TY  - JOUR
A1  - Gros, Claudius
A1  - Valentí, Roser
A1  - Schneider, Lukas
A1  - Valenti, Kilian
A1  - Gros, Daniel
T1  - Containment efficiency and control strategies for the corona pandemic costs
T2  - Scientific reports
N2  - The rapid spread of the Coronavirus (COVID-19) confronts policy makers with the problem of measuring the effectiveness of containment strategies, balancing public health considerations with the economic costs of social distancing measures. We introduce a modified epidemic model that we name the controlled-SIR model, in which the disease reproduction rate evolves dynamically in response to political and societal reactions. An analytic solution is presented. The model reproduces official COVID-19 cases counts of a large number of regions and countries that surpassed the first peak of the outbreak. A single unbiased feedback parameter is extracted from field data and used to formulate an index that measures the efficiency of containment strategies (the CEI index). CEI values for a range of countries are given. For two variants of the controlled-SIR model, detailed estimates of the total medical and socio-economic costs are evaluated over the entire course of the epidemic. Costs comprise medical care cost, the economic cost of social distancing, as well as the economic value of lives saved. Under plausible parameters, strict measures fare better than a hands-off policy. Strategies based on current case numbers lead to substantially higher total costs than strategies based on the overall history of the epidemic.
KW  - Diseases
KW  - Mathematics and computing
KW  - Physics
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/71013
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-710137
SN  - 2045-2322
N1  - We acknowledge financial support from the Horizon 2020 research and innovation program of the EU under grant agreement No. 101016233, H2020-SC1-PHE CORONAVIRUS-2020-2-RTD, PERISCOPE (Pan European Response to the Impacts of Covid-19 and future Pandemics and Epidemics) and from the Fulbright foundation (D.G.). Open Access funding enabled and organized by Projekt DEAL.
N1  - The COVID-19 data examined is publicly accessible via the COVID-19 Github repository of the Johns Hopkins Center of Systems Science and Engineering https://github.com/CSSEGISandData/COVID-19. Data for the 2015 MERS outbreak in South Korea is publicly available from the archive of the World Health organization (WHO), https://www.who.int/csr/disease/coronavirus_infections/archive-cases/en/.
VL  - 11
IS  - art. 6848
SP  - 1
EP  - 13
PB  - Macmillan Publishers Limited, part of Springer Nature
CY  - [London]
ER  -