What is she best known for?
The fields of study she is best known for:
- Computer network
- Statistics
- Artificial intelligence
Caterina Scoglio mainly investigates Epidemic model, Complex network, Robustness, Mathematical optimization and Computer network.
Her Epidemic model research is multidisciplinary, incorporating elements of Econometrics and Behavioral pattern.
Her work in Complex network tackles topics such as Graph theory which are related to areas like Adjacency matrix and Markov process.
Caterina Scoglio has researched Robustness in several fields, including Islanding, Electric power system, Multiple edges and Algebraic connectivity.
She frequently studies issues relating to Distributed computing and Computer network.
Her studies in Multiprotocol Label Switching integrate themes in fields like Traffic engineering and Bandwidth allocation.
Her most cited work include:
- Generalized epidemic mean-field model for spreading processes over multilayer complex networks (184 citations)
- Competitive epidemic spreading over arbitrary multilayer networks (140 citations)
- On the existence of a threshold for preventive behavioral responses to suppress epidemic spreading. (118 citations)
What are the main themes of her work throughout her whole career to date?
Caterina Scoglio spends much of her time researching Computer network, Distributed computing, Complex network, Mathematical optimization and Robustness.
Her research investigates the connection with Distributed computing and areas like Network topology which intersect with concerns in Upper and lower bounds and Network model.
Her Complex network study incorporates themes from Survivability, Epidemic model, Graph theory, Artificial intelligence and Graph.
Her study in Mathematical optimization focuses on Heuristics in particular.
Her biological study spans a wide range of topics, including Cascading failure, Electric power system and Topology.
Her Multiprotocol Label Switching research incorporates themes from Bandwidth allocation, Bandwidth, Path and Traffic engineering.
She most often published in these fields:
- Computer network (20.43%)
- Distributed computing (17.83%)
- Complex network (16.09%)
What were the highlights of her more recent work (between 2018-2021)?
- Outbreak (6.96%)
- Statistics (5.65%)
- Epidemic model (9.13%)
In recent papers she was focusing on the following fields of study:
Caterina Scoglio mainly focuses on Outbreak, Statistics, Epidemic model, Beef cattle and Complex network.
Her study focuses on the intersection of Outbreak and fields such as Risk assessment with connections in the field of Dengue fever, Host, Cartography and Probabilistic logic.
Caterina Scoglio has included themes like State parameter, Transmission, Feature selection and Dual in her Statistics study.
Her study in Epidemic model is interdisciplinary in nature, drawing from both Group, Network model, Contact tracing, Bounded function and Isoperimetric inequality.
The concepts of her Beef cattle study are interwoven with issues in Cattle movement and Disease transmission.
Her work deals with themes such as Probability density function, Mathematical optimization and Identification, which intersect with Complex network.
Between 2018 and 2021, her most popular works were:
- Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China (15 citations)
- A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus. (13 citations)
- Contact Adaption During Epidemics: A Multilayer Network Formulation Approach (12 citations)
In her most recent research, the most cited papers focused on:
- Computer network
- Statistics
- Artificial intelligence
Her main research concerns Outbreak, Network topology, Limiting, Robustness and Distributed computing.
Her Outbreak research is multidisciplinary, relying on both Evolutionary biology, Host and Zika virus.
Her research integrates issues of Mathematical optimization, Mathematical model, Monotonic function and Nonlinear system in her study of Network topology.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
