Long-term effect of COVID-19 social distancing measures on pertussis transmission in England; a mathematical modelling study

Long-term effect of COVID-19 social distancing measures on pertussis transmission in England; a mathematical modelling study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long-term effect of COVID-19 social distancing measures on pertussis transmission in England; a mathematical modelling study Yoon Hong Choi, Edwin Leeuwen, Elizabeth Miller This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5459094/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2026 Read the published version in BMC Infectious Diseases → Version 1 posted 12 You are reading this latest preprint version Abstract Background. In 2024 England, in common with many other countries, experienced a pertussis resurgence the cause of which is unclear. We used a pertussis transmission model, previously developed to investigate the cause of the pertussis resurgence in England in 2012, to explore potential factors contributing to the increase in pertussis cases observed in England in 2024. Methods An age-stratified dynamic transmission model fitted to pertussis notification data from England between 1953 and 2013 was run until 2054 with and without changes in social mixing as estimated from Google mobility and school attendance data during the COVID-19 pandemic. The model assumes vaccination protects better against disease than pertussis infection, and that an infection results in more durable immunity than vaccination. Counterfactual scenarios were also run to explore the effect of reductions in vaccine coverage during the pandemic and of the addition in 2014 of boosters in the 2nd year of life and in adolescence. Results A resurgence was only generated with reduced social mixing and could not be explained by short-term reductions in vaccine coverage. Additional boosters at 18 months and 14 years from 2014 would not have prevented a resurgence though would have reduced its magnitude. Peaks of increased pertussis incidence are predicted over the next decade. The parameter sets that generated a resurgence in 2024 had the shortest duration of acellular vaccine protection, median 5 years with 90% protection against infection. Conclusion This modelling study implicates reduced mixing in England during the COVID-19 pandemic as the cause of the pertussis resurgence in 2024 together with the short duration of protection from acellular vaccine. Interruption of the background rate of natural boosting during the pandemic increased the pool of susceptible individuals resulting in increased transmission post-pandemic with clinical cases in those with waned vaccine–induced protection and the unvaccinated, including infants of unvaccinated mothers. In countries using acellular pertussis vaccines, infection continues to play an important role in maintaining population immunity around an endemic equilibrium. Improved pertussis vaccines that provide more complete and more durable protection against infection are needed to improve pertussis control. pertussis resurgence dynamic transmission model social mixing COVID-19 pandemic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Pertussis is a highly transmissible respiratory infection which remains endemic despite longstanding immunisation programmes that have achieved high coverage. Infants too young to be vaccinated are most at risk of severe disease with high case fatality rates even in countries with advanced health care systems ( 1 ). Many countries experienced an increase in pertussis incidence following a change from whole cell pertussis (wP) to acellular pertussis (aP) vaccines which was likely attributable to the shorter duration of protection and lower efficacy against infection of aP than wP vaccines ( 2 , 3 ). This resulted in a higher endemic level of pertussis transmission in aP-using countries which could not be wholly mitigated by adding additional aP boosters to the immunisation programme ( 2 , 3 ). In response the UK introduced a maternal pertussis immunisation programme which was shown to be highly effective at preventing pertussis cases and associated deaths in infants too young to be vaccinated ( 4 , 5 ). Many aP-using countries subsequently introduced a maternal programme which is now recommended by WHO as the most effective and cost-effective way of protecting vulnerable young infants ( 2 ). In the first quarter of 2024 five infant deaths from pertussis were reported in England together with an increase in pertussis cases in all age groups ( 6 ). The European Centre for Disease prevention and Control (ECDC) also reported an increase in pertussis cases in all but two of the 27 European countries submitting surveillance reports to ECDC in the first quarter of 2024 ( 7 ). The resurgences in England and other European countries occurred after a period of reduced incidence associated with the social distancing measures introduced during the COVID-19 pandemic. Increases in pertussis incidence in 2023-24 have also been reported in other countries including North America, Brazil, Australia, Israel and China ( 7 ). These resurgences have occurred in countries with and without maternal pertussis vaccination programmes and in countries with vaccination schedules that recommended booster doses in adolescence and at regular intervals during adult life. A number of factors have been suggested as potentially contributing to the increase in pertussis cases reported in 2023-24 including improvements in sensitivity of surveillance methods, low vaccine coverage, occurrence of natural epidemic peaks and waning immunity together with what has been described as a “bounce back” after the period of reduced transmission during the COVID-19 pandemic ( 7 – 10 ). We used a previously developed pertussis transmission model ( 11 ) to investigate the cause of the pertussis resurgence in England in 2024 taking account of changes in population mixing that occurred as a result of the social distancing measures imposed in 2020 and 2022 ( 12 ). Methods Model assumptions We used a realistic age-structured compartmental deterministic model that does not include a maternal immunisation compartment (for full model details see ( 11 )). The model assumes that infants are born susceptible (S1) to pertussis and acquire natural immunity (R) according to the age-dependent force of infection (FOI), λ. A primary infection in an unvaccinated individual results in a proportion (α 1 ) developing notifiable pertussis. Natural immunity wanes to a secondary susceptible state (S2) in which individuals can become re-infected according to the same FOI as for a primary infection. The proportion of secondary infections that result in notifiable pertussis (α 2 ) is lower than α 1 with recovered individuals re-entering the R compartment. Vaccinated individuals prior to waning are completely protected against notifiable pertussis but can still be infected, though with a reduced FOI reflecting the efficacy of the vaccine against infection. After clearing infection, vaccinated individuals enter the R compartment from which protection wanes to S2; without infection vaccine protection also wanes to S2. An aP booster is assumed to restore waned protection to the vaccine-protected compartment which gives protection against clinical disease but only partial protection against infection. On waning, boosted individuals enter S2. A summary of the transitions between compartments for aP vaccinated cohorts is shown in Fig. 1. Figure 1. Flow diagram of the pertussis transmission dynamic model capturing the impact of acellular pertussis vaccination. Legend. S1: Susceptible to a first infection, λ: Force of infection, I1: Infectious with a first infection, α 1 : the proportion) of first infections developing notifiable pertussis, R: Natural Immunity, S2: Susceptible to a secondary infection, I2: Infectious with a secondary infection, α 2 ; the proportion of secondary infections developing notifiable pertussis, S aP : Susceptible to infection while acellular pertussis vaccine protected against clinical disease, VE: Vaccine Efficacy against infection, I aP : Infectious with a first infection while protected by acellular vaccine against clinical disease (* there is no α parameter as this is set to 0). Model fitting In this study we used the previously inferred transmission and vaccine parameter estimates from Choi et al. ( 11 ) to model the long-term impact of the COVID-19 pandemic on pertussis transmission. As described in Choi et al ( 11 ), the FOI in unvaccinated individuals was estimated from a static model fitted to pre-vaccination age-stratified pertussis notification data for 1956 with the parameters relating to natural and vaccine-induced protection estimated by fitting a dynamic model to age-stratified annual notification data for England from 1956 to 2013 using historical coverage data for primary and booster doses ( 11 ). The degree and duration of protection against infection afforded by vaccination was assumed to differ between the wP and aP vaccines. The wP vaccine was in use (up to 2004) when wP was fully replaced by aP vaccine. From 2001 on wards the aP booster was in use in pre-school children. The following parameters were estimated by selecting the 5% best fitting parameter sets; average duration of natural immunity; efficacy of wP and aP vaccines (VE) against infection while still vaccine-protected ; average duration of protection of wP and aP against infection and clinical disease; α 1 , and the ratio α 2 /α 1 . The following constraints were applied during the fitting: duration of natural immunity should not be less than that of wP and the duration of wP protection against infection should not be less than that of aP for which a minimum duration of 5 years was assumed based on aP effectiveness studies. The estimated parameter ranges for the (658) 5% best fitting parameter sets taken from Choi et al. ( 11 ) are shown in Table 1 . These were then used to describe the uncertainty (minimum to maximum range) of the model outputs for the long-term simulations for the current analyses. Table 1 Estimated parameter values (median and quantiles) from pertussis model in Choi et al. ( 11 ) Parameter Quantiles 0.025 0.25 0.5 (Median) 