Auflistung nach Autor:in "Grütter, Rolf"

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A transdisciplinary approach supporting the implementation of a big data project in livestock production: an example from the Swiss pig production industry
(Frontiers Research Foundation, 2019) Faverjon, Céline; Bernstein, Abraham; Grütter, Rolf; Nathues, Christina; Nathues, Heiko; Sarasua, Cristina; Sterchi, Martin; Vargas, Maria Elena; Berezowski, John
Big Data approaches offer potential benefits for improving animal health, but they have not been broadly implemented in livestock production systems. Privacy issues, the large number of stakeholders, and the competitive environment all make data sharing, and integration a challenge in livestock production systems. The Swiss pig production industry illustrates these and other Big Data issues. It is a highly decentralized and fragmented complex network made up of a large number of small independent actors collecting a large amount of heterogeneous data. Transdisciplinary approaches hold promise for overcoming some of the barriers to implementing Big Data approaches in livestock production systems. The purpose of our paper is to describe the use of a transdisciplinary approach in a Big Data research project in the Swiss pig industry. We provide a brief overview of the research project named “Pig Data,” describing the structure of the project, the tools developed for collaboration and knowledge transfer, the data received, and some of the challenges. Our experience provides insight and direction for researchers looking to use similar approaches in livestock production system research.
01A - Beitrag in wissenschaftlicher Zeitschrift
Active querying approach to epidemic source detection on contact networks
(Nature, 2023) Sterchi, Martin; Hilfiker, Lorenz; Grütter, Rolf; Bernstein, Abraham
The problem of identifying the source of an epidemic (also called patient zero) given a network of contacts and a set of infected individuals has attracted interest from a broad range of research communities. The successful and timely identification of the source can prevent a lot of harm as the number of possible infection routes can be narrowed down and potentially infected individuals can be isolated. Previous research on this topic often assumes that it is possible to observe the state of a substantial fraction of individuals in the network before attempting to identify the source. We, on the contrary, assume that observing the state of individuals in the network is costly or difficult and, hence, only the state of one or few individuals is initially observed. Moreover, we presume that not only the source is unknown, but also the duration for which the epidemic has evolved. From this more general problem setting a need to query the state of other (so far unobserved) individuals arises. In analogy with active learning, this leads us to formulate the active querying problem. In the active querying problem, we alternate between a source inference step and a querying step. For the source inference step, we rely on existing work but take a Bayesian perspective by putting a prior on the duration of the epidemic. In the querying step, we aim to query the states of individuals that provide the most information about the source of the epidemic, and to this end, we propose strategies inspired by the active learning literature. Our results are strongly in favor of a querying strategy that selects individuals for whom the disagreement between individual predictions, made by all possible sources separately, and a consensus prediction is maximal. Our approach is flexible and, in particular, can be applied to static as well as temporal networks. To demonstrate our approach’s practical importance, we experiment with three empirical (temporal) contact networks: a network of pig movements, a network of sexual contacts, and a network of face-to-face contacts between residents of a village in Malawi. The results show that active querying strategies can lead to substantially improved source inference results as compared to baseline heuristics. In fact, querying only a small fraction of nodes in a network is often enough to achieve a source inference performance comparable to a situation where the infection states of all nodes are known.
01A - Beitrag in wissenschaftlicher Zeitschrift
Maximizing the likelihood of detecting outbreaks in temporal networks
(Springer, 2020) Sterchi, Martin; Sarasua, Cristina; Grütter, Rolf; Bernstein, Abraham; Cherifi, Hocine; Gaito, Sabrina; Mendes, José Fernendo; Moro, Esteban; Rocha, Luis Mateus
Epidemic spreading occurs among animals, humans, or computers and causes substantial societal, personal, or economic losses if left undetected. Based on known temporal contact networks, we propose an outbreak detection method that identifies a small set of nodes such that the likelihood of detecting recent outbreaks is maximal. The two-step procedure involves (i) simulating spreading scenarios from all possible seed configurations and (ii) greedily selecting nodes for monitoring in order to maximize the detection likelihood. We find that the detection likelihood is a submodular set function for which it has been proven that greedy optimization attains at least 63% of the optimal (intractable) solution. The results show that the proposed method detects more outbreaks than benchmark methods suggested recently and is robust against badly chosen parameters. In addition, our method can be used for outbreak source detection. A limitation of this method is its heavy use of computational resources. However, for large graphs the method could be easily parallelized.
04B - Beitrag Konferenzschrift
Outbreak detection for temporal contact data
(Springer, 2021) Sterchi, Martin; Sarasua, Cristina; Grütter, Rolf; Bernstein, Abraham
Epidemic spreading is a widely studied process due to its importance and possibly grave consequences for society. While the classical context of epidemic spreading refers to pathogens transmitted among humans or animals, it is straightforward to apply similar ideas to the spread of information (e.g., a rumor) or the spread of computer viruses. This paper addresses the question of how to optimally select nodes for monitoring in a network of timestamped contact events between individuals. We consider three optimization objectives: the detection likelihood, the time until detection, and the population that is affected by an outbreak. The optimization approach we use is based on a simple greedy approach and has been proposed in a seminal paper focusing on information spreading and water contamination. We extend this work to the setting of disease spreading and present its application with two example networks: a timestamped network of sexual contacts and a network of animal transports between farms. We apply the optimization procedure to a large set of outbreak scenarios that we generate with a susceptible-infectious-recovered model. We find that simple heuristic methods that select nodes with high degree or many contacts compare well in terms of outbreak detection performance with the (greedily) optimal set of nodes. Furthermore, we observe that nodes optimized on past periods may not be optimal for outbreak detection in future periods. However, seasonal effects may help in determining which past period generalizes well to some future period. Finally, we demonstrate that the detection performance depends on the simulation settings. In general, if we force the simulator to generate larger outbreaks, the detection performance will improve, as larger outbreaks tend to occur in the more connected part of the network where the top monitoring nodes are typically located. A natural progression of this work is to analyze how a representative set of outbreak scenarios can be generated, possibly taking into account more realistic propagation models.
01A - Beitrag in wissenschaftlicher Zeitschrift
The pig transport network in Switzerland. Structure, patterns, and implications for the transmission of infectious diseases between animal holdings
(Public Library of Science, 2019) Sterchi, Martin; Faverjon, Céline; Sarasua, Cristina; Vargas, Maria Elena; Berezowski, John; Bernstein, Abraham; Grütter, Rolf; Nathues, Heiko
The topology of animal transport networks contributes substantially to how fast and to what extent a disease can transmit between animal holdings. Therefore, public authorities in many countries mandate livestock holdings to report all movements of animals. However, the reported data often does not contain information about the exact sequence of transports, making it impossible to assess the effect of truck sharing and truck contamination on disease transmission. The aim of this study was to analyze the topology of the Swiss pig transport network by means of social network analysis and to assess the implications for disease transmission between animal holdings. In particular, we studied how additional information about transport sequences changes the topology of the contact network. The study is based on the official animal movement database in Switzerland and a sample of transport data from one transport company. The results show that the Swiss pig transport network is highly fragmented, which mitigates the
01A - Beitrag in wissenschaftlicher Zeitschrift