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Internet searching may predict novel symptoms, but not new outbreaks. A long-term evaluation of infoveillance in COVID-19

100%
Radziwon J. A., Lubiejewski M., Waszak P., Heleniak Z., Dębska-Ślizień M. A.
European Journal of Translational and Clinical Medicine
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2023
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vol. 6
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issue 2
71-77
EN
Background: At the beginning of COVID-19 pandemic authors in several countries reported the possibility of predicting disease outbreaks using internet analysis and search tools like GoogleTrends™. Our aim was to investigate the impact of changes in COVID-19 symptomatology and pandemic intensity on those predictions. Material and methods: GoogleTrends™ was utilized to track online searches for COVID-19 symptoms in Poland during two years of the pandemic. Search volumes were then assessed for correlation with daily cases in each wave of infection separately. Results: The symptoms that correlated strongly with new cases were anosmia and ageusia (Spearman's rho=0.5230 and rho=0.4483 respectively, p<0.01). Searches for these symptoms preceded an outbreak by 12 days during the first wave of infections, but this gap was later shortened to five days. The frequency of searching for these symptoms markedly diminished during the last phase and was no longer adequate. Stronger correlations were then shown for fever, sore throat, and headache. Conclusions: In conclusion, COVID-19 case prediction using GoogleTrends™ did not remain possible later on in the pandemic course. However, noticeable changes reflecting novel features of emerging SARS-CoV-2 variants were observed. Therefore, monitoring symptom changes and virus evolution might be a promising application of internet search analysis in the future.
2
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Instance-based regression with missing data applied to a photocatalytic oxidation process

100%
Leon F., Piuleac C., Curteanu S., Poulios I.
Open Chemistry
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2012
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vol. 10
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issue 4
1149-1156
EN
In this paper, a modified nearest-neighbor regression method (kNN) is proposed to model a process with incomplete information of the measurements. This technique is based on the variation of the coefficients used to weight the distances of the instances. The case study selected for testing this algorithm was the photocatalytic degradation of Reactive Red 184 (RR184), a dye belonging to the group of azo compounds, which is widely used in manufacturing paint paper, leather and fabrics. The process is conducted with TiO2 as catalyst (an inexpensive semiconductor material, completely inert chemically and biologically), in the presence of H2O2 (with the role of increasing the rate of photo-oxidation), at different pH values. The final concentration of RR184 is predicted accurately with the modified kNN regression method developed in this article. A comparison with other machine learning methods (sequential minimal optimization regression, decision table, reduced error pruning tree, M5 pruned model tree) proves the superiority and efficiency of the proposed algorithm, not only for its results, but for its simplicity and flexibility in manipulating incomplete experimental data. [...]
3
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Modifying a kidney injury score by including perioperative data Comparison of three predictive scores

100%
Heise D., Kunze N., Bürger J., Quintel M., Perl T.
Open Medicine
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2012
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vol. 7
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issue 4
420-428
4
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Flash point of organic binary mixtures containing alcohols: experiment and prediction

100%
Hristova M., Damgaliev D.
Open Chemistry
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2013
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vol. 11
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issue 3
388-393
EN
The flash points of three organic binary mixtures containing alcohols were measured in the present work. The experimental data was obtained using the Pensky-Martens closed cup tester. The experimental data were compared with the values calculated by the Liaw model. Activity coefficients were calculated by the Wilson equation and NRTL equation. The accuracy of predicted flash point values is dependent on the thermodynamic model used for activity coefficient.
5
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Predictions of cancer incidence in Poland in 2019

88%
Wojtyś P., Godlewski D., Antczak A.
Open Medicine
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2013
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vol. 8
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issue 2
185-191
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