Short-term forecasting of COVID-19 in Germany and Poland during the second wave – a preregistered study

Bracher, Johannes; Wolffram, Daniel; Deuschel, Jannik; Goergen, Konstantin; Ketterer, Jakob; Ullrich, Alexander; Abbott, Sam; Barbarossa, Maria Vittoria; Bertsimas, Dimitris; Bhatia, Sangeeta; Bodych, Marcin; Bosse, Nikos I.; Burgard, Jan Pablo; Castro, Lauren; Fairchild, Geoffrey; Fuhrmann, Jan; Funk, Sebastian; Gogolewski, Krzysztof; Gu, Quanquan; Heyder, Stefan; Hotz, Thomas; Kheifetz, Yuri; Kirsten, Holger; Krueger, Tyll; Krymova, Ekaterina; Li, Michael Lingzhi; Meinke, Jan H.; Michaud, Isaac; Niedzielewski, Karol; Ożański, Tomasz; Rakowski, Franciszek; Scholz, Markus; Soni, Saksham; Srivastava, Ajitesh; Zieliński, Jakub; Zou, Difan; Gneiting, Tilmann; Schienle, Melanie

doi:10.1101/2020.12.24.20248826

Cited by 30 publications

(59 citation statements)

References 35 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Future research could investigate integrating more data streams into the models. In addition to the weekly reports that we publish, our work has also contributed to other international forecasting efforts [22,36,44].…”

Section: Discussionmentioning

confidence: 98%

“…Modelling groups around the world have attempted to meet one or both challenges with various analyses conducted at a sub-national scale [43], at a national scale for a specific country [22,[44][45][46], and for several countries across the globe [47][48][49]. In contrast to models built for a region or country and calibrated using local data, models that aim to provide a global overview must be sufficiently general to describe the epidemic trajectory in a range of countries/regions using widely available data that are consistently available over time.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment

Bhatia

Parag

Wardle

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

From 8th March to 29th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for 81 countries with evidence of sustained transmission. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3\% and 45.6\% of the observations lying in the 50\% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9\% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment

Bhatia

Parag

Wardle

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Modelling groups around the world have attempted to meet one or both challenges with various analyses conducted at a sub-national scale [54], at a national scale for a specific country [22,[55][56][57], and for several countries across the globe [41,58,59]. In contrast to models built for a region or country and calibrated using local data, models that aim to provide a global overview must be sufficiently general to describe the epidemic trajectory in a range of countries/regions using widely available data that are consistently available over time.…”

Section: Discussionmentioning

confidence: 99%

Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment

Bhatia

Parag

Wardle

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Background: As of July 2021, more than 180,000,000 cases of COVID-19 have been reported across the world, with more than 4 million deaths. Mathematical modelling and forecasting efforts have been widely used to inform policy-making and to create situational awareness. Methods and Findings: From 8th March to 29th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for countries with evidence of sustained transmission. The estimates and forecasts were based on an ensemble model comprising of three models that were calibrated using only the reported number of COVID-19 cases and deaths in each country. We also developed a novel heuristic to combine weekly estimates of transmissibility and potential changes in population immunity due to infection to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. Conclusions: During the 39-week period covered by this study, we produced short- and medium-term forecasts for 81 countries. Both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax stringent public health measures that were implemented to contain the pandemic.

show abstract

“…During a pandemic, not a lot of data is available to reliably run the aforementioned models. However, some of the models for infectious diseases were designed for determining longterm, instead of short-term, dynamics and projections [25]. In comparison, the data-driven PLOS ONE methods considered in this paper are less data-hungry, perform well for short-term forecasts (based on evaluation of 4-week ahead forecasts), and do not require as much level of detail in the datasets.…”

Section: Plos Onementioning

confidence: 99%

Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases

et al. 2021

View full text Add to dashboard Cite

Background The WHO announced the epidemic of SARS-CoV2 as a public health emergency of international concern on 30th January 2020. To date, it has spread to more than 200 countries and has been declared a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2. Methodology This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 187 countries, using four data-driven methodologies; autoregressive integrated moving average (ARIMA), exponential smoothing model (ETS), and random walk forecasts (RWF) with and without drift. For these forecasts, we evaluate the accuracy and systematic errors using the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), respectively. Findings The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generate heat maps to provide a pictorial representation of the countries at risk of having an increase in the cases in the coming 4 weeks of February 2021. Conclusion Due to limited data availability during the ongoing pandemic, less data-hungry short-term forecasting models, like ARIMA and ETS, can help in anticipating the future outbreaks of SARS-CoV2.

show abstract

Short-term forecasting of COVID-19 in Germany and Poland during the second wave – a preregistered study

Cited by 30 publications

References 35 publications

Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment

Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment

Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment

Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases

Contact Info

Product

Resources

About