Borough-level COVID-19 forecasting in London using deep learning techniques and a novel MSE-Moran’s I loss function
Following its identification in late 2019, COVID-19 has spread around the globe, and been declared a pandemic. With this in mind, modelling the spread of COVID-19 remains important for responding effectively. To date research has focused primarily on modelling the spread of COVID-19 on national and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-04-01
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| Series: | Results in Physics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211379722001450 |
| _version_ | 1828878385305092096 |
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| author | Frederik Olsen Calogero Schillaci Mohamed Ibrahim Aldo Lipani |
| author_facet | Frederik Olsen Calogero Schillaci Mohamed Ibrahim Aldo Lipani |
| author_sort | Frederik Olsen |
| collection | DOAJ |
| description | Following its identification in late 2019, COVID-19 has spread around the globe, and been declared a pandemic. With this in mind, modelling the spread of COVID-19 remains important for responding effectively. To date research has focused primarily on modelling the spread of COVID-19 on national and regional scales with just a few studies doing so on a city and sub-city scale. However, no attempts have yet been made to design and optimize a model explicitly for accurately forecasting the spread of COVID-19 at sub-city scale. This research aimed to address this research gap by developing an experimental LSTM-ANN deep learning model. The model is largely autoregressive in nature as it considers temporally lagged borough-level COVID-19 cases data from the last 9 days, but also considers temporally lagged (i) borough-level NO2 concentration data, (ii) government stringency data, and (iii) climatic data from the last 9 days, as well as non-temporally variable borough-level urban characteristics data when modelling and forecasting the spread of the disease. The model was also encouraged to learn the spatial relationships between boroughs with regards to the spread of COVID-19 by a novel MSE-Moran’s I loss function. Overall, the model’s performance appears promising and so the model represents a useful tool for assisting the decision making and interventions of governing bodies within cities. A sensitivity analysis also indicated that of the non COVID-19 variables, the government stringency is particularly important in the modelling process, with this being closely followed by the climatic variables, the NO2 concentration data, and finally the urban characteristics data. Additionally, the introduction of the novel MSE-Moran’s I loss function appeared to improve the model’s forecasting performance, and so this research has implications at the intersection of deep learning and disease modelling. It may also have implications within spatio-temporal forecasting more generally because such a feature may have the potential to improve forecasting in other spatio-temporal applications |
| first_indexed | 2024-12-13T09:05:45Z |
| format | Article |
| id | doaj.art-6bc3e6bee6e0440b9fb4935820bc29a7 |
| institution | Directory Open Access Journal |
| issn | 2211-3797 |
| language | English |
| last_indexed | 2024-12-13T09:05:45Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Physics |
| spelling | doaj.art-6bc3e6bee6e0440b9fb4935820bc29a72022-12-21T23:53:05ZengElsevierResults in Physics2211-37972022-04-0135105374Borough-level COVID-19 forecasting in London using deep learning techniques and a novel MSE-Moran’s I loss functionFrederik Olsen0Calogero Schillaci1Mohamed Ibrahim2Aldo Lipani3Department of Civil, Environmental and Geomatic Engineering, University College London (UCL), England; Corresponding author.Department of Agricultural and Environmental Science, University of Milan, Via Celoria 2 Milan, ItalyDepartment of Civil, Environmental and Geomatic Engineering, University College London (UCL), EnglandDepartment of Civil, Environmental and Geomatic Engineering, University College London (UCL), EnglandFollowing its identification in late 2019, COVID-19 has spread around the globe, and been declared a pandemic. With this in mind, modelling the spread of COVID-19 remains important for responding effectively. To date research has focused primarily on modelling the spread of COVID-19 on national and regional scales with just a few studies doing so on a city and sub-city scale. However, no attempts have yet been made to design and optimize a model explicitly for accurately forecasting the spread of COVID-19 at sub-city scale. This research aimed to address this research gap by developing an experimental LSTM-ANN deep learning model. The model is largely autoregressive in nature as it considers temporally lagged borough-level COVID-19 cases data from the last 9 days, but also considers temporally lagged (i) borough-level NO2 concentration data, (ii) government stringency data, and (iii) climatic data from the last 9 days, as well as non-temporally variable borough-level urban characteristics data when modelling and forecasting the spread of the disease. The model was also encouraged to learn the spatial relationships between boroughs with regards to the spread of COVID-19 by a novel MSE-Moran’s I loss function. Overall, the model’s performance appears promising and so the model represents a useful tool for assisting the decision making and interventions of governing bodies within cities. A sensitivity analysis also indicated that of the non COVID-19 variables, the government stringency is particularly important in the modelling process, with this being closely followed by the climatic variables, the NO2 concentration data, and finally the urban characteristics data. Additionally, the introduction of the novel MSE-Moran’s I loss function appeared to improve the model’s forecasting performance, and so this research has implications at the intersection of deep learning and disease modelling. It may also have implications within spatio-temporal forecasting more generally because such a feature may have the potential to improve forecasting in other spatio-temporal applicationshttp://www.sciencedirect.com/science/article/pii/S2211379722001450COVID-19Deep LearningLSTMEpidemiological ModellingPandemic |
| spellingShingle | Frederik Olsen Calogero Schillaci Mohamed Ibrahim Aldo Lipani Borough-level COVID-19 forecasting in London using deep learning techniques and a novel MSE-Moran’s I loss function Results in Physics COVID-19 Deep Learning LSTM Epidemiological Modelling Pandemic |
| title | Borough-level COVID-19 forecasting in London using deep learning techniques and a novel MSE-Moran’s I loss function |
| title_full | Borough-level COVID-19 forecasting in London using deep learning techniques and a novel MSE-Moran’s I loss function |
| title_fullStr | Borough-level COVID-19 forecasting in London using deep learning techniques and a novel MSE-Moran’s I loss function |
| title_full_unstemmed | Borough-level COVID-19 forecasting in London using deep learning techniques and a novel MSE-Moran’s I loss function |
| title_short | Borough-level COVID-19 forecasting in London using deep learning techniques and a novel MSE-Moran’s I loss function |
| title_sort | borough level covid 19 forecasting in london using deep learning techniques and a novel mse moran s i loss function |
| topic | COVID-19 Deep Learning LSTM Epidemiological Modelling Pandemic |
| url | http://www.sciencedirect.com/science/article/pii/S2211379722001450 |
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