Immunological characteristics govern the transition of COVID-19 to endemicity
Taming a pandemic
One year after its emergence, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become so widespread that there is little hope of elimination. There are, however, several other human coronaviruses that are endemic and cause multiple reinfections that engender sufficient immunity to protect against severe adult disease. By making assumptions about acquired immunity from its already endemic relatives, Lavine et al. developed a model with which to analyze the trajectory of SARS-CoV-2 into endemicity. The model accounts for SARS-CoV-2's age-structured disease profile and assesses the impact of vaccination. The transition from epidemic to endemic dynamics is associated with a shift in the age distribution of primary infections to younger age groups, which in turn depends on how fast the virus spreads. Longer-lasting sterilizing immunity will slow the transition to endemicity. Depending on the type of immune response it engenders, a vaccine could accelerate establishment of a state of mild disease endemicity.
Science, this issue p. 741
Abstract
We are currently faced with the question of how the severity of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may change in the years ahead. Our analysis of immunological and epidemiological data on endemic human coronaviruses (HCoVs) shows that infection-blocking immunity wanes rapidly but that disease-reducing immunity is long-lived. Our model, incorporating these components of immunity, recapitulates both the current severity of SARS-CoV-2 infection and the benign nature of HCoVs, suggesting that once the endemic phase is reached and primary exposure is in childhood, SARS-CoV-2 may be no more virulent than the common cold. We predict a different outcome for an emergent coronavirus that causes severe disease in children. These results reinforce the importance of behavioral containment during pandemic vaccine rollout, while prompting us to evaluate scenarios for continuing vaccination in the endemic phase.
Humans have regularly been threatened by emerging pathogens that kill a substantial fraction of all people born. Recent decades have seen multiple challenges from acute virus infections, including severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), Hendra, Nipah, and Ebola. Fortunately, all were locally contained. When containment is not immediately successful, as is likely for the novel betacoronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (1, 2), we need to understand and plan for the transition to endemicity and continued circulation, with possible changes in disease severity owing to virus evolution and buildup of host immunity and resistance.
SARS-CoV-2 is an emerging virus that causes COVID-19. The virus has a high basic reproductive number (R0) and is transmissible during the asymptomatic phase of infection, both of which make it hard to control (3). However, there are six other coronaviruses with known human chains of transmission, which may provide clues to future scenarios for the current pandemic. There are four human coronaviruses (HCoVs) that circulate endemically around the globe; these cause only mild symptoms and are not a considerable public health burden (4). Two other HCoV strains, SARS-CoV-1 and MERS-CoV, emerged in recent decades and have higher case fatality ratios (CFRs) and higher infection fatality ratios (IFRs) than COVID-19 but were contained and thus never spread widely (5, 6).
We propose a model to explore the potential changes in both transmission and disease severity of emerging HCoVs through the transition to endemicity. We focus on SARS-CoV-2 and discuss how the conclusions would differ for emerging coronaviruses more akin to SARS-CoV-1 and MERS-CoV. Our hypothesis is that all HCoVs elicit immunity with similar characteristics, and the current acute public health problem is a consequence of epidemic emergence into an immunologically naïve population in which older age groups with no previous exposure are most vulnerable to severe disease. We use our estimates of immunological and epidemiological parameters for endemic HCoVs to develop a quantitative model for endemic transmission of a virus with SARS-CoV-2–like characteristics, including the age dependence of severity. Our model explicitly considers three separate measures for immune efficacy that wane at different rates (fig. S1).
Building on ideas from the vaccine modeling literature, we suggest that immunity may provide protection in three ways (7). In its most robust form, sterilizing immunity can prevent a pathogen from replicating, thereby rendering the host refractory to reinfection. We term this property immune efficacy with respect to susceptibility (IES). If immunity does not prevent reinfection, it may still attenuate pathology due to reinfection (IEP) and/or reduce transmissibility or infectiousness (IEI). Indeed, experimental reexposure studies on endemic HCoVs provide evidence that the three immune efficacies do not wane at the same rate (8, 9). Callow et al.’s experimental study (8) shows that reinfection is possible within one year (relatively short IES); however, upon reinfection, symptoms are mild (high IEP) and the virus is cleared more quickly (moderate IEI). Details on the derivation of the model can be found in section 2 of the supplementary materials (SM).