0.75 0.975 Duration* natural protection (Years) 19.75 30 35 45 50 Duration* of wP protection (Years) 15 22.5 25 27.5 30 Duration* of aP protection (Years) 5 7.5 12.5 20 27.5 Efficacy wP vaccine against infection (VE) 0.8 0.8 0.8 0.8 0.9 Efficacy aP vaccine against infection (VE) 0.6 0.7 0.7 0.8 0.9 α 1 ** 0.1449 0.1455 0.1457 0.1457 0.146 α 2 ** 0.0016 0.0016 0.0021 0.0029 0.0048 Legend * Average duration of protection assuming an exponential decay ** α 1 is percentage of first infections in an unvaccinated individual that are notified; α 2 is the percentage of infections in vaccinated individuals with waned protection or reinfections in unvaccinated individuals that are notified. Mixing matrix The mixing matrix between age groups used for the fitting and for model predictions up to February 2020 was from the POLYMOD survey carried out in England in 2006 supplemented by an additional contact study in infants under one year of age who were under-represented in POLYMOD ( 13 , 14 ). From March 2020 the mixing matrix was scaled to reflect the reduced mixing that was associated with the social distancing measures that were imposed in England between March 2020 and October 2022 to reduce SARS-CoV-2 transmission. Following van Leeuwen et al. ( 15 ) each location (e.g. work, home, leisure etc.) in the POLYMOD survey was associated with one or more activity (e.g. school, social visits etc.) as defined in the time-use survey (UKTUS) ( 16 ). Activities were then scaled based on Google Mobility data, and school attendance data. For example, school associated contacts were scaled based on the school attendance data, and social visits were scaled based on the Google Mobility data for leisure activities. This results in a scaling matrix, where each element represents the relative number of contacts between the associated age groups, which can then be elementwise multiplied with the base contact matrix. Annual population sizes between 1956 and projected out to 2030 ( 17 ) were implemented to produce realistic mixing patterns with the 2030 population used for model simulations out to 2054. Model scenarios The model was run from 1956 to 2054 with the assumption that coverage for primary (96%) and booster (86%) doses from 2013 was the same as that in England in 2013. The following model scenarios were investigated: continuing with the existing primary immunisation and pre-school booster programme with and without a change in population mixing due to the COVID-19 social distancing measures, and with and without the addition in 2014 of an adolescent booster with 86% coverage and a booster dose at 18 months with 91% coverage. Coverage assumed for the 18 month and adolescent booster was respectively the primary vaccination coverage and pre-school booster coverage in England in 2020. The effect of adding these two boosters in 2025 on the pertussis cases in subsequent years was also investigated. Results The projected pertussis cases out to 2054 under the existing aP vaccination programme in England without and with the social distancing measures imposed during the COVID-19 pandemic are shown in Figs. 2A and 2B respetively. The 2012 resurgence and subsequent higher endemic level of pertussis transmission is evident but there is no predicted resurgence after the Covid-19 pandemic without the reduced social mixing during the pandemic. The exact timing of the predicted resurgence varied with some parameter sets predicting a resurgence in 2024 while others predicted a resurgence starting later. In order to help identify the vaccine parameter values that generate an early resurgence, those parameter sets that gave the highest 10% of predicted overall cases in 2024 (66 out of 658 scenarios) were selected and compared with parameter sets that predicted less than one case in 2024 (n = 273) (Figs. 2C and 2D respectively).The median duration of acellular vaccine protection for the parameter sets that generated a resurgence in 2024 was 5 years with a VE against infection of 0.9; in contrast for those that predicted a later resurgence the median duration of protection was 10 years with a VE of 0.9 (Appendix Tables S1 and S2). With the shorter duration of vaccine protection peaks of increased incidence are predicted to continue until around 2034 (Fig. 2C). Without reduced social mixing, falls of 10% in coverage for both the primary and pre-school booster doses in the 3 years between 2020 and 2022 would not have produced the resurgence that occurred in 2024 in England (Figure S1 ). Figure 2. Predicted overall notifications without and with the reduced population mixing during the COVID-19 pandemic. Legend. Graphs show model outputs for pertussis notifications all ages combined from 2006 to 2054 under the existing primary aP schedule at 2,3,4 months with a single booster before school entry. The median is shown as a black line (minimum to maximum range in the pink shaded area) of the predicted annual pertussis notifications using the best fitting parameter sets from Choi et al ( 11 ). Blue line shows NOIDs cases reported up to week 27 2024. A. Counterfactual scenario without reduced population mixing during the COVID-19 pandemic (all 658 parameter sets); B. With reduced population mixing during the pandemic (all 658 parameter sets); C. Restriction of parameter sets to those that generate a resurgence in 2024 (n = 66); D. Restriction of parameter sets to those that predict < 1 case in 2024 (n = 273). The predicted cases by age group out to 2054 with the restricted parameter sets that produced the top 10% of overall cases in 2024 are shown in Fig. 3. A resurgences in 2024 is predicted in all age groups with the biggest increases in those under 10 years of age. Apart from 15–24 year olds the first three post-pandemic peaks exceed those in the immediate pre-pandemic period. Figure 3. Model outputs by age-group under the existing schedule with reduced population mixing during the pandemic. Legend. Graphs show the median as a black line (minimum to maximum range in the pink shaded area) of the predicted annual pertussis notifications with restriction of parameter sets to those that generate a resurgence in 2024 (n = 66). In those under 4 years the majority of notifications come from those in the S1 compartment whereas notifications in older age groups notifications are mainly from those in the S2 compartment with waned immunity (Figure S2) The age-breakdown of the notified cases predicted by the model in 2024 with the 66 selected parameter sets compared with the NOIDs cases is shown in Fig. 4. The NOIDs data fell within the predicted range for all age groups below 10 years, although the median of the model estimates tended to be higher than the data, except in 1–4 year olds. The model underestimated the cases in the age groups 10 years and over. Figure 4. Predicted number of notified pertussis cases by age group in 2024 in England. Legend. Graphs show medians (error bars minimum to maximum range) of the model predictions with the reductions in population mxing during the COVID-19 pandemic and NOIDs cases up to week 27. Since the model is fitted to annual notifications up to 2013, its output is annual notifications so the predicted cases for 2024 were multiplied by 27/52 to generate cases up to week 27. The two alternative scenarios around the mitigating effects of a 18 month and 14 year booster programme starting in 2014 or 2025 respectively are shown in Tables S3-5 by age group using the 66 parameter sets that gave the top 10% of overall cases in 2024. Compared with the existing vaccination programme with a single booster before school entry, the model predicts that the addition of the two additional boosters in 2014 would have reduced the number of overall cases in 2024 by 21% (Tables S3 and S4). Addition of these boosters in 2025 would only start to have a significant effect from 2028 onwards (Table S5). The annual averted cases under these two mitigating scenarios compared with the existing vaccination schedule are shown in Fig. 5 and in Tables S6 and S7 by age group. Figure 5. Predicted annual averted notifications if 18 month and 14 year boosters added in A) 2014 or B) 2025 Legend. Boosters at 18 months and 14 years added to existing programme of primary aP doses at 2,3,4 months and an aP booster before school entry and with the the reductions in population mxing during the COVID-19 pandemic. Discussion This modelling study implicates the social distancing measures imposed during the COVID-19 pandemic as the cause of the pertussis resurgence in England that started in 2024. Our model predicted increases in cases across the age range consistent with the observed disease trends ( 18 ). The model predicted a resurgence without introducing in the model any drop in vaccine coverage associated with the pandemic. Implementing a 10% coverage drop for both the primary and pre-school booster doses for 3 years from January 2020 without reduced mixing did not produce a resurgence comparable to that seen in England in 2024. Our model predicted a resurgence following the reduced population mixing that occurred during the pandemic even under the counterfactual scenario in which 18 month and adolescent aP boosters were added to the national programme in 2014. This is consistent with the resurgences seen in settings where these additional boosters were already part of the schedule ( 7 ). In constructing our model we based the vaccine parameters on the protection against disease and infection documented in epidemiological studies and the baboon pertussis challenge model ( 2 , 3 ). These studies have shown more rapid waning of protection against clinical disease ( 2 ) and poorer protection against an infection ( 3 ) with acellular than whole cell vaccines. To generate robust protection against infection, acellular-vaccinated baboons need to experience one or more pertussis infections ( 19 ). In our model we reflected these properties by allowing acellular vaccinees to be susceptible to infection while still protected against notifiable (ie clinically typical) pertussis, and to have a shorter duration of protection than whole cell vaccinees or those who have experienced natural infection. Infection in an acellular vaccinee therefore acts as a booster generating more