The ongoing severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) pandemic has caused nearly 500,000 detected cases of coronavirus disease 2019 (COVID-19) illness and claimed >20,000 lives worldwide as of 26 March 2020 (
1). Experience from China, Italy, and the United States demonstrates that COVID-19 can overwhelm even the healthcare capacities of well-resourced nations (
2–
4). With no pharmaceutical treatments available, interventions have focused on contact tracing, quarantine, and social distancing. The required intensity, duration, and urgency of these responses will depend both on how the initial pandemic wave unfolds and on the subsequent transmission dynamics of SARS-CoV-2. During this initial pandemic wave, many countries have adopted social distancing measures and some, like China, are gradually lifting them after achieving adequate control of transmission. However, to mitigate the possibility of resurgences of infection, prolonged or intermittent periods of social distancing may be required. After the initial pandemic wave, SARS-CoV-2 might follow its closest genetic relative, SARS-CoV-1, and be eradicated by intensive public health measures after causing a brief but intense pandemic (
5). Increasingly, public health authorities consider this scenario unlikely (
6). Alternatively, the transmission of SARS-CoV-2 could resemble that of pandemic influenza by circulating seasonally after causing an initial global wave of infection (
7). Such a scenario could reflect the previous emergence of known human coronaviruses (HCoVs) from zoonotic origins, e.g., HCoV-OC43 (
8). Distinguishing between these scenarios is key for formulating an effective, sustained public health response to SARS-CoV-2.
The pandemic and postpandemic transmission dynamics of SARS-CoV-2 will depend on factors including the degree of seasonal variation in transmission, the duration of immunity, and the degree of cross-immunity between SARS-CoV-2 and other coronaviruses, as well as the intensity and timing of control measures. SARS-CoV-2 belongs to the
Betacoronavirus genus, which includes the SARS-CoV-1 coronavirus, the Middle East respiratory syndrome (MERS) coronavirus, and two other HCoVs, HCoV-OC43 and HCoV-HKU1. The SARS-CoV-1 and MERS coronaviruses cause severe illness with approximate case fatality rates of 9 and 36%, respectively, but the transmission of both has remained limited (
9). HCoV-OC43 and HCoV-HKU1 infections may be asymptomatic or associated with mild to moderate upper respiratory tract illness; these HCoVs are considered the second most common cause of the common cold (
9). HCoV-OC43 and HCoV-HKU1 cause annual wintertime outbreaks of respiratory illness in temperate regions (
10,
11), suggesting that wintertime climate and host behaviors may facilitate transmission, as is true for influenza (
12–
14). Immunity to HCoV-OC43 and HCoV-HKU1 appears to wane appreciably within 1 year (
15), whereas SARS-CoV-1 infection can induce longer-lasting immunity (
16). The betacoronaviruses can induce immune responses against one another: SARS-CoV-1 infection can generate neutralizing antibodies against HCoV-OC43 (
16) and HCoV-OC43 infection can generate cross-reactive antibodies against SARS-CoV-1 (
17). Although investigations into the spectrum of illness caused by SARS-CoV-2 are ongoing, recent evidence indicates that most patients experience mild to moderate illness with more limited occurrence of severe lower respiratory infection (
18). Current COVID-19 case fatality rates are estimated to lie between 0.6 and 3.5% (
19,
20), suggesting lower severity than SARS-CoV-1 and MERS but higher severity than HCoV-OC43 and HCoV-HKU1. The high infectiousness near the start of often mild symptoms makes SARS-CoV-2 considerably harder to control with case-based interventions such as intensive testing, isolation, and tracing compared with the SARS-CoV-1 and MERS coronaviruses (
21).
Intensive testing and case-based interventions have so far formed the centerpiece of control efforts in some places, including Singapore and Hong Kong (
22). Many other countries are adopting measures such as social distancing, closing schools and workplaces, and limiting the sizes of gatherings. The goal of these strategies is to reduce the peak intensity of the pandemic (i.e., “flatten the curve”) (
22), reducing the risk of overwhelming health systems and buying time to develop treatments and vaccines. For social distancing to have reversed the pandemic in China, the effective reproduction number (
Re; defined as the average number of secondary infections caused by a single infected individual in the population after there is some immunity or interventions have been put in place) must have declined by at least 50 to 60%, assuming a baseline basic reproduction number (
R0; defined as the average number of secondary infections caused by a single infected individual in a completely susceptible population) between 2 and 2.5 (
22). Through intensive control measures, Shenzhen was able to reduce the
Re by an estimated 85% (
23). However, it is unclear how well these declines in
R0 might generalize to other settings: recent data from Seattle suggest that the
R0 has only declined to about 1.4, or by about 30 to 45%, assuming a baseline
R0 between 2 and 2.5 (
24). Furthermore, social distancing measures may need to last for months to effectively control transmission and mitigate the possibility of resurgence (
25).
