Assistant Professor (Tenure Track) of Computational Immunology

We have an open Assistant Professor position (tenure track) @ethz.ch in Computational Immunology - ethz.ch/en/the-eth-z... Please apply / share!

And of course co-lead author Nicolas Banholzer 😅 (sorry Nico - too many drafts)

With Lukas Fenner, @phips81.bsky.social @ccattuto.bsky.social Tina Hascher, Mathias Egger, @tanjastadler.bsky.social Pascal Bittel @lo-dallamico.bsky.social Lavinia Furrer and Charlyne Bürki

Our results indicate that extended periods spent in poorly ventilated classrooms may be a stronger driver of within-school transmission than individual contact rates.

We found that while close contact contributed to elevated risk (rate ratio 1.16 per doubling daily time, 95%-CI 1.01–1.33), time spent in shared classrooms and poor air quality had larger effects (RR 3.17, 95%-CI 1.96–5.17 and RR 1.90 95%-CI 1.23–2.94) respectively).

Combining respiratory infection data for children in 4 school classes with proximity sensor, air quality and school records data in a pairwise survival analysis. We inferred the relative contribution of close contacts, shared classrooms and indoor air quality to respiratory transmission in schools.

Our paper modelling transmission risk in schools is published in Nature Communications. **The relative contribution of close-proximity contacts, shared classroom exposure and indoor air quality to respiratory virus transmission in schools** doi.org/10.1038/s414...

Thanks Duncan! These things are fun to dream up, eh.

NREM

Two more fully funded PhDs in real-time infectious disease modelling & forecasting — developing methods for operational use by @ukhsa.bsky.social and others in outbreak response. UK home students, starting April or September 2026.

www.lshtm.ac.uk/study/fees-a...

PhD position (Allschwil, Switzerland)
Forecast the spread and impact of resistance, guiding the optimal use of new insecticide-treated nets.
with Adrian Denz
at Swiss Tropical and Public Health Institute
More details: http://iddjobs.org/jobs/2429

Super cool PhD position up for grabs in our group with @adriandenz.bsky.social. Come and join us to study insecticide resistance in 🦟

PhD Student in Forecasting Resistance Spread and Epidemiological Impact (100%) - Swiss TPH

🌟 Hiring a PhD student! 🌟
📍 Swiss TPH, Basel
Model insecticide-resistance spread and its impact on malaria control.
Quantitative skills (Bayesian, ML, stats) and biology (evolution, genetics) needed.
👉 Apply: jobs.swisstph.ch/Vacancies/11...

We emphasize the need for detailed evaluation of this risk with the latest available data. We are grateful to the Dutch National Institute for Public Health and the Environment (RIVM) and Education Ministry (DUO) for their support of this work.

Figure 2. The evolution of measles outbreak risk on the school-household network in years since 2018. A) The spectral radius of the transmission probability network for each year. B) The proportion of outbreaks seeded in orthodox protestant schools that exceed final size thresholds of 1 to 125 schools. C) The distribution of the number of children infected in outbreaks simulated on the network, seeded in orthodox protestant schools. The red vertical line in each shows the year susceptible children first enter secondary school. D) Graph visualisations showing examples of largest out-components (outbreaks) for individual edge percolation instances for years 2023, 2025, 2026 and 2028, blue vertices show primary schools, red vertices show secondary schools

We evaluate outbreak risk across the school network in each academic year. While children born since the previous outbreak remain only in primary school, outbreaks remain small. As they enter secondary school (in 2025 and 2026) the expected outbreak sizes return to the order of that seen in 2013/14.

Figure 1. The progression of susceptible children through the school system after a large outbreak that provides near complete natural immunity to unvaccinated children. A) model of replenishment of susceptible children in schools over time. Circles represent primary schools and squares secondary schools. Green represents the proportion of the school vaccinated, red represents the proportion of the school with naturally acquired immunity, white represents the proportion of the school susceptible to measles infection. The arrows represent possible transmission paths between schools. B) Choropleths showing geographic distribution of susceptibility in schools in the phase where susceptible children enter secondary school.

In this paper we used a school-household network for the Netherlands constructed from national school records data. Using school-level MMR coverage estimates from 2013 we estimated annual susceptibility profiles of schools, as new cohorts of susceptible children progress through the year groups.

Estimating the risk and spatial spread of measles in populations with high MMR uptake: Using school-household networks to understand the 2013 to 2014 outbreak in the Netherlands Using Dutch household networks, James Munday and team investigate the risk of measles spreading in populations with high MMR uptake.

