Johannes Bracher

Collaborative nowcasting of COVID-19 hospitalization incidences in Germany

Real-time surveillance data are a crucial element in the response to infectious disease outbreaks. However, their interpretation is often hampered by delays occurring at various stages of data collection and reporting. These bias the most recent values downward, thus obscuring current trends. Statistical nowcasting techniques can be employed to correct these biases, allowing for accurate characterization of recent developments and thus enhance situational awareness. In this talk, we present a pre-registered real-time assessment of seven nowcasting approaches, applied by independent research teams to German 7-day hospitalization incidences. Due to their unusual definition where hospitalization counts are aggregated by the date of positive test rather than admission, German hospitalization incidences are particularly affected by delays and can take several weeks or months to fully stabilize. For this preregistered study, all methods were applied from 22 November 2021 to 29 April 2022, each day issuing probabilistic nowcasts for the current and 28 preceding days. Nowcasts were collected in the form of quantiles in a public repository and displayed in a dashboard. Moreover, a mean and a median ensemble nowcast were generated. We find that overall the compared methods were able to remove a large part of the biases introduced by delays. Most participating teams underestimated the importance of very long delays, though, resulting in nowcasts with a slight downward bias. Also, the accompanying uncertainty intervals were too narrow for almost all methods. Averaged over all nowcast horizons, the best performance was achieved by a model using case incidences as a covariate and taking into account longer delays than the other approaches. For the most recent days, which are often considered the most relevant in practice, a mean ensemble of the submitted nowcasts performed best.

https://community.epinowcast.org/t/community-seminar-2023-02-01-johannes-bracher-collaborative-nowcasting-of-covid-19-hospitalization-incidences-in-germany/116/3

Generative modeling approaches to nowcasting with incomplete line list data

Line list data offer the potential for a more timely tracking of transmission dynamics through the use of nowcasting. In practice, missing data and noise are important problems that need to be accounted for when nowcasting from line lists. In his talk, Adrian will discuss these challenges and how they can be addressed using Bayesian modeling in line with epinowcast’s generative modeling framework

https://community.epinowcast.org/t/community-seminar-2023-03-01-adrian-lison-generative-modeling-approaches-to-nowcasting-with-incomplete-line-list-data/122

Rafa Lopes and Leo Bastos

Nowcasting the notification delay for real-time decision making, with Nowcaster

Nowcaster is R package build to deal with the typical notification delays occurring on any surveillance systems. During the Covid-19 pandemic, and due to the specificities of Brazilian notification systems, delays where common place and has led to many misguided assertions about the real-time state of the pandemic. Nowcaster wrappers around a age-dependent model fitted in INLA, to estimate the occurring cases and deaths but not yet reported

https://community.epinowcast.org/t/community-seminar-2023-04-05-rafa-lopes-and-leo-bastos-nowcasting-the-notification-delay-for-real-time-decision-making-with-nowcaster/132

Nowcasting and Forecasting the 2022 U.S. Mpox Outbreak: Support for Public Health Decision Making and Lessons Learned

https://community.epinowcast.org/t/community-seminar-2023-05-03-kelly-charniga-nowcasting-and-forecasting-the-2022-u-s-mpox-outbreak-support-for-public-health-decision-making-and-lessons-learned/146

CFA: A new center at the US CDC focused on building response-ready modeling tools

https://community.epinowcast.org/t/community-seminar-2023-06-07-katelyn-gostic-cfa-a-new-center-at-the-us-cdc-focused-on-building-response-ready-modeling-tools/168

The most important operator in infectious disease modeling, and how to use it with biomarker data

Daniel Park/Samuel Brand

Estimating epidemiological delays in real-time/What model ingredients did we need to make medium-term forecasts of Mpox incidence?

The first significant outbreak of Mpox in high-income countries (HICs) happened across Europe and North America in 2022. In contrast to observed Mpox epidemiology in low- and middle-income countries (LMICs), the outbreak in HICs had gay, bisexual and other men who have sex with men (GBMSMs) as the core transmission group. Therefore, modelling groups attempting to create rapid reaction transmission models to inform public health scenarios and forecasting used modelling frameworks familiar from sexually transmissible diseases. In this talk, I’ll unpack the various assumptions, modelling decisions, inference/forecasting strategies and model validation steps that went into the various iterations of a particular Mpox model (recently published here).

Daily rapid testing in contact tracing for Covid-19: From models to trials to policy in the UK

Modelling and forecasting Mpox incidence in the United Kingdom using a randomly sized partitioning of the population

Rebecca Borchering

Informing short term forecasts of categorical trends in confirmed influenza hospital admissions in the US using real-time effective reproduction number estimates

Time to decide on chairs

Charlotte Hammer

Using Hawkes Processes to model infectious disease transmission

Globally there were an estimated 249 million malaria cases and 608,000 malaria deaths in 85 countries during 2022, predominantly in Africa, with 34 countries reporting fewer than 1000 indigenous cases of the disease. Modelling malaria in low transmission settings is challenging because prohibitively large sample sizes are needed to use traditional gold standard measures such as parasite prevalence. Instead, we propose using Hawkes Processes to capture malaria disease dynamics in countries that are close to eliminating malaria. Our model combines malaria specific information, such as the shape of the infectious profile, within a rigorous statistical framework to fit incidence data. We show that it is possible to accurately recreate the case counts over time with our Hawkes Process method. We also show that we can estimate the proportion of cases that are imported without using this information in our fitting process.

Friederike Becker

Scabies, who cares?

Dengue epidemic response 2025