durable protection against further infection without necessarily resulting in notifiable pertussis. The COVID-19 social distancing measures supressed this background rate of boosting allowing a pool of infection-susceptible acellular vaccinees to accumulate during the pandemic thereby disturbing the endemic equilibrium that existed before the COVID pandemic. On resumption of normal mixing patterns, transmission of pertussis increased and is predicted to exhibit cycles of increased oscillation before resuming the pre-COVID equilibrium with clinical cases apparent in unvaccinated individuals, including infants of unvaccinated mothers, and those whose protection against clinically typical disease has waned. Based on the timing of the resurgence in England our model implies that it was driven by short average duration of protection from aP vaccine (around 5 years) rather than poor protection against infection which was estimated to be around 90% prior to waning. The strength of our study is that we used an existing pertussis transmission model that reproduced the resurgence in England in 2012 which followed the change from wP to aP vaccines and correctly predicted the higher endemic level of transmission in England in all age groups since 2012 ( 20 ). The modifications to the mixing patterns that were incorporated to capture the restrictions imposed during the COVID-19 pandemic were based on empirical data and were used by modelling groups to refine model predictions on the impact of the evolving pandemic on cases and deaths ( 12 , 15 ). A limitation of our model is that the range of aP parameter values that fitted the observed notification patterns in England over the period 1956 to 2013 was wide (Table 1 ) but by selecting the parameter sets that gave the top 10% of overall cases in 2024, further refinement of the most likely parameter values for the degree and duration of protection afforded by aP vaccines was possible. Despite this restriction, there was still a wide range of model outputs with the minimum value showing no post-pandemic resurgence (Fig. 3). Furthermore, our model parameters α 1 and α 2 (which reflect the proportion of infections notified respectively in unvaccinated and previously infected/vaccinated individuals) are assumed to be constant over time. However, since 2013 the proportion of cases that are notified has increased due to the routine availability of serum and oral fluid testing for raised IgG levels to pertussis toxin that are indicative of recent infection ( 21 ). The improvements in notification efficiency resulting from IgG testing have been most marked in those aged 10 years and over in whom pertussis may be mild or clinically atypical ( 22 ). If our model had been fitted to NOIDs data since 2013 with age-dependent α 1 and α 2 parameters, it is probable that these estimated parameter values would have been higher for those aged 10 years and over. This may explain the underestimation of cases in those age 10 years and above predicted by the model when compared with NOIDs cases in 2024. Another limitation is that our model did not include a maternal immunisation component. While vaccination of pregnant women would have a negligible impact on overall transmission, it would mitigate the effect of a resurgence on infants either by passive protection through transfer of maternal antibodies ( 20 ) or by reduced exposure as mothers are a major source of infection for infants ( 14 ). The overestimation by the model of NOIDs cases in infants is consistent with this. Our model was originally developed to investigate the cause of the pertussis resurgence in England in 2012. At around the same time, a number of other groups developed pertussis transmission models to investigate the cause of the resurgences being reported in other aP-using countries. In common with our model, most of these models include waning immunity and allow for a substantial component of pertussis transmission to be driven by infections that are not reflected in notified disease ( 23 – 25 ). Differences with our model include allowing for the proportion of infections that are notified to be age-dependent ( 26 ) and for the use of sero-epidemiological data to estimate the incidence of boosting in the population by detection of high IgG levels to pertussis toxin ( 23 ), features that could potentially be incorporated in future iterations of our model. To date however we are unaware of any publications using such models to investigate the post-COVID-19 resurgences now being reported. The model predicts that peaks with an elevated incidence of pertussis are likely to occur over the next decade in England as the epidemiology of pertussis returns to the pre-pandemic equilibrium. It is therefore essential to achieve and sustain high levels of maternal immunisation to protect vulnerable infants too young to be vaccinated. Coverage of the maternal immunisation programme in England for 2023 to March 2024 was only 58.6%, a fall of 15.8% since 2017 ( 18 ). With an effectiveness of around 92% ( 18 ), 90% coverage of the maternal programme could prevent 83% of the cases predicted by our model in infants under 3 months of age in the coming years. The effect of the resurgence would also have been mitigated somewhat had additional 18 month and adolescent boosters been in place; their addition now is predicted to have some mitigating effect from 2028 onwards. The short duration of protection of 5 years estimated by the model for aP vaccine supports the recommendation that those who have regular contact with pregnant women or vulnerable infants in the hospital or community setting should receive 5-yearly booster doses ( 21 ). Our modelling analysis, together with other assessments ( 27 ), indicate the extent of pertussis infection that occurs despite high vaccine coverage and the deficiencies of the current generation of aP vaccines in providing durable, sterilising immunity. Research efforts are underway to develop improved pertussis vaccines by incorporating novel adjuvants to elicit persisting immunological memory ( 27 ) or by using intranasal administration of a live attenuated B. pertussis strain to generate mucosal responses that can protect against transmission ( 28 ). Optimising the deployment of such new pertussis vaccines, for example as boosters or in place of the current generation of aP vaccines, will require further refinement of pertussis transmission models to better capture the impact of improved surveillance methods and to validate the model predictions against direct measures of pertussis incidence such as those available from seroepidemiology. Declarations Ethics approval : The data used for fitting the pertussis model is routinely available anonymised notification data. Ethics approval for the study was there not required. Consent for publication: Not applicable Data availability : Competing interests: All authors declare no conflict of interest. EM receives support from the National Institute for Health Research (NIHR) Health Protection Research Unit in Immunisation at the London School of Hygiene and Tropical Medicine in partnership with Public Health England (Grant Reference NIHR200929). The funders had no input to the design of the study nor the interpretation of the results. Funding : UKHSA and the National Institute for Health Research. Contributions YHC and EM designed the study; YHC built the pertussis model and ran the simulations; EvL constructed the scaling matrix for the reduced social mixing during the COVID-19 pandemic; EM wrote the first draft of the paper; all authors critically reviewed the paper and approved the final version for submission. Acknowledgments We thank Sonia Robeiro for provision of the NOIDs data for 2024 and Prof Nick Andrews for helpful suggestions on parameter restriction and comments on the paper. References Kimberlin D, Barnett E, Lynfield R, Sawyer M, editors. Red Book: 2021 Report of the Committee on Infectious Diseases.: American Academy of Pediatrics (AAP). Pertussis (whooping cough). 2021. World Health Organization. Pertussis vaccine: WHO position paper - August 2015. WHO Weekly epidemiological record. 2015;90:433-60. Warfel JM, Zimmerman LI, Merkel TJ. Acellular pertussis vaccines protect against disease but fail to prevent infection and transmission in a nonhuman primate model. Proc Natl Acad Sci U S A. 2014;111(2):787-92. Dabrera G, Amirthalingam G, Andrews N, Campbell H, Ribeiro S, Kara E, et al. A case-control study to estimate the effectiveness of maternal pertussis vaccination in protecting newborn infants in England and Wales, 2012-2013. Clin Infect Dis. 2015;60(3):333-7. Amirthalingam G, Andrews N, Campbell H, Ribeiro S, Kara E, Donegan K, et al. Effectiveness of maternal pertussis vaccination in England: an observational study. The Lancet. 2014;384(9953):1521-8. UK Health Security Agency. Whooping cough cases continue to rise 2024 [Available from: Whooping cough cases continue to rise. European Centre for Disease Prevention and Control. Increase of pertussis cases in the EU/EEA 2024 [Available from: https://www.ecdc.europa.eu/en/publications-data/increase-pertussis-cases-eueea. Stein-Zamir C, Shoob H, Abramson N, Brown EH, Zimmermann Y. Pertussis outbreak mainly in unvaccinated young children in ultra-orthodox Jewish groups, Jerusalem, Israel 2023. Epidemiol Infect. 2023;151:e166. Wise J. Whooping cough: What’s behind the rise in cases and deaths in England? BMJ. 2024;385:q1118. Liu Y, Ye Q. Resurgence and the shift in the age of peak onset of pertussis in southern China. J Infect. 2024;89(2):106194. Choi YH, Campbell H, Amirthalingam G, van Hoek AJ, Miller E. Investigating the pertussis resurgence in England and Wales, and options for future control. BMC Medicine. 2016;14(1):121. Birrell P, Blake J, van Leeuwen E, Gent N, De Angelis D. Real-time nowcasting and forecasting of COVID-19 dynamics in England: the first wave. Philosophical Transactions of the Royal Society B: Biological Sciences. 2021;376(1829):20200279. Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, et al. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLOS Med. 2008;5(3):e74. van Hoek AJ, Andrews N, Campbell H, Amirthalingam G, Edmunds WJ, Miller E. The social life of infants in the context of infectious disease transmission; social contacts and mixing patterns of the very young. PLoS One. 2013;8(10):e76180. van Leeuwen E, Sandmann F. Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics: A modelling analysis. Stat Methods Med Res. 2022;31(9):1704-15. Gershuny J, Sullivan O. United Kingdom Time Use Survey, 2014-2015. Centre for Time Use Research, IOE, University College London. [data collection]. UK Data Service 2017 [Available from: https://www.timeuse.org/uk-time-use-survey-2014-2015. Office for National Statistics. 2020-based Interim National Population Projections 2024 [07/02/2024]. Available from: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationprojections /bulletins/nationalpopulationprojections/2020basedinterim. UK Health Security Agency. Confirmed cases of pertussis in England by month, to end May 2024 2024 [Available from: https://www.gov.uk/government/publications/pertussis-epidemiology-in-england-2024/confirmed-cases-of-pertussis-in-england-by-month. Kapil P, Wang Y, Zimmerman L, Gaykema M, Merkel TJ. Repeated Bordetella pertussis Infections Are Required to Reprogram Acellular Pertussis Vaccine-Primed Host Responses in the Baboon Model. J Infect Dis. 2024;229(2):376-83. Amirthalingam G, Campbell H, Ribeiro S, Stowe J, Tessier E, Litt D, et al. Optimization of Timing of Maternal Pertussis Immunization From 6 Years of Postimplementation Surveillance Data in England. Clin Infect Dis. 2023;76(3):e1129-e39. UK Health Security Agency. Guidance on the management of cases of pertussis in England during the re-emergence of pertussis in 2024 2024 [updated August 2024. Available from: https://assets.publishing.service.gov.uk/media/66c4a642808b8c0aa08fa7e7/UKHSA-guidance-on-the-management-of-cases-of-pertussis-during-high-activity-august-2024.pdf. Campbell H, Amirthalingam G, Fry NK, Litt D, Harrison TG, Wagner K, et al. Oral fluid testing for pertussis, England and wales, june 2007-august 2009. Emerg Infect Dis. 2014;20(6):968-75. Campbell PT, McCaw JM, McIntyre P, McVernon J. Defining long-term drivers of pertussis resurgence, and optimal vaccine control strategies. Vaccine. 2015;33(43):5794-800. Gambhir M, Clark TA, Cauchemez S, Tartof SY, Swerdlow DL, Ferguson NM. A change in vaccine efficacy and duration of protection explains recent rises in pertussis incidence in the United States. PLoS Comput Biol. 2015;11(4):e1004138. Althouse BM, Scarpino SV. Asymptomatic transmission and the resurgence of Bordetella pertussis. BMC Med. 2015;13:146. Domenech de Cellès M, Magpantay FMG, King AA, Rohani P. The impact of past vaccination coverage and immunity on pertussis resurgence. Sci Transl Med. 2018;10(434). Damron FH, Barbier M, Dubey P, Edwards KM, Gu XX, Klein NP, et al. Overcoming Waning Immunity in Pertussis Vaccines: Workshop of the National Institute of Allergy and Infectious Diseases. J Immunol. 2020;205(4):877-82. Keech C, Miller VE, Rizzardi B, Hoyle C, Pryor MJ, Ferrand J, et al. Immunogenicity and safety of BPZE1, an intranasal live attenuated pertussis vaccine, versus tetanus-diphtheria-acellular pertussis vaccine: a randomised, double-blind, phase 2b trial. Lancet. 2023;401(10379):843-55. Additional Declarations No competing interests reported. Supplementary Files PertussispaperAppendix.docx Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2026 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 31 Mar, 2025 Reviews received at journal 29 Mar, 2025 Reviews received at journal 19 Mar, 2025 Reviewers agreed at journal 13 Feb, 2025 Reviewers agreed at journal 11 Feb, 2025 Reviewers agreed at journal 12 Dec, 2024 Reviewers agreed at journal 25 Nov, 2024 Reviewers invited by journal 22 Nov, 2024 Editor invited by journal 18 Nov, 2024 Editor assigned by journal 17 Nov, 2024 Submission checks completed at journal 17 Nov, 2024 First submitted to journal 15 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5459094","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":390926786,"identity":"355443b3-75f8-4ead-928c-90fc86fc2f0f","order_by":0,"name":"Yoon Hong Choi","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Yoon","middleName":"Hong","lastName":"Choi","suffix":""},{"id":390926787,"identity":"e27c0487-6fb3-4891-89f7-a6b856dc17d8","order_by":1,"name":"Edwin Leeuwen","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Edwin","middleName":"","lastName":"Leeuwen","suffix":""},{"id":390926788,"identity":"737e72d2-a6e4-44d7-8c48-3b17ad6cd2a8","order_by":2,"name":"Elizabeth Miller","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3PMQrCMBSA4VcD7fJq1ojiGQIuDoIHcREEpxY8QqdOohfwEB0dUwK65ACOLYKTg2uhg02Lg0hj3RzyD00JfLwXAJvtD+sT/eXTEQBJhRO97kU7cRvCsPpdVoR3IM2hCfKOxPOy3mXDkA7VIy2O5QI8mRFUxsWWJKgWG+zCRPqKhxGuOcGLkYiacOUn0okrAgEQfJiIE9VkrjBLC03o/Rsh0ExBBOFrwvQU42IuSE2Ycrn040kYsxtPD4bnU3rOr0E5G9MtyfMiHod7usqz+6md6MT73I8bm81ms/3cE++RRqB5NUosAAAAAElFTkSuQmCC","orcid":"","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":true,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Miller","suffix":""}],"badges":[],"createdAt":"2024-11-15 08:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5459094/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5459094/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-026-12521-5","type":"published","date":"2026-01-23T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71876323,"identity":"cb9b5823-6464-4a51-bc81-ce4a834b8745","added_by":"auto","created_at":"2024-12-19 10:58:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97679,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the pertussis transmission dynamic model capturing the impact of acellular pertussis vaccination.\u003c/p\u003e\n\u003cp\u003eLegend. S1: Susceptible to a first infection, λ: Force of infection, I1: Infectious with a first infection, α\u003csub\u003e1\u003c/sub\u003e: the proportion) of first infections developing notifiable pertussis, R: Natural Immunity, S2: Susceptible to a secondary infection, I2: Infectious with a secondary infection, α\u003csub\u003e2\u003c/sub\u003e; the proportion of secondary infections developing notifiable pertussis, S\u003csub\u003eaP\u003c/sub\u003e: Susceptible to infection while acellular pertussis vaccine protected against clinical disease , VE: Vaccine Efficacy against infection, I\u003csub\u003eaP\u003c/sub\u003e: Infectious with a first infection while \u0026nbsp;protected by acellular vaccine against clinical disease (* there is no α parameter as this is set to 0).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5459094/v1/fbf9366827d121cc76dac65c.png"},{"id":71876322,"identity":"4dcb970a-130a-49b9-8998-81d881a030cb","added_by":"auto","created_at":"2024-12-19 10:58:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1956471,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted overall notifications without and with the reduced population mixing during the COVID-19 pandemic.\u003c/p\u003e\n\u003cp\u003eLegend. Graphs show model outputs for pertussis notifications all ages combined from 2006 to 2054 under the existing \u0026nbsp;primary aP schedule at 2,3,4 months with a single booster before school entry. The median is shown as a black line (minimum to maximum range in the pink shaded area) of the predicted annual pertussis notifications using the best fitting parameter sets from Choi et al (11). Blue line shows NOIDs cases reported up to week 27 2024. A. Counterfactual scenario without reduced population mixing during the COVID-19 pandemic (all 658 parameter sets); B. With reduced population mixing during the pandemic (all 658 parameter sets); C. Restriction of parameter sets to those that generate a resurgence in 2024 (n=66); D. Restriction of parameter sets to those that predict \u0026lt;1 case in 2024 (n=273).\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5459094/v1/83c512c438c7237af10149ab.png"},{"id":71876902,"identity":"b9ffd901-8b32-47ee-89c5-d5509cf94c44","added_by":"auto","created_at":"2024-12-19 11:06:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2787072,"visible":true,"origin":"","legend":"\u003cp\u003eModel outputs by age-group under the existing schedule with reduced population mixing during the pandemic.\u003c/p\u003e\n\u003cp\u003eLegend. Graphs show the median as a black line (minimum to maximum range in the pink shaded area) of the predicted annual pertussis notifications with restriction of parameter sets to those that generate a resurgence in 2024 (n=66).\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5459094/v1/aef05b11f7f988d4d05ca4d8.png"},{"id":71876925,"identity":"102a96f2-6bce-4cc4-b759-b931483fb99b","added_by":"auto","created_at":"2024-12-19 11:06:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":707138,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted number of notified pertussis cases by age group in 2024 in England.\u003c/p\u003e\n\u003cp\u003eLegend. Graphs show medians (error bars minimum to maximum range) \u0026nbsp;of the model predictions \u0026nbsp;with the reductions in population mxing during the COVID-19 pandemic \u0026nbsp;and NOIDs cases up to week 27. Since the model is fitted to annual notifications up to 2013, its output is annual notifications so the predicted cases for 2024 were multiplied by 27/52 to generate cases up to week 27.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5459094/v1/ef903269f3130f3a48c661c0.png"},{"id":71876321,"identity":"09cc56ff-bb5e-44d6-8481-5a4d4752b072","added_by":"auto","created_at":"2024-12-19 10:58:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":904392,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted annual averted notifications if 18 month and 14 year boosters added in A) 2014 or B) 2025\u003c/p\u003e\n\u003cp\u003eLegend. \u0026nbsp;Boosters at 18 months and 14 years added to existing programme of primary aP doses at 2,3,4 months and an aP booster before school entry and with the the reductions in population mxing during the COVID-19 pandemic.