Methicillin-resistant Staphylococcus aureus: phylogenetic relatedness between European epidemic clones and Swiss sporadic strains.

We have compared the phylogenetic diversity of methicillin-resistant Staphylococcus aureus (MRSA) strains from Switzerland and their phylogenetic relationships with European epidemic clones, using multiprimer random amplification polymorphic DNA (RAPD). Strains included 24 European epidemic clones (59 strains), 66 sporadic strains isolated in Switzerland in 1996-1997, and 15 reference strains of five other Staphylococcus species. Similarity and clustering analysis with the Jaccard's coefficient showed that the maximum genetic distance between MRSA strains was 0.43, whereas the minimum genetic distance between the six Staphylococcus species was 0.97, indicating that the method permits phylogenetic hierarchization. The 24 MRSA clones reported to be epidemic in European countries during the 1990s were distributed into seven different genetic clusters with a maximum distance of 0.29 among them. This clustering pattern was confirmed by the analysis of a subset of MRSA strains by multilocus enzyme electrophoresis at 12 loci. Most of the sporadic Swiss strains were distributed into these seven different genetic clusters, together with the epidemic MRSA clones. This suggests that there is no phylogenetic cluster specific to epidemic clones of MRSA.

Nature of the Epidemic Threshold for the Susceptible-Infected-Susceptible Dynamics in Networks

We develop an analytical approach to the susceptible-infected-susceptible epidemic model that allows us to unravel the true origin of the absence of an epidemic threshold in heterogeneous networks. We find that a delicate balance between the number of high degree nodes in the network and the topological distance between them dictates the existence or absence of such a threshold. In particular, small-world random networks with a degree distribution decaying slower than an exponential have a vanishing epidemic threshold in the thermodynamic limit.

Statistical Analysis of an SEIR Epidemic Model

This thesis was focussed on statistical analysis methods and proposes the use of Bayesian inference to extract information contained in experimental data by estimating Ebola model parameters. The model is a system of differential equations expressing the behavior and dynamics of Ebola. Two sets of data (onset and death data) were both used to estimate parameters, which has not been done by previous researchers in (Chowell, 2004). To be able to use both data, a new version of the model has been built. Model parameters have been estimated and then used to calculate the basic reproduction number and to study the disease-free equilibrium. Estimates of the parameters were useful to determine how well the model fits the data and how good estimates were, in terms of the information they provided about the possible relationship between variables. The solution showed that Ebola model fits the observed onset data at 98.95% and the observed death data at 93.6%. Since Bayesian inference can not be performed analytically, the Markov chain Monte Carlo approach has been used to generate samples from the posterior distribution over parameters. Samples have been used to check the accuracy of the model and other characteristics of the target posteriors.

Connections: can the 20th century coronary heart disease epidemic reveal something about the 1918 influenza lethality?

This essay proposes that the ecologic association shown between the 20th century coronary heart disease epidemic and the 1918 influenza pandemic could shed light on the mechanism associated with the high lethality of the latter. It suggests that an autoimmune interference at the apoB-LDL interface could explain both hypercholesterolemia and inflammation (through interference with the cellular metabolism of arachidonic acid). Autoimmune inflammation, then, would explain the 1950s-60s acute coronary events (coronary thrombosis upon influenza re-infection) and the respiratory failure seen among young adults in 1918. This hypothesis also argues that the lethality of the 1918 pandemic may have not depended so much on the 1918 virus as on an immune vulnerability to it, possibly resulting from an earlier priming of cohorts born around 1890 by the 1890 influenza pandemic virus.

Overview of Zika virus (ZIKV) infection in regards to the Brazilian epidemic

Zika virus (ZIKV), a mosquito-borne flavivirus, belongs to the Flaviviridae family, genus Flavivirus. ZIKV was initially isolated in 1947 from a sentinel monkey in the Zika forest, Uganda. Little clinical importance was attributed to ZIKV, once only few symptomatic cases were reported in some African and Southeast Asiatic countries. This situation changed in 2007, when a large outbreak was registered on the Yap Island, Micronesia, caused by the Asian ZIKV lineage. Between 2013 and 2014, ZIKV spread explosively and caused many outbreaks in different islands of the Southern Pacific Ocean and in 2015 autochthonous transmission was reported in Brazil. Currently, Brazil is the country with the highest number of ZIKV-positive cases in Latin America. Moreover, for the first time after the discovery of ZIKV, the Brazilian scientists are studying the possibility for the virus to cause severe congenital infection related to microcephaly and serious birth defects due to the time-spatial coincidence of the alarming increase of newborns with microcephaly and the Brazilian ZIKV epidemic. The present review summarizes recent information for ZIKV epidemiology, clinical picture, transmission, diagnosis and the consequences of this emerging virus in Brazil.

