A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from Early Warning Alert and Response System in Syria

Darwish, Ali; Rahhal, Yasser; Jafar, Assef

doi:10.1186/s13104-020-4889-5

Cited by 31 publications

(19 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the outbreak has differences with other recent outbreaks, which brings into question the ability of standard models to deliver accurate results [3]. In addition to the numerous known and unknown variables involved in the spread, the complexity of population-wide behavior in various geopolitical areas and differences in containment strategies dramatically increased model uncertainty [4]. Consequently, standard epidemiological models face new challenges to deliver more reliable results.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Outbreak Prediction with Machine Learning

et al. 2020

View full text Add to dashboard Cite

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.

show abstract

Section: Introductionmentioning

confidence: 99%

COVID-19 Outbreak Prediction with Machine Learning

et al. 2020

View full text Add to dashboard Cite

show abstract

“…There is a huge amount of data available throughout the world related to COVID-19, but the challenge is how to handle issues such as what are the variety, velocity and volume of data, and how to handle the complex data. Due to the complex behavior of the population in different geographical areas, several variables involved in the transmission of disease, and variation in the methods of containment leads to enhancement of uncertainty of any model [99].…”

Section: Challengesmentioning

confidence: 99%

A Review on Predictive Systems and Data Models for COVID-19

Khan

Khanam

Ramlal

et al. 2020

Studies in Computational Intelligence

View full text Add to dashboard Cite

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or novel Coronavirus, responsible for the transmission of Coronavirus disease , represents the causative agent of a conceivably deadly sickness, and a global public health concern. In December 2019 in China (Wuhan), the spread of SARS-CoV-2 has taken the shape of a pandemic and affects the respiratory system and manifests as pneumonia in humans, influencing more than 216 nations so far. On January 12th, 2020, the World Health Organization (WHO) gave the name "2019-nCoV" for 2019 novel Coronavirus, and the infection further on February 11th, 2020, is authoritatively named as COVID-19. Instead of using various predictive systems and data models, the prevalence of COVID-19 is continuously increasing, affecting millions of individuals. This chapter focuses on predictive systems and data models utilized from the beginning of COVID-19 outbreak that helped in predicting the cases and deaths qualities of COVID-19 in the desire for giving a reference to future investigations and help in controlling the spread of further epidemics. And also suggest how these data models can help and enable policymakers to plan the regional and national healthcare systems required and design monitored active plans.

show abstract

“…5/15 The weekly outbreak labelling in each region is done in two steps, given by Equation (10) and (11) respectively. An illustration of this labeling process is shown in Figure 4.…”

Section: • Model 1: Simple Thresholdmentioning

confidence: 99%

“…Ali Darwish investigated the performance of three different feature spaces in different models to forecast the weekly ILI rate in Syria using EWARS data from WHO 10 . In the same year, Yuzhou Zhang combined GFT together with WHO published data and developed a multivariate seasonal autoregressive integrated moving average model to track influenza epidemics in Australia, China, US, and UK.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data-Driven Methodology for Influenza Outbreak Detection and Prediction

Yan²

2020

SSRN Journal

View full text Add to dashboard Cite

Influenza is an infectious disease that leads to an estimated 5 million severe illness cases and 650,000 respiratory deaths worldwide each year. Early detection and prediction of influenza outbreaks are crucial to efficient resource planning to save patients' lives and healthcare costs. This paper proposes a novel data-driven methodology for influenza outbreaks detection and prediction. The doctor's diagnosis-based prescription dataset of Influenza-Like Illness (ILI) from more than 3,000 clinics in Malaysia is used in this study because the prescription data are reliable and can be captured timely. A new Region Index (RI) of the influenza outbreak is proposed based on the prescription dataset. With the newly proposed RI metric, statistical and machine learning models are developed to detect and predict influenza outbreaks. Cross-validation is conducted to evaluate the prediction model performance. The proposed methods are also validated by real-world evidence. It is proved to be sensitive and accurate in influenza outbreak prediction with 80 − 90% accuracy, 70 − 80% recall, and 70 − 80% precision scores.

show abstract

A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from Early Warning Alert and Response System in Syria

Cited by 31 publications

References 26 publications

COVID-19 Outbreak Prediction with Machine Learning

COVID-19 Outbreak Prediction with Machine Learning

A Review on Predictive Systems and Data Models for COVID-19

A Novel Data-Driven Methodology for Influenza Outbreak Detection and Prediction

Contact Info

Product

Resources

About