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Modeling of suppression and mitigation interventions in the COVID-19 epidemics.
Han, Yuexing; Xie, Zeyang; Guo, Yike; Wang, Bing.
  • Han Y; School of Computer Engineering and Science, Shanghai University, Shanghai, People's Republic of China.
  • Xie Z; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, People's Republic of China.
  • Guo Y; School of Computer Engineering and Science, Shanghai University, Shanghai, People's Republic of China.
  • Wang B; Hong Kong Baptist University, Hong Kong, People's Republic of China.
BMC Public Health ; 21(1): 723, 2021 04 14.
Article in English | MEDLINE | ID: covidwho-1183520
Semantic information from SemMedBD (by NLM)
1. Intervention regimes AFFECTS Negative
Subject
Intervention regimes
Predicate
AFFECTS
Object
Negative
2. Observation - diagnostic procedure MEASURES Dynamics
Subject
Observation - diagnostic procedure
Predicate
MEASURES
Object
Dynamics
3. Infection COEXISTS_WITH Short menstrual periods
Subject
Infection
Predicate
COEXISTS_WITH
Object
Short menstrual periods
4. Intervention regimes AFFECTS Negative
Subject
Intervention regimes
Predicate
AFFECTS
Object
Negative
5. Observation - diagnostic procedure MEASURES Dynamics
Subject
Observation - diagnostic procedure
Predicate
MEASURES
Object
Dynamics
6. Infection COEXISTS_WITH Short menstrual periods
Subject
Infection
Predicate
COEXISTS_WITH
Object
Short menstrual periods
ABSTRACT

BACKGROUND:

The global spread of the COVID-19 pandemic has become the most fundamental threat to human health. In the absence of vaccines and effective therapeutical solutions, non-pharmaceutic intervention has become a major way for controlling the epidemic. Gentle mitigation interventions are able to slow down the epidemic but not to halt it well. While strict suppression interventions are efficient for controlling the epidemic, long-term measures are likely to have negative impacts on economics and people's daily live. Hence, dynamically balancing suppression and mitigation interventions plays a fundamental role in manipulating the epidemic curve.

METHODS:

We collected data of the number of infections for several countries during the COVID-19 pandemics and found a clear phenomenon of periodic waves of infection. Based on the observation, by connecting the infection level with the medical resources and a tolerance parameter, we propose a mathematical model to understand impacts of combining intervention measures on the epidemic dynamics.

RESULTS:

Depending on the parameters of the medical resources, tolerance level, and the starting time of interventions, the combined intervention measure dynamically changes with the infection level, resulting in a periodic wave of infections controlled below an accepted level. The study reveals that, (a) with an immediate, strict suppression, the numbers of infections and deaths are well controlled with a significant reduction in a very short time period; (b) an appropriate, dynamical combination of suppression and mitigation may find a feasible way in reducing the impacts of epidemic on people's live and economics.

CONCLUSIONS:

While the assumption of interventions deployed with a cycle of period in the model is limited and unrealistic, the phenomenon of periodic waves of infections in reality is captured by our model. These results provide helpful insights for policy-makers to dynamically deploy an appropriate intervention strategy to effectively battle against the COVID-19.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Pandemics / COVID-19 / Models, Theoretical Subject: Pandemics / COVID-19 / Models, Theoretical Language: English Journal: BMC Public Health Year: 2021

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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Pandemics / COVID-19 / Models, Theoretical Subject: Pandemics / COVID-19 / Models, Theoretical Language: English Journal: BMC Public Health Year: 2021
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