Rapid simulation of spatial epidemics : a spectral method

Spatial structure and hence the spatial position of host populations plays a vital role in the spread of infection. In the majority of situations, it is only possible to predict the spatial spread of infection using simulation models, which can be computationally demanding especially for large population sizes. Here we develop an approximation method that vastly reduces this computational burden. We assume that the transmission rates between individuals or sub-populations are determined by a spatial transmission kernel. This kernel is assumed to be isotropic, such that the transmission rate is simply a function of the distance between susceptible and infectious individuals; as such this provides the ideal mechanism for modelling localised transmission in a spatial environment. We show that the spatial force of infection acting on all susceptibles can be represented as a spatial convolution between the transmission kernel and a spatially extended ‘image’ of the infection state. This representation allows the rapid calculation of stochastic rates of infection using fast-Fourier transform (FFT) routines, which greatly improves the computational efficiency of spatial simulations. We demonstrate the efficiency and accuracy of this fast spectral rate recalculation (FSR) method with two examples: an idealised scenario simulating an SIR-type epidemic outbreak amongst N habitats distributed across a two-dimensional plane; the spread of infection between US cattle farms, illustrating that the FSR method makes continental-scale outbreak forecasting feasible with desktop processing power. The latter model demonstrates which areas of the US are at consistently high risk for cattle-infections, although predictions of epidemic size are highly dependent on assumptions about the tail of the transmission kernel

The Parkinson disease pain classification system: Results from an international mechanism-based classification approach

Pain is a common nonmotor symptom in patients with Parkinson disease (PD) but the correct diagnosis of the respective cause remains difficult because suitable tools are lacking, so far. We developed a framework to differentiate PD- from non-PD-related pain and classify PD-related pain into 3 groups based on validated mechanistic pain descriptors (nociceptive, neuropathic, or nociplastic), which encompass all the previously described PD pain types. Severity of PD-related pain syndromes was scored by ratings of intensity, frequency, and interference with daily living activities. The PD-Pain Classification System (PD-PCS) was compared with classic pain measures (ie, brief pain inventory and McGill pain questionnaire [MPQ], PDQ-8 quality of life score, MDS-UPDRS scores, and nonmotor symptoms). 159 nondemented PD patients (disease duration 10.2 6 7.6 years) and 37 healthy controls were recruited in 4 centers. PDrelated pain was present in 122 patients (77%), with 24 (15%) suffering one or more syndromes at the same time. PD-related nociceptive, neuropathic, or nociplastic pain was diagnosed in 87 (55%), 25 (16%), or 35 (22%), respectively. Pain unrelated to PD was present in 35 (22%) patients. Overall, PD-PCS severity score significantly correlated with pain’s Brief Pain Inventory and MPQ ratings, presence of dyskinesia and motor fluctuations, PDQ-8 scores, depression, and anxiety measures. Moderate intrarater and interrater reliability was observed. The PD-PCS is a valid and reliable tool for differentiating PD-related pain from PD-unrelated pain. It detects and scores mechanistic pain subtypes in a pragmatic and treatment-oriented approach, unifying previous classifications of PD-pain.Fil: Mylius, Veit. Universitat Phillips; Alemania. Center for Neurorehabilitation; Suiza. Kantonsspital St; SuizaFil: Perez Lloret, Santiago. Universidad Abierta Interamericana. Secretaría de Investigación. Centro de Altos Estudios En Ciencias Humanas y de la Salud - Sede Buenos Aires.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires"; ArgentinaFil: Cury, Rubens G.. Universidade de Sao Paulo; BrasilFil: Teixeira, Manoel J.. Universidade de Sao Paulo; BrasilFil: Barbosa, Victor R.. Universidade de Sao Paulo; BrasilFil: Barbosa, Egberto R.. Universidade de Sao Paulo; BrasilFil: Moreira, Larissa I.. Universidade de Sao Paulo; BrasilFil: Listik, Clarice. Universidade de Sao Paulo; BrasilFil: Fernandes, Ana M.. Universidade de Sao Paulo; BrasilFil: de Lacerda Veiga, Diogo. Universidade de Sao Paulo; BrasilFil: Barbour, Julio. Universidade de Sao Paulo; BrasilFil: Hollenstein, Nathalie. Universidade de Sao Paulo; BrasilFil: Oechsner, Matthias. Center for Neurological Rehabilitation; SuizaFil: Walch, Julia. Kantonsspital St; SuizaFil: Brugger, Florian. Kantonsspital St; SuizaFil: Hägele Link, Stefan. Kantonsspital St; SuizaFil: Beer, Serafin. Center for Neurorehabilitation; SuizaFil: Rizos, Alexandra. King's College Hospital; Reino UnidoFil: Chaudhuri, Kallol Ray. The Maurice Wohl Clinical Neuroscience Institute; Reino Unido. King's College Hospital; Reino UnidoFil: Bouhassira, Didier. Université Versailles-Saint-Quentin; Francia. Hôpital Ambroise Paré; FranciaFil: Lefaucheur, Jean Pascal. Université Paris-Est-Créteil; FranciaFil: Timmermann, Lars. Universitat Phillips; AlemaniaFil: Gonzenbach, Roman. Center for Neurorehabilitation; SuizaFil: Kägi, Georg. Kantonsspital St; SuizaFil: Möller, Jens Carsten. Universitat Phillips; Alemania. Center for Neurological Rehabilitation; SuizaFil: Ciampi de Andrade, Daniel. Universidade de Sao Paulo; Brasi

