The itchy-scratchy misery of a chickenpox was until recently a rite of passage for children around the world. The varicella-zoster virus causes chickenpox infections. This virus persists in small numbers in nerve cells for many years after infection, and can reactivate from these cells. Often this reactivation causes no symptoms, but sometimes it results in a painful skin condition called shingles (or herpes zoster), especially in older adults.

Some countries—including the United States, Australia, Taiwan and Greece—have virtually wiped out childhood cases of chickenpox by requiring that children be vaccinated against the varicella-zoster virus. But some countries have hesitated. One reason for this hesitation is that exposure to individuals with a chickenpox infection helps boost the immunity of individuals who have previously been infected. This may help reduce the likelihood of these people developing shingles later in life. So, some countries have worried that chickenpox vaccinations might inadvertently increase the number of shingles cases. To assess this risk, many scientists have created computer models, but the models have some limitations.

Now, Ogunjimi et al. report a new individual-based model to assess the effect of childhood varicella vaccination on shingles cases that factors in the immune responses to varicella infection. The model suggests that re-exposure to the varicella virus through contact with infected people would only provide extra protection for about two years; this is much shorter than previous predictions that suggested it might last 20 years. The model also predicts that implementing a varicella vaccination program for children would almost double the number of shingles cases 31 years later. But this increase would be temporary.

The predicted increase in shingles cases is likely to disproportionately occur among 31- to 40-year-olds. This is unexpected because most previous models predict that older age groups would bear the brunt of a rise in shingles, but this younger population would be less likely to develop lasting complications of shingles. Together, these findings may allay some fears about implementing childhood varicella vaccination programs by showing that the benefits of re-exposure are limited.

Varicella-zoster virus (VZV) causes the itching, erythematous vesicular disease called varicella or chickenpox, mainly during childhood, and remains latent in neural ganglia afterwards. Latency can then be interrupted by episodes of (primarily subclinical) reactivation of VZV as shown by the detection of VZV in saliva or blood from otherwise healthy individuals (Schünemann et al., 1998; Nagel et al., 2011). VZV reactivations may also cause herpes zoster (HZ), which presents clinically as a painful dermatomal rash. HZ occurs most frequently in individuals with a drastically declined cellular immune status (Dolin et al., 1978), but ageing itself is often assumed to substantially reduce resilience of the VZV-specific immune response (Miller, 1980; Berger et al., 1981; Levin et al., 2003, 2008). Recent research supports the hypothesis that waning of VZV cellular mediated immunity (CMI) by age is also influenced by cytomegalovirus (CMV) infection (Ogunjimi et al., 2014).

Effective and safe pediatric vaccines against varicella exist and have been universally implemented in some countries including the US, Australia, Greece, Germany, Japan and Taiwan (Marin et al., 2008). However, in many other countries policy makers have been hesitant to introduce childhood VZV vaccination due to the general population's perception of varicella as a relatively mild disease, as well as the so-called exogenous boosting hypothesis (Hope-Simpson, 1965; Ogunjimi et al., 2013). This hypothesis is based on the concept of the secondary immune response and assumes that boosting of VZV-CMI occurs upon re-exposure to varicella. Boosted VZV-specific cellular immunity would subsequently reduce the risk of VZV reactivation and thence HZ. The introduction of widespread childhood VZV vaccination would reduce opportunities for varicella re-exposure and could therefore increase HZ incidence, as shown in model-based projections (Schuette and Hethcote, 1999; Brisson et al., 2000; Bilcke et al., 2013). Although current data on HZ-incidence post introduction of childhood VZV vaccination has caused controversy, a systematic review rating the quality of the evidence, concluded that exogenous boosting exists, but that its population-wide effect after widespread childhood VZV vaccination remains highly uncertain (Ogunjimi et al., 2013). One of the main points of criticism on current predictions by deterministic VZV simulation models is the entanglement of exogenous boosting, waning of immunity, immunosenescence and reactivation undermining the possibility of estimating the magnitude and duration of exogenous boosting accurately. A concern is that these entangled parameters can be chosen to fit these models well to observed HZ incidence data, but that they are too artificial to allow real and verifiable biological interpretations. This leads to currently unverifiable and potentially poor predictions of VZV dynamics beyond the fitted equilibrium states. An additional, hitherto ignored uncertainty, is the potential occurrence of endogenous boosting by subclinical VZV reactivation. Indeed, some studies have shown VZV to reactivate subclinically both in healthy individuals, immunocompromised and stressed individuals (Schünemann et al., 1997; Mehta et al., 2003; Nagel et al., 2011). However, the effect on VZV-CMI has not yet been quantified.