Here, we identify viral, environmental, and immunologic factors that in combination will determine the dynamics of SARS-CoV-2. We integrate our findings in a mathematical model to project potential scenarios for SARS-CoV-2 transmission through the pandemic and postpandemic periods and identify key data still needed to determine which scenarios are likely to play out. Then, using the model, we assess the duration and intensity of social distancing measures that might be needed to maintain control of SARS-CoV-2 in the coming months under both existing and expanded critical care capacities.
Transmission dynamics of HCoV-OC43 and HCoV-HKU1
We used data from the United States to model betacoronavirus transmission in temperate regions and to project the possible dynamics of SARS-CoV-2 infection through the year 2025. We first assessed the role of seasonal variation, duration of immunity, and cross-immunity on the transmissibility of HCoV-OC43 and HCoV-HKU1 in the United States. We used the weekly percentage of positive laboratory tests for HCoV-OC43 and HCoV-HKU1 (
31) multiplied by the weekly population-weighted proportion of physician visits for influenza-like illness (ILI) (
32,
33) to approximate historical betacoronavirus incidence in the United States to within a scaling constant. This proxy is proportional to incidence under a set of assumptions described in the supplementary materials and methods. To quantify variation in transmission strength over time, we estimated the weekly
Re (
34,
35). The
Res for each of the betacoronaviruses displayed a seasonal pattern, with annual peaks in the
Re slightly preceding those of the incidence curves (fig. S1). We limited our analysis to “in-season” estimates that were based on adequate samples, defined as week 40 through week 20 of the following year, roughly October to May. For both HCoV-OC43 and HCoV-HKU1, the
Re typically reached its peak between October and November and its trough between February and May. Over the five seasons included in our data (2014 to 2019), the median peak
Re was 1.85 (range: 1.61 to 2.21) for HCoV-HKU1 and 1.56 (range: 1.54 to 1.80) for HCoV-OC43 after removing outliers (five for HCoV-HKU1, zero for HCoV-OC43). Results were similar using various choices of incidence proxy and serial interval distributions (figs. S1 to S3).
To quantify the relative contribution of immunity versus seasonal forcing on the transmission dynamics of the betacoronaviruses, we adapted a regression model (
36) that expressed the
Re for each strain (HKU1 and OC43) as the product of a baseline transmissibility constant (related to the
R0) and the proportion of the population susceptible (hereafter referred to as “susceptibles”) at the start of each season, the depletion of susceptibles because of infection with the same strain, the depletion of susceptibles because of infection with the other strain, and a spline to capture further unexplained seasonal variation in transmission strength (seasonal forcing). These covariates were able to explain most of the observed variability in the
Res (adjusted
R2: 74.3%). The estimated multiplicative effects of each of these covariates on the weekly
Re are depicted in
Fig. 1. As expected, depletion of susceptibles for each betacoronavirus strain was negatively correlated with transmissibility of that strain. Depletion of susceptibles for each strain was also negatively correlated with the
Re of the other strain, providing evidence of cross-immunity. Per incidence proxy unit, the effect of the cross-immunizing strain was always less than the effect of the strain itself (table S1), but the overall impact of cross-immunity on the
Re could still be substantial if the cross-immunizing strain had a large outbreak (e.g., HCoV-OC43 in 2014–2015 and 2016–2017). The ratio of cross-immunization to self-immunization effects was larger for HCoV-HKU1 than for HCoV-OC43, suggesting that HCoV-OC43 confers stronger cross-immunity. Seasonal forcing appears to drive the rise in transmissibility at the start of the season (late October through early December), whereas depletion of susceptibles plays a comparatively larger role in the decline in transmissibility toward the end of the season. The strain-season coefficients were fairly consistent across seasons for each strain and lacked a clear correlation with incidence in prior seasons, consistent with experimental results showing substantial waning of immunity within 1 year (
15).
We integrated these findings into a two-strain ordinary differential equation susceptible-exposed-infectious-recovered-susceptible (SEIRS) compartmental model to describe the transmission dynamics of HCoV-OC43 and HCoV-HKU1 (fig. S4). The model provided a good fit to both the weekly incidence proxies for HCoV-OC43 and HCoV-HKU1 and to the estimated weekly
Res (
Fig. 2). According to the best-fit model parameters, the
R0 for HCoV-OC43 and HCoV-HKU1 varies between 1.7 in the summer and 2.2 in the winter and peaks in the second week of January, consistent with the seasonal spline estimated from the data. Also in agreement with the findings of the regression model, the duration of immunity for both strains in the best-fit SEIRS model is ~45 weeks, and each strain induces cross-immunity against the other, although the cross-immunity that HCoV-OC43 infection induces against HCoV-HKU1 is stronger than the reverse.