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5459094/v1/6c10ceba7a96a3416a747ae9.png"},{"id":101151715,"identity":"9fc40ac9-6e31-45b1-a667-1f08a705e525","added_by":"auto","created_at":"2026-01-26 16:02:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7719862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5459094/v1/2ff6f5d2-9369-410f-9d70-4e68d43ea410.pdf"},{"id":71876320,"identity":"102a514e-ce76-4347-b4fb-b294cf2b3358","added_by":"auto","created_at":"2024-12-19 10:58:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":460788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"PertussispaperAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5459094/v1/8973ff89eb8dd0c4f31fa92e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term effect of COVID-19 social distancing measures on pertussis transmission in England; a mathematical modelling study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePertussis is a highly transmissible respiratory infection which remains endemic despite longstanding immunisation programmes that have achieved high coverage. Infants too young to be vaccinated are most at risk of severe disease with high case fatality rates even in countries with advanced health care systems (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Many countries experienced an increase in pertussis incidence following a change from whole cell pertussis (wP) to acellular pertussis (aP) vaccines which was likely attributable to the shorter duration of protection and lower efficacy against infection of aP than wP vaccines (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This resulted in a higher endemic level of pertussis transmission in aP-using countries which could not be wholly mitigated by adding additional aP boosters to the immunisation programme (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In response the UK introduced a maternal pertussis immunisation programme which was shown to be highly effective at preventing pertussis cases and associated deaths in infants too young to be vaccinated (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Many aP-using countries subsequently introduced a maternal programme which is now recommended by WHO as the most effective and cost-effective way of protecting vulnerable young infants (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the first quarter of 2024 five infant deaths from pertussis were reported in England together with an increase in pertussis cases in all age groups (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The European Centre for Disease prevention and Control (ECDC) also reported an increase in pertussis cases in all but two of the 27 European countries submitting surveillance reports to ECDC in the first quarter of 2024 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The resurgences in England and other European countries occurred after a period of reduced incidence associated with the social distancing measures introduced during the COVID-19 pandemic. Increases in pertussis incidence in 2023-24 have also been reported in other countries including North America, Brazil, Australia, Israel and China (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). These resurgences have occurred in countries with and without maternal pertussis vaccination programmes and in countries with vaccination schedules that recommended booster doses in adolescence and at regular intervals during adult life.\u003c/p\u003e \u003cp\u003eA number of factors have been suggested as potentially contributing to the increase in pertussis cases reported in 2023-24 including improvements in sensitivity of surveillance methods, low vaccine coverage, occurrence of natural epidemic peaks and waning immunity together with what has been described as a \u0026ldquo;bounce back\u0026rdquo; after the period of reduced transmission during the COVID-19 pandemic (\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). We used a previously developed pertussis transmission model (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) to investigate the cause of the pertussis resurgence in England in 2024 taking account of changes in population mixing that occurred as a result of the social distancing measures imposed in 2020 and 2022 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eModel assumptions\u003c/h2\u003e \u003cp\u003eWe used a realistic age-structured compartmental deterministic model that does not include a maternal immunisation compartment (for full model details see (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)). The model assumes that infants are born susceptible (S1) to pertussis and acquire natural immunity (R) according to the age-dependent force of infection (FOI), λ. A primary infection in an unvaccinated individual results in a proportion (α\u003csub\u003e1\u003c/sub\u003e) developing notifiable pertussis. Natural immunity wanes to a secondary susceptible state (S2) in which individuals can become re-infected according to the same FOI as for a primary infection. The proportion of secondary infections that result in notifiable pertussis (α\u003csub\u003e2\u003c/sub\u003e) is lower than α\u003csub\u003e1\u003c/sub\u003e with recovered individuals re-entering the R compartment. Vaccinated individuals prior to waning are completely protected against notifiable pertussis but can still be infected, though with a reduced FOI reflecting the efficacy of the vaccine against infection. After clearing infection, vaccinated individuals enter the R compartment from which protection wanes to S2; without infection vaccine protection also wanes to S2. An aP booster is assumed to restore waned protection to the vaccine-protected compartment which gives protection against clinical disease but only partial protection against infection. On waning, boosted individuals enter S2. A summary of the transitions between compartments for aP vaccinated cohorts is shown in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eFigure 1. Flow diagram of the pertussis transmission dynamic model capturing the impact of acellular pertussis vaccination.\u003c/p\u003e \u003cp\u003eLegend. S1: Susceptible to a first infection, λ: Force of infection, I1: Infectious with a first infection, α\u003csub\u003e1\u003c/sub\u003e: the proportion) of first infections developing notifiable pertussis, R: Natural Immunity, S2: Susceptible to a secondary infection, I2: Infectious with a secondary infection, α\u003csub\u003e2\u003c/sub\u003e; the proportion of secondary infections developing notifiable pertussis, S\u003csub\u003eaP\u003c/sub\u003e: Susceptible to infection while acellular pertussis vaccine protected against clinical disease, VE: Vaccine Efficacy against infection, I\u003csub\u003eaP\u003c/sub\u003e: Infectious with a first infection while protected by acellular vaccine against clinical disease (* there is no α parameter as this is set to 0).\u003c/p\u003e \u003cp\u003eModel fitting\u003c/p\u003e \u003cp\u003eIn this study we used the previously inferred transmission and vaccine parameter estimates from Choi et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) to model the long-term impact of the COVID-19 pandemic on pertussis transmission. As described in Choi et al (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), the FOI in unvaccinated individuals was estimated from a static model fitted to pre-vaccination age-stratified pertussis notification data for 1956 with the parameters relating to natural and vaccine-induced protection estimated by fitting a dynamic model to age-stratified annual notification data for England from 1956 to 2013 using historical coverage data for primary and booster doses (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The degree and duration of protection against infection afforded by vaccination was assumed to differ between the wP and aP vaccines. The wP vaccine was in use (up to 2004) when wP was fully replaced by aP vaccine. From 2001 on wards the aP booster was in use in pre-school children. The following parameters were estimated by selecting the 5% best fitting parameter sets; average duration of natural immunity; efficacy of wP and aP vaccines (VE) against infection while still vaccine-protected ; average duration of protection of wP and aP against infection and clinical disease; α\u003csub\u003e1\u003c/sub\u003e, and the ratio α\u003csub\u003e2\u003c/sub\u003e/α\u003csub\u003e1\u003c/sub\u003e. The following constraints were applied during the fitting: duration of natural immunity should not be less than that of wP and the duration of wP protection against infection should not be less than that of aP for which a minimum duration of 5 years was assumed based on aP effectiveness studies. The estimated parameter ranges for the (658) 5% best fitting parameter sets taken from Choi et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These were then used to describe the uncertainty (minimum to maximum range) of the model outputs for the long-term simulations for the current analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated parameter values (median and quantiles) from pertussis model in Choi et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eQuantiles\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 (Median)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration* natural protection (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration* of wP protection (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration* of aP protection (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficacy wP vaccine against infection (VE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficacy aP vaccine against infection (VE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα\u003csub\u003e1\u003c/sub\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα\u003csub\u003e2\u003c/sub\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cp\u003eLegend\u003c/p\u003e \u003cp\u003e \u003csup\u003e*\u003c/sup\u003e Average duration of protection assuming an exponential decay\u003c/p\u003e \u003cp\u003e** α\u003csub\u003e1\u003c/sub\u003e is percentage of first infections in an unvaccinated individual that are notified; α\u003csub\u003e2\u003c/sub\u003e is the percentage of infections in vaccinated individuals with waned protection or reinfections in unvaccinated individuals that are notified.