short term forecasting of covid 19 in germany and poland during the second wave CORD-Papers-2022-06-02 (Version 1)

Title: Short-term forecasting of COVID-19 in Germany and Poland during the second wave - a preregistered study
Abstract: We report insights from ten weeks of collaborative COVID-19 forecasting for Germany and Poland (12 October - 19 December 2020). The study period covers the onset of the second wave in both countries with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks with evaluation focused on one- and two-week horizons which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance in particular in terms of coverage but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.
Published: 2020-12-26
DOI: 10.1101/2020.12.24.20248826
DOI_URL: http://doi.org/10.1101/2020.12.24.20248826
Author Name: Bracher J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bracher_j
Author Name: Wolffram D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wolffram_d
Author Name: Deuschel J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/deuschel_j
Author Name: Goergen K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/goergen_k
Author Name: Ketterer J L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ketterer_j_l
Author Name: Ullrich A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ullrich_a
Author Name: Abbott S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/abbott_s
Author Name: Barbarossa M V
Author link: https://covid19-data.nist.gov/pid/rest/local/author/barbarossa_m_v
Author Name: Bertsimas D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bertsimas_d
Author Name: Bhatia S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bhatia_s
Author Name: Bodych M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bodych_m
Author Name: Bosse N I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bosse_n_i
Author Name: Burgard J P
Author link: https://covid19-data.nist.gov/pid/rest/local/author/burgard_j_p
Author Name: Fuhrmann J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/fuhrmann_j
Author Name: Funk S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/funk_s
Author Name: Gogolewski K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gogolewski_k
Author Name: Gu Q
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gu_q
Author Name: Heyder S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/heyder_s
Author Name: Hotz T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hotz_t
Author Name: Kheifetz Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kheifetz_y
Author Name: Kirsten H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kirsten_h
Author Name: Krueger T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/krueger_t
Author Name: Krymova E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/krymova_e
Author Name: Li M L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/li_m_l
Author Name: Meinke J H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/meinke_j_h
Author Name: Niedzielewski K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/niedzielewski_k
Author Name: Ozanski T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ozanski_t
Author Name: Rakowski F
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rakowski_f
Author Name: Scholz M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/scholz_m
Author Name: Soni S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/soni_s
Author Name: Srivastava A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/srivastava_a
Author Name: Zielinski J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zielinski_j
Author Name: Zou D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zou_d
Author Name: Gneiting T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gneiting_t
Author Name: Schienle M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/schienle_m
sha: 2f26eb7ad6a7bef227fb7413b3c7c8435221fff0
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: https://doi.org/10.1101/2020.12.24.20248826 http://medrxiv.org/cgi/content/short/2020.12.24.20248826v1?rss=1
has_full_text: TRUE
Keywords Extracted from Text Content: COVID-19 https://www.dw.com/en/coronavirus-germany-to-imposeone-month-partial-lockdown/a-55421241 Germany and λ t+1 German 5/10 9/10 87,597 60,347 2/9 6/9 forecast::ets(ts OSF WIS Figures 3 and 4 layer lockdown https://doi.org/10.1101 https://doi.org/10 14,191 Poland-based teams MOCOS 5/10 9/10 55,931 Günther RKI passt Testempfehlungen KITCOVIDhub-inverse SARS-CoV-2-Diagnostik 5/10 8/10 CRPS Petropoulos MIMUW KIT-baseline Ärzteblatt · | X t−i−1 Semi-lockdown Germany Makridakis https://doi.org/10.1101/2020.12.24.20248826 doi left panel Vespignani, 2019 https://kitmetricslab.github.io/forecasthub LANL-GrowthRate -show re-format Epi1Ger 9/10 37,831 25,362 2/9 6/9 14,511 KIT-baseline 31,605 left J. Plucinska Johns Hopkins University Center for Systems Science and EpiExpert MOCOS-agent1 λ let α = 1 UK Figure 1 medRxiv preprint Figure 12 SECIR Two-week-ahead Germany and Poland One-week-ahead Viboud samples body takeaways medRxiv preprint Figure human Four-week-ahead Berlin/Warsaw semi-lockdown Figures 3 four-week-ahead LANL-GrowthRate 5 Dynamic SI epiforecasts-EpiExpert t+4 X t X t−1 Coronavirus t−i−1 6/10 9/10 40,453 26,329 2/9 6/9 16,541 10,373 4/10 8/10 55,827 35,166 1/9 5/9 Funk F one-week-ahead medRxiv COVID-19 Far LeipzigIMISE-SECIR Bicher COVIDAnalytics-Delphi US two-week-ahead inverse-WIS USC-SIkJalpha Supplementary Section C. 2020 Human jugdement A medRxiv preprint US COVID-19 Evan L. Nicholas G. SIMCARD Sangeeta Bhatia 15,554 10,086 4/10 9/10 40,120 25,588 1/9 6/9 16,068 10,633 Hub 55,290 36,153 6/10 Fabian Eckelmann
