| Literature DB >> 34972117 |
Michele Tizzani1, Violeta Muñoz-Gómez2,3, Marco De Nardi2, Daniela Paolotti1, Olga Muñoz4,5, Piera Ceschi5, Arvo Viltrop6, Ilaria Capua5.
Abstract
SARS-CoV-2 has clearly shown that efficient management of infectious diseases requires a top-down approach which must be complemented with a bottom-up response to be effective. Here we investigate a novel approach to surveillance for transboundary animal diseases using African Swine (ASF) fever as a model. We collected data both at a population level and at the local level on information-seeking behavior respectively through digital data and targeted questionnaire-based surveys to relevant stakeholders such as pig farmers and veterinary authorities. Our study shows how information-seeking behavior and resulting public attention during an epidemic, can be identified through novel data streams from digital platforms such as Wikipedia. Leveraging attention in a critical moment can be key to providing the correct information at the right moment, especially to an interested cohort of people. We also bring evidence on how field surveys aimed at local workers and veterinary authorities remain a crucial tool to assess more in-depth preparedness and awareness among front-line actors. We conclude that these two tools should be used in combination to maximize the outcome of surveillance and prevention activities for selected transboundary animal diseases such as ASF.Entities:
Mesh:
Year: 2021 PMID: 34972117 PMCID: PMC8719698 DOI: 10.1371/journal.pone.0252972
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Weekly moving average of Wikipedia page view count, colored-solid lines, news volume, dotted-black lines, and ASF surveillance cases reports for domestic pigs, dashed-red vertical lines, and wild boars, solid blue vertical lines.
Pearson and Spearman correlation between Wikipedia pageviews and news volume.
| Wikipedia—News by source country | Pearson | Spearman |
|---|---|---|
| ITALY | 0,55 | 0,6 |
| SOUTH KOREA | 0,75 | 0,7 |
| LATVIA | 0,42 | 0,22 |
| LITHUANIA | 0,53 | 0,23 |
| POLAND | 0,53 | 0,72 |
| ROMANIA | 0,64 | 0,71 |
| UKRAINE | 0,33 | 0,46 |
| CZECH REPUBLIC | 0,57 | 0,79 |
| ESTONIA | 0,65 | 0,23 |
| BELGIUM | 0,86 | 0,71 |
| CHINA | 0,74 | 0,73 |
| INDIA | 0,4 | 0,72 |
Adjusted R2 for the two linear regression models applied to predict Wikipedia visits.
| Countries | News | News+Memory |
|---|---|---|
| ITALY | 0.57 | 0.72 |
| SOUTH KOREA | 0.57 | 0.73 |
| LATVIA | 0.44 | 0.59 |
| LITHUANIA | 0.46 | 0.53 |
| POLAND | 0.36 | 0.37 |
| ROMANIA | 0.47 | 0.59 |
| UKRAINE | 0.51 | 0.61 |
| CZECH REPUBLIC | 0.43 | 0.48 |
| ESTONIA | 0.56 | 0.61 |
| BELGIUM | 0.78 | 0.79 |
| CHINA | 0.65 | 0.65 |
| INDIA | 0.28 | 0.64 |
Coefficient estimates for the model with memory.
| Countries | News | News+Memory |
|---|---|---|
| ITALY | 0.20 [0.03, 0.37] | 0.14 [0.11, 0.17] |
| SOUTH KOREA | 0.41 [0.16, 0.65] | 0.43 [0.19, 0.68] |
| LATVIA | 0.17 [0.00, 0.34] | 0.08 [0.06, 0.11] |
| LITHUANIA | 0.41 [0.04, 0.78] | 0.09 [0.04, 0.13] |
| POLAND | 0.29 [-0.05, 0.62] | -0.05 [-0.21, 0.11] |
| ROMANIA | 0.19 [-0.13, 0.51] | 0.25 [0.16, 0.35] |
| UKRAINE | 0.30 [0.14, 0.46] | 0.34 [0.25, 0.43] |
| CZECH REPUBLIC | 0.25 [0.07, 0.43] | 0.16 [0.04, 0.27] |
| ESTONIA | 0.25 [0.03, 0.48] | 0.16 [0.10, 0.23] |
| BELGIUM | 0.61 [0.36, 0.85] | 0.09 [-0.03, 0.20] |
| CHINA | 0.49 [0.17, 0.80] | -0.07 [-0.22, 0.09] |
| INDIA | -0.07 [-0.23, 0.08] | 0.36 [0.20, 0.53] |
The coefficients are all significant with p<0.001, except for Poland (p = 0.009), Romania (p = 0.013), Belgium (p = 0.003), and China (p = 0.02).
Fig 2Percentage of documents per topic defined.