A structured open dataset of government interventions in response to COVID-19

Desvars-Larrive, Amélie; Dervić, Elma; Haug, Nils; Niederkrotenthaler, Thomas; Chen, Jiaying; Natale, Anna Di; Lasser, Jana; Gliga, Diana S.; Roux, Alexandra; Chakraborty, Abhijit; Ten, Alexandr; Dervić, Alija; Pacheco, Andrea; Cserjan, David; Lederhilger, Diana; Berishaj, Dorontinë; Tames, Erwin Flores; Takriti, Huda; Korbel, Jan; Reddish, Jenny; Stangl, Johannes; Hadziavdic, Lamija; Stoeger, Laura; Gooriah, Leana; Geyrhofer, Lukas; Ferreira, Márcia Regina; Vierlinger, Rainer; Holder, Samantha; Álvarez, Samuel; Haberfellner, Simon; Ahne, Verena; Reisch, Viktoria; Servedio, Vito D. P.; Chen, Xiao; Pocasangre-Orellana, Xochilt; García, David; Thurner, Stefan

doi:10.1101/2020.05.04.20090498

Cited by 54 publications

(91 citation statements)

References 20 publications

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“…com/CCCSLgraph/. We have also established a GitHub repository available at: https://github.com/amel-github/ CCCSL-Codes and provide codes 19 for importing, exploring and visualising the data into R 20 . Furthermore, for purposes of transparency of data collection and to motivate collaborative validation process as well as a large use and development of the dataset, an open library is available, that contains all sources used to collect the data: https://www.zotero.org/groups/2488884/cccsl_covid_measure_project (>3,100 data sources are included as of date of submission).…”

Section: Data Recordsmentioning

confidence: 99%

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A structured open dataset of government interventions in response to COVID-19

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In response to the COVID-19 pandemic, governments have implemented a wide range of nonpharmaceutical 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.

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Section: Data Recordsmentioning

confidence: 99%

“…A live version of this project is accessible on GitHub at: https://github.com/amel-github/covid19interventionmeasures. The codes used to describe the CCCSL dataset and the codes used to explore the CCCSL dataset are written in R language 19 . They are available at: https://github.com/amel-github/CCCSL-Codes.…”

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confidence: 99%

A structured open dataset of government interventions in response to COVID-19

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“…Similarly, in Iceland in a random-population screening the prevalence of positively tested was found to be 0.8% (11). Clearly, at this time the COVID-19 outbreak has been far from the uncontrolled case as many countries have implemented nonpharmaceutical interventions (NPIs) to reduce infection rates (12).…”

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confidence: 96%

“…13. Many countries that implemented NPIs in response to the COVID-19 crisis (12) show a different pattern. They also show an extended linear growth; however, infection curves tend to bend and level off in response to the implemented measures (Fig.…”

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A network-based explanation of why most COVID-19 infection curves are linear

Thurner

Klimek

Hanel

2020

Proc. Natl. Acad. Sci. U.S.A.

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Many countries have passed their first COVID-19 epidemic peak. Traditional epidemiological models describe this as a result of nonpharmaceutical interventions pushing the growth rate below the recovery rate. In this phase of the pandemic many countries showed an almost linear growth of confirmed cases for extended time periods. This new containment regime is hard to explain by traditional models where either infection numbers grow explosively until herd immunity is reached or the epidemic is completely suppressed. Here we offer an explanation of this puzzling observation based on the structure of contact networks. We show that for any given transmission rate there exists a critical number of social contacts, Dc, below which linear growth and low infection prevalence must occur. Above Dc traditional epidemiological dynamics take place, e.g., as in susceptible–infected–recovered (SIR) models. When calibrating our model to empirical estimates of the transmission rate and the number of days being contagious, we find Dc∼7.2. Assuming realistic contact networks with a degree of about 5, and assuming that lockdown measures would reduce that to household size (about 2.5), we reproduce actual infection curves with remarkable precision, without fitting or fine-tuning of parameters. In particular, we compare the United States and Austria, as examples for one country that initially did not impose measures and one that responded with a severe lockdown early on. Our findings question the applicability of standard compartmental models to describe the COVID-19 containment phase. The probability to observe linear growth in these is practically zero.

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Assessing the potential impact of immunity waning on the dynamics of COVID-19 in South Africa: an endemic model of COVID-19

Rabiu

Iyaniwura

2022

Nonlinear Dyn

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We developed an endemic model of COVID-19 to assess the impact of vaccination and immunity waning on the dynamics of the disease. Our model exhibits the phenomenon of backward bifurcation and bi-stability, where a stable disease-free equilibrium coexists with a stable endemic equilibrium. The epidemiological implication of this is that the control reproduction number being less than unity is no longer sufficient to guarantee disease eradication. We showed that this phenomenon could be eliminated by either increasing the vaccine efficacy or by reducing the disease transmission rate (adhering to non-pharmaceutical interventions). Furthermore, we numerically investigated the impacts of vaccination and waning of both vaccine-induced immunity and post-recovery immunity on the disease dynamics. Our simulation results show that the waning of vaccine-induced immunity has more effect on the disease dynamics relative to post-recovery immunity waning and suggests that more emphasis should be on reducing the waning of vaccine-induced immunity to eradicate COVID-19.

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A structured open dataset of government interventions in response to COVID-19

Cited by 54 publications

References 20 publications

A structured open dataset of government interventions in response to COVID-19

A structured open dataset of government interventions in response to COVID-19

A network-based explanation of why most COVID-19 infection curves are linear

Assessing the potential impact of immunity waning on the dynamics of COVID-19 in South Africa: an endemic model of COVID-19

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