Deciphering the impacts of vaccination and immunity on pertussis epidemiology in Thailand

Blackwood, Julie C.; Cummings, Derek A. T.; Broutin, Hélène; Iamsirithaworn, Sopon; Rohani, Pejman

doi:10.1073/pnas.1220908110

Cited by 51 publications

(60 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We did not include this possibility on the basis of the epidemiological data. Some studies have found no empirical evidence in support of the immune boosting hypothesis (17,21), whereas others have found that its epidemiological impact is small, even if statistically significant (52).…”

Section: Discussionmentioning

confidence: 99%

“…After recovering, individuals are immune to further infection. We assume infection-derived immunity to be lifelong, based on previous epidemiological studies of pertussis in Sweden (22), England and Wales (21), and Thailand (17), which found that repeat infections contributed very little to transmission (57). The number of individuals of each age in each state (susceptible, exposed, infectious, recovered, and vaccinated) is updated stochastically (56).…”

Section: Methodsmentioning

confidence: 99%

“…Even if vaccination is widespread and initially efficacious, infant immunization programs alone may fail to achieve elimination if vaccine-derived immunity is temporary. Some waning of vaccine-derived immunity to pertussis is supported by data at both the population and individual scales, but whereas population-level patterns of incidence generally suggest long-lasting immunity for both wholecell and acellular vaccines (17,21,22,32), individual-level data on reinfections are better explained by a much shorter duration of protection (18). In this scenario, we assumed 100% uptake with a fully efficacious vaccine, with a mean duration of vaccinederived immunity of 15 y (Fig.…”

Section: Significancementioning

confidence: 99%

“…Data from serological studies (16,18) and animal models (19) paint a picture of a vaccine that protects against disease for a limited duration and may afford little protection against transmission. In contrast, large-scale pertussis incidence data from countries such as Denmark (20), England and Wales (21), Thailand (17), and Sweden (22,23) are consistent with long-lasting vaccine-derived immunity and sufficiently little transmission among vaccinated and previously infected individuals as to generate herd immunity.…”

mentioning

confidence: 97%

See 3 more Smart Citations

Combating pertussis resurgence: One booster vaccination schedule does not fit all

Riolo

Rohani

2015

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

Pertussis has reemerged as a major public health concern in many countries where it was once considered well controlled. Although the mechanisms responsible for continued pertussis circulation and resurgence remain elusive and contentious, many countries have nevertheless recommended booster vaccinations, the timing and number of which vary widely. Here, using a stochastic, age-stratified transmission model, we searched for cost-effective booster vaccination strategies using a genetic algorithm. We did so assuming four hypothesized mechanisms underpinning contemporary pertussis epidemiology: (I) insufficient coverage, (II) frequent primary vaccine failure, (III) waning of vaccine-derived protection, and (IV) vaccine "leakiness." For scenarios I-IV, successful booster strategies were identified and varied considerably by mechanism. Especially notable is the inability of booster schedules to alleviate resurgence when vaccines are leaky. Critically, our findings argue that the ultimate effectiveness of vaccine booster schedules will likely depend on correctly pinpointing the causes of resurgence, with misdiagnosis of the problem epidemiologically ineffective and economically costly.R econciling the historical successes of routine infant pertussis vaccination programs in reducing incidence (1-3) with the recent resurgence in a number of highly vaccinated countries (4, 5) has proved challenging, especially in the context of the global heterogeneity in contemporary pertussis epidemiology (6). A variety of explanations for pertussis resurgence have been proposed, ranging from improvements in surveillance and diagnostics (7) to reduced protection afforded by vaccination, whether due to the waning of vaccine-derived immunity (8), the evolution of the etiological agent (the bacterium Bordetella pertussis) (9), the switch from whole-cell to acellular vaccines (10), or the invasion and spread of Bordetella congeners (11). It has also been demonstrated that even in the absence of changes in the nature of transmission, vaccine, or reporting, pertussis resurgence might be expected in some countries as an inevitable consequence of insufficient historical vaccination (12).Disentangling the many pathways to pertussis resurgence is particularly difficult because pertussis immunity and, in particular, vaccine-derived immunity are not well understood (13). With no known reliable serological marker of protection (14), the properties of infection-and vaccine-derived immunity against pertussis must be inferred indirectly (15). However, the models of pertussis immunity that best reconcile individual-level clinical data (16) and population-level incidence data (17), respectively, are strikingly different. Data from serological studies (16, 18) and animal models (19) paint a picture of a vaccine that protects against disease for a limited duration and may afford little protection against transmission. In contrast, large-scale pertussis incidence data from countries such as Denmark (20), England and Wales (21), Thailand (17), and Sweden (2...

