Post-processing and weighted combination of infectious disease nowcasts

André Victor Ribeiro Amaral, Daniel Wolffram, Paula Moraga and Johannes Bracher

PLOS Computational Biology, 2025, vol. 21, issue 3, 1-24

Abstract: In infectious disease surveillance, incidence data are frequently subject to reporting delays and retrospective corrections, making it hard to assess current trends in real time. A variety of probabilistic nowcasting methods have been suggested to correct for the resulting biases. Building upon a recent comparison of eight of these methods in an application to COVID-19 hospitalization data from Germany, the objective of this paper is twofold. Firstly, we investigate how nowcasts from different models can be improved using statistical post-processing methods as employed, e.g., in weather forecasting. Secondly, we assess the potential of weighted ensemble nowcasts, i.e., weighted combinations of different probabilistic nowcasts. These are a natural extension of unweighted nowcast ensembles, which have previously been found to outperform most individual models. Both in post-processing and ensemble building, specific challenges arise from the fact that data are constantly revised, hindering the use of standard approaches. We find that post-processing can improve the individual performance of almost all considered models both in terms of evaluation scores and forecast interval coverage. Improving upon the performance of unweighted ensemble nowcasts via weighting schemes, on the other hand, poses a substantial challenge. Across an array of approaches, we find modest improvement in scores for some and decreased performance for most, with overall more favorable results for simple methods. In terms of forecast interval coverage, however, our methods lead to rather consistent improvements over the unweighted ensembles.Author summary: Infectious disease surveillance data are often subject to reporting delays, which cause recent data points to be incomplete. This leads to spurious dips towards the end of incidence time series, and hampers the real-time assessment of trends. Statistical nowcasts aim to predict how many cases will still be added to the record and thus reveal current trends. In an application to COVID-19 hospitalization data from Germany, we study two extensions to classic disease nowcasting. Firstly, as it is known that nowcasts often have systematic shortcomings, such as biases or too narrow uncertainty intervals, we develop statistical post-processing methods inspired by similar approaches from meteorology. We find that these lead to quite consistent improvements in nowcasting performance. Secondly, previous research has shown that simple unweighted averages of nowcasts from different models can achieve more robust performance than individual models. We assess if this can be further enhanced by weighting member models in a data-driven manner. Here we find that it is very challenging to improve upon unweighted averages. We discuss possible reasons for this phenomenon, which in the forecasting literature is known as the “forecast combination puzzle”.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1012836

DOI: 10.1371/journal.pcbi.1012836

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