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Since outbreak A was the largest outbreak in California since elimination, this may have garnered increased, and perhaps undue, interest in the causal effects of genotype B3 and winter season on measles transmissibility.

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To address this possible fallacy, we omitted outbreak A and recalculated permutation P values for all predictors of interest and analyzed genotype and season as a random effect. Our result that genotype is a predictor of transmissibility was robust to inclusion or exclusion of the outbreak A and random-effects modeling.

However, the effect of season is more questionable given that the P value was significant when comparing winter to all other seasons, but not in a random-effects model. Last, for outbreak A, we only included known transmission events that occurred outside of the Disneyland theme parks; this transmission was likely more typical of transmission in California [ 6 ].

In addition, if there had been a known single index case, the decision to model this outbreak as an outbreak with 45 index cases effectively reduced our statistical power to detect a difference between B3 and non-B3 genotypes; given that we did detect a significant effect, this lends credibility to the hypothesis that there are genotype-specific differences in transmissibility.

We note that the number of index cases we use has been updated and thus differs from the 40 used in [ 6 ]. A weakness of this study is that we were limited by the number of California measles cases since elimination; thus, the study may have been underpowered to detect significant effects for key predictors. For example, in only 16 of single-index-case outbreaks was the index case vaccinated the remainder had unknown vaccination status or were unvaccinated. While these outbreaks were on average characterized by less transmission, we did not detect a significant effect.

In addition, we cannot distinguish between biological differences between genotypes eg, greater viral replication in the lungs for B3 strains [ 36 ] and differences in networks in which specific genotypes may tend to circulate eg, a larger number of contacts in networks where B3 strains tend to be introduced. While we would argue that biological differences between genotype B3 and other genotypes are plausible [ 36 ], as we did not include all possible confounders, our models cannot necessarily distinguish between competing explanations for differing transmissibility.

While measles case ascertainment is thought to be high, missed cases are a possibility and could result in collider selection bias. For this to bias the observed association between genotype and outbreak in the observed direction, the B3 genotype would have to cause less severe disease. Case detection would be enhanced, not diminished, during large outbreaks. While this would be at odds with [ 36 ], the B3 genotype could cause less severe disease if the measles vaccine were less effective against this genotype. That is, genotype B3 cases would be enriched with vaccinated and thus partially protected individuals compared with other outbreaks.

In addition, if the B3 genotype causes more severe disease, it is possible that B3 outbreaks would appear larger due to improved case finding. While we cannot rule out that genotype B3 outbreaks appear larger because genotype B3 causes more severe disease with available data, the apparent public health impact of the B3 genotype nonetheless appears to be greater than that of other genotypes.

In conclusion, we have found that genotype appears to be a significant predictor of measles transmissibility, with genotype B3 being more transmissible compared with all other genotypes combined. School age of the index case also appears to be a significant predictor of transmissibility. While we do not find compelling evidence that the relationship between genotype B3 and outbreak size is confounded by other factors, it remains to be seen whether this result could be replicated in other highly vaccinated populations. Furthermore, outbreak sizes are variable and, for example, genotype B3 outbreaks will overlap substantially in size with non-B3 outbreaks.

Thus, more data are needed before one could make a firm recommendation that genotype or age of index case could be used for guiding contact investigation. The view that non-B3 outbreaks or outbreaks with index cases not of school age are of lesser public health importance is unwarranted. While high levels of population immunity achieved through routine measles vaccination remains the cornerstone of control, variability in measles transmissibility may nonetheless have important implications for measles control: the vaccination threshold required for elimination may not be the same for all genotypes or age groups.

Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.


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Potential conflicts of interest. All authors: No reported conflicts of interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed. Europe PMC requires Javascript to function effectively. Recent Activity. Background:Substantial heterogeneity in measles outbreak sizes may be due to genotype-specific transmissibility. Using a branching process analysis, we characterize differences in measles transmission by estimating the association between genotype and the reproduction number R among postelimination California measles cases during cases, outbreaks.

Methods:Assuming a negative binomial secondary case distribution, we fit a branching process model to the distribution of outbreak sizes using maximum likelihood and estimated the reproduction number R for a multigenotype model. Conclusions:Variability in measles transmissibility may have important implications for measles control; the vaccination threshold required for elimination may not be the same for all genotypes or age groups. The snippet could not be located in the article text. This may be because the snippet appears in a figure legend, contains special characters or spans different sections of the article.

Introduction to Public Health Surveillance

Clin Infect Dis. Published online Nov 6. PMID: Sarah F Ackley 1 Francis I. Lee Worden 1 Francis I. Seth Blumberg 1 Francis I. Travis C Porco 1 Francis I. Received Jun 12; Accepted Nov 3. This article has been cited by other articles in PMC. Abstract Background Substantial heterogeneity in measles outbreak sizes may be due to genotype-specific transmissibility. Methods Assuming a negative binomial secondary case distribution, we fit a branching process model to the distribution of outbreak sizes using maximum likelihood and estimated the reproduction number R for a multigenotype model.

Conclusions Variability in measles transmissibility may have important implications for measles control; the vaccination threshold required for elimination may not be the same for all genotypes or age groups. Keywords: Measles, branching process, mathematical model, disease elimination, subcritical diseases. Analysis Branching process theory [ 16 ] has been used to estimate the reproduction numbers of many subcritical diseases [ 17 , 18 ].

Table 1. Open in a separate window. Table 2. Table 3. Figure 1. Supplementary Material Supplementary Appendix Click here for additional data file. Notes Acknowledgments. References 1.

Bayesian Monitoring of Emerging Infectious Diseases

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Background

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BMC Public Health ; 15 Branching process models for surveillance of infectious diseases controlled by mass vaccination. Biostatistics , 4 2 pp. Mass vaccination programmes aim to maintain the effective reproduction number R of an infection below unity. We describe methods for monitoring the value of R using surveillance data. The models are based on branching processes in which R is identified with the offspring mean.

We derive unconditional likelihoods for the offspring mean using data on outbreak size and outbreak duration. We also discuss Bayesian methods, implemented by Metropolis—Hastings sampling. We investigate by simulation the validity of the models with respect to depletion of susceptibles and under-ascertainment of cases.