Developing infectious disease surveillance systems

doi:10.1038/s41467-020-18798-7

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…Also, it forms the basis for a systematic evaluation of performance. This is a prerequisite for model consolidation and improvement, and a need repeatedly expressed 8 . It has been highlighted that such modeling studies should be prospective 9 and ideally follow pre-registered protocols 10 in order to prevent selective reporting and hindsight bias (i.e., the tendency to overstate the predictability of past events in hindsight).…”

Section: Introductionmentioning

confidence: 99%

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021

et al. 2022

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Background During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021. Methods We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess calibration. The presented work is part of a pre-registered evaluation study. Results We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (Alpha) variant in March 2021, prove challenging to predict. Conclusions Multi-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance.

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Section: Introductionmentioning

confidence: 99%

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021

et al. 2022

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“…Efforts to emphasize on developing effective and advanced infectious disease surveillance and monitoring systems for emerging and re-emerging infectious diseases in underserved communities of low- and middle-income counties to access epidemic waves and be able to come up with strong international and local policies to tackle the future threats of climatic changes on the rise of infectious diseases 26 .…”

Section: Environmental and Climatic Changes In Infectious Diseasesmentioning

confidence: 99%

Impacts of environmental and climatic changes on future infectious diseases

Uwishema

Masunga

Naisikye

et al. 2023

International Journal of Surgery

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Pandemetrics: systematically assessing, monitoring, and controlling the evolution of a pandemic

Barone

Chakhunashvili

2022

Qual Quant

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The still ongoing pandemic of SARS-CoV-2 virus and COVID-19 disease, affecting the population worldwide, has demonstrated the need of more accurate methodologies for assessing, monitoring, and controlling an outbreak of such devastating proportions. Authoritative attempts have been made in traditional fields of medicine (epidemiology, virology, infectiology) to address these shortcomings, mainly by relying on mathematical and statistical modeling. However, here, we propose approaching the methodological work from a different, and to some extent alternative, standpoint. Applied systematically, the concepts and tools of statistical engineering and quality management, developed not only in healthcare settings, but also in other scientific contexts, can be very useful in assessing, monitoring, and controlling pandemic events. We propose a methodology based on a set of tools and techniques, formulas, graphs, and tables to support the decision-making concerning the management of a pandemic like COVID-19. This methodological body is hereby named Pandemetrics. This name intends to emphasize the peculiarity of our approach to measuring, and graphically presenting the unique context of the COVID-19 pandemic.

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Developing infectious disease surveillance systems

Cited by 9 publications

References 11 publications

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021

Impacts of environmental and climatic changes on future infectious diseases

Pandemetrics: systematically assessing, monitoring, and controlling the evolution of a pandemic

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