Bayesian analysis of SEIR epidemic models

This thesis concerns the analysis of epidemic models. We adopt the Bayesian paradigm and develop suitable Markov Chain Monte Carlo (MCMC) algorithms. This is done by considering an Ebola outbreak in the Democratic Republic of Congo, former Zaïre, 1995 as a case of SEIR epidemic models. We model the Ebola epidemic deterministically using ODEs and stochastically through SDEs to take into account a possible bias in each compartment. Since the model has unknown parameters, we use different methods to estimate them such as least squares, maximum likelihood and MCMC. The motivation behind choosing MCMC over other existing methods in this thesis is that it has the ability to tackle complicated nonlinear problems with large number of parameters. First, in a deterministic Ebola model, we compute the likelihood function by sum of square of residuals method and estimate parameters using the LSQ and MCMC methods. We sample parameters and then use them to calculate the basic reproduction number and to study the disease-free equilibrium. From the sampled chain from the posterior, we test the convergence diagnostic and confirm the viability of the model. The results show that the Ebola model fits the observed onset data with high precision, and all the unknown model parameters are well identified. Second, we convert the ODE model into a SDE Ebola model. We compute the likelihood function using extended Kalman filter (EKF) and estimate parameters again. The motivation of using the SDE formulation here is to consider the impact of modelling errors. Moreover, the EKF approach allows us to formulate a filtered likelihood for the parameters of such a stochastic model. We use the MCMC procedure to attain the posterior distributions of the parameters of the SDE Ebola model drift and diffusion parts. In this thesis, we analyse two cases: (1) the model error covariance matrix of the dynamic noise is close to zero , i.e. only small stochasticity added into the model. The results are then similar to the ones got from deterministic Ebola model, even if methods of computing the likelihood function are different (2) the model error covariance matrix is different from zero, i.e. a considerable stochasticity is introduced into the Ebola model. This accounts for the situation where we would know that the model is not exact. As a results, we obtain parameter posteriors with larger variances. Consequently, the model predictions then show larger uncertainties, in accordance with the assumption of an incomplete model.

Use and abuse of mathematical models: an illustration from the 2001 foot and mouth disease epidemic in the United Kingdom

Foot and mouth disease (FMD) is a major threat, not only to countries whose economies rely on agricultural exports, but also to industrialised countries that maintain a healthy domestic livestock industry by eliminating major infectious diseases from their livestock populations. Traditional methods of controlling diseases such as FMD require the rapid detection and slaughter of infected animals, and any susceptible animals with which they may have been in contact, either directly or indirectly. During the 2001 epidemic of FMD in the United Kingdom (UK), this approach was supplemented by a culling policy driven by unvalidated predictive models. The epidemic and its control resulted in the death of approximately ten million animals, public disgust with the magnitude of the slaughter, and political resolve to adopt alternative options, notably including vaccination, to control any future epidemics. The UK experience provides a salutary warning of how models can be abused in the interests of scientific opportunism.

Risk of foot and mouth disease associated with proximity in space and time to infected premises and the implications for control policy during the 2001 epidemic in Cumbria

An analysis was made that calculated the risk of disease for premises in the most heavily affected parts of the county of Cumbria during the foot-and-mouth disease epidemic in the UK in 2001. In over half the cases the occurrence of the disease was not directly attributable to a recently infected premises being located within 1.5 km. Premises more than 1.5 km from recently infected premises faced sufficiently high infection risks that culling within a 1.5 km radius of the infected premises alone could not have prevented the progress of the epidemic. A comparison of the final outcome in two areas of the county, south Penrith and north Cumbria, indicated that focusing on controlling the potential spread of the disease over short distances by culling premises contiguous to infected premises, while the disease continued to spread over longer distances, may have resulted in excessive numbers of premises being culled. Even though the contiguous cull in south Penrith appeared to have resulted in a smaller proportion of premises becoming infected, the overall proportion of premises culled was considerably greater than in north Cumbria, where, because of staff and resource limitations, a smaller proportion of premises contiguous to infected premises was culled