Assessing intervention scenarios during the initial pandemic wave
Regardless of the postpandemic transmission dynamics of SARS-CoV-2, urgent measures are required to address the ongoing pandemic. Pharmaceutical treatments and vaccines may require months to years to develop and test, leaving nonpharmaceutical interventions as the only immediate means of curbing SARS-CoV-2 transmission. Social distancing measures have been adopted in many countries with widespread SARS-CoV-2 transmission. The necessary duration and intensity of these measures has yet to be characterized. To address this, we adapted the SEIRS transmission model (fig. S9) to capture moderate, mild, or asymptomatic infections (95.6% of infections), infections that lead to hospitalization but not critical care (3.08%), and infections that require critical care (1.32%) (
26). We assumed the worst-case scenario of no cross-immunity from HCoV-OC43 and HCoV-HKU1 against SARS-CoV-2, which makes the SARS-CoV-2 model unaffected by the transmission dynamics of those viruses. Informed by the transmission model fits, we assumed a latent period of 4.6 days and an infectious period of 5 days, in agreement with estimates from other studies (
26). The mean duration of noncritical hospital stay was 8 days for those not requiring critical care and 6 days for those requiring critical care, and the mean duration of critical care was 10 days (
26). We varied the peak (wintertime)
R0 between 2.2 and 2.6 and allowed the summertime
R0 to vary between 60% (i.e., relatively strong seasonality) and 100% (i.e., no seasonality) of the wintertime
R0, guided by the inferred seasonal forcing for HCoV-OC43 and HCoV-HKU1 (table S8).
We used the open critical care capacity of the United States, 0.89 free beds per 10,000 adults, as a benchmark for critical care demand (
2). We simulated pandemic trajectories that were based on a pandemic establishment time of 11 March 2020. We simulated social distancing by reducing
R0 by a fixed proportion, which ranged between 0 and 60%. We assessed “one-time” social distancing interventions, for which
R0 was reduced by up to 60% for a fixed duration of time (up to 20 weeks) or indefinitely starting 2 weeks after pandemic establishment. We also assessed intermittent social distancing measures, for which social distancing was turned “on” when the prevalence of infection rose above a threshold and “off” when it fell below a second, lower threshold, with the goal of keeping the number of critical care patients below 0.89 per 10,000 adults. An “on” threshold of 35 cases per 10,000 people achieved this goal in both the seasonal and nonseasonal cases with wintertime
R0 = 2.2. We chose five cases per 10,000 adults as the “off” threshold. These thresholds were chosen to qualitatively illustrate the intermittent intervention scenario; in practice, the thresholds will need to be tuned to local epidemic dynamics and hospital capacities. We performed a sensitivity analysis around these threshold values (figs. S10 and S11) to assess how they affected the duration and frequency of the interventions. We also implemented a model with extra compartments for the latent period, infectious period, and each hospitalization period so that the waiting times in these states were gamma distributed instead of being exponentially distributed (see the supplementary materials and methods and figs. S16 and S17). Finally, we assessed the impact of doubling critical care capacity (and the associated on/off thresholds) on the frequency and overall duration of the social distancing measures.
We evaluated the impact of one-time social distancing efforts of varying effectiveness and duration on the peak and timing of the pandemic with and without seasonal forcing. When transmission was not subject to seasonal forcing, one-time social distancing measures reduced the pandemic peak size (
Fig. 4 and fig. S12). Under all scenarios, there was a resurgence of infection when the simulated social distancing measures were lifted. However, longer and more stringent temporary social distancing did not always correlate with greater reductions in pandemic peak size. In the case of a 20-week period of social distancing with a 60% reduction in
R0, for example (
Fig. 4D), the resurgence peak size was nearly the same as the peak size of the uncontrolled pandemic: the social distancing was so effective that virtually no population immunity was built. The greatest reductions in peak size come from social distancing intensity and duration that divide cases approximately equally between peaks (
42).
For simulations with seasonal forcing, the postintervention resurgent peak could exceed the size of the unconstrained pandemic (
Fig. 5 and fig. S13), both in terms of peak prevalence and in terms of total number infected. Strong social distancing maintains a high proportion of susceptible individuals in the population, leading to an intense resurgence when
R0 rises in the late autumn and winter. None of the one-time interventions was effective at maintaining the prevalence of critical cases below the critical care capacity.
