Modelling the dynamics of Streptococcus pneumoniae transmission in children

Tese de mestrado. Bioinformática e Biologia Computacional. Universidade de Lisboa, Faculdade de Ciências, 2010Streptococcus pneumoniae, also known as pneumococcus, is a commensal bacterium very common in the nasopharynx of young children but that can also be found in older children and adults. Carriage may lead to infection. Although this is a rare event, it has a significant impact on human health. Indeed, diseases caused by pneumococcus include infections as common as otitis media and as dangerous as pneumonia or meningitis. It is therefore important to gain more knowledge of its transmission dynamics as modulated by environmental factors and of how it is affected by host population specificities. To characterize pneumococcus transmission dynamics in a Portuguese day-care center, data was used from a one-year longitudinal study on the state of colonization by S. pneumoniae in children attending a day-care center in Lisboa, Portugal [Sá-Leão et al.2008]. The data refer to 1998, before the introduction of the seven-valent pneumococcal conjugate vaccine. A conceptual model for pneumococcus transmission was built, which considered genotype colonizations and clearances as dependent on the number of carriers, the number of non-carriers and the values of four parameters: the clearance rate m, the within-group transmission parameter b, the community rate of acquisition k and the between-genotypes competition parameter f. Bayesian inference was used to estimate these parameters. Colonizations and clearances were modeled as Poisson processes and the joint posterior probability distributions of the model’s parameters were estimated by Markov Chain Monte Carlo sampling. The number of transitions that occurred in each sampling interval was counted directly from the sampled states, assuming that children did not undergo more than one transition per sampling interval. The posterior mean for the transmission parameters were 0.5974for b, 0.0107for k, 0.6280 for f and 0.3059 for m. Data was simulated using the posterior estimates for these parameters from a study of Finnish DCCs [Hoti et al.2009]. Sampling this data monthly, the method was found to give biased estimations, since the assumption that children did not undergo more than one transition per sampling interval did not hold. The precision could be improved by sampling for a longer period, 30 months were used. To significantly improve the accuracy, the sampling interval needed to be extremely short, daily samples were taken from the simulation. The estimation model used was found to be impractical. Another estimation method should be used that infers the possible carriage histories consistent with the observed states.Streptococcus pneumoniae, também denominado pneumococo, é uma bactéria comensal que só coloniza o homem. A aquisição desta bactéria começa pela colonização da nasofaringe, o indivíduo torna-se portador e pode transmitir a bactéria a outras pessoas através de contacto directo. Devido aos contactos frequentes e próximos entre crianças a frequentar infantários e à imaturidade do seu sistema imunitário, as crianças até aos seis anos de idade representam um grupo onde a prevalência de portadores de pneumococo é elevada [Bogaert et al.2004]. Estudos transversais realizados em crianças a frequentar infantários, mostraram que a prevalência de pneumococo nestas crianças pode ser superior a 70% [Mato et al. 2005]. Embora a colonização seja geralmente assintomática, pode, por vezes, evoluír para infecção. Na verdade, o pneumococo é uma causa comum de otite média, pneumonia e meningite, sendo um agente principal de doenças graves em crianças jovens [Centers for Disease Control and Prevention2000]. É, por isso, especialmente importante estudar a dinâmica de transmissão de pneumococo entre as crianças que frequentam infantários, bem como tentar perceber de que forma esta dinâmica pode ser afectada por factores ambientais e características populacionais. Existe uma grande variedade de estirpes de pneumococo, tendo sido descritos mais de noventa de cápsulas, ou serótipos, a que correspondem diferentes propriedades, tais como, um potencial patogénico variável [Brueggemann et al. 2003]. Um estudo recente, usando testes in vitro sobre amostras clínicas, encontrou uma possível relação, com significado biológico, entre o tipo de cápsula e a capacidade de resistir ao sistema imunitário do hospedeiro [Weinberger et al. 2009]. é possível que várias estirpes de pneumococo compitam na nasofaringe. Diversos tipos de competição foram estudados a nível teórico [Lipsitch 1997, Zhang et al. 2004] e os diferentes potenciais de colonização ou resistência a colonização foram testados para alguns serótipos em ratos [Lipsitch et al. 2000]. Um número limitado de serótipos foi estudado e poucos deram diferenças estatisticamente significativas. O primeiro tratamento utilizado contra pneumococo foi a soroterapia em estados iniciais de doença [Klugman 2008]. A partir de uma amostra de expectoração, determinava-se o serótipo e injectava-se no paciente soro animal contendo anticorpos contra esse serótipo. Em 1940 esta terapia estava disponível para cinco serótipos. Entretanto, foram descobertos antibióticos eficazes, sendo de destacar a penicilina. Embora a mortalidade por doença pneumocócica nos muito jovens e idosos tenha continuado alta, a penicilina parecia uma droga milagrosa por ser eficaz contra inúmeras doenças, algumas das quais quase sempre fatais, sem necessidade determinar o serótipo. Com o aumento do uso de penicilina no mundo, os tipos epidémicos mais invasivos, geralmente transmitidos por indivíduos doentes a pessoas saudáveis, mas que raramente se encontravam em indivíduos saudáveis, tornaram-se menos frequentes. Por outro lado, serótipos pediátricos menos virulentos, geralmente colonizadores por períodos prolongados e causa comum de otite média, começaram a substituir os tipos epidémicos nas infecç ões graves e a ganhar resistência aos antibióticos. Finalmente, na década de 80, uma vacina polissacárida eficaz contra as doenças pneumocócicas chegou ao mercado. Infelizmente, esta vacina não era eficiente em crianças. A primeira vacina conjugada a proteger contra a doença e o transporte de sete serótipos (PCV-7), também eficaz em crianças de menos de dois anos, só foi licenciada no ano 2000. Após o uso disseminado da vacina, a doença invasiva diminuiu, embora o transporte de pneumococo se tivesse mantido [Revai et al. 2006]. A última vacina conjugada aprovada protege contra 13 serótipos (PCV-13). O objectivo deste trabalho é estudar a dinâmica de transmissão de S. pneumoniae usando dados de um estudo longitudinal do estado de colonização, por esta bactéria, de crianças que frequentavam um infantário de Lisboa, Portugal [Sá-Leão et al. 2008]. Estes dados foram obtidos em 1998, antes da introdução da vacina PCV7. Ao todo, 21 clones de pneumococo foram identificados correspondentes a 13 serótipos. Neste trabalho, o termo genótipo foi usado para designar o clone. A análise exploratória permitiu perceber que os dados eram insuficientes para modelar a transmissão para genótipos diferentes de forma diferencial. Considerou-se, por isso, que todos os genótipos tinham igual capacidade de ser transmitidos e de resistir à eliminação. Para caracterizar a dinâmica de transmissão de pneumococo no infantário, construiu-se um modelo conceitual da transmissão de pneumococo que pressupõe que todas as crianças amostradas estão em contacto entre si e se misturam, que a colonização de uma criança portadora de pneumococo pode ser diferente da colonização de uma criança não portadora e que as taxas de transmissão e de clearance se mantêm constantes. Embora tendo em conta o número de portadores de cada genótipo, considera-se que todos têm características iguais. Este modelo depende de quatro parâmetros: uma taxa de transmissão entre as crianças do infantário b, uma taxa de aquisição de pneumococo pela comunidade k, um factor de competição entre genótipos f e uma taxa de clearance m. O valor destes parâmetros determina a dinâmica de transmissão própria ao infantário e foi estimado por inferência bayesiana. O modelo de verosimilhança individuais a partir dos estados observados como método alternativo que, por não depender deste pressuposto, permite estimar os parâmetros usando dados amostrados com um intervalo mais viável