In the current paper, we describe the first individual-based VZV model, explicitly combining within and between-host dynamics. This model is based on experimental viro-immunological data and allows an accurate estimation of the exogenous boosting characteristics and explicit insertion or validation of experimental data.

It is hypothesized that exogenous re-exposure to varicella increases VZV-specific cellular immunity (VZV-CMI). This natural consequence of the secondary immune response will reduce an individual's risk for HZ. When the probability of contact per unit-time between currently infectious and previously recovered varicella patients reduces, through a reduction in varicella incidence, it can be expected that HZ incidence temporarily increases due to a lack of exogenous boosting (Hope-Simpson, 1965; Ogunjimi et al., 2013).

To our knowledge, this study is the first to use an individual-based dynamic transmission model for VZV. This model allowed us to combine immunological and virological data to estimate key parameters in VZV population dynamics, such as the peak CMI response following re-exposure, duration of boosting and VZV subclinical reactivation characteristics. This means that in contrast to the deterministic models where abstract and ad hoc compartments are created to define the transition between different epidemiological states (for example the transition of a varicella recovered state to a zoster susceptible state), we can actually work with true biological, and verifiable, concepts such as VZV-CMI. Our best fitting parameter sets for Belgium suggest the effective duration of exogenous boosting to last only between 1 and 2 years. These predictions are significantly lower than those from the highly cited deterministic VZV model by Brisson et al., in which the average duration was predicted to be 20 years ([7–41 years] 95%CI) (Brisson et al., 2002). Our peak immunological response was estimated to be 2.8–4.0 times larger than the pre-re-exposure value. These predictions are consistent with those experimentally found in adults re-exposed to VZV by varicella contacts in the household (Arvin et al., 1983; Vossen et al., 2004) or by vaccination (Levin et al., 2008). A possible limitation of our study was that all close contacts with varicella patients were assumed to exert an equal ‘average’ boosting effect on the exposed individual. Future studies could assess how important it would be to incorporate variability in the impact of an exposure through direct contact with a varicella case, based on characteristics of both the exposed and the infectious person (e.g., age, comorbidity).

The VZV reactivation probability was estimated to be 5% per week. Observed data on VZV reactivation probability are rather sparse and highly divergent regarding study design, sampling site (saliva, blood, cerebrospinal fluid) and results (Schünemann et al., 1997; Mehta et al., 2003; Engelmann et al., 2008; Birlea et al., 2011; Nagel et al., 2011; van Velzen et al., 2013). The observed weekly VZV reactivation probability is a topic of discussion in the literature and varies between 0 and 71%. As such it is difficult to compare our estimates with the range of observed values. We also note that VZV reactivation in our model might only be detectable at the neural ganglia at not (always) necessarily in peripheral tissues. In order for HZ to occur, we assumed that VZV-CMI should be below a relative threshold (following a specific distribution) during VZV reactivation. None of our best fitting parameter sets predicted a significant effect of VZV reactivation on VZV-CMI, implying that endogenous boosting most likely does not have an impact on the occurrence of HZ. This finding is important since the existence of endogenous boosting has been proposed to have an effect in reducing the negative effects of varicella vaccination on HZ incidence. Future experimental studies should focus on confirming our predicted VZV reactivation probability and the lack of endogenous boosting.

