Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods
Bibliographic Data
| ID | 22034656 |
|---|---|
| Authors | Wolfgang Hamer (0000-0002-5943-5020, Christian-Albrechts-Universität zu Kiel, corresponding author), Tim Birr (0000-0001-8679-0573, Christian-Albrechts-Universität zu Kiel), Joseph-Alexander Verreet (Christian-Albrechts-Universität zu Kiel), Joseph‐Alexander Verreet (0000-0001-5839-7432, Christian-Albrechts-Universität zu Kiel), Rainer Duttmann (0000-0001-5606-2938, Christian-Albrechts-Universität zu Kiel), Holger Klink (Christian-Albrechts-Universität zu Kiel) |
| Year | 2020 |
| Volume | 9 |
| Issue | 1 |
| Pages | 44 |
| Publication date | 2020-01-15 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9010044 |
| OpenAlex | W2999115404 |
| Language | EN |
| Citations received | 2 |
| References cited | 28 |
Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted
Biological dispersal · Biology · Cartography · Geography · Machine learning · Population · Powdery mildew · Regionalisation · Computer Science · Genetics and Plant Breeding · Powdery Mildew Fungal Diseases · Wheat and Barley Genetics and Pathology · Artificial Intelligence · Ecology
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2020 - 2023 (4) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 2 |