Intermittent social distancing could prevent critical care capacity from being exceeded (
Fig. 6 and fig. S14). Because of the natural history of infection, there is an ~3-week lag between the start of social distancing and the peak critical care demand. When transmission is seasonally forced, summertime social distancing can be less frequent than when
R0 remains constant at its maximal wintertime value throughout the year. The length of time between distancing measures increases as the pandemic continues because the accumulation of immunity in the population slows the resurgence of infection. Under current critical care capacities, however, the overall duration of the SARS-CoV-2 pandemic could last into 2022, requiring social distancing measures to be in place between 25% (for wintertime
R0 = 2 and seasonality; fig. S11A) and 75% (for wintertime
R0 = 2.6 and no seasonality; fig. S10C) of that time. When the latent, infectious, and hospitalization periods are gamma distributed, incidence rises more quickly, requiring a lower threshold for implementing distancing measures (25 cases per 10,000 individuals for
R0 = 2.2 in our model) and more frequent interventions (fig. S16).
Increasing critical care capacity allows population immunity to be accumulated more rapidly, reducing the overall duration of the pandemic and the total length of social distancing measures (
Fig. 6, C and D). Although the frequency and duration of the social distancing measures were similar between the scenarios with current and expanded critical care capacity, the pandemic would conclude by July 2022 and social distancing measures could be fully relaxed by early to mid-2021, depending again on the degree of seasonal forcing of transmission (
Fig. 6, C and D). Introducing a hypothetical treatment that halved the proportion of infections that required hospitalization had a similar effect as doubling critical care capacity (fig. S15).
Discussion
Here, we examined a range of likely SARS-CoV-2 transmission scenarios through 2025 and assessed nonpharmaceutical interventions that could mitigate the intensity of the current outbreak. If immunity to SARS-CoV-2 wanes in the same manner as related coronaviruses, then recurrent wintertime outbreaks are likely to occur in coming years. The total incidence of SARS-CoV-2 through 2025 will depend crucially on this duration of immunity and, to a lesser degree, on the amount of cross-immunity that exists between HCoV-OC43/HCoV-HKU1 and SARS-CoV-2. The intensity of the initial pandemic wave will depend fundamentally on the
R0 at the time of pandemic establishment: If establishment occurs in the autumn when the
Re is rising, which could occur in countries that maintain pandemic control by contact tracing and quarantine through the summer, or if SARS-CoV-2 is not subject to the same summertime decline in transmissibility as HCoV-OC43 and HCoV-HKU1, then a high peak prevalence of infection is likely. One-time social distancing efforts may push the SARS-CoV-2 pandemic peak into the autumn, potentially exacerbating the load on critical care resources if there is increased wintertime transmissibility. Intermittent social distancing might maintain critical care demand within current thresholds, but widespread surveillance will be required to time the distancing measures correctly and avoid overshooting critical care capacity. New therapeutics, vaccines, or other interventions such as aggressive contact tracing and quarantine—impractical now in many places but more practical once case numbers have been reduced and testing scaled up (
43)—could alleviate the need for stringent social distancing to maintain control of the pandemic. In the absence of such interventions, surveillance and intermittent distancing (or sustained distancing if it is highly effective) may need to be maintained into 2022, which would present a substantial social and economic burden. To shorten the SARS-CoV-2 pandemic and to ensure adequate care for the critically ill, increasing critical care capacity and developing additional interventions are urgent priorities. Meanwhile, serological testing is required to understand the extent and duration of immunity to SARS-CoV-2, which will help to determine the postpandemic dynamics of the virus. Sustained, widespread surveillance will be needed both in the short term to effectively implement intermittent social distancing measures and in the long term to assess the possibility of resurgences of SARS-CoV-2 infection, which could occur as late as 2025 even after a prolonged period of apparent elimination.
Our observations are consistent with other predictions of how SARS-CoV-2 transmission might unfold and with assessments of the mitigation efforts that might be needed to curb the current outbreak. A modeling study using data from Sweden found that seasonal establishment of SARS-CoV-2 transmission is likely in the postpandemic period (
11). Observational and modeling studies (
2,
26) have found that early implementation of strong social distancing is essential for controlling the spread of SARS-CoV-2 and that, in the absence of the development of new therapies or preventative measures such as aggressive case finding and quarantining (
21), intermittent distancing measures may be the only way to avoid overwhelming critical care capacity while building population immunity. The observation that strong, temporary social distancing can lead to especially large resurgences agrees with data from the 1918 influenza pandemic in the United States (
44), in which the size of the autumn 1918 peak of infection was inversely associated with that of a subsequent winter peak after interventions were no longer in place.
