Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic
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Abstract
Background
The global impact of COVID-19 and the country-specific responses to the pandemic provide an unparalleled opportunity to learn about different patterns of the outbreak and interventions. We model the global pattern of reported COVID-19 cases during the primary response period, with the aim of learning from the past to prepare for the future.
Methods
Using Bayesian methods, we analyse the response to the COVID-19 outbreak for 158 countries for the period 22 January to 9 June 2020. This encompasses the period in which many countries imposed a variety of response measures and initial relaxation strategies. Instead of modelling specific intervention types and timings for each country explicitly, we adopt a stochastic epidemiological model including a feedback mechanism on virus transmission to capture complex nonlinear dynamics arising from continuous changes in community behaviour in response to rising case numbers. We analyse the overall effect of interventions and community responses across diverse regions. This approach mitigates explicit consideration of issues such as period of infectivity and public adherence to government restrictions.
Results
Countries with the largest cumulative case tallies are characterised by a delayed response, whereas countries that avoid substantial community transmission during the period of study responded quickly. Countries that recovered rapidly also have a higher case identification rate and small numbers of undocumented community transmission at the early stages of the outbreak. We also demonstrate that uncertainty in numbers of undocumented infections dramatically impacts the risk of multiple waves. Our approach is also effective at pre-empting potential flare-ups.
Conclusions
We demonstrate the utility of modelling to interpret community behaviour in the early epidemic stages. Two lessons learnt that are important for the future are: i) countries that imposed strict containment measures early in the epidemic fared better with respect to numbers of reported cases; and ii) broader testing is required early in the epidemic to understand the magnitude of undocumented infections and recover rapidly. We conclude that clear patterns of containment are essential prior to relaxation of restrictions and show that modelling can provide insights to this end.
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SciScore for 10.1101/2020.04.30.20085662: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The data on reported daily cases from the Johns Hopkins University coronavirus resource center have some limitations [12]. Firstly, when aggregated at a country level, these data do not take into account high levels of spatial heterogeneity. To account for potential bias, future work could consider a sensitivity analysis on the level of individual cities or provinces where available. The delay between onset dates and reported dates for …
SciScore for 10.1101/2020.04.30.20085662: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The data on reported daily cases from the Johns Hopkins University coronavirus resource center have some limitations [12]. Firstly, when aggregated at a country level, these data do not take into account high levels of spatial heterogeneity. To account for potential bias, future work could consider a sensitivity analysis on the level of individual cities or provinces where available. The delay between onset dates and reported dates for new case also potentially introduces bias and data spikes (as noted in Methods and Results). Lastly, the Johns Hopkins data does is not curated to distinguish between cases acquired through local transmission as opposed to imported cases. Further modelling extensions that account for details captured in alternative data sources, such as the European Center for Disease Control and Prevention (ECDC) [13], should also be considered. Our model, like any model, has fundamental assumptions that are necessarily introduced. We treat each country as a single well-mixed population. While our approach does include important features such as undocumented infections and a variable transmission rate, more advanced analysis could be performed by considering disease spread through a network [17, 53–55] of well-mixed populations, such as provinces, states or cities. This would assist is capturing social factors that could also influence COVID-19 transmission, such as spatial variation in population density and large population movement such as those that occur ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
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