The annual decline of VZV-CMI was predicted to be between 1 and 1.5%. This result is lower than the 2.7–3.9% experimentally observed by Levin et al. for individuals older than 60 years (Levin et al., 2008). The twofold difference in waning rate can be explained by the explicit disentanglement of waning and boosting (with younger age groups having—on average—higher probabilities of being boosted recently thereby actually increasing the observed waning rate). A limitation in our study is the use of a fixed waning rate for all ages. Our results might be interpreted as an averaged result of lower waning rates for younger age groups and higher waning rates for older age groups (as documented by Levin et al. (2008)). A higher waning rate in older age groups could for example be caused by chronic CMV infection (Ogunjimi et al., 2014). Although different types of waning (both in model specification and age dependency) can be used in this kind of simulation models, we believe that further experimental data documenting VZV specific cellular memory as a function of age is needed so that new waning models can be appropriately formulated. One future avenue of research could be the fitting of our predicted VZV-CMI to observed VZV-CMI data, as this will be a mixture of waning, immunosenescence and boosting. Better VZV-CMI datasets than those currently available are needed to be able to do this. These datasets could and should contain immune responses against different VZV peptides and could differentiate between the different cellular immunity compartments (CD4 vs CD8 and central vs effector memory cells). The use of our VZV IBM could help us identify which VZV-CMI compartment is of importance in controlling HZ.

We used our best fitting models to analyze the effects of a 100% effective varicella vaccine implemented for all 1-year-old children. We predicted a net increase in HZ incidence during 50 years and a 1.75 peak fold increase 31 years after introduction of the vaccination program. This delay in the HZ peak incidence is caused by cohorts born close to the time varicella vaccination was introduced experiencing less repeated boosting instances during their childhood than previously born cohorts (a mechanism that is similar to the progressive immunity model proposed in the deterministic VZV model by Guzzetta et al. (2013)). Although increases in HZ incidences following universal childhood varicella vaccination have been noticed, some authors have attributed these increases to a background evolution that was already present prior to CP vaccination (see discussion in Ogunjimi et al. (2013)). However, our analysis shows that proper documentation of significant increases in HZ incidence might not be possible during the first 10 years, even if a 100% effective vaccine would be used. For instance, during the first 10 years of the US program, both uptake and efficacy with the initial single dose vaccination programme were far below 100%. Although our model predicts a much lower duration of boosting than used hitherto in compartmental models (Ogunjimi et al., 2013), some of our overall HZ projections are qualitatively similar. However, in contrast to earlier model estimates our VZV IBM predicts that 31–40 year olds contribute the most to the peak in HZ incidence following varicella vaccination. Some observational studies found no effect of varicella vaccination on HZ incidence for those aged 60 years and older (Hales et al., 2013). This could be compatible with an overall HZ incidence increase due to rising HZ incidence among 31–40 year olds. Importantly, younger adults have been shown to be less likely to develop post-herpetic neuralgia (Opstelten et al., 2002; Drolet et al., 2010). Thus although our aggregated predictions regarding the increase in HZ incidence following varicella vaccination may appear similar to those published using deterministic models, cost-effectiveness analyses using our VZV IBM would be more in favor of universal childhood varicella vaccination.

A major benefit of our modeling approach is the possibility to verify our best–fit parameter values via experimental studies, or vice versa, to adapt parameter values when empiricism delivers new insights. For example, if a new experimental study would prove the existence of endogenous boosting, this could be readily implemented in our VZV IBM so that parameter sets could be estimated, conditional on the existence of endogenous boosting. Although our IBM has given us valuable insights into between-host and within-host VZV dynamics, other influential factors could be introduced in future versions. These factors could relate to CMV-immunosenescence (Ogunjimi et al., 2014), maternal antibodies, reduced VZV-CMI induction if infected during the first year of life as this might improve the prediction of the teenage group (Terada et al., 1994), co-infection with other viruses (Ogunjimi et al., 2015), risk groups (for e.g., immunosuppressed individuals) and HLA types (Meysman et al., 2015). Although current deterministic compartmental models should be able to partially account for these effects, a VZV IBM seems better suited to directly model the influence of these immunity-perturbing factors. Future VZV IBM should also explore more realistic vaccination scenarios as well as inter-country variability (Poletti et al., 2013).

We conclude that our VZV individual-based model has explicitly estimated the duration of exogenous boosting to be limited to only 1 or 2 years and that there was no significant effect from endogenous boosting.