Our study was subject to a variety of limitations. Only five seasons of observational data on coronaviruses were available, although the incidence patterns resemble those from 10 years of data from a hospital in Sweden (
11). We assumed that the spline coefficients were constant across all seasons but seasonal forcing likely differed from year to year because of underlying drivers. To keep the transmission model from becoming unreasonably complex, we assumed that there was no difference in the seasonal forcing, per-case force of infection, latent period, or infectious period across betacoronaviruses. However, our estimates for these values lie within the range of estimates from the literature. Although disease dynamics may differ by age, we did not have sufficient data to parameterize an age-structured model. We also did not directly model any effect from the opening of schools, which could lead to an additional boost in transmission strength in the early autumn (
45). The transmission model is deterministic, so it cannot capture the possibility of SARS-CoV-2 extinction. It also does not incorporate geographic structure, so the possibility of spatially heterogeneous transmission cannot be assessed. The construction of spatially explicit models will become more feasible as more data on SARS-CoV-2 incidence become available; these will help to determine whether there are differences in seasonal forcing between geographic locations, as is the case for influenza (
12), and will also help to assess the possibility of pandemic extinction while accounting for reintroductions. The timing and strength of postpandemic outbreaks may also depend on stochastic introductions from abroad, which can be assessed using more complex, global models.
We used percent test-positive multiplied by percent ILI to approximate coronavirus incidence up to a proportional constant; results were similar when using the raw number of positive tests and the raw percent test-positive as incidence proxies (fig. S1). Although the percent test-positive multiplied by percent ILI has been shown to be one of the best available proxies for influenza incidence (
32), the conversion between this measure and the true incidence of coronavirus infections is unclear, so we do not make precise estimates of the overall coronavirus incidence. This conversion will undoubtedly depend on the particular population for which these estimates are being made. In a recent study, an estimated 4% of individuals with coronavirus sought medical care, and only a fraction of these were tested (
46). In addition, the method that we adopted to estimate the
Re depends on the serial interval distribution, which has not been well studied for commonly circulating human coronaviruses; we used the best-available evidence from SARS-CoV-1, the most closely related coronavirus to SARS-CoV-2.
Our findings generalize only to temperate regions, which contain 60% of the world’s population (
47), and the size and intensity of outbreaks could be further modulated by differences in average interpersonal contact rates by location and the timing and effectiveness of nonpharmaceutical and pharmaceutical interventions. The transmission dynamics of respiratory illnesses in tropical regions can be much more complex. However, we expect that if postpandemic transmission of SARS-CoV-2 does take hold in temperate regions, there will also be continued transmission in tropical regions seeded by the seasonal outbreaks to the north and south. With such reseeding, long-term disappearance of any strain becomes less likely (
48), but according to our model, the
Re of SARS-CoV-2 remains <1 during most of each period when that strain disappears, meaning that reseeding would shorten these disappearances only modestly.
Our findings indicate key data required to know how the current SARS-CoV-2 outbreak will unfold. Most crucially, serological studies could indicate the extent of population immunity and whether immunity wanes and at what rate. In our model, this rate is the key modulator of the total SARS-CoV-2 incidence in the coming years. Although long-lasting immunity would lead to lower overall incidence of infection, it would also complicate vaccine efficacy trials by contributing to low case numbers when those trials are conducted, as occurred with Zika virus (
49). In our assessment of control measures in the initial pandemic period, we assumed that SARS-CoV-2 infection induces immunity that lasts for at least 2 years, but social distancing measures may need to be extended if SARS-CoV-2 immunity wanes more rapidly. In addition, if serological data reveal the existence of many undocumented asymptomatic infections that lead to immunity (
50), less social distancing may be required. Serology could also indicate whether cross-immunity exists among SARS-CoV-2, HCoV-OC43, and HCoV-HKU1, which could affect the postpandemic transmission of SARS-CoV-2. We anticipate that such cross-immunity would lessen the intensity of SARS-CoV-2 outbreaks, though some speculate that antibody-dependent enhancement (ADE) induced by prior coronavirus infection may increase susceptibility to SARS-CoV-2 and exacerbate the severity of infection (
51,
52). At present, there are limited data describing ADE between coronaviruses, but if it does exist, it may promote the cocirculation of betacoronavirus strains.
