TY  - JOUR
A1  - Gros, Claudius
A1  - Valentí, Roser
A1  - Schneider, Lukas
A1  - Gutsche, Benedikt
A1  - Marković, Dimitrije
T1  - Predicting the cumulative medical load of COVID-19 outbreaks after the peak in daily fatalities
T2  - PLOS ONE
N2  - The distinct ways the COVID-19 pandemic has been unfolding in different countries and regions suggest that local societal and governmental structures play an important role not only for the baseline infection rate, but also for short and long-term reactions to the outbreak. We propose to investigate the question of how societies as a whole, and governments in particular, modulate the dynamics of a novel epidemic using a generalization of the SIR model, the reactive SIR (short-term and long-term reaction) model. We posit that containment measures are equivalent to a feedback between the status of the outbreak and the reproduction factor. Short-term reaction to an outbreak corresponds in this framework to the reaction of governments and individuals to daily cases and fatalities. The reaction to the cumulative number of cases or deaths, and not to daily numbers, is captured in contrast by long-term reaction. We present the exact phase space solution of the controlled SIR model and use it to quantify containment policies for a large number of countries in terms of short and long-term control parameters. We find increased contributions of long-term control for countries and regions in which the outbreak was suppressed substantially together with a strong correlation between the strength of societal and governmental policies and the time needed to contain COVID-19 outbreaks. Furthermore, for numerous countries and regions we identified a predictive relation between the number of fatalities within a fixed period before and after the peak of daily fatality counts, which allows to gauge the cumulative medical load of COVID-19 outbreaks that should be expected after the peak. These results suggest that the proposed model is applicable not only for understanding the outbreak dynamics, but also for predicting future cases and fatalities once the effectiveness of outbreak suppression policies is established with sufficient certainty. Finally, we provide a web app (https://itp.uni-frankfurt.de/covid-19/) with tools for visualising the phase space representation of real-world COVID-19 data and for exporting the preprocessed data for further analysis.
KW  - COVID 19
KW  - Social distancing
KW  - Epidemiology
KW  - Pandemics
KW  - Epidemiological statistics
KW  - Simulation and modeling
KW  - Social systems
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/61189
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-611893
SN  - 1932-6203
VL  - 16
IS  - 4, art. e0247272
SP  - 1
EP  - 20
PB  - Public Library of Science
CY  - San Francisco
ER  -