Towards a standardization of biomethane potential tests

8 PáginasProduction of biogas from different organic materials is a most interesting source of renewable energy. The biomethane potential (BMP) of these materials has to be determined to get insight in design parameters for anaerobic digesters. A workshop was held in June 2015 in Leysin Switzerland to agree on common solutions to the conundrum of inconsistent BMP test results. A discussion covers actions and criteria that are considered compulsory ito accept and validate a BMP test result; and recommendations concerning the inoculum substrate test setup and data analysis and reporting ito obtain test results that can be validated and reproduced.The workshop in Leysin, Switzerland, has been financed by the Swiss Federal Office for Energy, and co-sponsored by Bioprocess Control Sweden AB, Lund, Sweden. The authors thank Alexandra Maria Murray for editing the English

Towards a standardization of biomethane potential tests

Production of biogas from different organic materials is a most interesting source of renewable energy. The biomethane potential (BMP) of these materials has to be determined to get insight in design parameters for anaerobic digesters. Although several norms and guidelines for BMP tests exist, inter-laboratory tests regularly show high variability of BMPs for the same substrate. A workshop was held in June 2015, in Leysin, Switzerland, with over 40 attendees from 30 laboratories around the world, to agree on common solutions to the conundrum of inconsistent BMP test results. This paper presents the consensus of the intense roundtable discussions and cross-comparison of methodologies used in respective laboratories. Compulsory elements for the validation of BMP results were defined. They include the minimal number of replicates, the request to carry out blank and positive control assays, a criterion for the test duration, details on BMP calculation, and last but not least criteria for rejection of the BMP tests. Finally, recommendations on items that strongly influence the outcome of BMP tests such as inoculum characteristics, substrate preparation, test setup, and data analysis are presented to increase the probability of obtaining validated and reproducible results.info:eu-repo/semantics/publishedVersio

Requirements for measurement and validation of biochemical methane potential (BMP).

This document presents the minimal requirements for measurement and validation of biochemical methane potential (also called biomethane potential) (BMP) in batch tests. It represents the consensus of more than 50 biogas researchers. The list of requirements is the same as in the open-access commentary by Holliger et al. [2021]. For details on development of these requirements see the open-access papers Holliger et al. [2016] and Hafner et al. [2020c]