To implement intermittent social distancing, it will be necessary to carry out widespread viral testing for surveillance to monitor when the prevalence thresholds that trigger the beginning or end of distancing have been crossed. Without such surveillance, critical care bed availability might be used as a proxy for prevalence, but this metric is far from optimal because the lag between distancing and peak critical care demand could lead to frequent overrunning of critical care resources. Critical care resources are also at greater risk of being overrun if the infectious, latent, and hospitalized periods follow peaked distributions (e.g., gamma versus exponential). Measuring the distributions of these times, and not just their means, will help to set more effective thresholds for distancing interventions. Under some circumstances, intense social distancing may be able to reduce the prevalence of COVID-19 enough to warrant a shift in strategy to contact tracing and containment efforts, as has occurred in many parts of China (
21,
23,
53). Still, countries that have achieved this level of control of the outbreak should prepare for the possibility of substantial resurgences of infection and a return to social distancing measures, especially if seasonal forcing contributes to a rise in transmissibility in the winter. Moreover, a winter peak for COVID-19 would coincide with peak influenza incidence (
54), further straining health care systems.
Treatments or vaccines for SARS-CoV-2 would reduce the duration and intensity of the social distancing required to maintain control of the pandemic. Treatments could reduce the proportion of infections that require critical care and the duration of infectiousness, which would both directly and indirectly (through a reduction in R0) reduce the demand for critical care resources. A vaccine would accelerate the accumulation of immunity in the population, reducing the overall length of the pandemic and averting infections that might have resulted in a need for critical care. Furthermore, if there have been many undocumented immunizing infections, then the herd immunity threshold may be reached sooner than our models suggest. Nevertheless, SARS-CoV-2 has demonstrated an ability to challenge robust healthcare systems, and the development and widespread adoption of pharmaceutical interventions will take months at best, so a period of sustained or intermittent social distancing will almost certainly be necessary.
In summary, the total incidence of COVID-19 illness over the next 5 years will depend critically upon whether it enters into regular circulation after the initial pandemic wave, which in turn depends primarily upon the duration of immunity that SARS-CoV-2 infection imparts. The intensity and timing of pandemic and postpandemic outbreaks will depend on the time of year when widespread SARS-CoV-2 infection becomes established and, to a lesser degree, upon the magnitude of seasonal variation in transmissibility and the level of cross-immunity that exists between the betacoronaviruses. Social distancing strategies could reduce the extent to which SARS-CoV-2 infections strain health care systems. Highly effective distancing could reduce SARS-CoV-2 incidence enough to make a strategy that is based on contact tracing and quarantine feasible, as in South Korea and Singapore. Less effective one-time distancing efforts may result in a prolonged single-peak pandemic, with the extent of strain on the healthcare system and the required duration of distancing depending on the effectiveness. Intermittent distancing may be required into 2022 unless critical care capacity is increased substantially or a treatment or vaccine becomes available. The authors are aware that prolonged distancing, even if intermittent, is likely to have profoundly negative economic, social, and educational consequences. Our goal in modeling such policies is not to endorse them, but rather to identify likely trajectories of the pandemic under alternative approaches, to identify complementary interventions such as expanding ICU capacity and identifying treatments to reduce ICU demand, and to spur innovative ideas (
55) to expand the list of options to bring the pandemic under long-term control. Our model presents a variety of scenarios intended to anticipate possible SARS-CoV-2 transmission dynamics under specific assumptions. We do not take a position on the advisability of these scenarios given the economic burden that sustained distancing may impose, but we note the potentially catastrophic burden on the healthcare system that is predicted if distancing is poorly effective and/or not sustained for long enough. The model will have to be tailored to local conditions and updated as more accurate data become available. Longitudinal serological studies are urgently required to determine the extent and duration of immunity to SARS-CoV-2, and epidemiological surveillance should be maintained in the coming years to anticipate the possibility of resurgence.
RE: Transmission Dynamics for a Seemingly Never Ending COVID-19 Pandemic
In May 2020, it might have seemed as though the COVID-219 pandemic was going to end sooner rather than later.
As the world approaches Christmas 2020 and New Year 2021, the seemingly never ending waves of the pandemic are difficult to predict.
To state the obvious, an increasingly large number of confirmed cases and deaths indicate that COVID-19 is now out of control, apart from some isolated countries territories.
The transmission curves are not flattening, there is no herd immunity, and the rejection of the best healthcare advice, including masking and social distancing, by so many in so many countries indicate it will be a long time before the pandemic is mitigated or controlled.
RE: New evidences don't support "SARS-CoV-2 surveillance should be maintained for 4 more years or longer"
The following evidence can question "SARS-CoV-2 surveillance should be maintained to late 2024" as concluded in the article (1).