We reanalyze a detailed dataset that estimates age-specific seroprevalence on the basis of both immunoglobulin M (IgM; acute response) and IgG (long-term memory) against all four circulating HCoVs in children and adults (10) to estimate parameter ranges for transmission and waning of immunity (Fig. 1A). The rapid rise in both IgM and IgG seroprevalence indicates that primary infection with all four endemic HCoV strains happens early in life, and our analysis of these data gives us an estimate for the mean age of primary infection (MAPI) between 3.4 and 5.1 years, with almost everyone infected by age 15 (see SM section 1 for details). The absence of detectable IgM titers in any individual over the age of 15 years suggests that reinfection of adults causes a recall response, indicating that while HCoV-specific immunity may wane, it is not lost. Whether immunity would wane to naïve levels in the absence of high pathogen circulation remains an open question.
Fig. 1 A low mean age of primary infection suggests that partially transmissible reinfections are common.
(A) Mean proportion seropositive for IgG (green, top lines) and IgM (purple, bottom lines) against the four endemic HCoV strains [dots connected by dashed lines; vertical lines represent the 95% confidence interval (CI); data from (10)]. The mean age of primary infection (MAPI) based on IgM data with 95% CI is shown in the inset of each panel (see SM for details). (B) MAPI as a function of waning of sterilizing immunity ω (y axis) and transmissibility of reinfections ρ (x axis). The MAPI was calculated from the equilibrium dynamics of the model shown in fig. S1 and supplementary equations 3 to 9 with a plausible basic reproductive number (R0 = 5), 0 < ω < 2, and 0 < ρ < 1. See SM section 2.1 for details. The inset shows the plausible combinations of values of ρ and ω consistent with the MAPI for HCoVs estimated in (A). F.O.I., force of infection; I2, reinfection. [See fig. S1 for parallel figures calculated at extreme plausible values for R0 (i.e., R0 = 2 and 10).]
For most people to be infected so early in life—younger even than measles in the pre-vaccine era—the attack rate must exceed transmission from primary infections alone. The model shows that a high attack rate can arise from a combination of high transmissibility from primary infections (i.e., high R0), waning of sterilizing immunity, and substantial transmission from reinfections in older individuals. The rapid waning of sterilizing immunity is also reported in experimental HCoV infections of humans, which showed that reinfection is possible 1 year after an earlier infection, albeit with milder symptoms (IEP) and a shorter duration (IEI) (8). Figure 1B shows the plausible combinations of waning immunity and transmission from reinfected individuals that are required to produce the MAPI observed in Fig. 1A, based on steady-state infection levels (see SM section 2.1 for details). Table 1 shows the ranges of the parameters used in our simulations.
| Characteristic and symbol | Estimates from literature | Value (range) | Citations |
|---|---|---|---|
| Primary infectious period (1/γ) |
≥5.6 days ~10 days |
9 days | (8) (30) |
| Primary transmissibility [R0 = β/(γ + μ)] |
4 to 9 | 2 to 10 | (31) |
| Secondary transmissibility (relative to primary, ρ) |
0.35 0.04 to 0.97 |
0 to 1 | (8) Fig. 2 and fig. S2 |
| Duration of sterilizing immunity (1/ω) | 0.91 years 1.67 years 0.5 to 2 years |
0.5 to 10 years | (8) and SM section 5 (8) and SM section 5 (11, 32) |
| Relative pathology of reinfections | Mild | – | (8) |
| Age-specific IFR (primary infections) |
SARS MERS COVID-19 |
See Fig. 3 |
(33) (5) (34) |
At the beginning of an outbreak, the age distribution of cases mirrors that of the population (Fig. 2A). However, once the demographics of infection reaches a steady state, our model predicts that primary cases occur almost entirely in babies and young children, who, in the case of COVID-19, experience a low CFR and a concomitantly low IFR. Reinfections in older individuals are predicted to be common during the endemic phase and to contribute to transmission, but in this steady-state population, older individuals, who would be at risk for severe disease from a primary infection, have acquired disease-reducing immunity after infection during childhood. The top panel of Fig. 3B illustrates how the overall IFR for SARS-CoV-2 drops drastically, eventually falling below that of seasonal influenza (~0.001) once the endemic steady-state is reached.
