O'Reilly, KM; Grassly, NC; Allen, DJ; Bannister-Tyrrell, M; Cameron, A; Carrion Martin, AI; Ramsay, M; Pebody, R; Zambon, M; (2020) Surveillance optimisation to detect poliovirus in the pre-eradication era: a modelling study of England and Wales. Epidemiology and Infection, 148. e157-. ISSN 0950-2688 DOI: https://doi.org/10.1017/S0950268820001004
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Abstract
Surveillance for acute flaccid paralysis (AFP) cases are essential for polio eradication. However, as most poliovirus infections are asymptomatic and some regions of the world are inaccessible, additional surveillance tools require development. Within England and Wales, we demonstrate how inclusion of environmental sampling (ENV) improves the sensitivity of detecting both wild and vaccine-derived polioviruses (VDPVs) when compared to current surveillance. Statistical modelling was used to estimate the spatial risk of wild and VDPV importation and circulation in England and Wales. We estimate the sensitivity of each surveillance mode to detect poliovirus and the probability of being free from poliovirus, defined as being below a pre-specified prevalence of infection. Poliovirus risk was higher within local authorities in Manchester, Birmingham, Bradford and London. The sensitivity of detecting wild poliovirus within a given month using AFP and enterovirus surveillance was estimated to be 0.096 (95% CI 0.055-0.134). Inclusion of ENV in the three highest risk local authorities and a site in London increased surveillance sensitivity to 0.192 (95% CI 0.191-0.193). The sensitivity of ENV strategies can be compared using the framework by varying sites and the frequency of sampling. The probability of being free from poliovirus slowly increased from the date of the last case in 1993. ENV within areas thought to have the highest risk improves detection of poliovirus, and has the potential to improve confidence in the polio-free status of England and Wales and detect VDPVs.
Item Type | Article |
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Faculty and Department |
Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology & Dynamics (2023-) Faculty of Infectious and Tropical Diseases > Department of Infection Biology |
Research Centre | Centre for the Mathematical Modelling of Infectious Diseases |
PubMed ID | 32398193 |
Elements ID | 147616 |
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