Unveiling time in dose-response models to infer host susceptibility to pathogens

The biological effects of interventions to control infectious diseases typically depend on the intensity of pathogen challenge. As much as the levels of natural pathogen circulation vary over time and geographical location, the development of invariant efficacy measures is of major importance, even if only indirectly inferrable. Here a method is introduced to assess host susceptibility to pathogens, and applied to a detailed dataset generated by challenging groups of insect hosts (Drosophila melanogaster) with a range of pathogen (Drosophila C Virus) doses and recording survival over time. The experiment was replicated for flies carrying the Wolbachia symbiont, which is known to reduce host susceptibility to viral infections. The entire dataset is fitted by a novel quantitative framework that significantly extends classical methods for microbial risk assessment and provides accurate distributions of symbiont-induced protection. More generally, our data-driven modeling procedure provides novel insights for study design and analyses to assess interventions

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Fit of time-dependent dose-response model to survival curves.

<p>Black and blue dots are the observed proportions surviving over time for Wolb⁻ and Wolb⁺ groups, respectively. The curve is the fitted mean posterior survival over time and the shaded area is the 95% CI. Fifty flies per group were pricked with: A, buffer solution (shown for comparison but not used on this analysis); and B, ; C, ; D, ; E, ; F, ; G, ; H, TCID₅₀<i>D</i>CV.</p

Parameters governing estimated number infected per dose of <i>D</i>CV challenge and time to death from infection using time-dependent dose-response models described in Methods.

<p>Parameters with superscripts ⁻ and ⁺ relate to Wolb⁻ and Wolb⁺ groups, respectively.</p

Estimated parameters governing time to death from causes other than <i>D</i>CV infection.

<p>Estimated parameters governing time to death from causes other than <i>D</i>CV infection.</p

Selection of optimal days to collect mortality measurements for traditional dose-response models.

<p>The red line traces a score for how well mortality at any given day represents infection estimated by the time-dependent model (refer to axis on the right). The score is given by , where Δ denotes the number of doses in the dataset, () represents the proportion infected in the Wolb⁻ (Wolb⁺) group subject to <i>D</i>CV dose <i>j</i>, and () the observed mortality proportion over time in the Wolb⁻ (Wolb⁺) group subject to <i>D</i>CV dose <i>j</i>. Gray vertical lines mark the optimal day to measure mortality for dose-response models (day 30, dash-dotted line) and the limits of the acceptable range (days 17 and 46). Dashed lines represent the Gamma distributions that describe old-age mortality, and black (blue) full curves refer to the Gamma distributions that describe infection-induced mortality in Wolb⁻ (Wolb⁺) (refer to axis on the left). Curves are the mean posterior probabilities and shaded areas represent the 95% CI.</p