1. 1. Arvin AM
   2. Koropchak CM
   3. Wittek AE

   (1983) Immunological evidence of reinfection with varicella-zoster virus

   The Journal of Infectious Diseases 148:200–205.

   https://doi.org/10.1093/infdis/148.2.200

   • Google Scholar
2. 1. Berger R
   2. Florent G
   3. Just M

   (1981)

   Decrease of the lymphoproliferative response to varicella-zoster virus antigen in the aged

   Infection and Immunity 32:24–27.

   • Google Scholar
3. 1. Bernstein HH
   2. Rothstein EP
   3. Watson BM
   4. Reisinger KS
   5. Blatter MM
   6. Wellman CO
   7. Chartrand SA
   8. Cho I
   9. Ngai A
   10. White CJ

   (1993)

   Clinical survey of natural varicella compared with breakthrough varicella after immunization with live attenuated Oka/Merck varicella vaccine

   Pediatrics 92:833–837.

   • Google Scholar
4. 1. Bilcke J
   2. Jan van Hoek A
   3. Beutels P

   (2013) Childhood varicella-zoster virus vaccination in Belgium: cost-effective only in the long run or without exogenous boosting?

   Human Vaccines & Immunotherapeutics 9:812–822.

   https://doi.org/10.4161/hv.23334

   • Google Scholar
5. 1. Bilcke J
   2. Ogunjimi B
   3. Marais C
   4. de Smet F
   5. Callens M
   6. Callaert K
   7. van Kerschaver E
   8. Ramet J
   9. van Damme P
   10. Beutels P

   (2012) The health and economic burden of chickenpox and herpes zoster in Belgium

   Epidemiology and Infection 140:2096–2109.

   https://doi.org/10.1017/S0950268811002640

   • Google Scholar
6. 1. Birlea M
   2. Arendt G
   3. Orhan E
   4. Schmid DS
   5. Bellini WJ
   6. Schmidt C
   7. Gilden D
   8. Cohrs RJ

   (2011) Subclinical reactivation of varicella zoster virus in all stages of HIV infection

   Journal of the Neurological Sciences 304:22–24.

   https://doi.org/10.1016/j.jns.2011.02.030

   • Google Scholar
7. 1. Brisson M
   2. Edmunds WJ
   3. Gay NJ
   4. Law B
   5. De Serres G

   (2000) Modelling the impact of immunization on the epidemiology of varicella zoster virus

   Epidemiology and Infection 125:651–669.

   https://doi.org/10.1017/S0950268800004714

   • Google Scholar
8. 1. Brisson M
   2. Gay NJ
   3. Edmunds WJ
   4. Andrews NJ

   (2002) Exposure to varicella boosts immunity to herpes-zoster: implications for mass vaccination against chickenpox

   Vaccine 20:2500–2507.

   https://doi.org/10.1016/S0264-410X(02)00180-9

   • Google Scholar
9. 1. Castro Sanchez AY
   2. Aerts M
   3. Shkedy Z
   4. Vickerman P
   5. Faggiano F
   6. Salamina G
   7. Hens N

   (2013) A mathematical model for HIV and hepatitis C co-infection and its assessment from a statistical perspective

   Epidemics 5:56–66.

   https://doi.org/10.1016/j.epidem.2013.01.002

   • Google Scholar
10. 1. Dolin R
    2. Reichman RC
    3. Mazur MH
    4. Whitley RJ

    (1978) NIH conference. Herpes zoster-varicella infections in immunosuppressed patients

    Annals of Internal Medicine 89:375–388.

    https://doi.org/10.7326/0003-4819-89-3-375

    • Google Scholar
11. 1. Drolet M
    2. Brisson M
    3. Levin MJ
    4. Schmader KE
    5. Oxman MN
    6. Johnson RW
    7. Camden S
    8. Mansi JA

    (2010) A prospective study of the herpes zoster severity of illness

    The Clinical Journal of Pain 26:656–666.

    https://doi.org/10.1097/AJP.0b013e3181eef686

    • Google Scholar
12. 1. Engelmann I
    2. Petzold DR
    3. Kosinska A
    4. Hepkema BG
    5. Schulz TF
    6. Heim A

    (2008) Rapid quantitative PCR assays for the simultaneous detection of herpes simplex virus, varicella zoster virus, cytomegalovirus, Epstein-Barr virus, and human herpesvirus 6 DNA in blood and other clinical specimens