\u003c/p\u003e\n\u003ch3\u003eMixing matrix\u003c/h3\u003e\n\u003cp\u003eThe mixing matrix between age groups used for the fitting and for model predictions up to February 2020 was from the POLYMOD survey carried out in England in 2006 supplemented by an additional contact study in infants under one year of age who were under-represented in POLYMOD (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). From March 2020 the mixing matrix was scaled to reflect the reduced mixing that was associated with the social distancing measures that were imposed in England between March 2020 and October 2022 to reduce SARS-CoV-2 transmission. Following van Leeuwen et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) each location (e.g. work, home, leisure etc.) in the POLYMOD survey was associated with one or more activity (e.g. school, social visits etc.) as defined in the time-use survey (UKTUS) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Activities were then scaled based on Google Mobility data, and school attendance data. For example, school associated contacts were scaled based on the school attendance data, and social visits were scaled based on the Google Mobility data for leisure activities. This results in a scaling matrix, where each element represents the relative number of contacts between the associated age groups, which can then be elementwise multiplied with the base contact matrix. Annual population sizes between 1956 and projected out to 2030 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) were implemented to produce realistic mixing patterns with the 2030 population used for model simulations out to 2054.\u003c/p\u003e\n\u003ch3\u003eModel scenarios\u003c/h3\u003e\n\u003cp\u003eThe model was run from 1956 to 2054 with the assumption that coverage for primary (96%) and booster (86%) doses from 2013 was the same as that in England in 2013. The following model scenarios were investigated: continuing with the existing primary immunisation and pre-school booster programme with and without a change in population mixing due to the COVID-19 social distancing measures, and with and without the addition in 2014 of an adolescent booster with 86% coverage and a booster dose at 18 months with 91% coverage. Coverage assumed for the 18 month and adolescent booster was respectively the primary vaccination coverage and pre-school booster coverage in England in 2020. The effect of adding these two boosters in 2025 on the pertussis cases in subsequent years was also investigated.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe projected pertussis cases out to 2054 under the existing aP vaccination programme in England without and with the social distancing measures imposed during the COVID-19 pandemic are shown in Figs.\u0026nbsp;2A and 2B respetively. The 2012 resurgence and subsequent higher endemic level of pertussis transmission is evident but there is no predicted resurgence after the Covid-19 pandemic without the reduced social mixing during the pandemic. The exact timing of the predicted resurgence varied with some parameter sets predicting a resurgence in 2024 while others predicted a resurgence starting later. In order to help identify the vaccine parameter values that generate an early resurgence, those parameter sets that gave the highest 10% of predicted overall cases in 2024 (66 out of 658 scenarios) were selected and compared with parameter sets that predicted less than one case in 2024 (n\u0026thinsp;=\u0026thinsp;273) (Figs.\u0026nbsp;2C and 2D respectively).The median duration of acellular vaccine protection for the parameter sets that generated a resurgence in 2024 was 5 years with a VE against infection of 0.9; in contrast for those that predicted a later resurgence the median duration of protection was 10 years with a VE of 0.9 (Appendix Tables S1 and S2). With the shorter duration of vaccine protection peaks of increased incidence are predicted to continue until around 2034 (Fig.\u0026nbsp;2C). Without reduced social mixing, falls of 10% in coverage for both the primary and pre-school booster doses in the 3 years between 2020 and 2022 would not have produced the resurgence that occurred in 2024 in England (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure 2. Predicted overall notifications without and with the reduced population mixing during the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eLegend. Graphs show model outputs for pertussis notifications all ages combined from 2006 to 2054 under the existing primary aP schedule at 2,3,4 months with a single booster before school entry. The median is shown as a black line (minimum to maximum range in the pink shaded area) of the predicted annual pertussis notifications using the best fitting parameter sets from Choi et al (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Blue line shows NOIDs cases reported up to week 27 2024. A. Counterfactual scenario without reduced population mixing during the COVID-19 pandemic (all 658 parameter sets); B. With reduced population mixing during the pandemic (all 658 parameter sets); C. Restriction of parameter sets to those that generate a resurgence in 2024 (n\u0026thinsp;=\u0026thinsp;66); D. Restriction of parameter sets to those that predict\u0026thinsp;\u0026lt;\u0026thinsp;1 case in 2024 (n\u0026thinsp;=\u0026thinsp;273).\u003c/p\u003e \u003cp\u003eThe predicted cases by age group out to 2054 with the restricted parameter sets that produced the top 10% of overall cases in 2024 are shown in Fig.\u0026nbsp;3. A resurgences in 2024 is predicted in all age groups with the biggest increases in those under 10 years of age. Apart from 15\u0026ndash;24 year olds the first three post-pandemic peaks exceed those in the immediate pre-pandemic period.\u003c/p\u003e \u003cp\u003eFigure 3. Model outputs by age-group under the existing schedule with reduced population mixing during the pandemic.\u003c/p\u003e \u003cp\u003eLegend. Graphs show the median as a black line (minimum to maximum range in the pink shaded area) of the predicted annual pertussis notifications with restriction of parameter sets to those that generate a resurgence in 2024 (n\u0026thinsp;=\u0026thinsp;66).\u003c/p\u003e \u003cp\u003eIn those under 4 years the majority of notifications come from those in the S1 compartment whereas notifications in older age groups notifications are mainly from those in the S2 compartment with waned immunity (Figure S2)\u003c/p\u003e \u003cp\u003eThe age-breakdown of the notified cases predicted by the model in 2024 with the 66 selected parameter sets compared with the NOIDs cases is shown in Fig.\u0026nbsp;4. The NOIDs data fell within the predicted range for all age groups below 10 years, although the median of the model estimates tended to be higher than the data, except in 1\u0026ndash;4 year olds. The model underestimated the cases in the age groups 10 years and over.\u003c/p\u003e \u003cp\u003eFigure 4. Predicted number of notified pertussis cases by age group in 2024 in England.\u003c/p\u003e \u003cp\u003eLegend. Graphs show medians (error bars minimum to maximum range) of the model predictions with the reductions in population mxing during the COVID-19 pandemic and NOIDs cases up to week 27. Since the model is fitted to annual notifications up to 2013, its output is annual notifications so the predicted cases for 2024 were multiplied by 27/52 to generate cases up to week 27.\u003c/p\u003e \u003cp\u003eThe two alternative scenarios around the mitigating effects of a 18 month and 14 year booster programme starting in 2014 or 2025 respectively are shown in Tables S3-5 by age group using the 66 parameter sets that gave the top 10% of overall cases in 2024. Compared with the existing vaccination programme with a single booster before school entry, the model predicts that the addition of the two additional boosters in 2014 would have reduced the number of overall cases in 2024 by 21% (Tables S3 and S4). Addition of these boosters in 2025 would only start to have a significant effect from 2028 onwards (Table S5). The annual averted cases under these two mitigating scenarios compared with the existing vaccination schedule are shown in Fig.\u0026nbsp;5 and in Tables S6 and S7 by age group.\u003c/p\u003e \u003cp\u003eFigure 5. Predicted annual averted notifications if 18 month and 14 year boosters added in A) 2014 or B) 2025\u003c/p\u003e \u003cp\u003eLegend. Boosters at 18 months and 14 years added to existing programme of primary aP doses at 2,3,4 months and an aP booster before school entry and with the the reductions in population mxing during the COVID-19 pandemic.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis modelling study implicates the social distancing measures imposed during the COVID-19 pandemic as the cause of the pertussis resurgence in England that started in 2024. Our model predicted increases in cases across the age range consistent with the observed disease trends (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The model predicted a resurgence without introducing in the model any drop in vaccine coverage associated with the pandemic. Implementing a 10% coverage drop for both the primary and pre-school booster doses for 3 years from January 2020 without reduced mixing did not produce a resurgence comparable to that seen in England in 2024. Our model predicted a resurgence following the reduced population mixing that occurred during the pandemic even under the counterfactual scenario in which 18 month and adolescent aP boosters were added to the national programme in 2014. This is consistent with the resurgences seen in settings where these additional boosters were already part of the schedule (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn constructing our model we based the vaccine parameters on the protection against disease and infection documented in epidemiological studies and the baboon pertussis challenge model (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These studies have shown more rapid waning of protection against clinical disease (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) and poorer protection against an infection (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) with acellular than whole cell vaccines. To generate robust protection against infection, acellular-vaccinated baboons need to experience one or more pertussis infections (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In our model we reflected these properties by allowing acellular vaccinees to be susceptible to infection while still protected against notifiable (ie clinically typical) pertussis, and to have a shorter duration of protection than whole cell vaccinees or those who have experienced natural infection. Infection in an acellular vaccinee therefore acts as a booster generating more durable protection against further infection without necessarily resulting in notifiable pertussis. The COVID-19 social distancing measures supressed this background rate of boosting allowing a pool of infection-susceptible acellular vaccinees to accumulate during the pandemic thereby disturbing the endemic equilibrium that existed before the COVID pandemic. On resumption of normal mixing patterns, transmission of pertussis increased and is predicted to exhibit cycles of increased oscillation before resuming the pre-COVID equilibrium with clinical cases apparent in unvaccinated individuals, including infants of unvaccinated mothers, and those whose protection against clinically typical disease has waned. Based on the timing of the resurgence in England our model implies that it was driven by short average duration of protection from aP vaccine (around 5 years) rather than poor protection against infection which was estimated to be around 90% prior to waning.