Extracted Text Content in Record: First 5000 Characters:We report insights from ten weeks of collaborative COVID-19 forecasting for Germany and Poland (12 October -19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one-and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic. This is a preprint. It has not yet undergone peer review. Forecasting is one of the key purposes of epidemic modelling, and despite being related to the understanding of underlying mechanisms, it is a conceptually distinct task (Keeling and Rohani, 2008) . Accurate disease forecasts can improve situational awareness of decision makers and facilitate tasks such as resource allocation or planning of vaccine trials (Dean et al., 2020) . During the COVID-19 pandemic, there has been a major surge in research activity on epidemic forecasting with a plethora of approaches being pursued. Contributions vary greatly in terms of purpose, forecast targets, methods, and evaluation criteria. An important distinction is between longer-term scenario or what-if projections and short-term forecasts (Reich and Rivers, 2020) . The former attempt to discern the consequences of hypothetical scenarios and typically cannot be evaluated directly using subsequently observed data. The latter, which are the focus of this work, quantitatively describe expectations and uncertainties in the short run. They refer to quantities expected to be largely unaffected by yet unknown changes in public health interventions. This makes them particularly suitable to assess the predictive power of computational models, a need repeatedly expressed during the pandemic (Nature Publishing Group, 2020) . In this work we present results and takeaways from a collaborative and prospective short-term COVID-19 forecasting project in Germany and Poland. The evaluation period extends from 12 October 2020 (first forecasts issued) to 19 December 2020 (last observations made), thus covering the onset of the second epidemic wave in both countries. We gathered a total of 13 modelling teams from Germany, Poland, Switzerland, the United Kingdom and the United States to generate forecasts of confirmed cases and deaths in a standardized and thus comparable manner. These are publicly available in an online repository (https://github.com/KITmetricslab/covid19-forecast-hub-de) called the German and Polish COVID-19 Forecast Hub and can be explored interactively in a dashboard (https://kitmetricslab.github.io/forecasthub). On 8 October 2020, we deposited a study protocol (Bracher et al., 2020b) at the registry of the Open Science Foundation (OSF), predefining the study period and procedures for a prospective forecast evaluation study. Here we report on results from this effort, addressing in particular the following questions: • At which forecast horizons can one expect to obtain reliable forecasts for various targets? • Are the forecasts calibrated, i.e. are they able to accurately quantify their own uncertainty? • How good is the agreement between different forecast methods? • Are there prediction approaches which prove to be particularly reliable? • Can combined ensemble forecasts lead to improved performance? The study period is marked by overall strong virus circulation and changes in intervention measures and testing strategies. This makes for a situation in which reliable short-term predictions are both particularly useful and particularly challenging to produce. Conclusions from ten weeks of real-time forecasting are necessarily preliminary, but we hope to contribute to an ongoing exchange on best practices in the field. Our study will be followed up until at least March 2021 and may be extended beyond. The project follows several principles which we consider key for a rigorous assessment of forecasting methods. Firstly, forecasts are made in real time, as retrospective forecasting often leads to overly optimistic conclusions about performance. Real-time forecasting poses many specific challenges (Desai et al., 2019) , including noisy or delayed data, incomplete knowledge on testing and interventions as well as time pressure. Even if these are mimicked in retrospective studies, some benefit of hindsight remains. Secondly, in a pandemic situation with presumably low predictability we consider it of central importance to explicitly quantify forecast u
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