Forecasting the COVID-19 Pandemic: Lessons learned and future directions

Sundar, Saketh; Schwab, Patrick; Tan, Jack; Romero-Brufau, Santiago; Celi, Leo; Wangmo, Dechen; Penna, Nicolás Della

doi:10.1101/2021.11.06.21266007

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…However the government banned all kinds of public gatherings and social activities, closed schools, cinemas, shopping malls and other public places, and people were asked to stop all unnecessary going out. Through lockdown, the frequency of people going out is reduced and the transmission chain of the virus is effectively cut off [16]. These measures widened the distance between people and prevented the population from gathering, effectively mitigating the surge in infections and flattening the pandemic curve in the short term.…”

Section: Discussionmentioning

confidence: 99%

“…The effectiveness of the pandemic prevention control in the UK shows that mitigation strategy can slow the spread of COVID-19 in the short term and successfully reduce the number of new infections. However, without isolating the source of infection and completely blocking the spread of the virus, there is a risk that COVID-19 will rebound several times when herd immunity is insufficient [16].…”

Section: Comparison Of Policies Between Two Wavesmentioning

confidence: 99%

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Critical policies disparity of the first and second waves of COVID-19 in the United Kingdom

Shi

Chen

Wang

et al. 2022

Int J Equity Health

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Objective This study aims to compare the differences in COVID-19 prevention and control policies adopted by the United Kingdom (UK) during the first wave (31 January 2020 to 6 September 2020) and the second wave (7 September 2020 to 12 April 2021), and analyze the effectiveness of the policies, so as to provide empirical experience for the prevention and control of COVID-19. Methods We systematically summarized the pandemic prevention and control policies of the UK from official websites and government documents, collated the epidemiological data from 31 January 2020 to 12 April 2021, and analyzed the effectiveness of the two waves of pandemic prevention and control policies. Results The main pandemic prevention and control policies adopted by the UK include surveillance and testing measures, border control measures, community and social measures, blockade measures, health care measures, COVID-19 vaccination measure, and relaxed pandemic prevention measures. The new cases per day curve showed only one peak in the first wave and two peaks in the second wave. The number of new cases per million in the second wave was much higher than that in the first wave, and the curve fluctuated less. The difference between mortality per million was small, and the curve fluctuated widely. Conclusion During the first and second waves of COVID-19, the UK implemented three lockdowns and managed to slow the spread of the pandemic. The UK’s experience in mitigating the second wave proves that advancing COVID-19 vaccination needs to be accompanied by ongoing implementation of non-pharmacological interventions to reduce the transmission rate of infection. And a stricter lockdown ensures that the containment effect is maximized during the lockdown period. In addition, these three lockdowns featured distinct mitigation strategies and the UK’s response to COVID-19 is mitigation strategy that reduce new cases in the short term, but with the risk of the pandemic rebound.

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Section: Discussionmentioning

confidence: 99%

Section: Comparison Of Policies Between Two Wavesmentioning

confidence: 99%

Critical policies disparity of the first and second waves of COVID-19 in the United Kingdom

Shi

Chen

Wang

et al. 2022

Int J Equity Health

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“…Non-mechanistic methods differ from mechanistic methods in that they do not require an explicit model of the dynamics [15,16]. As such, they can be similarly applied to data from any domain so long as the data meets the method's assumptions.…”

Section: Introductionmentioning

confidence: 99%

Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization

et al. 2023

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Forecasting methods are notoriously difficult to interpret, particularly when the relationship between the data and the resulting forecasts is not obvious. Interpretability is an important property of a forecasting method because it allows the user to complement the forecasts with their own knowledge, a process which leads to more applicable results. In general, mechanistic methods are more interpretable than non-mechanistic methods, but they require explicit knowledge of the underlying dynamics. In this paper, we introduce EpiForecast, a tool which performs interpretable, non-mechanistic forecasts using interactive visualization and a simple, data-focused forecasting technique based on empirical dynamic modelling. EpiForecast’s primary feature is a four-plot interactive dashboard which displays a variety of information to help the user understand how the forecasts are generated. In addition to point forecasts, the tool produces distributional forecasts using a kernel density estimation method–these are visualized using color gradients to produce a quick, intuitive visual summary of the estimated future. To ensure the work is FAIR and privacy is ensured, we have released the tool as an entirely in-browser web-application.

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“…Indeed, even when the correct mechanistic model is chosen, it may be outperformed by a welltuned model-free (non-mechanistic) method (14). Non-mechanistic methods differ from mechanistic methods in that they do not require an explicit model of the dynamics (15,16). As such, they can be similarly applied to data from any domain so long as the data meets the method's assumptions.…”

Section: Introductionmentioning

confidence: 99%

Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization

Mason

González

García‐Closas

et al. 2022

Preprint

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Forecasting the COVID-19 Pandemic: Lessons learned and future directions

Cited by 4 publications

References 27 publications

Critical policies disparity of the first and second waves of COVID-19 in the United Kingdom

Critical policies disparity of the first and second waves of COVID-19 in the United Kingdom

Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization

Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization

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