A decision support framework for prediction of avian influenza

Yousefinaghani, Samira; Dara, Rozita; Poljak, Zvonimir; Sharif, Shayan

doi:10.1038/s41598-020-75889-7

Cited by 11 publications

(7 citation statements)

References 36 publications

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“…One of these initiatives consisted of developing decision support systems (DSS) 15 to assist policymakers in taking effective decisions for the management of infectious disease outbreaks. 16 In this context, knowledge-based DSS systems are ensured to provide more accurate decision-making by successfully using timely and relevant data, information and knowledge management with prediction and suggestion techniques to aid decision-making. 17 …”

Section: Introductionmentioning

confidence: 99%

“…In this way, Twitter data is used to suggest to the user which geographical area needs particular attention and dedicated monitoring and can be used as a source of disease surveillance and hence can bypass formal information channels and enhance the response speed of control measures. 16 On the other hand, Twitter is a valuable source of data for identifying ‘hot spots’ and assigning remote-sensing data acquisition tasks. 27 Satellite images detect features that indicate crowding by seeing vehicles in parking lots.…”

Section: Introductionmentioning

confidence: 99%

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A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning

Atek

Bianchini²,

Vito

et al. 2023

DIGITAL HEALTH

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Objective Coronavirus disease 2019 demonstrated the inconsistencies in adequately responding to biological threats on a global scale due to a lack of powerful tools for assessing various factors in the formation of the epidemic situation and its forecasting. Decision support systems have a role in overcoming the challenges in health monitoring systems in light of current or future epidemic outbreaks. This paper focuses on some applied examples of logistic planning, a key service of the Earth Cognitive System for Coronavirus Disease 2019 project, here presented, evidencing the added value of artificial intelligence algorithms towards predictive hypotheses in tackling health emergencies. Methods Earth Cognitive System for Coronavirus Disease 2019 is a decision support system designed to support healthcare institutions in monitoring, management and forecasting activities through artificial intelligence, social media analytics, geospatial analysis and satellite imaging. The monitoring, management and prediction of medical equipment logistic needs rely on machine learning to predict the regional risk classification colour codes, the emergency rooms attendances, and the forecast of regional medical supplies, synergically enhancing geospatial and temporal dimensions. Results The overall performance of the regional risk colour code classifier yielded a high value of the macro-average F1-score (0.82) and an accuracy of 85%. The prediction of the emergency rooms attendances for the Lazio region yielded a very low root mean square error (<11 patients) and a high positive correlation with the actual values for the major hospitals of the Lazio region which admit about 90% of the region's patients. The prediction of the medicinal purchases for the regions of Lazio and Piemonte has yielded a low root mean squared percentage error of 16%. Conclusions Accurate forecasting of the evolution of new cases and drug utilisation enables the resulting excess demand throughout the supply chain to be managed more effectively. Forecasting during a pandemic becomes essential for effective government decision-making, managing supply chain resources, and for informing tough policy decisions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning

Atek

Bianchini²,

Vito

et al. 2023

DIGITAL HEALTH

View full text Add to dashboard Cite

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“…Providing early signals ahead of outbreaks is essential for early public health responses. Prediction systems for other diseases have been built to facilitate management in disease emergencies and making rapid policy decisions (1,2).…”

Section: Introductionmentioning

confidence: 99%

“…The present study aimed to examine the potential of online platforms in providing early warnings of first and second waves of COVID-19 outbreaks in the US and Canada for an 8-month period. The main objectives were: (1) to visualize the correlation between digital data sources and COVID-19 official cases; (2) to compare various sources of internet-driven data in terms of their timeliness and precision in providing alert signals of disease waves; and (3) to prioritize COVID-19 symptoms by their values in detecting disease trends.…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 Waves Using Social Media and Google Search: A Case Study of the US and Canada

Yousefinaghani

Dara

Mubareka

et al. 2021

Front. Public Health

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The ongoing COVID-19 pandemic has posed a severe threat to public health worldwide. In this study, we aimed to evaluate several digital data streams as early warning signals of COVID-19 outbreaks in Canada, the US and their provinces and states. Two types of terms including symptoms and preventive measures were used to filter Twitter and Google Trends data. We visualized and correlated the trends for each source of data against confirmed cases for all provinces and states. Subsequently, we attempted to find anomalies in indicator time-series to understand the lag between the warning signals and real-word outbreak waves. For Canada, we were able to detect a maximum of 83% of initial waves 1 week earlier using Google searches on symptoms. We divided states in the US into two categories: category I if they experienced an initial wave and category II if the states have not experienced the initial wave of the outbreak. For the first category, we found that tweets related to symptoms showed the best prediction performance by predicting 100% of first waves about 2–6 days earlier than other data streams. We were able to only detect up to 6% of second waves in category I. On the other hand, 78% of second waves in states of category II were predictable 1–2 weeks in advance. In addition, we discovered that the most important symptoms in providing early warnings are fever and cough in the US. As the COVID-19 pandemic continues to spread around the world, the work presented here is an initial effort for future COVID-19 outbreaks.

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“…These approaches were used to predict the Influenza A virus affecting humans and have some limitations. Previously, surveillance tools for predicting the spread of avian influenza virus was developed ( Yousefinaghani et al, 2020 ). Furthermore, a machine learning method was also devised to predict global reservoirs for low pathogenic avian influenza virus using big data ( Gulyaeva et al, 2020 ) but there was no specific strategy to classify only Avian Influenza A virus subtypes, so there was a need to develop a method.…”

Section: Introductionmentioning

confidence: 99%

Computational Method for Classification of Avian Influenza A Virus Using DNA Sequence Information and Physicochemical Properties

Humayun

Khan

Fawad³

et al. 2021

Front. Genet.

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Accurate and fast characterization of the subtype sequences of Avian influenza A virus (AIAV) hemagglutinin (HA) and neuraminidase (NA) depends on expanding diagnostic services and is embedded in molecular epidemiological studies. A new approach for classifying the AIAV sequences of the HA and NA genes into subtypes using DNA sequence data and physicochemical properties is proposed. This method simply requires unaligned, full-length, or partial sequences of HA or NA DNA as input. It allows for quick and highly accurate assignments of HA sequences to subtypes H1–H16 and NA sequences to subtypes N1–N9. For feature extraction, k-gram, discrete wavelet transformation, and multivariate mutual information were used, and different classifiers were trained for prediction. Four different classifiers, Naïve Bayes, Support Vector Machine (SVM), K nearest neighbor (KNN), and Decision Tree, were compared using our feature selection method. This comparison is based on the 30% dataset separated from the original dataset for testing purposes. Among the four classifiers, Decision Tree was the best, and Precision, Recall, F1 score, and Accuracy were 0.9514, 0.9535, 0.9524, and 0.9571, respectively. Decision Tree had considerable improvements over the other three classifiers using our method. Results show that the proposed feature selection method, when trained with a Decision Tree classifier, gives the best results for accurate prediction of the AIAV subtype.

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A decision support framework for prediction of avian influenza

Cited by 11 publications

References 36 publications

A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning

A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning

Prediction of COVID-19 Waves Using Social Media and Google Search: A Case Study of the US and Canada

Computational Method for Classification of Avian Influenza A Virus Using DNA Sequence Information and Physicochemical Properties

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