Fig. 2 The time scale of the transition from epidemic to endemic dynamics for emerging coronaviruses depends on R0 and the rate of immune waning.
Transition from epidemic to endemic dynamics for emerging HCoVs, simulated from an extension of the model presented in fig. S1 that includes age structure. Demographic characteristics (age distribution, birth, and age-specific death rates) are taken from the United States, and seasonality is incorporated via a sinusoidal forcing function (see SM section 2.2). Weak social distancing is approximated by R0 = 2. (See figs. S9 to S11 for strong social distancing results, R0 < 1.5.) (A) Daily number of new infections (black line; calculations in SM section 2.3). An initial peak is followed by a low-incidence endemic state (years 5 to 10 shown in the inset). A higher R0 results in a larger and faster initial epidemic and a more rapid transition to endemic dynamics. The proportion of primary cases in different age groups changes over time (plotted in different colors), and the transition from epidemic to endemic dynamics results in primary cases being restricted to younger age groups. Parameters for simulations: ω = 1 and ρ = 0.7. (B) Time for the average IFR (6-month moving average) to fall to 0.001, which is the IFR associated with seasonal influenza. Gray areas represent simulations where the IFR did not reach 0.001 within 30 years. The time to IFR = 0.001 decreases as the transmissibility (R0) increases and the duration of sterilizing immunity becomes shorter. Results are shown for ρ = 0.7. See SM section 2.3 and figs. S4 to S7 for sensitivity analyses and model specifications.
Fig. 3 The overall infection fatality ratio (IFR) of emerging coronaviruses once they reach endemicity is strongly influenced by the IFR of young children in the initial epidemic.
The age dependence of the IFR determines how the overall IFR changes during the transition from epidemic to endemic dynamics for emerging HCoVs. (A) Age dependence of the IFRs for the three emerging HCoVs. Primary infections with MERS-CoV and SARS-CoV-1 are consistently symptomatic, and the IFR and CFR are therefore assumed to be the same. SARS-CoV-1 and SARS-CoV-2 have J-shaped profiles, with a monotonic increase in IFR with age. The age-specific IFR for MERS-CoV is U-shaped, with high mortality in both the young and old age groups. Details of the statistical smoothing are described in SM section 6. (B) The overall IFR changes during the transition to endemic dynamics. These calculations assume that deaths due to reinfections are negligible. We relax this assumption to allow for a slower buildup of immunity and possible death due to secondary infection in figs. S5 to S9 and show that the qualitative results do not change.
The time it takes to complete the shift in IFR as endemicity develops depends on both transmission (R0) and loss of immunity [waning of sterilizing immunity (ω) and transmissibility of reinfections (ρ)], as shown in Fig. 2B and fig. S4. The transition from epidemic to endemic dynamics is associated with a shift in the age distribution of primary infections to lower age groups (Fig. 2A). This transition may take anywhere from a few years to a few decades, depending on how quickly the pathogen spreads. The rate of spread, measured by R0, is determined by a combination of viral properties and the frequency of social contacts and may therefore be reduced by social distancing. The top panel in Fig. 2A shows the effect of reducing R0 to 2, whereas the middle and bottom panels show the dynamics for higher R0, which are more akin to those of SARS-CoV-2 in the absence of control measures. If transmission is high, the model predicts a high case load and high death rate in earlier years following emergence (Fig. 2 and fig. S5). We see that, as might be expected, longer-lasting sterilizing immunity slows down the transition to endemicity (Fig. 2B). These results are robust to a more biologically realistic distribution for the duration of sterilizing immunity and the possibility that the generation of protective immunity requires more than one infection (see SM section 3 and figs. S5 to S9).
Slowing down the epidemic through social distancing measures that reduce R0 to close to 1 flattens the curve, thus delaying infections and preventing most deaths from happening early on, affording critical time for the development of an effective vaccine (fig. S10). If vaccine-induced IES and IEP immunity is similar to that induced by HCoV infections, the vaccine may usher in the endemic regime more quickly. The model code (see the acknowledgments) provides a flexible scaffolding for studying alternative vaccination scenarios. Notably, the model predicts that once the endemic state is reached, mass vaccination may no longer be necessary to save lives (see SM section 4 and fig. S11).