\u003c/p\u003e \u003cp\u003eThe strength of our study is that we used an existing pertussis transmission model that reproduced the resurgence in England in 2012 which followed the change from wP to aP vaccines and correctly predicted the higher endemic level of transmission in England in all age groups since 2012 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The modifications to the mixing patterns that were incorporated to capture the restrictions imposed during the COVID-19 pandemic were based on empirical data and were used by modelling groups to refine model predictions on the impact of the evolving pandemic on cases and deaths (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). A limitation of our model is that the range of aP parameter values that fitted the observed notification patterns in England over the period 1956 to 2013 was wide (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) but by selecting the parameter sets that gave the top 10% of overall cases in 2024, further refinement of the most likely parameter values for the degree and duration of protection afforded by aP vaccines was possible. Despite this restriction, there was still a wide range of model outputs with the minimum value showing no post-pandemic resurgence (Fig.\u0026nbsp;3). Furthermore, our model parameters α\u003csub\u003e1\u003c/sub\u003e and α\u003csub\u003e2\u003c/sub\u003e (which reflect the proportion of infections notified respectively in unvaccinated and previously infected/vaccinated individuals) are assumed to be constant over time. However, since 2013 the proportion of cases that are notified has increased due to the routine availability of serum and oral fluid testing for raised IgG levels to pertussis toxin that are indicative of recent infection (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The improvements in notification efficiency resulting from IgG testing have been most marked in those aged 10 years and over in whom pertussis may be mild or clinically atypical (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). If our model had been fitted to NOIDs data since 2013 with age-dependent α\u003csub\u003e1\u003c/sub\u003e and α\u003csub\u003e2\u003c/sub\u003e parameters, it is probable that these estimated parameter values would have been higher for those aged 10 years and over. This may explain the underestimation of cases in those age 10 years and above predicted by the model when compared with NOIDs cases in 2024. Another limitation is that our model did not include a maternal immunisation component. While vaccination of pregnant women would have a negligible impact on overall transmission, it would mitigate the effect of a resurgence on infants either by passive protection through transfer of maternal antibodies (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) or by reduced exposure as mothers are a major source of infection for infants (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The overestimation by the model of NOIDs cases in infants is consistent with this.\u003c/p\u003e \u003cp\u003eOur model was originally developed to investigate the cause of the pertussis resurgence in England in 2012. At around the same time, a number of other groups developed pertussis transmission models to investigate the cause of the resurgences being reported in other aP-using countries. In common with our model, most of these models include waning immunity and allow for a substantial component of pertussis transmission to be driven by infections that are not reflected in notified disease (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Differences with our model include allowing for the proportion of infections that are notified to be age-dependent (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and for the use of sero-epidemiological data to estimate the incidence of boosting in the population by detection of high IgG levels to pertussis toxin (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), features that could potentially be incorporated in future iterations of our model. To date however we are unaware of any publications using such models to investigate the post-COVID-19 resurgences now being reported.\u003c/p\u003e \u003cp\u003eThe model predicts that peaks with an elevated incidence of pertussis are likely to occur over the next decade in England as the epidemiology of pertussis returns to the pre-pandemic equilibrium. It is therefore essential to achieve and sustain high levels of maternal immunisation to protect vulnerable infants too young to be vaccinated. Coverage of the maternal immunisation programme in England for 2023 to March 2024 was only 58.6%, a fall of 15.8% since 2017 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). With an effectiveness of around 92% (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), 90% coverage of the maternal programme could prevent 83% of the cases predicted by our model in infants under 3 months of age in the coming years. The effect of the resurgence would also have been mitigated somewhat had additional 18 month and adolescent boosters been in place; their addition now is predicted to have some mitigating effect from 2028 onwards. The short duration of protection of 5 years estimated by the model for aP vaccine supports the recommendation that those who have regular contact with pregnant women or vulnerable infants in the hospital or community setting should receive 5-yearly booster doses (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur modelling analysis, together with other assessments (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), indicate the extent of pertussis infection that occurs despite high vaccine coverage and the deficiencies of the current generation of aP vaccines in providing durable, sterilising immunity. Research efforts are underway to develop improved pertussis vaccines by incorporating novel adjuvants to elicit persisting immunological memory (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) or by using intranasal administration of a live attenuated \u003cem\u003eB. pertussis\u003c/em\u003e strain to generate mucosal responses that can protect against transmission (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Optimising the deployment of such new pertussis vaccines, for example as boosters or in place of the current generation of aP vaccines, will require further refinement of pertussis transmission models to better capture the impact of improved surveillance methods and to validate the model predictions against direct measures of pertussis incidence such as those available from seroepidemiology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e: The \u0026nbsp;data used for fitting the pertussis model is routinely available anonymised notification data. Ethics approval for the study was there not required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eAll authors declare no conflict of interest. EM receives support from the National Institute for Health Research (NIHR) Health Protection Research Unit in Immunisation at the London School of Hygiene and Tropical Medicine in partnership with Public Health England (Grant Reference NIHR200929). The funders had no input to the design of the study nor the interpretation of the results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: UKHSA and the\u0026nbsp;National Institute for Health Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u0026nbsp;\u003c/strong\u003eYHC and EM designed the study; YHC built the pertussis model and ran the simulations; \u0026nbsp;EvL constructed the scaling matrix for the reduced social mixing during the COVID-19 pandemic; EM wrote the first draft of the paper; \u0026nbsp;all authors critically reviewed the paper and approved the final version for submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eWe thank Sonia Robeiro for provision of the NOIDs data for 2024 and Prof Nick Andrews for helpful suggestions on parameter restriction and comments on the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKimberlin D, Barnett E, Lynfield R, Sawyer M, editors. Red Book: 2021 Report of the Committee on Infectious Diseases.: American Academy of Pediatrics (AAP). Pertussis (whooping cough). 2021.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Pertussis vaccine: WHO position paper - August 2015. WHO Weekly epidemiological record. 2015;90:433-60.\u003c/li\u003e\n\u003cli\u003eWarfel JM, Zimmerman LI, Merkel TJ. Acellular pertussis vaccines protect against disease but fail to prevent infection and transmission in a nonhuman primate model. Proc Natl Acad Sci U S A. 2014;111(2):787-92.\u003c/li\u003e\n\u003cli\u003eDabrera G, Amirthalingam G, Andrews N, Campbell H, Ribeiro S, Kara E, et al. A case-control study to estimate the effectiveness of maternal pertussis vaccination in protecting newborn infants in England and Wales, 2012-2013. Clin Infect Dis. 2015;60(3):333-7.