Changes in transmission of Enterovirus D68 (EV-D68) in England inferred from seroprevalence data

  1. Margarita Pons-Salort
  2. Ben Lambert
  3. Everlyn Kamau
  4. Richard Pebody
  5. Heli Harvala
  6. Peter Simmonds
  7. Nicholas C Grassly

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    The authors use data from 3 cross-sectional age-stratified serosurveys on Enterovirus D68 from England between 2006 and 2017 to examine the transmission dynamics of this pathogen in this setting. Understanding these dynamics, including how it changes over time, may help uncover potential changes in the transmissibility of the virus. While the topic is relevant, interpretation of the results challenging largely due to the great uncertainty around how to interpret the serological (serostatus) data, and the impact this has on the inferences made. We ask the authors to perform some additional analyses and to provide more intuition to understand some of the key findings of this analysis.

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Abstract

The factors leading to the global emergence of Enterovirus D68 (EV-D68) in 2014 as a cause of acute flaccid myelitis (AFM) in children are unknown. To investigate potential changes in virus transmissibility or population susceptibility, we measured the seroprevalence of EV-D68-specific neutralising antibodies in serum samples collected in England in 2006, 2011, and 2017. Using catalytic mathematical models, we estimate an approximately 50% increase in the annual probability of infection over the 10-year study period, coinciding with the emergence of clade B around 2009. Despite such increase in transmission, seroprevalence data suggest that the virus was already widely circulating before the AFM outbreaks and the increase of infections by age cannot explain the observed number of AFM cases. Therefore, the acquisition of or an increase in neuropathogenicity would be additionally required to explain the emergence of outbreaks of AFM. Our results provide evidence that changes in enterovirus phenotypes cause major changes in disease epidemiology.

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  1. Version published to 10.7554/elife.76609 on eLife
  2. eLife

    Author Response

    Reviewer #2 (Public Review):

    The authors use data from 3 cross-sectional age-stratified serosurveys on Enterovirus D68 from England between 2006 and 2017 to examine the transmission dynamics of this pathogen in this setting. A key public health challenge on EV-D68 has been its implication in outbreaks of acute flaccid myelitis over the past decade, and past circulation patterns and population immunity to this pathogen are not yet well-understood. Towards this end, the authors develop and compare a suite of catalytic models as fitted to this dataset and incorporate different assumptions on how the force of infection varies over time and age. They find high overall EV-D68 seroprevalence as measured by neutralizing antibodies, and detect increased transmission during this time period as measured by the annual probability of infection and basic reproduction number. Interestingly, their data indicate very high seroprevalence in the youngest children (1 year-olds), and to accommodate this observation, the authors separate the force of infection in this age class from the other groups. They then reconstruct the historical patterns of EV-D68 circulation using their models and conclude that, while the serologic data suggest that transmissibility has increased between serosurvey rounds, additional factors not accounted for here (e.g., changes in pathogenicity) are likely necessary to explain the recent emergence of AFM outbreaks, particularly given the broader age-profile of reported AFM cases. The Discussion mentions important current unknowns on the biological interpretation of EV-D68 neutralizing antibody titers for protection against infection and disease. The analysis is rigorous and the conclusions are well-supported, but a few aspects of the work need to be clarified and extended, detailed below:

    1. Due to the lack of a clear single cut-point for seropositivity on this assay, the authors sensibly present results for two cut-points in the main text (1:16 and 1:64). While some differences that stem from using different cut-points are fully expected (i.e., seroprevalence being higher using the less stringent cut-point), differences that are less expected should be further discussed. For instance, it was not clear in Figure 2 why the annual probability of infection decreased after 2010 using the 1:64 cut-point, while it continued to increase using the 1:16 cut-point. It would also be helpful to explain why overall seroprevalence and R0 continue to increase over this time period using the 1:64 cut-point. Lastly, it would be useful to see the x-axis in Figure 4 extended to the start of the time period that FOI is estimated, with accompanying credible intervals.

    For the discussion on differences between the two cut-offs, please see response to essential comment 1.

    Extending the x-axis before 2006 in Figure 4 is not possible. Estimates of the overall seroprevalence at a year y require FOI estimates up until y-40. This implies the first estimates we can provide are for 2006.

    Credible intervals have been added to Figure 4.