We can extend our predictions to two other potentially emerging coronavirus infections, SARS and MERS. Our model predicts that in the endemic state, the IFR of a circulating HCoV depends primarily on the severity of childhood infections. In the case of SARS-CoV-1, which is more pathogenic than SARS-CoV-2, we still expect a low disease burden in the endemic phase, because SARS-CoV-1, like SARS-CoV-2, has a low IFR in young people (Fig. 3). However, data suggest that not all emerging HCoVs follow this optimistic pattern; the overall IFR of an endemic MERS-like virus would not decrease during the transition to endemicity, as seen in Fig. 3B, and this is because disease severity (and IFR) is high in children, the age group expected to experience the bulk of primary cases during the endemic phase. In the endemic phase, a vaccination program against MERS would therefore be necessary to avoid excess mortality (fig. S11).
The key result from our model framework that explicitly recognizes that functional immunity to reinfection, disease, and shedding are different is that, in contrast with infections that are severe in childhood, SARS-CoV-2 could join the ranks of mild, cold-causing endemic HCoVs in the long run. A critical prediction is that the severity of emergent HCoVs once they reach endemicity depends only on the severity of infection in children (Fig. 3), because all available evidence suggests that immunity to HCoVs has short IES and moderate IEI, leading to frequent reinfection throughout adulthood (11, 12), but strong IEP such that childhood infection provides protection from pathology upon reinfection in adulthood, as evidenced by the rarity of severe infections or detectable IgM titers in adults. Strain-specific virulence factors, such as the shared cellular receptor, angiotensin-converting enzyme 2 (ACE-2), to which SARS-CoV-1, SARS-CoV-2, and the endemic strain NL63 all bind (13–16), may affect the CFR during the emergence phase but have little impact on the severity of disease in the endemic phase. Because the four endemic HCoVs have been globally circulating for a long time and almost everyone is infected at a young age, we cannot ascertain how much pathology would result from a primary or even a secondary case of any of these in an elderly or otherwise vulnerable person.
The key insights come from how our model explicitly incorporates different components of immunological protection with respect to susceptibility, pathology, and infectivity (IES, IEP, and IEI, respectively) and their different rates of waning. In our analysis, we hypothesized that these components of immunity for SARS-CoV-2 are comparable to those of endemic HCoVs, and this needs to be determined. Additionally, during the transition to endemicity, we need to consider how the immune efficacies depend on primary and secondary infections across ages (17) and how responses differ between vaccination and natural infection.
Longitudinal analysis of SARS patients provides an opportunity to measure the durability of immune memory in the absence of reexposure. The only long-term study we know of that follows SARS-CoV-1–specific antibodies suggests that they wane faster than antibodies to other live viruses and vaccines such as measles, mumps, rubella, and smallpox (18) and fall below the threshold of detection in 6 years (19). In contrast to antibody responses, memory T cells persist for much longer periods (19, 20) and confer protection in animal model systems (21).
We further consider the effects of strain variation both for natural infection and vaccination. Strain variation and antibody escape may occur in endemic strains (22); however, the fact that symptoms are mild suggests that immunity induced by previously seen strains is nonetheless strong enough to prevent severe disease. Indeed, among HCoVs, frequent reinfections appear to boost immunity against related strains (12). However, the effect of strain variation may differ for vaccine-induced immunity, especially in light of the narrower epitope repertoire of many currently authorized vaccines.
If frequent boosting of immunity by ongoing virus circulation is required to maintain protection from pathology, then it may be best for the vaccine to mimic natural immunity insofar as preventing pathology without blocking ongoing virus circulation. Preliminary results suggest the adenovirus-based vaccine is better at preventing severe than mild or asymptomatic infections (23), and it will be important to collect similar data for the other vaccines. Should the vaccine cause a major reduction in transmission, it might be important to consider strategies that target delivery to older individuals for whom infection can cause higher morbidity and mortality, while allowing natural immunity and transmission to be maintained in younger individuals. During the transition to endemicity, primary SARS-CoV-2 infections will frequently occur in older individuals, and we need to determine whether immunity induced by infection or vaccination in adulthood is similar to that produced by natural infections in childhood. Thus far, there have been few reinfections reported with SARS-CoV-2, and disease severity has varied (24); the only population-level study of reinfection that we are aware of estimates a low rate of reinfection in the first 6 months after primary infection and mild disease upon reinfection (25), but further analysis and monitoring are vital.
