SCouT · Sparse Composite Likelihood Inference in Count Time Series
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2016-06-01 → 2018-05-31
- EU contribution
- €168,277
- Participants
- 1
- Scheme
- MSCA-IF-EF-ST
Lines connect the coordinator with its partners.
Results in brief
Sparse Composite Likelihood Inference in Count Time Series
The analysis of infectious disease data is essential for strengthening surveillance systems and supporting the response to public health threats. Statistical modelling of infectious disease data needs to account for a variety of aspects including seasonality, trends, the effect of concomitant infectious diseases, human activities that may favor the disease diffusion and external factors such as weather conditions. Infectious disease data typically consist of numbers of cases that are recorded sequentially in time, usually on a daily or monthly basis, and present some temporal dependence, e.g. the evolution of a disease today depends on its evolution the day before. On the other hand, monitoring of a single disease might be of limited interest for outbreak detection purposes, especially in situations where the same underlying process, such as a common pathogen, has generated multiple disease series introducing dependencies between them. In such cases, there is an emerging need for multivariate surveillance techniques that can account for such dynamics and thus improve the effectiveness and timeliness in outbreak detection. Failure to account properly for these data features can result in incorrect statistical results. Nowadays, a variety of sophisticated statistical methods able to account for the characteristics of infectious disease data is available in the literature. However, the computational difficulties that often come along with the implementation of these methods, limit their practical usefulness. The SCouT project copes with methodological and implementation aspects of advanced statistical techniques aiming to enhance their flexibility and applicability for health surveillance purposes including identification of disease outbreaks. An example of the application of the developed methodology to the analysis of infectious disease data is displayed in Figure 1. The figure shows the monthly number of invasive meningococcal disease cases in Greece for the years 1999-2016 as obtained by the European Center of Disease Control (ECDC) Surveillance Atlas. Appropriate statistical modeling of such data allows for an effective prediction of the number of future disease cases and thus identification of disease outbreaks. For example, modeling of the invasive meningococcal disease data available until the year 2015 allows us to predict the 2016 cases and identify disease outbreaks, i.e. points outside the shaded area in Figure 1. The statistical methods developed in line with the research objectives of the SCouT project enrich the available toolbox for public health surveillance and will hopefully contribute in the effective and timely detection of disease outbreaks.
Data: CORDIS, © European Union
Project objective
The availability of many interesting datasets consisting of count time series has motivated a steadily increasing research activity towards the development of appropriate statistical models. Nowadays, a variety of time series models constructed on the basis of the integer-valued property of count data is available. However, their practical usefulness is often limited because of difficulties to implement efficient estimation procedures. These difficulties grow significantly when higher-order autoregressive and moving average terms are included in the model or when multivariate time series data are considered. The SCouT project aims to enhance the flexibility of time series models for counts and facilitate their application to data characterized by complex dependence structures. To this end, the SCouT project proposes an innovative methodological approach that integrates two powerful statistical tools, that is sparsity techniques and composite likelihood methods. The project will provide a unified framework for simultaneous order selection and estimation of autoregressive and moving average terms in count time series models, reducing considerably the computational burden of traditional model selection approaches and improving the predictive performance of the fitted model. The potential of the suggested methodology will be further highlighted by investigating its extensions to spatial and spatio-temporal data that are usually characterized by complex dependence structures. Such data are often met in the field of temporal and spatial analysis of public health surveillance data that will be the main application field of the project. The simultaneous order selection and estimation procedure established by the SCouT project will offer great support to statistical methods for public health surveillance and significantly contribute to the achievement of the effective and timely detection of disease outbreaks.
Original text from CORDIS.
Participants
- UNIVERSITA CA' FOSCARI VENEZIA · VeneziaCoordinatorItaly
Links
Data: CORDIS, © European Union