    1. Additional context of EV-D68 in the study setting of England would be useful. While the Introduction does mention AFM cases "in the UK and elsewhere in Europe" (line 53), a summary of reported data on EV-D68/AFM in England prior to this study would provide important context. The Methods refers to "whether transmission had increased over time (before the first reported big outbreak of EV-D68 in the US in 2014)" (lines 133-134), rather than in this setting. It would be useful to summarize the viral genomic data from the region for additional context - particularly since the emergence of a viral clade is highlighted as a co-occurrence with the increased transmissibility detected in this analysis.

    We have added a figure (new Figure 1 – figure supplement 1) showing the annual number of EV-D68 detections reported by Public Health England from 2004 to 2020.

    We have also added the following text to the introduction: “Similarly, in the UK, reported EV-D68 virus detections also show a biennial pattern between 2014 and 2018 (Figure 1 – figure supplement 1).”

    We have also amended the sentence in the Methods.

    Finally, below is a screenshot of the nexstrain tree for EV-D68 based on the VP1 region and with tips representing sequences from the UK (light blue) and European countries in colour. There is a lot of mixing between sequences from different regions, indicating widespread transmission and small regional clustering. We have added the following text to the Discussion: “Reported EV-D68 outbreaks in 2014 and 2016 were due to clade B viruses, while the 2018 outbreaks were reported to be linked to both B3 and A2 clade viruses in the UK (10), France (32) and elsewhere.”

    Reviewer #3 (Public Review):

    In the proposed manuscript, the authors use cross-sectional seroprevalence data from blood samples that were tested for evidence of antibodies against D68 for the UK. Samples were collected at 3 time points from individuals of all ages. The authors then fit a suite of serocatalytic models to explain the changing level of seropositivity by age. From each model they estimate the force of infection and assess whether there have been changes in transmissibility over the study period. D68 is an important pathogen, especially due to its links with acute flaccid myelitis, and its transmission intensity remains poorly understood.

    Serocatalytic models appear to be appropriate here. I have a few comments.

    The biggest challenge to this project is the difficulty in assigning individuals as seronegative or seropositive. There is no clear bimodal distribution in titers that would allow obvious discrimination and apparently no good validation data with controls with known serostatus. The authors tackle this problem by presenting results to four different cut-points (1:16 to 1:128) - resulting in seropositivity ranging from around 50% to around 80%. They then run the serocatalytic models with two of these (1:16 and 1:64) - leading to a range of FoI values of 0.25-0.90 for the 1 year olds and 0.05-0.25 for older age groups (depending on model and cutpoint). This represents a substantial amount of variability. While I certainly see the benefit of attacking this uncertainty head on, it does ultimately limit the inferences that can be made about the underlying risk of infection in UK communities, except that it's very uncertain and possibly quite high.

    I find the force of infection in 1 year olds very high (with a suggestion that up to 75% get infected within a year) and difficult to believe, especially as the force of infection is assumed much lower for all other ages.

    The authors exclude all <1s due to maternal antibodies, which seems sensible, however, does this mean that it is impossible for <1s to become infected in the model? We know for other pathogens (e.g., dengue virus) with protection from maternal antibodies that the protection from infection is gone after a few months. Maybe allowing for infections in the first year of life too would reduce the very large, and difficult to believe, difference in risk between 1 year olds and older age groups. I suspect you wouldn't need to rely on <1 serodata - just allow for infections in this time period.

    Relatedly, would it be possible to break the age data into months rather than years in these infants to help tease apart what happens in the critical early stages of life.

    Yes. We have added two figures (new Figures 1C and 1D) showing the prevalence of antibodies in children <1 yo. We show these data for the three serosurveys combined, because the number of individuals per month of age is very small.

    One of the major findings of the paper is that there is a steadily increasing R0. This again is difficult to understand. It would suggest there are either year on year increases in inherent transmissibility of the virus through fitness changes, or year on year increases in the mixing of the population. It would be useful for the authors to discuss potential explanations for an inferred gradual increase in R0.

    We have removed the estimates of R0 from the manuscript.

    On a similar note, I struggle to reconcile evidence of a stable or even small drop in FoI in the 1:64 models 4 and 5 from 2010/11 (Figure 3) with steadily increasing R0 in this period (Figure 4). Is this due to changes in the susceptibility proportion. It would be good to understand if there are important assumptions in the Farrington approach that may also contribute to this discrepancy.

    We have removed the estimates of R0 from the manuscript and only present the reconstruction of the annual number of new infections per age class and year (new Figure 5). We think this measure is more adapted to the discussion of the results.

    In addition, when using the classical expression R{0t}=1/(1-S(t)), with S(t) the annual proportion seropositive, the high seroprevalence estimates (new Figure 4) result in extremely high estimates of the basic reproduction number (median ranges: 11.6 – 29.7 for 1:16 and 3.3 – 7.6 for 1:64 during the period 2006 to 2017).