The findings presented here suggest that using symptoms as a surveillance tool to curb the spread of SARS-CoV-2 will become more difficult, as milder reinfections increasingly contribute to chains of transmission and population-level attack rates. In addition, infection or vaccination may protect against disease but not provide the type of transmission-blocking immunity that allows for shielding (26) or the generation of long-term herd immunity (2).
The details of the change in overall IFR through the transient period will be affected by a wide array of factors, such as age-specific human contact rates (27) and susceptibility to infection (28) as well as improvement in treatment protocols, hospital capacity, and virus evolution. The qualitative result of mild disease in the endemic phase is robust to these complexities, but quantitative predictions for the transient phase will depend on a careful consideration of these realities and how they interact with the dynamics of infection and components of immunity (29).
The changes in the IFR over time predicted by the model have implications for vaccination strategy against current and future emerging HCoVs. Social distancing and an effective vaccine are critical for control during a virgin epidemic and the transition out of it, but once we enter the endemic phase, mass vaccination may no longer be necessary. The necessity for continual vaccination will depend on the age-dependence of the IFR. If primary infections of children are mild (as for SARS-CoV-1 and SARS-CoV-2), continued vaccination may not be needed as primary cases recede to mild childhood sniffles. If, on the other hand, primary infection in children is severe (as for MERS), then vaccination of children will need to be continued.
From an ecological and evolutionary perspective, our study opens the door to questions regarding the within-host and between-host dynamics of human immunity and pathogen populations in the face of immune efficacies with different kinetics. It also opens the question of how these immune efficacies interplay with strain cross-immunity, which is likely relevant within the alpha- and betacoronaviruses. Considering data and model predictions from emergence through endemicity of HCoVs revealed a framework for understanding immunity and vaccination that may apply to a variety of infections, such as respiratory syncytial virus and seasonal influenza, which share similar age distributions and immune responses.
Acknowledgments
We gratefully acknowledge helpful conversations with H. Ahmed, V. Zarnitsyna, and K. Fuller. Funding: This work was funded by NIH grants U01 AI150747, U01 HL139483, and U01 AI144616. Author contributions: J.S.L.: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing – original draft, Writing – review and editing, and Visualization. R.A.: Conceptualization, Methodology, Writing, Visualization, Supervision, and Funding acquisition. O.N.B.: Conceptualization, Methodology, and Writing – review and editing. Competing interests: The authors declare no competing interests. Data and materials availability: The scripts and data used to perform the analysis and generate the figures in this paper are available on GitHub (https://github.com/JennieLavine/covid-immunity-endemicity) and archived in Zenodo (35). This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material.
Supplementary Material
Summary
Supplementary Text
Figs. S1 to S13
MDAR Reproducibility Checklist
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References and Notes
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Volume 371 | Issue 6530
12 February 2021
12 February 2021
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Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
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Received: 4 September 2020
Accepted: 7 January 2021
Published in print: 12 February 2021
Acknowledgments
We gratefully acknowledge helpful conversations with H. Ahmed, V. Zarnitsyna, and K. Fuller. Funding: This work was funded by NIH grants U01 AI150747, U01 HL139483, and U01 AI144616. Author contributions: J.S.L.: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing – original draft, Writing – review and editing, and Visualization. R.A.: Conceptualization, Methodology, Writing, Visualization, Supervision, and Funding acquisition. O.N.B.: Conceptualization, Methodology, and Writing – review and editing. Competing interests: The authors declare no competing interests. Data and materials availability: The scripts and data used to perform the analysis and generate the figures in this paper are available on GitHub (https://github.com/JennieLavine/covid-immunity-endemicity) and archived in Zenodo (35). This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material.
