Wang, Xia, Li, Qian, He, Sha, Fan, Xia, Song, Pengfei, Shao, Yiming, Wu, Jianhong, Cheke, Robert ORCID: https://orcid.org/0000-0002-7437-1934, Tang, Sanyi and Xiao, Yanni (2021) Effects of medical resource capacities and intensities of public mitigation measures on outcomes of COVID-19 outbreaks. BMC Public Health, 21:605. pp. 1-11. ISSN 1471-2458 (Online) (doi:10.1186/s12889-021-10657-4)

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Abstract

Background:
The COVID-19 pandemic is complex and is developing in different ways according to the country involved.

Methods:
To identify the key parameters or processes that have the greatest effects on the pandemic and reveal the different progressions of epidemics in different countries, we quantified enhanced control measures and the dynamics of the production and provision of medical resources. We then nested these within a COVID-19 epidemic transmission model, which is parameterized by multi-source data. We obtained rate functions related to the intensity of mitigation measures, the effective reproduction numbers and the timings and durations of runs on medical resources, given differing control measures implemented in various countries.

Results:
Increased detection rates may induce runs on medical resources and prolong their durations, depending on resource availability. Nevertheless, improving the detection rate can effectively and rapidly reduce the mortality rate, even after runs on medical resources. Combinations of multiple prevention and control strategies and timely improvement of abilities to supplement medical resources are key to effective control of the COVID-19 epidemic. A 50% reduction in comprehensive control measures would have led to the cumulative numbers of confirmed cases and deaths exceeding 590,000 and 60,000, respectively, by 27 March 2020 in mainland China.

Conclusions:
Multiple data sources and cross validation of a COVID-19 epidemic model, coupled with a medical resource logistic model, revealed the key factors that affect epidemic progressions and their outbreak patterns in different countries. These key factors are the type of emergency medical response to avoid runs on medical resources, especially improved detection rates, the ability to promote public health measures, and the synergistic effects of combinations of multiple prevention and control strategies. The proposed model can assist health authorities to predict when they will be most in need of hospital beds and equipment such as ventilators, personal protection equipment, drugs, and staff.

Item Type:	Article
Additional Information:	© The Author(s). 2021 Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Uncontrolled Keywords:	Pandemic, COVID-19, Model, Runs on medical resources, Inter-country comparisons, Prediction, Epidemiology
Subjects:	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 Faculty of Engineering & Science > Natural Resources Institute > Centre for Sustainable Agriculture 4 One Health Faculty of Engineering & Science > Natural Resources Institute > Centre for Sustainable Agriculture 4 One Health > Behavioural Ecology
Last Modified:	27 Nov 2024 14:29
URI:	http://gala.gre.ac.uk/id/eprint/31956

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