    Journal of Medical Virology 80:467–477.

    https://doi.org/10.1002/jmv.21095

    • Google Scholar
13. 1. Guzzetta G
    2. Poletti P
    3. Del Fava E
    4. Ajelli M
    5. Scalia Tomba GP
    6. Merler S
    7. Manfredi P

    (2013) Hope-Simpson's progressive immunity hypothesis as a possible explanation for herpes zoster incidence data

    American Journal of Epidemiology 177:1134–1142.

    https://doi.org/10.1093/aje/kws370

    • Google Scholar
14. 1. Hales CM
    2. Harpaz R
    3. Joesoef MR
    4. Bialek SR

    (2013) Examination of links between herpes zoster incidence and childhood varicella vaccination

    Annals of Internal Medicine 159:739–745.

    https://doi.org/10.7326/0003-4819-159-11-201312030-00006

    • Google Scholar
15. Book

    1. Hens N

    (2012)

    Modeling infectious disease parameters based on serological and social contact data : a modern statistical perspective

    New York: Springer.

    • Google Scholar
16. 1. Hope-Simpson RE

    (1965)

    The nature of herpes zoster: a long-term study and a new hypothesis

    Proceedings of the Royal Society of Medicine 58:9–20.

    • Google Scholar
17. 1. Levin MJ
    2. Oxman MN
    3. Zhang JH
    4. Johnson GR
    5. Stanley H
    6. Hayward AR
    7. Caulfield MJ
    8. Irwin MR
    9. Smith JG
    10. Clair J
    11. Chan IS
    12. Williams H
    13. Harbecke R
    14. Marchese R
    15. Straus SE
    16. Gershon A
    17. Weinberg A

    (2008) Varicella-zoster virus-specific immune responses in elderly recipients of a herpes zoster vaccine

    Journal of Infectious Diseases 197:825–835.

    https://doi.org/10.1086/528696

    • Google Scholar
18. 1. Levin MJ
    2. Smith JG
    3. Kaufhold RM
    4. Barber D
    5. Hayward AR
    6. Chan CY
    7. Chan IS
    8. Li DJ
    9. Wang W
    10. Keller PM
    11. Shaw A
    12. Silber JL
    13. Schlienger K
    14. Chalikonda I
    15. Vessey SJ
    16. Caulfield MJ

    (2003) Decline in varicella-zoster virus (VZV)-specific cell-mediated immunity with increasing age and boosting with a high-dose VZV vaccine

    The Journal of Infectious Diseases 188:1336–1344.

    https://doi.org/10.1086/379048

    • Google Scholar
19. 1. Malavige GN
    2. Rohanachandra LT
    3. Jones L
    4. Crack L
    5. Perera M
    6. Fernando N
    7. Guruge D
    8. Ogg GS

    (2010) IE63-specific T-cell responses associate with control of subclinical varicella zoster virus reactivation in individuals with malignancies

    British Journal of Cancer 102:727–730.

    https://doi.org/10.1038/sj.bjc.6605542

    • Google Scholar
20. 1. Marin M
    2. Meissner HC
    3. Seward JF

    (2008) Varicella prevention in the United States: a review of successes and challenges

    Pediatrics 122:e744–e751.

    https://doi.org/10.1542/peds.2008-0567

    • Google Scholar
21. 1. Mehta SK
    2. Cohrs RJ
    3. Forghani B
    4. Zerbe G
    5. Gilden DH
    6. Pierson DL

    (2003) Stress-induced subclinical reactivation of varicella zoster virus in astronauts

    Journal of Medical Virology 72:174–179.

    https://doi.org/10.1002/jmv.10555

    • Google Scholar
22. 1. Meysman P
    2. Ogunjimi B
    3. Naulaerts S
    4. Beutels P
    5. Van Tendeloo V
    6. Laukens K

    (2015) Varicella-zoster virus-derived major histocompatibility complex class i-restricted peptide affinity is a determining factor in the HLA risk profile for the development of postherpetic neuralgia

    Journal of Virology 89:962–969.