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RE: Immunological characteristics govern the transition of COVID-19 to endemicity
We are impressed by your study "Immunological characteristics govern the transition of COVID-19 to endemicity" [1], which highlights the necessity of behavioral containment in the years ahead during the COVID-19 vaccine rollout. However, we noticed two technical issues related to the accuracy of the prediction, which is worthy of discussion considering the potential impact of this piece of work on public health policy worldwide.
The primary infectious period in your modelling is set as 9 days. Nevertheless, the mean duration of viral shedding does not necessarily equal the mean infectious period in a compartmental model to simulate the pandemic. In the model setting (the classic SIR model), the mean infectious period should equal the mean generation time (see [2]), there are at least three studies which showed that the mean generation time is 5 days. Therefore, the time interval of 9 days is obviously longer than the widely reported 5 days [3].
The Infection Fatality Ratio (IFR) for data analysis comes from the work by Verity et al., [4] which was based on data mainly from mainland China in February 2020, when the COVID-19 not yet prevailed around the globe. Nevertheless, on April 17, 2020, China revised the reported COVID-19 deaths from 3352 to 4642. Verity et al. was not able to correspondingly update the data considering the publication date. Even if Verity et al. could have updated their results based on the revised deaths, the population context was mostly limited in mainland China in that time (February 2020) without much incorporation of the global data. It should be suggested to apply IFRs reported by more comprehensive research on global population after the COVID-19 became a pandemic.
It has been almost a year since the early outbreak of COVID-19 in China. Given the sheer number of cases and deaths worldwide, it is not reasonable anymore to carry out analysis based on the estimated IFR in February 2020. There should be more reliable and updated IFR available. For instance, we used the covid-19 cases and deaths in Hong Kong (HK), China, which spans from February 2020 up to now [5]. The number of deaths from COVID-19 in HK collected by the HK surveillance data is carefully conducted and reliable. There were four waves of community outbreaks in HK since 2020 and the age structure of COVID-19 cases is representative of the general population. There has been extensive testing conducted in HK, including one universal community testing of 1.78 million out of 7.5 million population carried out in early September 2020. The local outbreaks in HK were gradually under control without much overwhelming the healthcare system and the testing capacity. The COVID-19 cases identified by the PCR-tests, which are composed of a reasonable proportion of asymptomatic and symptomatic cases, should picked up most of the infections in the City.
Though a considerable proportion of cases are imported, if we focus on the number of local cases and deaths, the Case Fatality Ratio (approximately equals IFR in Hong Kong) should be a good standard for an international city with functioning medical system. The comparison of the age specific IFR estimated by Verity et al. and the surveillance data in HK was shown in Table 1. Here we assume that the CFR approximately equals the IFR considering the extensive community testing of COVID-19 in HK. Although the case numbers are typically underreported, the case ascertainment rate in HK should be closer to 1 compared with 0.5 estimated by Verity et al. [4]. Supposing an existing 50% ascertainment rate, it is still not sufficient to explain the huge gap between the two IFR age profiles. Therefore, it is highly suggested to apply an IFR estimated by more updated and accurate data in the published modelling work titled "Immunological characteristics govern the transition of COVID-19 to endemicity".
Table 1. Comparison of IFR in Verity et al and in Hong Kong.
age, Verity et al., Hong Kong
0-5,0,0
5-10,1e-04,0
10-15,1e-04,0
15-20,2e-04,0
20-25,3e-04,0
25-30,4e-04,0
30-35,6e-04,0
35-40,0.001,0.0014
40-45,0.0016,0.0015
45-50,0.0024,0.004
50-55,0.0038,0
55-60,0.006,0.0099
60-65,0.0094,0.011
65-70,0.0147,0.0279
70-75,0.0231,0.047
75-80,0.0361,0.1339
80-85,0.0566,0.1966
85-90,0.0886,0.2903
90-95,0.1737,0.3188
Reference
[1] Lavine JS, Bjornstad ON, Antia R. Immunological characteristics govern the transition of COVID-19 to endemicity. Science. 2021 Jan 12.
[2] Svensson Å. A note on generation times in epidemic models. Mathematical biosciences. 2007;208(1):300-11.
[3] Griffin J, Casey M, Collins Á, Hunt K, McEvoy D, Byrne A, McAloon C, Barber A, Lane EA, More S. Rapid review of available evidence on the serial interval and generation time of COVID-19. BMJ open. 2020;10(11):e040263.
