Looking in all the wrong places: A rationale for signal detection for pandemics based on existing data sources
Global surveillance systems did not detect the early stages of the COVID-19 pandemic. We argue this is because the national surveillance systems which report to centralised systems are not designed to detect the emergence of novel infectious diseases. Likewise, substantial resources devoted to hunti...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/131544 |
| _version_ | 1855526794918100992 |
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| author | Dogra, A.E.K. Munyasa, W.L. Hung Nguyen-Viet Grace, Delia |
| author_browse | Dogra, A.E.K. Grace, Delia Hung Nguyen-Viet Munyasa, W.L. |
| author_facet | Dogra, A.E.K. Munyasa, W.L. Hung Nguyen-Viet Grace, Delia |
| author_sort | Dogra, A.E.K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Global surveillance systems did not detect the early stages of the COVID-19 pandemic. We argue this is because the national surveillance systems which report to centralised systems are not designed to detect the emergence of novel infectious diseases. Likewise, substantial resources devoted to hunting for deadly new viruses in obscure places did not predict COVID-19. We suggest an alternative approach to make better use of baseline human mortality and morbidity data to detect anomalies, building on existing frameworks for data collection and standardisation and drawing on data from individual medical facilities. While most emerging diseases in humans originate in animals, focusing on animal surveillance may be an ignis fatuus, and detection should focus on human cases as early as possible after spillover. Animal-based surveillance for pandemic prevention is warranted for recurring outbreaks of known zoonotic pathogens when it can inform the detection of human cases. Further research is suggested in surveillance for pandemic preparedness utilising human baseline data, using available routine health data, as well as other data sources generated outside the health sector which could detect anomalies. The methodology is potentially highly cost effective, and applicable to low-and-middle income countries (LMICs). Data sources can be evaluated with historical data, where evidence of detection should be seen in the early stages of within-country spread of COVID-19. |
| format | Journal Article |
| id | CGSpace131544 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1315442025-10-26T12:51:41Z Looking in all the wrong places: A rationale for signal detection for pandemics based on existing data sources Dogra, A.E.K. Munyasa, W.L. Hung Nguyen-Viet Grace, Delia one health approach emerging diseases pandemics covid-19 Global surveillance systems did not detect the early stages of the COVID-19 pandemic. We argue this is because the national surveillance systems which report to centralised systems are not designed to detect the emergence of novel infectious diseases. Likewise, substantial resources devoted to hunting for deadly new viruses in obscure places did not predict COVID-19. We suggest an alternative approach to make better use of baseline human mortality and morbidity data to detect anomalies, building on existing frameworks for data collection and standardisation and drawing on data from individual medical facilities. While most emerging diseases in humans originate in animals, focusing on animal surveillance may be an ignis fatuus, and detection should focus on human cases as early as possible after spillover. Animal-based surveillance for pandemic prevention is warranted for recurring outbreaks of known zoonotic pathogens when it can inform the detection of human cases. Further research is suggested in surveillance for pandemic preparedness utilising human baseline data, using available routine health data, as well as other data sources generated outside the health sector which could detect anomalies. The methodology is potentially highly cost effective, and applicable to low-and-middle income countries (LMICs). Data sources can be evaluated with historical data, where evidence of detection should be seen in the early stages of within-country spread of COVID-19. 2023-12 2023-08-14T15:38:39Z 2023-08-14T15:38:39Z Journal Article https://hdl.handle.net/10568/131544 en Open Access Elsevier Dogra, A.E.K., Munyasa, W.L., Hung Nguyen-Viet and Grace, D. 2023. Looking in all the wrong places: A rationale for signal detection for pandemics based on existing data sources. IJID One Health 1: 100003. |
| spellingShingle | one health approach emerging diseases pandemics covid-19 Dogra, A.E.K. Munyasa, W.L. Hung Nguyen-Viet Grace, Delia Looking in all the wrong places: A rationale for signal detection for pandemics based on existing data sources |
| title | Looking in all the wrong places: A rationale for signal detection for pandemics based on existing data sources |
| title_full | Looking in all the wrong places: A rationale for signal detection for pandemics based on existing data sources |
| title_fullStr | Looking in all the wrong places: A rationale for signal detection for pandemics based on existing data sources |
| title_full_unstemmed | Looking in all the wrong places: A rationale for signal detection for pandemics based on existing data sources |
| title_short | Looking in all the wrong places: A rationale for signal detection for pandemics based on existing data sources |
| title_sort | looking in all the wrong places a rationale for signal detection for pandemics based on existing data sources |
| topic | one health approach emerging diseases pandemics covid-19 |
| url | https://hdl.handle.net/10568/131544 |
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