State variables (population compartments) and model parameters for the model investigated.

<p>State variables (population compartments) and model parameters for the model investigated.</p

Cross-sectional incidence of disease over age of population.

<p>Annual age-dependent (to age 30 years) incidence of pertussis disease cases for the years 1994–2012 in 2 year intervals. Gray shaded envelopes indicate 50%, 90%, 95%, and 99% credible intervals from the model parameter and uncertainty estimation. Black points (1994–2008) indicate disease incidence data collected by the NNDSS. The red crosses on the 2010 and 2012 plots also represent NNDSS incidence data; however, the model was not fitted to these data and so model outputs represent out-of-sample predicted age-dependent incidence curves.</p

Case-control study results compared with modeled values.

<p>Vaccine Effectiveness (VE) as measured in the case-control study of Misegades et al [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004138#pcbi.1004138.ref022" target="_blank">22</a>] from 2010 in California (black crosses) compared with VE values generated by simulations of the case-control study using the model fitted to the incidence and these VE data (gray region). The gray shaded region represents the 95% credible interval of the model outputs. The dotted curve lying above the 2010 data and simulations was calculated by simulating a 1990 case-control study, and shows significantly slower waning of the VE value.</p

Descriptions of the nested models that were fitted to the NNDSS incidence data.

<p>The mean posterior values of the Deviance Information Criterion (DIC) of the models are given in the rightmost column.</p><p>Descriptions of the nested models that were fitted to the NNDSS incidence data.</p

Parameter estimates for the best-fitting model, Model 8 (models outlined in Table 1).

<p>Parameter estimates for the best-fitting model, Model 8 (models outlined in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004138#pcbi.1004138.t001" target="_blank">Table 1</a>).</p

Epidemiological model diagram.

<p>The compartmental model for pertussis infection and disease used, modified from the basic model of Aguas <i>et al</i> [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004138#pcbi.1004138.ref044" target="_blank">44</a>]. There are two infected compartments for primary (<i>I</i>₁) and secondary (or higher) infection (<i>I</i>₂). We assume that surveillance systems only capture those who are experiencing primary infection. Once an individual has recovered from primary or secondary infection, induced immunity may wane, whereupon they will re-enter the susceptible state. Rates of flow between compartments are defined in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004138#pcbi.1004138.t003" target="_blank">Table 3</a>.</p

Incidence of disease in the United States, compared with modeled values.

<p>The time varying incidence of disease cases in the model and the US data (log scale) after 1990. The black dots are US disease incidence data, and the shaded regions represent the credible intervals (50% and 95%) obtained through model parameter estimation of model 8. The model has been run beyond the time over which it was trained to illustrate its continued correspondence with the 2010–2012 data (red crosses).</p

Identifying Optimal Vaccination Strategies for Serogroup A <i>Neisseria meningitidis</i> Conjugate Vaccine in the African Meningitis Belt

<div><p>Objective</p><p>The optimal long-term vaccination strategies to provide population-level protection against serogroup A Neisseria meningitidis (MenA) are unknown. We developed an age-structured mathematical model of MenA transmission, colonization, and disease in the African meningitis belt, and used this model to explore the impact of various vaccination strategies.</p><p>Methods</p><p>The model stratifies the simulated population into groups based on age, infection status, and MenA antibody levels. We defined the model parameters (such as birth and death rates, age-specific incidence rates, and age-specific duration of protection) using published data and maximum likelihood estimation. We assessed the validity of the model by comparing simulated incidence of invasive MenA and prevalence of MenA carriage to observed incidence and carriage data.</p><p>Results</p><p>The model fit well to observed age- and season-specific prevalence of carriage (mean pseudo-R2 0.84) and incidence of invasive disease (mean R2 0.89). The model is able to reproduce the observed dynamics of MenA epidemics in the African meningitis belt, including seasonal increases in incidence, with large epidemics occurring every eight to twelve years. Following a mass vaccination campaign of all persons 1–29 years of age, the most effective modeled vaccination strategy is to conduct mass vaccination campaigns every 5 years for children 1–5 years of age. Less frequent campaigns covering broader age groups would also be effective, although somewhat less so. Introducing conjugate MenA vaccine into the EPI vaccination schedule at 9 months of age results in higher predicted incidence than periodic mass campaigns.</p><p>Discussion</p><p>We have developed the first mathematical model of MenA in Africa to incorporate age structures and progressively waning protection over time. Our model accurately reproduces key features of MenA epidemiology in the African meningitis belt. This model can help policy makers consider vaccine program effectiveness when determining the feasibility and benefits of MenA vaccination strategies.</p></div

Leptospirosis Outbreak following Severe Flooding: A Rapid Assessment and Mass Prophylaxis Campaign; Guyana, January–February 2005

<div><h3>Background</h3><p>Leptospirosis is a zoonosis usually transmitted through contact with water or soil contaminated with urine from infected animals. Severe flooding can put individuals at greater risk for contracting leptospirosis in endemic areas. Rapid testing for the disease and large-scale interventions are necessary to identify and control infection. We describe a leptospirosis outbreak following severe flooding and a mass chemoprophylaxis campaign in Guyana.</p> <h3>Methodology/Principal Findings</h3><p>From January–March 2005, we collected data on suspected leptospirosis hospitalizations and deaths. Laboratory testing included anti-leptospiral dot enzyme immunoassay (DST), immunohistochemistry (IHC) staining, and microscopic agglutination testing (MAT). DST testing was conducted for 105 (44%) of 236 patients; 52 (50%) tested positive. Four (57%) paired serum samples tested by MAT were confirmed leptospirosis. Of 34 total deaths attributed to leptospirosis, postmortem samples from 10 (83%) of 12 patients were positive by IHC. Of 201 patients interviewed, 89% reported direct contact with flood waters. A 3-week doxycycline chemoprophylaxis campaign reached over 280,000 people.</p> <h3>Conclusions</h3><p>A confirmed leptospirosis outbreak in Guyana occurred after severe flooding, resulting in a massive chemoprophylaxis campaign to try to limit morbidity and mortality.</p> </div