[4] Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, Cuomo-Dannenburg G, Thompson H, Walker PG, Fu H, Dighe A. Estimates of the severity of coronavirus disease 2019: a model-based analysis. The Lancet infectious diseases. 2020 Jun 1;20(6):669-77.
[5] https://www.chp.gov.hk/files/pdf/local_situation_covid19_en.pdf
Spread of SARS-CoV-2 variants that escape the immune system in Japan
As of February 12, 2021, more than 100 people were infected with the SARS-CoV-2 variants in Japan. In February 2021, a large-scale cluster (infected population) of SARS-CoV-2 variants-infected persons occurred in Saitama Prefecture in Japan. Infection with SARS-CoV-2 variants is gradually spreading in Japan. The World Health Organization (WHO) reports that "the infectivity of SARS-CoV-2 variants is 1.4 to 1.7 times higher than that of conventional SARS-CoV-2" (1). Infectious disease experts in Japan are becoming more cautious about the spread of infection by SARS-CoV-2 variants.
As of February 12, 2021, in Japan, 93 people were infected with the SARS-CoV-2 variant derived from the United Kingdom (UK), 11 people were infected with the SARS-CoV-2 variant derived from South Africa, and 4 people are infected with the SARS-CoV-2 variant derived of Brazil (2). As of February 8, 2021, WHO reports that cases of SARS-CoV-2 variants derived from the UK have been identified in 86 countries around the world.
In 2020 of October, in the Manaus city in Brazil, 76% of residents of Manaus city, had acquired the anti-SARS-CoV-2 antibody. However, since the detection of the SARS-CoV-2 variant derived from Brazil in mid-December 2020, the number of people infected with the SARS-CoV-2 variant derived from Brazil has increased significantly. In other words, the immunological antiviral effect of the neutralizing antibody against SARS-CoV-2 has not been observed for the SARS-CoV-2 variant derived from Brazil.
Recent studies have revealed the following matters (3).
The N501Y mutation does not affect the three-dimensional structure of conventional SARS-CoV-2 spike glycoproteins. From this research result, it was clarified that the binding property of the spike glycoprotein N501Y mutant to human ACE2 was stronger than that of the conventional SARS-CoV-2 spike glycoprotein.
From this study, it was revealed that the affinity between the spike glycoprotein N501Y mutant and 24 of the 27 neutralizing antibodies examined was clearly weak compared to the conventional SARS-CoV-2 spike glycoprotein.
With the advent of the variants of the SARS-CoV-2, there is a possibility that the vaccine effect is inhibited. Therefore, the development of antiviral drugs against SARS-CoV-2 is even more important.
Ethics statement
This study was reviewed and approved by the Central Ethics Review Board of the National Hospital Organization of Japan. In order to carry out this research, the authors attended a research ethics education course (e-APRIN) conducted by Association for the Promotion of Research Integrity (APRIN; Shinjuku, Tokyo, Japan).
Disclosure
The authors declare no potential conflicts of interest. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgments
We thank medical staffs for the research assistance at National Hospital Organization Kyoto Medical Center.
References
1. https://www.who.int/csr/don/21-december-2020-sars-cov2-variant-united-ki...
2. Experts urge caution as coronavirus variants spread in Japan. February 11, 2021 (Mainichi Japan)
3. Hayashi T. et al. bioRxiv Cold Spring Harbor
https://www.biorxiv.org/content/10.1101/2020.11.27.401893v1
RE: Emergence of SARS-CoV-2 vaccine escape variants
Incorporating information from the four endemic human coronaviruses on a mathematical model, J. Lavine et al. (Jan 12th 2021) predict that over the next few years a growing proportion of SARS-CoV-2 infections in adults will be re-infections and less severe. Once the endemic phase will be reached, primary exposures will be in childhood. Then, 'COVID-19 may be no more virulent than the common cold'(1).
The authors assume that 'infection-blocking immunity wanes rapidly, but disease-reducing immunity is long-lived'(1). Accordingly, a successful vaccine rollout would accelerate the shift from pandemic to endemic.
