A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada
During a pandemic, data are very “noisy” with enormous amounts of local variation in daily counts, compared with any rapid changes in trend. Accurately characterizing the trends and reliable predictions on future trajectories are important for planning and public situation awareness. We describe a s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-03-01
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| Series: | Epidemics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1755436522000019 |
| _version_ | 1818137740502368256 |
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| author | Muhammad Abu Shadeque Mullah Ping Yan |
| author_facet | Muhammad Abu Shadeque Mullah Ping Yan |
| author_sort | Muhammad Abu Shadeque Mullah |
| collection | DOAJ |
| description | During a pandemic, data are very “noisy” with enormous amounts of local variation in daily counts, compared with any rapid changes in trend. Accurately characterizing the trends and reliable predictions on future trajectories are important for planning and public situation awareness. We describe a semi-parametric statistical model that is used for short-term predictions of daily counts of cases and deaths due to COVID-19 in Canada, which are routinely disseminated to the public by Public Health Agency of Canada. The main focus of the paper is the presentation of the model. Performance indicators of our model are defined and then evaluated through extensive sensitivity analyses. We also compare our model with other commonly used models such as generalizations of logistic models for similar purposes. The proposed model is shown to describe the historical trend very well with excellent ability to predict the short-term trajectory. |
| first_indexed | 2024-12-11T10:01:06Z |
| format | Article |
| id | doaj.art-316edcd8e2a143578cadbd0f7fc0faf9 |
| institution | Directory Open Access Journal |
| issn | 1755-4365 |
| language | English |
| last_indexed | 2024-12-11T10:01:06Z |
| publishDate | 2022-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Epidemics |
| spelling | doaj.art-316edcd8e2a143578cadbd0f7fc0faf92022-12-22T01:12:06ZengElsevierEpidemics1755-43652022-03-0138100537A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in CanadaMuhammad Abu Shadeque Mullah0Ping Yan1Correspondence to: Public Helath Agency of Canada (PHAC), 130 Colonnade Road, Ottawa, Ontario, K1A 0K9, Canada.; Public Health Agency of Canada, CanadaPublic Health Agency of Canada, CanadaDuring a pandemic, data are very “noisy” with enormous amounts of local variation in daily counts, compared with any rapid changes in trend. Accurately characterizing the trends and reliable predictions on future trajectories are important for planning and public situation awareness. We describe a semi-parametric statistical model that is used for short-term predictions of daily counts of cases and deaths due to COVID-19 in Canada, which are routinely disseminated to the public by Public Health Agency of Canada. The main focus of the paper is the presentation of the model. Performance indicators of our model are defined and then evaluated through extensive sensitivity analyses. We also compare our model with other commonly used models such as generalizations of logistic models for similar purposes. The proposed model is shown to describe the historical trend very well with excellent ability to predict the short-term trajectory.http://www.sciencedirect.com/science/article/pii/S1755436522000019Poisson–Gamma mixturePenalized splinesGeneralized linear mixed effects modelSemi-parametric mixed modelBayesian Markov chain Monte Carlo |
| spellingShingle | Muhammad Abu Shadeque Mullah Ping Yan A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada Epidemics Poisson–Gamma mixture Penalized splines Generalized linear mixed effects model Semi-parametric mixed model Bayesian Markov chain Monte Carlo |
| title | A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada |
| title_full | A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada |
| title_fullStr | A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada |
| title_full_unstemmed | A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada |
| title_short | A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada |
| title_sort | semi parametric mixed model for short term projection of daily covid 19 incidence in canada |
| topic | Poisson–Gamma mixture Penalized splines Generalized linear mixed effects model Semi-parametric mixed model Bayesian Markov chain Monte Carlo |
| url | http://www.sciencedirect.com/science/article/pii/S1755436522000019 |
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