1. The center of COVID-19 epidemic in China was Wuhan. On Feb. 18, the total cases were 44,412 with death 1,497. Owed to multiple measures, new infection went down and stopped from June 4. Since then, none of new cases were found in local people for 151 days already (counted until Nov. 2). None of COVID-19 was shown in Wuhan residents for 151 days, except a few imported cases.
2. COVID-19 caused the highest daily infection (295 new cases) in Beijing was on Feb. 12. After 4 months of fighting COVID-19, Beijing witnessed 1 new case only on June 8. However, 3 days later, the infected new patients were 6, the next day was increased to 36, indicating SARS-CoV-2 came back. After one-month the anti-virus war, Beijing finally reduced the new cases to zero on July 6. Since then, none of the new infection was detected in Beijing residents, and it has been 120 days already that Beijing people were free from the virus.
3. The highest daily new patients (97 cases) in Liaoning Province were on Feb. 11. Liaoning successfully knocked down new cases to zero on May 25. Unfortunately, 19 days later, 2 new patients appeared, and 26 new cases were confirmed on June 25-26. Following the enforced the anti-virus war, the new infection cases were decreased to zero again on Aug. 9. Then, none of the new infection was found in the Liaoning residents for 85 days already.
Coronavirus can survive on human skin for maximal 9 hours, and its survivability was between 3 and 14 days on smooth surface (2-3). A recent report suggested that coronavirus could survive up to 28 days on glass surface at 20 Co (4), notably, which was the longest surviving-time as recorded. Interestingly and importantly, no any newly infected case occurred in residents in Wuhan, Beijing, and Liaoning for continuous 151, 120, or 85 days, respectively, except the imported cases. Even for the imported, the total cases were continuously decreased without further transmission under the control. Such as 151 or 120 days without new infection are critical evidence that SARS-CoV-2 could be wiped out from the areas, just like the gone of SARS-virus in 2003. If Wuhan, Beijing, and Liaoning could do that, why other areas should accept that SARS-CoV-2 will stay for 4 more years or longer?
Supported by the grants (2019YFA0802600, 81771592, and Jiangsu Key Fetology Discipline/Laboratory).
1Contributed equally,*The correspondence: [email protected]
References
1. S. M. Kissler, C. Tedijanto, E. Goldstein, Y. H. Grad, M. Lipsitch, Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science, eabb5793 (2020).
2. N. van Doremalen et al., Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. NEJM. 382, 1564-1567 (2020).
3. K. H. Chan et al., The effects of temperature and relative humidity on the viability of the SARS coronavirus. Advances in Virology 2011,1-7 (2011).
4. S. Riddell, S. Goldie, A. Hill, D. Eagles, T. W. Drew, The effect of temperature on persistence of SARS-CoV-2 on common surfaces. Virology. 17, 145 (2020).
RE: The complexity of the clinical practice in comparison to this mathematical model
We have read carefully the article by Kissler et al. projecting the transmission dynamics of SARS-CoV-2 through the post-pandemic period (1) and although really interesting, we would like to highlight the following points. To make their projections authors consider an incubation time of 4.6 days and an infectious period of 5 days. Nowadays, SARS-CoV-2 infectivity is considered really low during the incubation period, until the two days before symptoms onset. Second, the infectious period lasts much longer with a peak in viral load at around 10 days after symptoms onset (2). For the infective period Kissler et al. refer to the article of Ferguson et al. which does not provide such information, reporting only a median of 5 days between symptoms and hospitalization (3). Making a comparison with influenza authors suggest a seasonal variation of R0. For influenza, a major role is played by genetic drift allowing immunological escape. On the contrary, coronaviruses are considered genetically poorly variable and SARS-CoV-2 seems to have a very low mutation rate (4).
Discussing the impact of serology on disease spread authors should mention concerns about serology accuracy. Remarkably, the sole FDA approved serological test has an unknown sensitivity with a false-positive ratio between 3.3 and 7.1%, possibly related to cross-reactivity (as declared by manufacturer information https://www.fda.gov/media/136625/download).
The possibility of precocious reinfection can't be entirely excluded as current evidence negating it is based solely on a very small trial on an animal model (5). Authors suggest a threshold to activate social distancing of 35 cases every 10.000 people.
Such threshold may be considered politically and socially unachievable because, despite more than 600,000 cases already registered in the US as of April 15th 2020 there is considerable economic, political and social pressure to interrupt social distancing. Such a decision could lead to profound changes in respect to actual projections.
The future of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission is certain.
The future of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission is certain.
