Nowcasting the 2022 mpox outbreak in England.
In May 2022, a cluster of mpox cases were detected in the UK that could not be traced to recent travel history from an endemic region. Over the coming months, the outbreak grew, with over 3000 total cases reported in the UK, and similar outbreaks occurring worldwide. These outbreaks appeared linked...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011463&type=printable |
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| _version_ | 1850162868744355840 |
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| author | Christopher E Overton Sam Abbott Rachel Christie Fergus Cumming Julie Day Owen Jones Rob Paton Charlie Turner Thomas Ward |
| author_facet | Christopher E Overton Sam Abbott Rachel Christie Fergus Cumming Julie Day Owen Jones Rob Paton Charlie Turner Thomas Ward |
| author_sort | Christopher E Overton |
| collection | DOAJ |
| description | In May 2022, a cluster of mpox cases were detected in the UK that could not be traced to recent travel history from an endemic region. Over the coming months, the outbreak grew, with over 3000 total cases reported in the UK, and similar outbreaks occurring worldwide. These outbreaks appeared linked to sexual contact networks between gay, bisexual and other men who have sex with men. Following the COVID-19 pandemic, local health systems were strained, and therefore effective surveillance for mpox was essential for managing public health policy. However, the mpox outbreak in the UK was characterised by substantial delays in the reporting of the symptom onset date and specimen collection date for confirmed positive cases. These delays led to substantial backfilling in the epidemic curve, making it challenging to interpret the epidemic trajectory in real-time. Many nowcasting models exist to tackle this challenge in epidemiological data, but these lacked sufficient flexibility. We have developed a nowcasting model using generalised additive models that makes novel use of individual-level patient data to correct the mpox epidemic curve in England. The aim of this model is to correct for backfilling in the epidemic curve and provide real-time characteristics of the state of the epidemic, including the real-time growth rate. This model benefited from close collaboration with individuals involved in collecting and processing the data, enabling temporal changes in the reporting structure to be built into the model, which improved the robustness of the nowcasts generated. The resulting model accurately captured the true shape of the epidemic curve in real time. |
| format | Article |
| id | doaj-art-bb8558ce776b4a1a9ead6b60a74b9124 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-bb8558ce776b4a1a9ead6b60a74b91242025-08-20T02:22:26ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-09-01199e101146310.1371/journal.pcbi.1011463Nowcasting the 2022 mpox outbreak in England.Christopher E OvertonSam AbbottRachel ChristieFergus CummingJulie DayOwen JonesRob PatonCharlie TurnerThomas WardIn May 2022, a cluster of mpox cases were detected in the UK that could not be traced to recent travel history from an endemic region. Over the coming months, the outbreak grew, with over 3000 total cases reported in the UK, and similar outbreaks occurring worldwide. These outbreaks appeared linked to sexual contact networks between gay, bisexual and other men who have sex with men. Following the COVID-19 pandemic, local health systems were strained, and therefore effective surveillance for mpox was essential for managing public health policy. However, the mpox outbreak in the UK was characterised by substantial delays in the reporting of the symptom onset date and specimen collection date for confirmed positive cases. These delays led to substantial backfilling in the epidemic curve, making it challenging to interpret the epidemic trajectory in real-time. Many nowcasting models exist to tackle this challenge in epidemiological data, but these lacked sufficient flexibility. We have developed a nowcasting model using generalised additive models that makes novel use of individual-level patient data to correct the mpox epidemic curve in England. The aim of this model is to correct for backfilling in the epidemic curve and provide real-time characteristics of the state of the epidemic, including the real-time growth rate. This model benefited from close collaboration with individuals involved in collecting and processing the data, enabling temporal changes in the reporting structure to be built into the model, which improved the robustness of the nowcasts generated. The resulting model accurately captured the true shape of the epidemic curve in real time.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011463&type=printable |
| spellingShingle | Christopher E Overton Sam Abbott Rachel Christie Fergus Cumming Julie Day Owen Jones Rob Paton Charlie Turner Thomas Ward Nowcasting the 2022 mpox outbreak in England. PLoS Computational Biology |
| title | Nowcasting the 2022 mpox outbreak in England. |
| title_full | Nowcasting the 2022 mpox outbreak in England. |
| title_fullStr | Nowcasting the 2022 mpox outbreak in England. |
| title_full_unstemmed | Nowcasting the 2022 mpox outbreak in England. |
| title_short | Nowcasting the 2022 mpox outbreak in England. |
| title_sort | nowcasting the 2022 mpox outbreak in england |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011463&type=printable |
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