    https://doi.org/10.1128/JVI.02500-14

    • Google Scholar
23. 1. Miller AE

    (1980) Selective decline in cellular immune response to varicella-zoster in the elderly

    Neurology 30:582–587.

    https://doi.org/10.1212/WNL.30.6.582

    • Google Scholar
24. 1. Nagel MA
    2. Choe A
    3. Cohrs RJ
    4. Traktinskiy I
    5. Sorensen K
    6. Mehta SK
    7. Pierson DL
    8. Tyring SK
    9. Haitz K
    10. DiGiorgio C
    11. LaPolla W
    12. Gilden D

    (2011) Persistence of varicella zoster virus DNA in saliva after herpes zoster

    The Journal of Infectious Diseases 204:820–824.

    https://doi.org/10.1093/infdis/jir425

    • Google Scholar
25. 1. Ogunjimi B
    2. Buntinx F
    3. Bartholomeeusen S
    4. Terpstra I
    5. De Haes I
    6. Willem L
    7. Elli S
    8. Bilcke J
    9. Van Damme P
    10. Coenen S
    11. Beutels P

    (2015) Herpes zoster is associated with herpes simplex and other infections in under 60 year-olds

    The Journal of Infection 70:171–177.

    https://doi.org/10.1016/j.jinf.2014.08.016

    • Google Scholar
26. 1. Ogunjimi B
    2. Hens N
    3. Goeyvaerts N
    4. Aerts M
    5. Van Damme P
    6. Beutels P

    (2009) Using empirical social contact data to model person to person infectious disease transmission: an illustration for varicella

    Mathematical Biosciences 218:80–87.

    https://doi.org/10.1016/j.mbs.2008.12.009

    • Google Scholar
27. 1. Ogunjimi B
    2. Smits E
    3. Heynderickx S
    4. Van den Bergh J
    5. Bilcke J
    6. Jansens H
    7. Malfait R
    8. Ramet J
    9. Maecker HT
    10. Cools N
    11. Beutels P
    12. Van Damme P

    (2014) Influence of frequent infectious exposures on general and varicella-zoster virus-specific immune responses in pediatricians

    Clinical and Vaccine Immunology 21:417–426.

    https://doi.org/10.1128/CVI.00818-13

    • Google Scholar
28. 1. Ogunjimi B
    2. Theeten H
    3. Hens N
    4. Beutels P

    (2014) Serology indicates cytomegalovirus infection is associated with varicella-zoster virus reactivation

    Jornal of Medical Virology 86:812–819.

    https://doi.org/10.1002/jmv.23749

    • Google Scholar
29. 1. Ogunjimi B
    2. Van Damme P
    3. Beutels P

    (2013) Herpes zoster risk reduction through exposure to chickenpox patients: a systematic multidisciplinary review

    PLOS ONE 8:e66485.

    https://doi.org/10.1371/journal.pone.0066485

    • Google Scholar
30. 1. Opstelten W
    2. Mauritz JW
    3. de Wit NJ
    4. van Wijck AJ
    5. Stalman WA
    6. van Essen GA

    (2002) Herpes zoster and postherpetic neuralgia: incidence and risk indicators using a general practice research database

    Family Practice 19:471–475.

    https://doi.org/10.1093/fampra/19.5.471

    • Google Scholar
31. 1. Poletti P
    2. Melegaro A
    3. Ajelli M
    4. Del Fava E
    5. Guzzetta G
    6. Faustini L
    7. Scalia Tomba G
    8. Lopalco P
    9. Rizzo C
    10. Merler S
    11. Manfredi P

    (2013) Perspectives on the impact of varicella immunization on herpes zoster. A model-based evaluation from three European countries

    PLOS ONE 8:e60732.

    https://doi.org/10.1371/journal.pone.0060732

    • Google Scholar
32. 1. Schuette MC
    2. Hethcote HW

    (1999) Modeling the effects of varicella vaccination programs on the incidence of chickenpox and shingles

    Bulletin of Mathematical Biology 61:1031–1064.

    https://doi.org/10.1006/bulm.1999.0126

    • Google Scholar
33. 1. Schünemann S
    2. Mainka C
    3. Wolff MH

    (1998) Subclinical reactivation of varicella-zoster virus in immunocompromised and immunocompetent individuals