    We had previously used the Farrington approach as it is adapted to cases when the force of infections is different for different age classes.

    The R0 estimates (Figure 4) should also be presented with uncertainty.

    R0 no longer presented, but estimates of overall seroprevalence now presented with uncertainty.

    Finally, given the substantial uncertainty in the assay, it seems optimistic to attempt to fit annual force of infections in the 30 year period prior to the start of the sampling periods. I would be tempted to include a constant lambda prior to the dates of the first study across the models considered.

    We thank the reviewers for the suggestion.

    We implemented this change (constant FOI before 2006) in the previous models without maternal antibodies and the result for the random-walk-based models was that the variance of the random walk was estimated over a very short period, thus resulting in a rather non- smoothed FOI.

    Implementing this change with the new models with maternal antibodies and random-walk on the FOI was technically a bit complex. We therefore kept the simple random-walk over the whole period and added the following paragraph to the Discussion:

    “It is important to interpret well the results for the estimates of the FOI over time from our analysis under the assumptions of the models. First, as the best model uses a random walk on the FOI, the change in transmission that we infer happens continuously over several years. In reality, this may have occurred differently (e.g. in a shorter period of time). Our ability to recover more complex changes in transmission is limited by the data available. It would not be surprising if EV-D68 has exhibited biennial (or longer) cycles of transmission in England over the last few years, as it has been shown in the US (7) and is common for other enteroviruses (30). However, it is difficult to recover changes at this finer time scale with serology data unless sampling is very frequent (at least annual). Therefore, our study can only reveal broader long-term secular changes. Second, interpretation of the results before 2006 must be avoided for two resasons. On the one hand, as we go backwards in time, there is more uncertaintly about the time of seroconversion of the individuals informing the estimates of the FOI. On the other hand, because age and time are confounded in cross-sectional seroprevalence measurements, the random walk on time may account for possible differences in the FOI through age (possibly higher in the youngest age classes, and lowest in the oldest), which are note explicitly accounted for here. This may explain the decline in FOI when going backwards in time before the first cross-sectional study in 2006.”

  3. eLife

    eLife assessment

    The authors use data from 3 cross-sectional age-stratified serosurveys on Enterovirus D68 from England between 2006 and 2017 to examine the transmission dynamics of this pathogen in this setting. Understanding these dynamics, including how it changes over time, may help uncover potential changes in the transmissibility of the virus. While the topic is relevant, interpretation of the results challenging largely due to the great uncertainty around how to interpret the serological (serostatus) data, and the impact this has on the inferences made. We ask the authors to perform some additional analyses and to provide more intuition to understand some of the key findings of this analysis.

  4. eLife

    Reviewer #1 (Public Review):

    In this paper the authors are estimating the amount of transmission (via the force of infection) of EV-D^8 in England. The strengths of the study are the use of serological data for understanding underlying transmission, and the assessment of the sensitivity of the conclusions to the seropositivity cut off and the model form used. The weaknesses are the data not being annually and the lack of link to HFMD cases,, but these do not detract from the conclusions that can be drawn from the paper. The results do support the conclusions.

  5. eLife

    Reviewer #2 (Public Review):

    The authors use data from 3 cross-sectional age-stratified serosurveys on Enterovirus D68 from England between 2006 and 2017 to examine the transmission dynamics of this pathogen in this setting. A key public health challenge on EV-D68 has been its implication in outbreaks of acute flaccid myelitis over the past decade, and past circulation patterns and population immunity to this pathogen are not yet well-understood. Towards this end, the authors develop and compare a suite of catalytic models as fitted to this dataset and incorporate different assumptions on how the force of infection varies over time and age. They find high overall EV-D68 seroprevalence as measured by neutralizing antibodies, and detect increased transmission during this time period as measured by the annual probability of infection and basic reproduction number. Interestingly, their data indicate very high seroprevalence in the youngest children (1 year-olds), and to accommodate this observation, the authors separate the force of infection in this age class from the other groups. They then reconstruct the historical patterns of EV-D68 circulation using their models and conclude that, while the serologic data suggest that transmissibility has increased between serosurvey rounds, additional factors not accounted for here (e.g., changes in pathogenicity) are likely necessary to explain the recent emergence of AFM outbreaks, particularly given the broader age-profile of reported AFM cases. The Discussion mentions important current unknowns on the biological interpretation of EV-D68 neutralizing antibody titers for protection against infection and disease. The analysis is rigorous and the conclusions are well-supported, but a few aspects of the work need to be clarified and extended, detailed below:

