Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations

Accurate time series forecasting is a key issue to support individual and or- ganizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neu- ral networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on sea- sonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series.The research was supported by the European Regional Development Fund in the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070). Furthermore, we gratefully acknowledge partial support of the project KON- TAKT II - LH12229 of MSˇMT CˇR

Price Trackers Inspired by Immune Memory

In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. Tests are performed to examine the algorithm’s ability to successfully identify trends in a small data set. The influence of the long term memory pool is then examined. We find the algorithm is able to identify price trends presented successfully and efficiently

Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK

Following the UK Government's Living with COVID-19 Strategy and the end of universal testing, hospital admissions are an increasingly important measure of COVID-19 pandemic pressure. Understanding leading indicators of admissions at National Health Service (NHS) Trust, regional and national geographies help health services plan capacity needs and prepare for ongoing pressures. We explored the spatio-temporal relationships of leading indicators of hospital pressure across successive waves of SARS-CoV-2 incidence in England. This includes an analysis of internet search volume values from Google Trends, NHS triage calls and online queries, the NHS COVID-19 App, lateral flow devices and the ZOE App. Data sources were analysed for their feasibility as leading indicators using linear and non-linear methods; granger causality, cross correlations and dynamic time warping at fine spatial scales. Consistent temporal and spatial relationships were found for some of the leading indicators assessed across resurgent waves of COVID-19. Google Trends and NHS queries consistently led admissions in over 70% of Trusts, with lead times ranging from 5-20 days, whereas an inconsistent relationship was found for the ZOE app, NHS COVID-19 App, and rapid testing, that diminished with granularity, showing limited autocorrelation of leads between -7 to 7 days. This work shows that novel syndromic surveillance data has utility for understanding the expected hospital burden at fine spatial scales. The analysis shows at low level geographies that some surveillance sources can predict hospital admissions, though care must be taken in relying on the lead times and consistency between waves

Frontiers of parasitology research in the People's Republic of China : infection, diagnosis, protection and surveillance

ABSTRACT: Control and eventual elimination of human parasitic diseases in the People's Republic of China (P.R. China) requires novel approaches, particularly in the areas of diagnostics, mathematical modelling, monitoring, evaluation, surveillance and public health response. A comprehensive effort, involving the collaboration of 188 scientists (<85% from P.R. China) from 48 different institutions and universities (80% from P.R. China), covers this collection of 29 articles published in Parasites & Vectors. The research mainly stems from a research project entitled 'Surveillance and diagnostic tools for major parasitic diseases in P.R. China' (grant no. 2008ZX10004-011) and highlights the frontiers of research in parasitology. The majority of articles in this thematic series deals with the most important parasitic diseases in P.R. China, emphasizing Schistosoma japonicum, Plasmodium vivax and Clonorchis sinensis plus some parasites of emerging importance such as Angiostrongylus cantonensis. Significant achievements have been made through the collaborative research programme in the following three fields: (i) development of control strategies for the national control programme; (ii) updating the surveillance data of parasitic infections both in human and animals; and (iii) improvement of existing, and development of novel, diagnostic tools to detect parasitic infections. The progress is considerable and warrants broad validation efforts. Combined with the development of improved tools for diagnosis and surveillance, integrated and multi-pronged control strategies now pave the way for elimination of parasitic diseases in P.R. China. Experiences and lessons learned can stimulate control and elimination efforts of parasitic diseases in other parts of the world

Detecting COVID-19 Outbreak with Anomalous Term Frequency

Previously many studies have aimed at predicting the trend of a disease through time series forecasting using machine learning methods. However, data extracted from the real world is often noisy, which can pose numerous challenges for directly predicting the trend, and therefore leading to suboptimal prediction results. Furthermore, real-world data is usually very large, that is, having very long time periods. When it comes to data of such scale, trend forecasting becomes intractable even to state-of-the-art forecasting algorithms such as RNN-LSTM. In the past, not much research has been conducted in applying anomaly detection for disease outbreak detection, including the most recent COVID-19 pandemic. Consequently, in this research, we propose redefining the problem into outbreak detection, which aims to predict whether a future point is or is not a sign of a large scaled COVID-19 outbreak. Through simplifying a complex regression problem into a binary classification problem, the requirements of the learning model may be decreased and therefore the learning performance may be enhanced

Forecasting the 2013--2014 Influenza Season using Wikipedia

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects between 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013--2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method projected the actual outcome with a high probability. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has past.Comment: Second version. In previous version 2 figure references were compiling wrong due to error in latex sourc

A data-driven epidemic model to analyse the course of covid-19 in the Veneto region

The current COVID-19 pandemic is an unprecedented global health crisis, with severe economic impacts and social damages. Mathematical models are playing an important role in this ongoing emergency, providing scientific support to inform public policies worldwide. In this thesis work, an epidemic model for the spread of the novel Coronavirus disease in the Veneto region has been proposed. Starting from the available local Health System data to examine past year contagion numbers and other features potentiality, a SEIQRD (Susceptible Exposed Infected Quarantined Removed Deceased) compartmental schema has been designed generalizing the classic SIR model. Then, the infection dynamics have been practically implemented in two versions: as a Deterministic Equation-based formulation and as an Agent-based model. While the former has been maintained simple and computationally inexpensive in order to serve as a baseline and to quickly provide parameter estimates, for the latter a detailed metapopulation of agents with personalized attributes and network of contacts has been developed to recreate as realistic as possible simulations. Once these models have been trained and validated, they could became valuable tools for various types of analysis and predictions. In particular, the agent-based version, thanks to its flexibility as well as to its higher resolution, could be exploited for exclusive a posteriori evaluations of the effectiveness of the adopted containment measures in reducing the pandemic in Veneto.ope

T-Growth Stochastic Model: Simulation and Inference via Metaheuristic Algorithms

The main objective of this work is to introduce a stochastic model associated with the one described by the T-growth curve, which is in turn a modification of the logistic curve. By conveniently reformulating the T curve, it may be obtained as a solution to a linear differential equation. This greatly simplifies the mathematical treatment of the model and allows a diffusion process to be defined, which is derived from the non-homogeneous lognormal diffusion process, whose mean function is a T curve. This allows the phenomenon under study to be viewed in a dynamic way. In these pages, the distribution of the process is obtained, as are its main characteristics. The maximum likelihood estimation procedure is carried out by optimization via metaheuristic algorithms. Thanks to an exhaustive study of the curve, a strategy is obtained to bound the parametric space, which is a requirement for the application of various swarm-based metaheuristic algorithms. A simulation study is presented to show the validity of the bounding procedure and an example based on real data is provided.Ministerio de Economía, Industria y Competitividad, Spain, under Grant MTM2017-85568-PFEDER/Junta de Andalucía-Consejería de Economía y Conocimiento, Spain, Grant A-FQM-456-UGR1