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J R Soc Interface ; 18(185): 20210608, 2021 12.
Article in English | MEDLINE | ID: covidwho-1865053


Due to its high lethality among older people, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures and vaccines becoming available at scale, nursing homes might relax prohibitory measures while controlling the spread of infections. By control we mean that each index case infects less than one other person on average. Here, we develop an agent-based epidemiological model for the spread of SARS-CoV-2 calibrated to Austrian nursing homes to identify optimal prevention strategies. We find that the effectiveness of mitigation testing depends critically on test turnover time (time until test result), the detection threshold of tests and mitigation testing frequencies. Under realistic conditions and in absence of vaccinations, we find that mitigation testing of employees only might be sufficient to control outbreaks if tests have low turnover times and detection thresholds. If vaccines that are 60% effective against high viral load and transmission are available, control is achieved if 80% or more of the residents are vaccinated, even without mitigation testing and if residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular testing and sequencing of virus genomes is advised to enable early identification of new variants of concern.

COVID-19 , Pandemics , Aged , Epidemiological Models , Humans , Nursing Homes , SARS-CoV-2 , Vaccination , Vaccine Efficacy
Nat Hum Behav ; 4(12): 1303-1312, 2020 12.
Article in English | MEDLINE | ID: covidwho-926236


Assessing the effectiveness of non-pharmaceutical interventions (NPIs) to mitigate the spread of SARS-CoV-2 is critical to inform future preparedness response plans. Here we quantify the impact of 6,068 hierarchically coded NPIs implemented in 79 territories on the effective reproduction number, Rt, of COVID-19. We propose a modelling approach that combines four computational techniques merging statistical, inference and artificial intelligence tools. We validate our findings with two external datasets recording 42,151 additional NPIs from 226 countries. Our results indicate that a suitable combination of NPIs is necessary to curb the spread of the virus. Less disruptive and costly NPIs can be as effective as more intrusive, drastic, ones (for example, a national lockdown). Using country-specific 'what-if' scenarios, we assess how the effectiveness of NPIs depends on the local context such as timing of their adoption, opening the way for forecasting the effectiveness of future interventions.

Basic Reproduction Number/statistics & numerical data , COVID-19/prevention & control , Global Health/statistics & numerical data , Government , Artificial Intelligence , Datasets as Topic , Humans , Models, Theoretical
Sci Data ; 7(1): 285, 2020 08 27.
Article in English | MEDLINE | ID: covidwho-733508


In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.

Coronavirus Infections/epidemiology , Government , Pneumonia, Viral/epidemiology , Betacoronavirus , COVID-19 , Communicable Disease Control , Coronavirus Infections/diagnosis , Coronavirus Infections/prevention & control , Coronavirus Infections/therapy , Humans , Pandemics/prevention & control , Pneumonia, Viral/diagnosis , Pneumonia, Viral/prevention & control , Pneumonia, Viral/therapy , SARS-CoV-2