The recent coronavirus pneumonia that emerged in Wuhan (SARS-CoV2) at the end of 2019 quickly spread to all over the world, as of April 14, 2020, confirmed cases was reported has reached 2100000 worldwide according to statistical data, which attracted global attention (https://www.who.int/). Therefore, I read the recent report by Kissler et al with great interest (1). Nonetheless, there are obvious problems in this report.
Firstly, Kissler et al stated that "It is urgent to understand the future of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission" in their manuscript (1). Nevertheless, China has completely contained the dissemination of novel coronavirus (SARS-CoV2) through drastic measures as of April 14, 2020 (2). Therefore, the future of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission is certain in China. Furthermore, as the authors have made a strong comment as " During the initial pandemic wave, many countries have adopted social distancing measures, and some, like China, are gradually lifting them after achieving adequate control of transmission" (1), Indeed, China has completely contained the dissemination of novel coronavirus (SARS-CoV2) (2-4).
Secondly, the authors stated that "Experience from China, Italy, and the United States demonstrates that COVID-19 can overwhelm even the healthcare capacities of well-resourced nations" (1). However, Chinese government provided a quick response and took drastic measures such as controlling the source of infection, cutting off the route of transmission, and protecting vulnerable groups including travel restrictions and quarantining Wuhan City on January 23, therefore, China has completely contained the dissemination of novel coronavirus (SARS-CoV2) (2-4), which demonstrated that experience from China demonstrates that COVID-19 can't overwhelm even the healthcare capacities of well-resourced nations.
Thirdly, Kissler et al stated that "The high infectiousness near the start of often mild symptoms makes SARS-CoV-2 considerably harder to control with case-based interventions such as intensive testing, isolation and tracing, compared to SARS-CoV-1 and MERS coronaviruses" (1), and "Then, using the model, we assess the duration and intensity of social distancing measures that might be needed to maintain control of SARS-CoV-2 in the coming months under both existing and expanded critical care capacities" (1). However, China has completely contained the dissemination of novel coronavirus (SARS-CoV2) through drastic measures such as controlling the source of infection, cutting off the route of transmission, and protecting vulnerable groups including travel restrictions and quarantining Wuhan City on January 23. Thus, what's value of these old findings in controlling the outbreak of SARS-CoV2 for China and the world?
Also the discussion in their manuscript is stated " Although disease dynamics may differ by age, we did not have sufficient data to parameterize an age-structured model. We also did not directly model any effect from the opening of schools, which could lead to an additional boost in transmission strength in the early autumn" (1). However, the opening of schools hasn't led to an additional boost in China.
This work was supported by grants from the National Undergraduate Training Program for Innovation and Entrepreneurship (no. 201811646025 to YW), the Student Research and Innovation Program of Ningbo University (no. 2017SRIP1918, no. 2018SRIP2507 and no. 2019SRIP1902 to YW). The author has nothing to disclose.
References:
1. S.M. Kissler, C. Tedijanto, E. Goldstein, Y.H. Grad, M. Lipsitch. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science. 2020; eabb5793. doi: 10.1126/science.abb5793
2. W.J. Guan, Z.Y. Ni, Y. Hu, W.H. Liang, C.Q. Ou, J.X. He, L. Liu, H. Shan, C.L. Lei, D.S.C. Hui, B. Du, L.J. Li, G. Zeng, K.Y. Yuen, R.C. Chen, C.L. Tang, T. Wang, P.Y. Chen, J. Xiang, S.Y.Li, J.L. Wang, Z.J. Liang, Y.X. Peng, L. Wei, Y. Liu, Y.H. Hu, P. Peng, J.M. Wang, J.Y. Liu, Z. Chen, G. Li, Z.J. Zheng, S.Q. Qiu, J. Luo, C.J. Ye, S.Y. Zhu, N.S. Zhong; China Medical Treatment Expert Group for Covid-19. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020 Feb 28. doi: 10.1056/NEJMoa2002032.
3. C. Huang, Y. Wang, X. Li, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 395 497–506 (2020). doi:10.1016/S0140-6736(20)30183-5
4. Yezhao Wang. The effectiveness of efforts to control SARS-CoV2 is known.https://science.sciencemag.org/content/early/2020/03/24/science.abb3221/....
Yezhao Wang*
Department of Biochemistry and Molecular Biology, and Zhejiang Key Laboratory of Pathophysiology, Ningbo University School of Medicine, Ningbo, 315211, China.
*Correspondence to Yezhao Wang
Department of Biochemistry and Molecular Biology, and Zhejiang Key Laboratory of Pathophysiology, Ningbo University School of Medicine, Ningbo, 315211, China.
E-mail: [email protected].