The Netherlands has historically experienced major outbreaks of Measles - particularly concentrated in school-aged children who attend schools connected to the orthodox Protestant church. We previously connected the 2013 outbreak to the structure of the school system: doi.org/10.1371/jour...

New Paper 🍃! *Projected increase in risk of large measles outbreaks in the Netherlands as susceptible children enter secondary school in 2025/26* out now in the International Journal of Infectious Diseases doi.org/10.1016/j.ij...

... Would you like me to generate a simple workflow to get you started?

There is an obvious solution here Sam - only communicate with humans via an LLM intermediary

Combining demographic shifts with age-based resistance prevalence to estimate future antimicrobial resistance burden in Europe and implications for targets: A modelling study Author summary Why was this study done? Infections caused by bacteria that are resistant to antibiotics are a major and growing threat to public health. Older adults and men are at higher risk of...

How will population shifts affect the future burden of #AMR? 👶👩‍🦳 🧫

We use a new modelling framework to explore future projections of #AMR by age and by sex, as well as interventions for control, building on our earlier findings that AMR isn’t uniform across demographics.

doi.org/10.1371/jour...

We are really grateful to all the people who worked hard to process the wastewater samples and resulting data as part of this national monitoring program. Particular shout out to those at @eawag.bsky.social and the Swiss FOPH. You can find more about Swiss wastewater surveillance here: wise.ethz.ch

Fig 4. (A) Communities of WWTPs identified using hierarchical clustering of time-series in phases 1, 2 and 5 (B–D) time-series of local deviation from national wastewater concentration trends in their relevant clusters for phases 1, 2 and 5 respectively. E) Sankey plot describing how WWTPs flow between partitions in each analysis phase.

Analysing the regional and local deviations, we found varying evidence for correlations with hospitalisation data and geographical clustering through the different phases of the epidemic, which broadly reflect changes in infection prevalence and population mobility patterns.

Fig 2. (A) Association between laboratories or changes in methods within laboratories and SARS-CoV-2 wastewater viral load [VL] (reference is laboratory A). (B) Association between characteristics of the population living in the wastewater treatment plants catchment area and VL (per unit-increase in standard deviation). (C) Association between calendar changes and VL (reference is no public holiday and week day, respectively).

Applying a Bayesian spatio-temporal model, we quantified the contributions of various factors to the observed variability in the time series considering covariates related to processing protocol, socioeconomic factors and regional and local deviations from the global trend.

Fig 1. (A) Catchment areas of wastewater treatment plants [WWTPs] participating to SARS-CoV-2 surveillance by region, with the number of measurements in each WWTP over the study period (light grey <200, grey 200 to 500, black ≥500). (B) All measurements of SARS-CoV-2 viral load in wastewater (values below the limit of quantification are removed; labels 1 to 5 correspond to ad hoc periods aligned with epidemic waves). (C) Weekly median SARS-CoV-2 viral load by WWTP (WWTPs are numbered alphabetically within regions indicated by the coloured scale on the right side; vertical dashed lines delimit the five periods labelled in panel B).

We analysed SARS-CoV-2 RNA concentrations quantified in wastewater samples taken regularly from 120 treatment plants between February 2022 and December 2023.

Fig 3. (A) Adjusted time trends in SARS-CoV-2 wastewater viral load in seven regions of Switzerland (uncertainty is not shown for clarity). (B) Correlation between adjusted time trends in viral load and laboratory-confirmed SARS-CoV-2 hospitalisations in the same region. (C) Adjusted bias in viral load by wastewater treatment plant.

New Paper🚀! Our work analysing the Swiss national SARS-CoV-2 wastewater 🚱 monitoring data is now published in PLOS Water 💧 @plos.org journals.plos.org/water/articl... (Julien Riou, @tanjastadler.bsky.social @trj2.bsky.social Christoph Ort et al)

We promote further investigation of integrating expert opinion in short-term forecasting, especially the development of scalable systems to elicit forecasts. 5/5

In general, the experts tended to over-predict small numbers of cases in more geographical regions than the models but were less likely to predict larger flare-ups. 4/5

We found that while no individual expert could consistently compete with the mathematical models, the performance of the ensemble forecast of the experts was comparable. 3/5

We evaluated the relative performance of a panel of experts with two mathematical models when forecasting the number and spatial distribution of future cases of Ebola Virus Disease in real-time during the 2018-2020 outbreak in North-eastern DRC. 2/5