Using multi-event hydrologic and hydraulic signatures from water level sensors to diagnose locations of uncertainty in integrated urban drainage models used in living digital twins.

Digital twins of urban drainage systems require simulation models that can adequately replicate the physical system. All models have their limitations, and it is important to investigate when and where simulation results are acceptable and to communicate the level of performance transparently to end users. This paper first defines a classification of four possible 'locations of uncertainty' in integrated urban drainage models. It then develops a structured framework for identifying and diagnosing various types of errors. This framework compares model outputs with in-sewer water level observations based on hydrologic and hydraulic signatures. The approach is applied on a real case study in Odense, Denmark, with examples from three different system sites: a typical manhole, a small flushing chamber, and an internal overflow structure. This allows diagnosing different model errors ranging from issues in the underlying asset database and missing hydrologic processes to limitations in the model software implementation. Structured use of signatures is promising for continuous, iterative improvements of integrated urban drainage models. It also provides a transparent way to communicate the level of model adequacy to end users.

Estimation of the transmission dynamics of African swine fever virus within a swine house.

The spread of African swine fever virus (ASFV) threatens to reach further parts of Europe. In countries with a large swine production, an outbreak of ASF may result in devastating economic consequences for the swine industry. Simulation models can assist decision makers setting up contingency plans. This creates a need for estimation of parameters. This study presents a new analysis of a previously published study. A full likelihood framework is presented including the impact of model assumptions on the estimated transmission parameters. As animals were only tested every other day, an interpretation was introduced to cover the weighted infectiousness on unobserved days for the individual animals (WIU). Based on our model and the set of assumptions, the within- and between-pen transmission parameters were estimated to ß w = 1·05 (95% CI 0·62-1·72), ß b = 0·46 (95% CI 0·17-1·00), respectively, and the WIU = 1·00 (95% CI 0-1). Furthermore, we simulated the spread of ASFV within a pig house using a modified SEIR-model to establish the time from infection of one animal until ASFV is detected in the herd. Based on a chosen detection limit of 2·55% equivalent to 10 dead pigs out of 360, the disease would be detected 13-19 days after introduction.

A Comparison of Dynamics in Two Models for the Spread of a Vector-Borne Disease.

In 2007, bluetongue virus (BTV) was introduced to both Denmark (DK) and the United Kingdom (UK). For this reason, simulation models were built to predict scenarios for future incursions. The DK and UK models have a common description of within-herd dynamics, but differ greatly in their descriptions of between-herd spread, one using an explicit representation of vector dispersal, the other a transmission kernel. Here, we compare model predictions for the dynamics of bluetongue in the UK, based on the 2007 incursion and vaccination rollout in 2008. We demonstrate how an agent-based model shows greater sensitivity to the level of vaccine uptake and has lower variability compared with a kernel-based model. However, a model using a transmission kernel requires less detailed data and is often faster.

Decisions on control of foot-and-mouth disease informed using model predictions.

The decision on whether or not to change the control strategy, such as introducing emergency vaccination, is perhaps one of the most difficult decisions faced by the veterinary authorities during a foot-and-mouth disease (FMD) epidemic. A simple tool that may predict the epidemic outcome and consequences would be useful to assist the veterinary authorities in the decision-making process. A previously proposed simple quantitative tool based on the first 14 days outbreaks (FFO) of FMD was used with results from an FMD simulation exercise. Epidemic outcomes included the number of affected herds, epidemic duration, geographical size and costs. The first 14 days spatial spread (FFS) was also included to further support the prediction. The epidemic data was obtained from a Danish version (DTU-DADS) of a pre-existing FMD simulation model (Davis Animal Disease Spread - DADS) adapted to model the spread of FMD in Denmark. The European Union (EU) and Danish regulations for FMD control were used in the simulation. The correlations between FFO and FFS and the additional number of affected herds after day 14 following detection of the first infected herd were 0.66 and 0.82, respectively. The variation explained by the FFO at day 14 following detection was high (P-value<0.001). This indicates that the FFO may take a part in the decision of whether or not to intensify FMD control, for instance by introducing emergency vaccination and/or pre-emptive depopulation, which might prevent a "catastrophic situation". A significant part of the variation was explained by supplementing the model with the FFS (P-value<0.001). Furthermore, the type of the index-herd was also a significant predictor of the epidemic outcomes (P-value<0.05). The results of the current study suggest that national veterinary authorities should consider to model their national situation and to use FFO and FFS to help planning and updating their contingency plans and FMD emergency control strategies.

Comparing control strategies against foot-and-mouth disease: will vaccination be cost-effective in Denmark?

