Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting
Professor Pierre Magal made important contributions to the field of mathematical biology before his death on February 20, 2024, including research in which epidemiological models were used to study the ends of infectious disease outbreaks. In related work, there has been interest in inferring (in re...
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| Format: | Article |
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KeAi Communications Co., Ltd.
2025-09-01
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| Series: | Infectious Disease Modelling |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042725000260 |
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| author | I. Ogi-Gittins J. Polonsky M. Keita S. Ahuka-Mundeke W.S. Hart M.J. Plank B. Lambert E.M. Hill R.N. Thompson |
| author_facet | I. Ogi-Gittins J. Polonsky M. Keita S. Ahuka-Mundeke W.S. Hart M.J. Plank B. Lambert E.M. Hill R.N. Thompson |
| author_sort | I. Ogi-Gittins |
| collection | DOAJ |
| description | Professor Pierre Magal made important contributions to the field of mathematical biology before his death on February 20, 2024, including research in which epidemiological models were used to study the ends of infectious disease outbreaks. In related work, there has been interest in inferring (in real-time) when outbreaks have ended and control interventions can be relaxed. Here, we analyse data from the 2018 Ebola outbreak in Équateur Province, Democratic Republic of the Congo, during which an Ebola Response Team (ERT) was deployed to implement public health measures. We use a renewal equation transmission model to perform a quasi real-time investigation into when the ERT could be withdrawn safely at the tail end of the outbreak. Specifically, each week following the arrival of the ERT, we calculate the probability of future cases if the ERT is withdrawn. First, we show that similar estimates of the probability of future cases can be obtained from either daily or weekly case reports. This demonstrates that high temporal resolution case reporting may not always be necessary to determine when interventions can be relaxed. Second, we demonstrate how case under-reporting can be accounted for rigorously when estimating the probability of future cases. We find that, the lower the level of case reporting, the longer it is necessary to wait after the apparent final case before interventions can be removed safely (with only a small probability of additional cases). Finally, we show how uncertainty in the extent of case reporting can be included in estimates of the probability of future cases. Our research highlights the importance of accounting for under-reporting in deciding when to remove interventions at the tail ends of infectious disease outbreaks. |
| format | Article |
| id | doaj-art-a69739246cb842418f2fe79b5656fae0 |
| institution | DOAJ |
| issn | 2468-0427 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj-art-a69739246cb842418f2fe79b5656fae02025-08-20T02:57:21ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272025-09-0110393594510.1016/j.idm.2025.03.009Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reportingI. Ogi-Gittins0J. Polonsky1M. Keita2S. Ahuka-Mundeke3W.S. Hart4M.J. Plank5B. Lambert6E.M. Hill7R.N. Thompson8Mathematics Institute, University of Warwick, Coventry, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry, UKGeneva Centre of Humanitarian Studies, University of Geneva, Geneva, SwitzerlandWorld Health Organization, Regional Office for Africa, Brazzaville, Republic of the Congo; Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, SwitzerlandNational Institute of Biomedical Research, Kinshasa, Democratic Republic of the CongoMathematical Institute, University of Oxford, Oxford, UKSchool of Mathematics and Statistics, University of Canterbury, Christchurch, New ZealandDepartment of Statistics, University of Oxford, Oxford, UK; Pandemic Sciences Institute, University of Oxford, Oxford, UKCivic Health Innovation Labs and Institute of Population Health, University of Liverpool, Liverpool, UK; NIHR Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, UKMathematical Institute, University of Oxford, Oxford, UK; Corresponding author.Professor Pierre Magal made important contributions to the field of mathematical biology before his death on February 20, 2024, including research in which epidemiological models were used to study the ends of infectious disease outbreaks. In related work, there has been interest in inferring (in real-time) when outbreaks have ended and control interventions can be relaxed. Here, we analyse data from the 2018 Ebola outbreak in Équateur Province, Democratic Republic of the Congo, during which an Ebola Response Team (ERT) was deployed to implement public health measures. We use a renewal equation transmission model to perform a quasi real-time investigation into when the ERT could be withdrawn safely at the tail end of the outbreak. Specifically, each week following the arrival of the ERT, we calculate the probability of future cases if the ERT is withdrawn. First, we show that similar estimates of the probability of future cases can be obtained from either daily or weekly case reports. This demonstrates that high temporal resolution case reporting may not always be necessary to determine when interventions can be relaxed. Second, we demonstrate how case under-reporting can be accounted for rigorously when estimating the probability of future cases. We find that, the lower the level of case reporting, the longer it is necessary to wait after the apparent final case before interventions can be removed safely (with only a small probability of additional cases). Finally, we show how uncertainty in the extent of case reporting can be included in estimates of the probability of future cases. Our research highlights the importance of accounting for under-reporting in deciding when to remove interventions at the tail ends of infectious disease outbreaks.http://www.sciencedirect.com/science/article/pii/S2468042725000260Ebola virus diseaseInterventionsEnd-of-outbreak probabilityRenewal equationEpidemic modelling |
| spellingShingle | I. Ogi-Gittins J. Polonsky M. Keita S. Ahuka-Mundeke W.S. Hart M.J. Plank B. Lambert E.M. Hill R.N. Thompson Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting Infectious Disease Modelling Ebola virus disease Interventions End-of-outbreak probability Renewal equation Epidemic modelling |
| title | Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting |
| title_full | Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting |
| title_fullStr | Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting |
| title_full_unstemmed | Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting |
| title_short | Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting |
| title_sort | real time inference of the end of an outbreak temporally aggregated disease incidence data and under reporting |
| topic | Ebola virus disease Interventions End-of-outbreak probability Renewal equation Epidemic modelling |
| url | http://www.sciencedirect.com/science/article/pii/S2468042725000260 |
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