    Intervirology 41:98–102.

    https://doi.org/10.1159/000024920

    • Google Scholar
34. 1. Terada K
    2. Kawano S
    3. Yoshihiro K
    4. Morita T

    (1994) Varicella-zoster virus (VZV) reactivation is related to the low response of VZV-specific immunity after chickenpox in infancy

    The Journal of Infectious Diseases 169:650–652.

    https://doi.org/10.1093/infdis/169.3.650

    • Google Scholar
35. 1. van Velzen M
    2. Ouwendijk WJ
    3. Selke S
    4. Pas SD
    5. van Loenen FB
    6. Osterhaus AD
    7. Wald A
    8. Verjans GM

    (2013) Longitudinal study on oral shedding of herpes simplex virus 1 and varicella-zoster virus in individuals infected with HIV

    Journal of Medical Virology 85:1669–1677.

    https://doi.org/10.1002/jmv.23634

    • Google Scholar
36. 1. Vossen MT
    2. Gent MR
    3. Weel JF
    4. de Jong MD
    5. van Lier RA
    6. Kuijpers TW

    (2004) Development of virus-specific CD4+ T cells on reexposure to Varicella-Zoster virus

    The Journal of Infectious Diseases 190:72–82.

    https://doi.org/10.1086/421277

    • Google Scholar
37. 1. Weinberg A
    2. Zhang JH
    3. Oxman MN
    4. Johnson GR
    5. Hayward AR
    6. Caulfield MJ
    7. Irwin MR
    8. Clair J
    9. Smith JG
    10. Stanley H
    11. Marchese RD
    12. Harbecke R
    13. Williams HM
    14. Chan IS
    15. Arbeit RD
    16. Gershon AA
    17. Schodel F
    18. Morrison VA
    19. Kauffman CA
    20. Straus SE
    21. Schmader KE
    22. Davis LE
    23. Levin MJ

    (2009) Varicella-zoster virus-specific immune responses to herpes zoster in elderly participants in a trial of a clinically effective zoster vaccine

    The Journal of Infectious Diseases 200:1068–1077.

    https://doi.org/10.1086/605611

    • Google Scholar
38. 1. Westera L
    2. Drylewicz J
    3. den Braber I
    4. Mugwagwa T
    5. van der Maas I
    6. Kwast L
    7. Volman T
    8. van de Weg-Schrijver EH
    9. Bartha I
    10. Spierenburg G
    11. Gaiser K
    12. Ackermans MT
    13. Asquith B
    14. de Boer RJ
    15. Tesselaar K
    16. Borghans JAM

    (2013) Closing the gap between T-cell life span estimates from stable isotope-labeling studies in mice and humans

    Blood 122:2205–2212.

    https://doi.org/10.1182/blood-2013-03-488411

    • Google Scholar
39. 1. Wood SN

    (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models

    Journal of the Royal Statistical Society Series B-Statistical Methodology 73:3–36.

    https://doi.org/10.1111/j.1467-9868.2010.00749.x

    • Google Scholar
40. 1. Yawn BP
    2. Wollan PC
    3. Kurland MJ
    4. St Sauver JL
    5. Saddier P

    (2011) Herpes zoster recurrences more frequent than previously reported

    Mayo Clinic Proceedings 86:88–93.

    https://doi.org/10.4065/mcp.2010.0618

    • Google Scholar

Author details

1. Benson Ogunjimi

   1. Centre for Health Economics Research and Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
   2. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium

   Contribution

   BO, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   benson.ogunjimi@uantwerp.be

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-0831-2063

2. Lander Willem

   1. Centre for Health Economics Research and Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
   2. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium
   3. Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium

   Contribution

   LW, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Philippe Beutels

   1. Centre for Health Economics Research and Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
   2. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium
   3. School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia

   Contribution

   PB, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Niel Hens

   1. Centre for Health Economics Research and Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
   2. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium

   Contribution

   NH, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

• 4,511

  views

• 420

  downloads

• 28

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Citations by DOI

• 28

  citations for umbrella DOI https://doi.org/10.7554/eLife.07116