    1. Due to the lack of a clear single cut-point for seropositivity on this assay, the authors sensibly present results for two cut-points in the main text (1:16 and 1:64). While some differences that stem from using different cut-points are fully expected (i.e., seroprevalence being higher using the less stringent cut-point), differences that are less expected should be further discussed. For instance, it was not clear in Figure 2 why the annual probability of infection decreased after 2010 using the 1:64 cut-point, while it continued to increase using the 1:16 cut-point. It would also be helpful to explain why overall seroprevalence and R0 continue to increase over this time period using the 1:64 cut-point. Lastly, it would be useful to see the x-axis in Figure 4 extended to the start of the time period that FOI is estimated, with accompanying credible intervals.

    2. Additional context of EV-D68 in the study setting of England would be useful. While the Introduction does mention AFM cases "in the UK and elsewhere in Europe" (line 53), a summary of reported data on EV-D68/AFM in England prior to this study would provide important context. The Methods refers to "whether transmission had increased over time (before the first reported big outbreak of EV-D68 in the US in 2014)" (lines 133-134), rather than in this setting. It would be useful to summarize the viral genomic data from the region for additional context - particularly since the emergence of a viral clade is highlighted as a co-occurrence with the increased transmissibility detected in this analysis.

  6. eLife

    Reviewer #3 (Public Review):

    In the proposed manuscript, the authors use cross-sectional seroprevalence data from blood samples that were tested for evidence of antibodies against D68 for the UK. Samples were collected at 3 time points from individuals of all ages. The authors then fit a suite of serocatalytic models to explain the changing level of seropositivity by age. From each model they estimate the force of infection and assess whether there have been changes in transmissibility over the study period. D68 is an important pathogen, especially due to its links with acute flaccid myelitis, and its transmission intensity remains poorly understood. Serocatalytic models appear to be appropriate here. I have a few comments.

    The biggest challenge to this project is the difficulty in assigning individuals as seronegative or seropositive. There is no clear bimodal distribution in titers that would allow obvious discrimination and apparently no good validation data with controls with known serostatus. The authors tackle this problem by presenting results to four different cut-points (1:16 to 1:128) - resulting in seropositivity ranging from around 50% to around 80%. They then run the serocatalytic models with two of these (1:16 and 1:64) - leading to a range of FoI values of 0.25-0.90 for the 1 year olds and 0.05-0.25 for older age groups (depending on model and cutpoint). This represents a substantial amount of variability. While I certainly see the benefit of attacking this uncertainty head on, it does ultimately limit the inferences that can be made about the underlying risk of infection in UK communities, except that it's very uncertain and possibly quite high.

    I find the force of infection in 1 year olds very high (with a suggestion that up to 75% get infected within a year) and difficult to believe, especially as the force of infection is assumed much lower for all other ages.

    The authors exclude all <1s due to maternal antibodies, which seems sensible, however, does this mean that it is impossible for <1s to become infected in the model? We know for other pathogens (e.g., dengue virus) with protection from maternal antibodies that the protection from infection is gone after a few months. Maybe allowing for infections in the first year of life too would reduce the very large, and difficult to believe, difference in risk between 1 year olds and older age groups. I suspect you wouldn't need to rely on <1 serodata - just allow for infections in this time period.

    Relatedly, would it be possible to break the age data into months rather than years in these infants to help tease apart what happens in the critical early stages of life.

    One of the major findings of the paper is that there is a steadily increasing R0. This again is difficult to understand. It would suggest there are either year on year increases in inherent transmissibility of the virus through fitness changes, or year on year increases in the mixing of the population. It would be useful for the authors to discuss potential explanations for an inferred gradual increase in R0.

    On a similar note, I struggle to reconcile evidence of a stable or even small drop in FoI in the 1:64 models 4 and 5 from 2010/11 (Figure 3) with steadily increasing R0 in this period (Figure 4). Is this due to changes in the susceptibility proportion. It would be good to understand if there are important assumptions in the Farrington approach that may also contribute to this discrepancy.

    The R0 estimates (Figure 4) should also be presented with uncertainty.

    Finally, given the substantial uncertainty in the assay, it seems optimistic to attempt to fit annual force of infections in the 30 year period prior to the start of the sampling periods. I would be tempted to include a constant lambda prior to the dates of the first study across the models considered.

  7. Version published to 10.1101/2021.11.08.21265913v1 on medRxiv