Clarifying predictions for COVID-19 from testing data: The example of New York State
With the spread of COVID-19 across the world, a large amount of data on reported cases has become available. We are studying here a potential bias induced by the daily number of tests which may be insufficient or vary over time. Indeed, tests are hard to produce at the early stage of the epidemic an...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2021-01-01
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| Series: | Infectious Disease Modelling |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042721000026 |
| _version_ | 1797203330967011328 |
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| author | Quentin Griette Pierre Magal |
| author_facet | Quentin Griette Pierre Magal |
| author_sort | Quentin Griette |
| collection | DOAJ |
| description | With the spread of COVID-19 across the world, a large amount of data on reported cases has become available. We are studying here a potential bias induced by the daily number of tests which may be insufficient or vary over time. Indeed, tests are hard to produce at the early stage of the epidemic and can therefore be a limiting factor in the detection of cases. Such a limitation may have a strong impact on the reported cases data. Indeed, some cases may be missing from the official count because the number of tests was not sufficient on a given day. In this work, we propose a new differential equation epidemic model which uses the daily number of tests as an input. We obtain a good agreement between the model simulations and the reported cases data coming from the state of New York. We also explore the relationship between the dynamic of the number of tests and the dynamics of the cases. We obtain a good match between the data and the outcome of the model. Finally, by multiplying the number of tests by 2, 5, 10, and 100 we explore the consequences for the number of reported cases. |
| first_indexed | 2024-04-24T08:17:38Z |
| format | Article |
| id | doaj.art-db56cfd599b14d24bfeedd57a9b96a71 |
| institution | Directory Open Access Journal |
| issn | 2468-0427 |
| language | English |
| last_indexed | 2024-04-24T08:17:38Z |
| publishDate | 2021-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj.art-db56cfd599b14d24bfeedd57a9b96a712024-04-17T03:15:08ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272021-01-016273283Clarifying predictions for COVID-19 from testing data: The example of New York StateQuentin Griette0Pierre Magal1Univ. Bordeaux, IMB, UMR 5251, F-33400, Talence, France; CNRS, IMB, UMR 5251, F-33400, Talence, FranceUniv. Bordeaux, IMB, UMR 5251, F-33400, Talence, France; CNRS, IMB, UMR 5251, F-33400, Talence, France; Corresponding author. Univ. Bordeaux, IMB, UMR 5251, F-33400, Talence, France.With the spread of COVID-19 across the world, a large amount of data on reported cases has become available. We are studying here a potential bias induced by the daily number of tests which may be insufficient or vary over time. Indeed, tests are hard to produce at the early stage of the epidemic and can therefore be a limiting factor in the detection of cases. Such a limitation may have a strong impact on the reported cases data. Indeed, some cases may be missing from the official count because the number of tests was not sufficient on a given day. In this work, we propose a new differential equation epidemic model which uses the daily number of tests as an input. We obtain a good agreement between the model simulations and the reported cases data coming from the state of New York. We also explore the relationship between the dynamic of the number of tests and the dynamics of the cases. We obtain a good match between the data and the outcome of the model. Finally, by multiplying the number of tests by 2, 5, 10, and 100 we explore the consequences for the number of reported cases.http://www.sciencedirect.com/science/article/pii/S2468042721000026Corona virusTesting dataReported and unreported casesIsolationQuarantinePublic closings |
| spellingShingle | Quentin Griette Pierre Magal Clarifying predictions for COVID-19 from testing data: The example of New York State Infectious Disease Modelling Corona virus Testing data Reported and unreported cases Isolation Quarantine Public closings |
| title | Clarifying predictions for COVID-19 from testing data: The example of New York State |
| title_full | Clarifying predictions for COVID-19 from testing data: The example of New York State |
| title_fullStr | Clarifying predictions for COVID-19 from testing data: The example of New York State |
| title_full_unstemmed | Clarifying predictions for COVID-19 from testing data: The example of New York State |
| title_short | Clarifying predictions for COVID-19 from testing data: The example of New York State |
| title_sort | clarifying predictions for covid 19 from testing data the example of new york state |
| topic | Corona virus Testing data Reported and unreported cases Isolation Quarantine Public closings |
| url | http://www.sciencedirect.com/science/article/pii/S2468042721000026 |
| work_keys_str_mv | AT quentingriette clarifyingpredictionsforcovid19fromtestingdatatheexampleofnewyorkstate AT pierremagal clarifyingpredictionsforcovid19fromtestingdatatheexampleofnewyorkstate |