In Spain, vaccination begun on Dec 27th with roughly 500,000 people vaccinated within the first two weeks. Unexpectedly, new diagnoses of COVID-19 were reported after first-dose vaccination(2). We hypothesize that if any of the current licensed vaccines are given to persons during the incubation period -as it seems to be the case during the ongoing third wave surge-, selection of vaccine-escape mutants will inevitably occur, as already shown in experimental models(3).
The mutation rate for SARS-CoV-2 is lower than for other RNA viruses, such as HIV or hepatitis C virus, given the presence of a nuclease that repairs mistakes. Even so, evolution of viral genomes follows the rule of quasispecies. A constellation of variants evolves around a consensus sequence over time and across tissues and individuals. Accordingly, mutations that would result in antiviral resistance or escape to neutralizing antibodies pre-exist and would expand under drug or vaccine pressure, respectively (4).
Interventions that would reduce the risk for selection of vaccine-escape variants include: rapid and wider vaccination; refraining from vaccination those presumably having acute infection, perhaps using rapid antigen testing; developing new vaccines with much broader antigenicity; and administration throughout inhalation for inducing mucosal immunity including IgA antibodies. The latest would halt viral replication in the respiratory airway and ultimately transmissions.
Vicente Soriano
UNIR Health Sciences School & Medical Center, Madrid, Spain
Email: [email protected]
Pablo Barreiro
Hospital Carlos III-La Paz University Hospital, Madrid, Spain
Email: [email protected]
Carmen de Mendoza
Puerta de Hierro Research Institute & University Hospital, Majadahonda, Spain
Email: [email protected]
REFERENCES AND NOTES
1. J. S. Lavine, et al., Science. 10.1126/science.abe6522 (2021).
2. E. Calvo. ABC, Madrid 16-1-2021, https://www.abc.es/sociedad/abci-agujeros-vacunacion-ante-segunda-dosis-... (2020)
3. Y. Weisblum, et al. Elife, 9: e61312 (2020)
4. J. M. Coffin. Science. 267, 483 (1995)
COMPETING INTERESTS:
None to report for all authors
RE: COVID-19, Immunity, and Endemicity
In a fascinating research report, the expert authors evaluate data on endemic human coronaviruses, and show that infection-blocking immunity wanes rapidly, but disease-reducing immunity does not.
It begs the question as to what is the best strategy for transitioning from a pandemic to a locally-contained endemic, possibly based on a high threshold of the population having been reached for viral infections, or a successful vaccination program, or both, to achieve herd immunity?
This leads to the following issues:
(1) How does viral evolution develop?
(2) Do mutated strains become more resistant to currently approved vaccines?
(3) Can vaccines be adapted to deal with the more highly transmissible and infectious strains or variants that have been discovered in the UK and South Africa??
(4) Will the mutated strains lead to variations in the percentage of the population that is asymptomatic?
(5) Furthermore, will the mutated strains lead to variations in the percentage of the population that might suffer from long haul COVID-19 effects?
(6) How large are the regions for purposes of defining endemicity, such as city, state, province, prefecture, country, sub-continent, continent, or hemisphere?
(7) How does the SARS-CoV-2 virus that causes COVID-19 differ from SARS (or SARS-CoV-1) and MERS?
(8) Can specific immunity types be predicted in the short run and long run?
(9) Can the duration of a pandemic such as COVID-19 be predicted?
(10) Can the duration of the effectiveness of alternative approved vaccines be predicted?
(11) At what point will the age distribution of infected cases differ from that of the population?
(12) What happens if older individuals have not acquired immunity through having been infected, vaccinated, or both, at an early age?
(13) How many times will an individual need to be infected to acquire immunity from the virus?
(14) How many times will an individual need to be vaccinated to acquire immunity from the virus?
(15) What are the interactive effects of infection and subsequent vaccination, or the reverse?
(16) What is the likely duration of the transition from a pandemic to an endemic status, and how long with endemicity last?
(17) What are the effects of existing comorbidities on the transition to, and duration of, endemicity?
(18) What are the effects of the age structure on the transition to, and duration of, endemicity?
(19) Are the properties of the approved vaccines likely to perform differently in the pandemic and endemic states?
(20) Are the properties of the mutated strains likely to be different in the pandemic and endemic states?