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Significancementioning

confidence: 99%

mentioning

confidence: 97%

See 2 more Smart Citations

Combating pertussis resurgence: One booster vaccination schedule does not fit all

Riolo

Rohani

2015

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

show abstract

“…IF1 approximates the Fisher score function, whereas IF2 implements an iterated Bayes map. IF1 has been used in applications for which no other computationally feasible algorithm for statistically efficient, likelihoodbased inference was known (10)(11)(12)(13)(14)(15). The extra capabilities offered by IF2 open up further possibilities for drawing inferences about nonlinear partially observed stochastic dynamic models from time series data.…”

Section: Markov Processmentioning

confidence: 99%

Inference for dynamic and latent variable models via iterated, perturbed Bayes maps

Ionides¹,

Nguyen²,

Atchadé³

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

125

185

View full text Add to dashboard Cite

Iterated filtering algorithms are stochastic optimization procedures for latent variable models that recursively combine parameter perturbations with latent variable reconstruction. Previously, theoretical support for these algorithms has been based on the use of conditional moments of perturbed parameters to approximate derivatives of the log likelihood function. Here, a theoretical approach is introduced based on the convergence of an iterated Bayes map. An algorithm supported by this theory displays substantial numerical improvement on the computational challenge of inferring parameters of a partially observed Markov process. sequential Monte Carlo | particle filter | maximum likelihood | Markov processA n iterated filtering algorithm was originally proposed for maximum likelihood inference on partially observed Markov process (POMP) models by Ionides et al. (1). Variations on the original algorithm have been proposed to extend it to general latent variable models (2) and to improve numerical performance (3,4). In this paper, we study an iterated filtering algorithm that generalizes the data cloning method (5, 6) and is therefore also related to other Monte Carlo methods for likelihood-based inference (7-9). Data cloning methodology is based on the observation that iterating a Bayes map converges to a point mass at the maximum likelihood estimate. Combining such iterations with perturbations of model parameters improves the numerical stability of data cloning and provides a foundation for stable algorithms in which the Bayes map is numerically approximated by sequential Monte Carlo computations.We investigate convergence of a sequential Monte Carlo implementation of an iterated filtering algorithm that combines data cloning, in the sense of Lele et al. (5), with the stochastic parameter perturbations used by the iterated filtering algorithm of (1). Lindström et al. (4) proposed a similar algorithm, termed fast iterated filtering, but the theoretical support for that algorithm involved unproved conjectures. We present convergence results for our algorithm, which we call IF2. Empirically, it can dramatically outperform the previous iterated filtering algorithm of ref. 1, which we refer to as IF1. Although IF1 and IF2 both involve recursively filtering through the data, the theoretical justification and practical implementations of these algorithms are fundamentally different. IF1 approximates the Fisher score function, whereas IF2 implements an iterated Bayes map. IF1 has been used in applications for which no other computationally feasible algorithm for statistically efficient, likelihoodbased inference was known (10-15). The extra capabilities offered by IF2 open up further possibilities for drawing inferences about nonlinear partially observed stochastic dynamic models from time series data.Iterated filtering algorithms implemented using basic sequential Monte Carlo techniques have the property that they do not need to evaluate the transition density of the latent Markov process.Algorithms with this property...

show abstract

The impact of infection-derived immunity on disease dynamics

King

Magpantay

et al. 2021

J. Math. Biol.

Self Cite

View full text Add to dashboard Cite

When modeling infectious diseases, it is common to assume that infection-derived immunity is either (1) non-existent or (2) perfect and lifelong. However there are many diseases in which infection-derived immunity is known to be present but imperfect. There are various ways in which infection-derived immunity can fail, which can ultimately impact the probability that an individual be reinfected by the same pathogen, as well as the long-run population-level prevalence of the pathogen. Here we discuss seven different models of imperfect infection-derived immunity, including waning, leaky and all-or-nothing immunity. For each model we derive the probability that an infected individual becomes reinfected during their lifetime, given that the system is at endemic equilibrium. This can be thought of as the impact that each of these infection-derived immunity failures have on reinfection. This measure is useful because it provides us with a way to compare different modes of failure of infection-derived immunity.

show abstract

Deciphering the impacts of vaccination and immunity on pertussis epidemiology in Thailand

Cited by 51 publications

References 38 publications

Combating pertussis resurgence: One booster vaccination schedule does not fit all

Combating pertussis resurgence: One booster vaccination schedule does not fit all

Inference for dynamic and latent variable models via iterated, perturbed Bayes maps

The impact of infection-derived immunity on disease dynamics

Contact Info

Product

Resources

About