Hindsight is 2020 Vision: A Characterisation of the Global Response to the COVID-19 Pandemic

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 trajectories 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 second waves. Our approach is also effective at pre-empting potential second waves and 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.

Modeling the Covid-19 Pandemic Response of the US States

“…(4), and make comparisons, which may be useful for contributing to the evaluation of different underlying factors and different policies followed. Table II presents (4). NT is the asymptotic value after all waves have decayed, given in Table I, normalized by the state's population.…”

“…The Covid-19 pandemic has generated a plethora of studies aspiring to understand the dynamics of the disease spread and predict its future evolution (see, for example, [1][2][3][4][5][6][7][8][9]). Different types of models can be assumed to describe this dynamic evolution.…”

Fighting the tragedy of nursing homes in the COVID-19 pandemic

“…Countries articulating an early response, with more restrictive measures in terms of stopping the main routes of transmission, and more coherent, homogeneous and clear guidelines, developing actions in the main "hot spots," have obtained better results in terms of SARS-CoV-2 spread, morbidity, and mortality. In contrast, in countries that decided more permissive measures to generate herd immunity or to safeguard the economy, the impact of the epidemic has been much more devastating 31 . This proofs the importance of global political measures, which should always be guided by scientific knowledge.…”

“…Focusing on specific public health measures, there are some which have been performed and/or proposed by some countries and institutions, as detailed in table 1 [5][6][7][14][15][16][17]25,[31][32][33][34][35] . These actions are oriented to three levels: to the political institutions and society as a whole to make the vulnerability of NH visible and to raise awareness among the entire population of the importance of devoting resources to these institutions; to the social and health systems to improve their coordination, teamwork, and cooperation actions; and finally, to workers and residents of NH to ensure the necessary structural changes in the centers, the professional skills in the prevention and management of COVID-19, the necessary equipment and protection measures, and the universal and regular screening of workers and residents.…”

On Identifying and Mitigating Bias in the Estimation of the COVID-19 Case Fatality Rate

“…Interventions to improve the quality of medical care can cause a drastic decrease in CFR, particularly when treatment options (e.g., drugs, blood transfusions, ventilators) become available or if training of health care workers (HCWs) improves. The effect can be highly pronounced in developing countries (Siddique, 1994) and is the subject of active study today (Hsiang et al, 2020; Warne et al, 2020). By the same logic as above, these interventions can lead to a spuriously increased CFR estimate.…”