\u003c/li\u003e\n\u003cli\u003eAmirthalingam G, Andrews N, Campbell H, Ribeiro S, Kara E, Donegan K, et al. Effectiveness of maternal pertussis vaccination in England: an observational study. The Lancet. 2014;384(9953):1521-8.\u003c/li\u003e\n\u003cli\u003eUK Health Security Agency. Whooping cough cases continue to rise 2024 [Available from: Whooping cough cases continue to rise.\u003c/li\u003e\n\u003cli\u003eEuropean Centre for Disease Prevention and Control. Increase of pertussis cases in the EU/EEA 2024 [Available from: https://www.ecdc.europa.eu/en/publications-data/increase-pertussis-cases-eueea.\u003c/li\u003e\n\u003cli\u003eStein-Zamir C, Shoob H, Abramson N, Brown EH, Zimmermann Y. Pertussis outbreak mainly in unvaccinated young children in ultra-orthodox Jewish groups, Jerusalem, Israel 2023. Epidemiol Infect. 2023;151:e166.\u003c/li\u003e\n\u003cli\u003eWise J. Whooping cough: What\u0026rsquo;s behind the rise in cases and deaths in England? BMJ. 2024;385:q1118.\u003c/li\u003e\n\u003cli\u003eLiu Y, Ye Q. Resurgence and the shift in the age of peak onset of pertussis in southern China. J Infect. 2024;89(2):106194.\u003c/li\u003e\n\u003cli\u003eChoi YH, Campbell H, Amirthalingam G, van Hoek AJ, Miller E. Investigating the pertussis resurgence in England and Wales, and options for future control. BMC Medicine. 2016;14(1):121.\u003c/li\u003e\n\u003cli\u003eBirrell P, Blake J, van Leeuwen E, Gent N, De Angelis D. Real-time nowcasting and forecasting of COVID-19 dynamics in England: the first wave. Philosophical Transactions of the Royal Society B: Biological Sciences. 2021;376(1829):20200279.\u003c/li\u003e\n\u003cli\u003eMossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, et al. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLOS Med. 2008;5(3):e74.\u003c/li\u003e\n\u003cli\u003evan Hoek AJ, Andrews N, Campbell H, Amirthalingam G, Edmunds WJ, Miller E. The social life of infants in the context of infectious disease transmission; social contacts and mixing patterns of the very young. PLoS One. 2013;8(10):e76180.\u003c/li\u003e\n\u003cli\u003evan Leeuwen E, Sandmann F. Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics: A modelling analysis. Stat Methods Med Res. 2022;31(9):1704-15.\u003c/li\u003e\n\u003cli\u003eGershuny J, Sullivan O. United Kingdom Time Use Survey, 2014-2015. Centre for Time Use Research, IOE, University College London. [data collection]. UK Data Service 2017 [Available from: https://www.timeuse.org/uk-time-use-survey-2014-2015.\u003c/li\u003e\n\u003cli\u003eOffice for National Statistics. 2020-based Interim National Population Projections 2024 [07/02/2024]. Available from: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationprojections\u003cbr\u003e/bulletins/nationalpopulationprojections/2020basedinterim.\u003c/li\u003e\n\u003cli\u003eUK Health Security Agency. Confirmed cases of pertussis in England by month, to end May 2024 2024 [Available from: https://www.gov.uk/government/publications/pertussis-epidemiology-in-england-2024/confirmed-cases-of-pertussis-in-england-by-month.\u003c/li\u003e\n\u003cli\u003eKapil P, Wang Y, Zimmerman L, Gaykema M, Merkel TJ. Repeated Bordetella pertussis Infections Are Required to Reprogram Acellular Pertussis Vaccine-Primed Host Responses in the Baboon Model. J Infect Dis. 2024;229(2):376-83.\u003c/li\u003e\n\u003cli\u003eAmirthalingam G, Campbell H, Ribeiro S, Stowe J, Tessier E, Litt D, et al. Optimization of Timing of Maternal Pertussis Immunization From 6 Years of Postimplementation Surveillance Data in England. Clin Infect Dis. 2023;76(3):e1129-e39.\u003c/li\u003e\n\u003cli\u003eUK Health Security Agency. Guidance on the management of cases of pertussis in England during the re-emergence of pertussis in 2024 2024 [updated August 2024. Available from: https://assets.publishing.service.gov.uk/media/66c4a642808b8c0aa08fa7e7/UKHSA-guidance-on-the-management-of-cases-of-pertussis-during-high-activity-august-2024.pdf.\u003c/li\u003e\n\u003cli\u003eCampbell H, Amirthalingam G, Fry NK, Litt D, Harrison TG, Wagner K, et al. Oral fluid testing for pertussis, England and wales, june 2007-august 2009. Emerg Infect Dis. 2014;20(6):968-75.\u003c/li\u003e\n\u003cli\u003eCampbell PT, McCaw JM, McIntyre P, McVernon J. Defining long-term drivers of pertussis resurgence, and optimal vaccine control strategies. Vaccine. 2015;33(43):5794-800.\u003c/li\u003e\n\u003cli\u003eGambhir M, Clark TA, Cauchemez S, Tartof SY, Swerdlow DL, Ferguson NM. A change in vaccine efficacy and duration of protection explains recent rises in pertussis incidence in the United States. PLoS Comput Biol. 2015;11(4):e1004138.\u003c/li\u003e\n\u003cli\u003eAlthouse BM, Scarpino SV. Asymptomatic transmission and the resurgence of Bordetella pertussis. BMC Med. 2015;13:146.\u003c/li\u003e\n\u003cli\u003eDomenech de Cell\u0026egrave;s M, Magpantay FMG, King AA, Rohani P. The impact of past vaccination coverage and immunity on pertussis resurgence. Sci Transl Med. 2018;10(434).\u003c/li\u003e\n\u003cli\u003eDamron FH, Barbier M, Dubey P, Edwards KM, Gu XX, Klein NP, et al. Overcoming Waning Immunity in Pertussis Vaccines: Workshop of the National Institute of Allergy and Infectious Diseases. J Immunol. 2020;205(4):877-82.\u003c/li\u003e\n\u003cli\u003eKeech C, Miller VE, Rizzardi B, Hoyle C, Pryor MJ, Ferrand J, et al. Immunogenicity and safety of BPZE1, an intranasal live attenuated pertussis vaccine, versus tetanus-diphtheria-acellular pertussis vaccine: a randomised, double-blind, phase 2b trial. Lancet. 2023;401(10379):843-55.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"pertussis resurgence, dynamic transmission model, social mixing, COVID-19 pandemic","lastPublishedDoi":"10.21203/rs.3.rs-5459094/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5459094/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eIn 2024 England, in common with many other countries, experienced a pertussis resurgence the cause of which is unclear. We used a pertussis transmission model, previously developed to investigate the cause of the pertussis resurgence in England in 2012, to explore potential factors contributing to the increase in pertussis cases observed in England in 2024.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn age-stratified dynamic transmission model fitted to pertussis notification data from England between 1953 and 2013 was run until 2054 with and without changes in social mixing as estimated from Google mobility and school attendance data during the COVID-19 pandemic. The model assumes vaccination protects better against disease than pertussis infection, and that an infection results in more durable immunity than vaccination. Counterfactual scenarios were also run to explore the effect of reductions in vaccine coverage during the pandemic and of the addition in 2014 of boosters in the 2nd year of life and in adolescence.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA resurgence was only generated with reduced social mixing and could not be explained by short-term reductions in vaccine coverage. Additional boosters at 18 months and 14 years from 2014 would not have prevented a resurgence though would have reduced its magnitude. Peaks of increased pertussis incidence are predicted over the next decade. The parameter sets that generated a resurgence in 2024 had the shortest duration of acellular vaccine protection, median 5 years with 90% protection against infection.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis modelling study implicates reduced mixing in England during the COVID-19 pandemic as the cause of the pertussis resurgence in 2024 together with the short duration of protection from acellular vaccine. Interruption of the background rate of natural boosting during the pandemic increased the pool of susceptible individuals resulting in increased transmission post-pandemic with clinical cases in those with waned vaccine\u0026ndash;induced protection and the unvaccinated, including infants of unvaccinated mothers. In countries using acellular pertussis vaccines, infection continues to play an important role in maintaining population immunity around an endemic equilibrium. Improved pertussis vaccines that provide more complete and more durable protection against infection are needed to improve pertussis control.\u003c/p\u003e","manuscriptTitle":"Long-term effect of COVID-19 social distancing measures on pertussis transmission in England; a mathematical modelling study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-19 10:57:56","doi":"10.21203/rs.3.rs-5459094/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-31T09:18:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-30T00:37:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-19T16:43:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82913383607278696845004471466980414084","date":"2025-02-13T15:05:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332692978268528403825044832874611397158","date":"2025-02-12T01:21:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250829400130766954356515644384320905972","date":"2024-12-12T20:10:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60525715450354734359252669077150722977","date":"2024-11-25T22:24:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-23T04:22:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-18T08:50:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-18T03:22:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-18T03:22:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-11-15T08:48:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d67291d8-05d4-45cf-8726-bc2d1738205d","owner":[],"postedDate":"December 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:00:12+00:00","versionOfRecord":{"articleIdentity":"rs-5459094","link":"https://doi.org/10.1186/s12879-026-12521-5","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2026-01-23 15:57:10","publishedOnDateReadable":"January 23rd, 2026"},"versionCreatedAt":"2024-12-19 10:57:56","video":"","vorDoi":"10.1186/s12879-026-12521-5","vorDoiUrl":"https://doi.org/10.1186/s12879-026-12521-5","workflowStages":[]},"version":"v1","identity":"rs-5459094","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5459094","identity":"rs-5459094","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}