shows the results with confidence intervals. The confidence intervals were obtained via the sensitivity analysis.

Comments #7: It is not clear how confidence intervals in section "Results" are computed, as the model authors describe is deterministic.

Response: Thanks for the comment. The confidence intervals were estimated from the sensitivity analysis output. As explained in the manuscript, multivariate sensitivity analysis that varies selected parameters by ±25% (in Table 1, parameters with * were included in the sensitivity analysis) was performed with random draws from uniform distributions over the designated range to explore how a change in these parameters influences the outcome variables namely cumulative infected cases, deaths, and fraction infection. The simulation model was run 500 times and the output was exported to excel for analysis. The mean, and 95% confidence interval were estimated and reported in Table 2.

Comments #8: On page 14, authors claim that they “explored the impact of (a) immunity on the scenarios explored, and (b) what would have happened if Singapore allowed the virus to take it natural course without intervention approach”, however no results are provided either in the manuscript of in the supplementary material. Moreover, they do not explain how they modified the model (and set the parameters) in order to relax the immunisation hypothesis.

Response: Thanks for the comment. We have provided the results and model changes made for the immunity assumption and the uninhibited spread/herd immunity assumptions. S8 provides details for the immunity assumption and S9 for the uninhibited spread/herd immunity assumption.

Minor issues

Comments #9: Figures S1 and S2 of the Supplementary Material are missing. All the supplementary file should be revised, as there are several figures which are not completely readable, whereas captions and title graphs do not adequately clarify what information is plotted.

Response: Thanks for the suggestion. We have reorganised the supplementary file.

Comments #10: Second and third items on page 9 would be more easy-to-read if parameters were reported (also ?) along with definitions.

Response: Thanks for the suggestion. Because table 1 list all these parameters with their values, we are of the opinion we do not need to provide it again in the manuscript.

Some typos and other minor details

Comments #10: [p. 3, sect. "Why this study was done?", point 1, 3rd line] A quotation mark is missing for keyword "2019 nCoV".

Response: Thanks for the suggestion. We have made the correction.

Comments #10: [p. 3, sect. "Why this study was done?", point 3] Last sentence says "The list of 30 papers included are provided in the appendix". If authors refer to Appendix S10 of the supplementary material, they could explicitly cite the reference label "S10". Moreover, Appendix S10 actually consists of 31 instead of 30 references.

Response: Thanks for the suggestion. We have made the correction.

Comments #11: [line 8] Please, specify the date adverb "currently" refers to.

Response: Thanks for the suggestion. We have made the correction.

Comments #12: [line 82] I think that figures authors refer to are those with numbers 2 and 3.

Response: Thanks for the suggestion. We have made the correction.

Comments #13: [page 8] Equation number (11) has not been used.

Response: Thanks for the suggestion. We have made the correction.

Comments #14: [equation 13] Right parenthesis is missing.

Response: Thanks for the suggestion. We have made the correction.

Comments #15: [line 146] Remove comma after "alpha".

Response: Thanks for the suggestion. We have made the correction.

Comments #16: [line 154] "denotes" should be "denote", and a space is missing before "kappa".

Response: Thanks for the suggestion. We have made the correction.

Comments #17: [line 163] "deMotes" should be "denote".

Response: Thanks for the suggestion. We have made the correction.

Comments #18: [line 213] "will diagnosed" should be "will be diagnosed".

Response: Thanks for the suggestion. We have made the correction.

Comments #19: [line 220] "...these parameters influence..." should be "...these parameters influence...".

Response: Thanks for the suggestion. We have made the correction.

Comments #20: [line 260] "it natural course" should be "its natural course".

Response: Thanks for the suggestion. We have made the correction

Submitted filename: Reviewer comments.docx

Click here for additional data file.

5 Mar 2021

The Effectiveness of Public Health Interventions Against COVID-19: Lessons from the Singapore Experience.

PONE-D-20-19492R1

Dear Dr. Ansah,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Kannan Navaneetham, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: I am satisfied with the authors' answers and revision of the manuscript, which may be accepted for publication.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

15 Mar 2021

PONE-D-20-19492R1

The Effectiveness of Public Health Interventions Against COVID-19: Lessons from the Singapore Experience

Dear Dr. Ansah:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

Professor Kannan Navaneetham

Academic Editor

PLOS ONE