Zhou, Hao, Sha, He, Cheke, Robert ORCID: 0000-0002-7437-1934 and Tang, Sanyi (2024) Model analysis and data validation of structured prevention and control interruptions of emerging infectious diseases. Journal of Mathematical Biology, 88:62. pp. 1-34. ISSN 0303-6812 (Print), 1432-1416 (Online) (doi:https://doi.org/10.1007/s00285-024-02083-y)

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Abstract

The design of optimized non-pharmaceutical interventions (NPIs) is critical to the effective control of emergent outbreaks of infectious diseases such as SARS, A/H1N1 and COVID-19 and to ensure that numbers of hospitalized cases do not exceed the carrying capacity of medical resources. To address this issue, we formulated a classic SIR model to include a close contact tracing strategy and structured prevention and control interruptions (SPCIs). The impact of the timing of SPCIs on the maximum number of non-isolated infected individuals and on the duration of an infectious disease outside quarantined areas (i.e. implementing a dynamic zero-case policy) were analyzed numerically and theoretically. These analyses revealed that to minimize the maximum number of non-isolated infected individuals, the optimal time to initiate SPCIs is when they can control the peak value of a second rebound of the epidemic to be equal to the first peak value. More individuals may be infected at the peak of the second wave with a stronger intervention during SPCIs. The longer the duration of the intervention and the stronger the contact tracing intensity during SPCIs, the more effective they are in shortening the duration of an infectious disease outside quarantined areas. The dynamic evolution of the number of isolated and non-isolated individuals, including two peaks and long tail patterns, have been confirmed by various real data sets of multiple-wave COVID-19 epidemics in China. Our results provide important theoretical support for the adjustment of NPI strategies in relation to a given carrying capacity of medical resources.

Item Type:	Article
Uncontrolled Keywords:	emerging infectious diseases; structured prevention and control interruptions; multiple peaks; optimal strategy
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics S Agriculture > S Agriculture (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Natural Resources Institute Faculty of Engineering & Science > Natural Resources Institute > Agriculture, Health & Environment Department
Last Modified:	17 Apr 2024 13:28
URI:	http://gala.gre.ac.uk/id/eprint/46757

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