Modeling and forecasting the COVID-19 pandemic time-series data
<p><strong>Objective</strong> We analyze the number of recorded cases and deaths of COVID-19 in many parts of the world, with the aim to understand the complexities of the data, and produce regular forecasts.</p> <p><strong>Methods</strong> The SARS-CoV-2 v...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Wiley
2021
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| _version_ | 1797073826491662336 |
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| author | Doornik, JA Castle, JL Hendry, DF |
| author_facet | Doornik, JA Castle, JL Hendry, DF |
| author_sort | Doornik, JA |
| collection | OXFORD |
| description | <p><strong>Objective</strong>
We analyze the number of recorded cases and deaths of COVID-19 in many parts of the world, with the aim to understand the complexities of the data, and produce regular forecasts.</p>
<p><strong>Methods</strong>
The SARS-CoV-2 virus that causes COVID-19 has affected societies in all corners of the globe but with vastly differing experiences across countries. Health-care and economic systems vary significantly across countries, as do policy responses, including testing, intermittent lockdowns, quarantine, contact tracing, mask wearing, and social distancing. Despite these challenges, the reported data can be used in many ways to help inform policy. We describe how to decompose the reported time series of confirmed cases and deaths into a trend, seasonal, and irregular component using machine learning methods.</p>
<p><strong>Results</strong>
This decomposition enables statistical computation of measures of the mortality ratio and reproduction number for any country, and we conduct a counterfactual exercise assuming that the United States had a summer outcome in 2020 similar to that of the European Union. The decomposition is also used to produce forecasts of cases and deaths, and we undertake a forecast comparison which highlights the importance of seasonality in the data and the difficulties of forecasting too far into the future.</p>
<p><strong>Conclusion</strong>
Our adaptive data-based methods and purely statistical forecasts provide a useful complement to the output from epidemiological models.</p> |
| first_indexed | 2024-03-06T23:27:35Z |
| format | Journal article |
| id | oxford-uuid:6aebd8d3-f90b-4532-a6cd-b6f093cba071 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:27:35Z |
| publishDate | 2021 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:6aebd8d3-f90b-4532-a6cd-b6f093cba0712022-03-26T19:00:28ZModeling and forecasting the COVID-19 pandemic time-series dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6aebd8d3-f90b-4532-a6cd-b6f093cba071EnglishSymplectic ElementsWiley2021Doornik, JACastle, JLHendry, DF<p><strong>Objective</strong> We analyze the number of recorded cases and deaths of COVID-19 in many parts of the world, with the aim to understand the complexities of the data, and produce regular forecasts.</p> <p><strong>Methods</strong> The SARS-CoV-2 virus that causes COVID-19 has affected societies in all corners of the globe but with vastly differing experiences across countries. Health-care and economic systems vary significantly across countries, as do policy responses, including testing, intermittent lockdowns, quarantine, contact tracing, mask wearing, and social distancing. Despite these challenges, the reported data can be used in many ways to help inform policy. We describe how to decompose the reported time series of confirmed cases and deaths into a trend, seasonal, and irregular component using machine learning methods.</p> <p><strong>Results</strong> This decomposition enables statistical computation of measures of the mortality ratio and reproduction number for any country, and we conduct a counterfactual exercise assuming that the United States had a summer outcome in 2020 similar to that of the European Union. The decomposition is also used to produce forecasts of cases and deaths, and we undertake a forecast comparison which highlights the importance of seasonality in the data and the difficulties of forecasting too far into the future.</p> <p><strong>Conclusion</strong> Our adaptive data-based methods and purely statistical forecasts provide a useful complement to the output from epidemiological models.</p> |
| spellingShingle | Doornik, JA Castle, JL Hendry, DF Modeling and forecasting the COVID-19 pandemic time-series data |
| title | Modeling and forecasting the COVID-19 pandemic time-series data |
| title_full | Modeling and forecasting the COVID-19 pandemic time-series data |
| title_fullStr | Modeling and forecasting the COVID-19 pandemic time-series data |
| title_full_unstemmed | Modeling and forecasting the COVID-19 pandemic time-series data |
| title_short | Modeling and forecasting the COVID-19 pandemic time-series data |
| title_sort | modeling and forecasting the covid 19 pandemic time series data |
| work_keys_str_mv | AT doornikja modelingandforecastingthecovid19pandemictimeseriesdata AT castlejl modelingandforecastingthecovid19pandemictimeseriesdata AT hendrydf modelingandforecastingthecovid19pandemictimeseriesdata |