Recent outbreaks of foot-and-mouth disease (FMD) in Europe have highlighted the need for assessment of control strategies to optimise control of the spread of FMD. Our objectives were to assess the epidemiological and financial impact of simulated FMD outbreaks in Denmark and the effect of using ring depopulation or emergency vaccination to control these outbreaks. Two stochastic simulation models (InterSpreadPlus (ISP) and the modified Davis Animal Disease Simulation model (DTU-DADS)) were used to simulate the spread of FMD in Denmark using different control strategies. Each epidemic was initiated in one herd (index herd), and a total of 5000 index herds were used. Four types of control measures were investigated: (1) a basic scenario including depopulation of detected herds, 3 km protection and 10 km surveillance zones, movement tracing and a three-day national standstill, (2) the basic scenario plus depopulation in ring zones around detected herds (Depop), (3) the basic scenario plus protective vaccination within ring zones around detected herds, and (4) the basic scenario plus protective vaccination within ring zones around detected herds. Disease spread was simulated through direct animal movements, medium-risk contacts (veterinarians, artificial inseminators or milk controllers), low-risk contacts (animal feed and rendering trucks, technicians or visitors), market contacts, abattoir trucks, milk tanks, or local spread. The two simulation models showed different results in terms of the estimated numbers. However, the tendencies in terms of recommendations of strategies were similar for both models. Comparison of the different control strategies showed that, from an epidemiological point of view, protective vaccination would be preferable if the epidemic started in a cattle herd in an area with a high density of cattle, whereas if the epidemic started in an area with a low density of cattle or in other species, protective vaccination or depopulation would have almost the same preventive effect. Implementing additional control measures either 14 days after detection of the first infected herd or when 10 herds have been diagnosed would be more efficient than implementing additional control measures when more herds have been diagnosed. Protective vaccination scenarios would never be cost-effective, whereas depopulation or suppressive vaccination scenarios would most often be recommended. Looking at the median estimates of the cost-benefit analysis, depopulation in zones would most often be recommended, although, in extreme epidemics, suppressive vaccination scenarios could be less expensive. The vast majority of the costs and losses associated with a Danish epidemic could be attributed to export losses.

Modelling conjugation with stochastic differential equations.

Conjugation is an important mechanism involved in the transfer of resistance between bacteria. In this article a stochastic differential equation based model consisting of a continuous time state equation and a discrete time measurement equation is introduced to model growth and conjugation of two Enterococcus faecium strains in a rich exhaustible media. The model contains a new expression for a substrate dependent conjugation rate. A maximum likelihood based method is used to estimate the model parameters. Different models including different noise structure for the system and observations are compared using a likelihood-ratio test and Akaike's information criterion. Experiments indicating conjugation on the agar plates selecting for transconjugants motivates the introduction of an extended model, for which conjugation on the agar plate is described in the measurement equation. This model is compared to the model without plate conjugation. The modelling approach described in this article can be applied generally when modelling dynamical systems.

Antibiotic resistance in Pseudomonas aeruginosa strains with increased mutation frequency due to inactivation of the DNA oxidative repair system.

The chronic Pseudomonas aeruginosa infection of the lungs of cystic fibrosis (CF) patients is characterized by the biofilm mode of growth and chronic inflammation dominated by polymorphonuclear leukocytes (PMNs). A high percentage of P. aeruginosa strains show high frequencies of mutations (hypermutators [HP]). P. aeruginosa is exposed to oxygen radicals, both those generated by its own metabolism and especially those released by a large number of PMNs in response to the chronic CF lung infection. Our work therefore focused on the role of the DNA oxidative repair system in the development of HP and antibiotic resistance. We have constructed and characterized mutT, mutY, and mutM mutants in P. aeruginosa strain PAO1. The mutT and mutY mutants showed 28- and 7.5-fold increases in mutation frequencies, respectively, over that for PAO1. These mutators had more oxidative DNA damage (higher levels of 7,8-dihydro-8-oxodeoxyguanosine) than PAO1 after exposure to PMNs, and they developed resistance to antibiotics more frequently. The mechanisms of resistance were increased beta-lactamase production and overexpression of the MexCD-OprJ efflux-pump. Mutations in either the mutT or the mutY gene were found in resistant HP clinical isolates from patients with CF, and complementation with wild-type genes reverted the phenotype. In conclusion, oxidative stress might be involved in the development of resistance to antibiotics. We therefore suggest the possible use of antioxidants for CF patients to prevent the development of antibiotic resistance.

Maximum Likelihood based comparison of the specific growth rates for P. aeruginosa and four mutator strains.

The specific growth rate for P. aeruginosa and four mutator strains mutT, mutY, mutM and mutY-mutM is estimated by a suggested Maximum Likelihood, ML, method which takes the autocorrelation of the observation into account. For each bacteria strain, six wells of optical density, OD, measurements are used for parameter estimation. The data is log-transformed such that a linear model can be applied. The transformation changes the variance structure, and hence an OD-dependent variance is implemented in the model. The autocorrelation in the data is demonstrated, and a correlation model with an exponentially decaying function of the time between observations is suggested. A model with a full covariance structure containing OD-dependent variance and an autocorrelation structure is compared to a model with variance only and with no variance or correlation implemented. It is shown that the model that best describes data is a model taking into account the full covariance structure. An inference study is made in order to determine whether the growth rate of the five bacteria strains is the same. After applying a likelihood-ratio test to models with a full covariance structure, it is concluded that the specific growth rate is the same for all bacteria strains. This study highlights the importance of carrying out an explorative examination of residuals in order to make a correct parametrization of a model including the covariance structure. The ML method is shown to be a strong tool as it enables estimation of covariance parameters along with the other model parameters and it makes way for strong statistical tools for inference studies.

Thymic squamous cell carcinoma.

In the case described, a 43-year-old man had an asymptomatic mass involving the right hilar and superior mediastinum. At surgery, the tumor was found to have invaded the right middle lobe of the lung and the pericardium. The mass was found to be a thymic squamous cell carcinoma, a rare tumor. Two serum tumor markers--lipid-associated sialic acid and squamous cell carcinoma antigen--were elevated. The patient underwent postoperative radiation therapy and has responded favorably to treatment. He had no clinical evidence of metastasis or recurrence as of 43 months postoperatively.