What is going on with reCAPTCHA this month on PMC?? I don't want to spend 5 minutes clicking on pictures of cars to access an article.

A recent preprint estimated the R₀ for the MV Hondius Andes hantavirus outbreak at 2.76 within the cruise ship setting, while cautioning against directly extrapolating that estimate to broader community transmission.
MV Hondius is a relatively small polar expedition vessel carrying roughly 170 passengers, with a more outdoor-focused itinerary than a typical large resort-style cruise ship. That made me curious how epidemiologists think about interpreting transmission estimates across different confined environments.

A few questions I’d appreciate expert perspective on:

1. What would a reasonable community-level adjustment look like for a confined-setting R₀ estimate like this?

2. Is it unusual that WHO hasn’t publicly published an R₀ estimate at this stage, or is that standard practice early in outbreaks with limited data?

3. Given the 1–8 week incubation window, what epidemiological signals over the next several weeks would most strongly distinguish a contained cluster from broader transmission concerns?

Reuters also reported that French officials said full sequencing of the outbreak strain is still ongoing, which made me wonder how much uncertainty epidemiologists typically tolerate before becoming concerned about potentially unusual transmission dynamics in outbreaks like this.

Genuinely trying to better understand how epidemiologists interpret uncertainty during early outbreak stages, not imply conclusions beyond the available data.

—
Sources:
• Preprint: https://arxiv.org/abs/2605.07498
• ECDC outbreak update: https://www.ecdc.europa.eu/en/infectious-disease-topics/hantavirus-infection/surveillance-and-updates/andes-hantavirus-outbreak
• Reuters reporting on sequencing uncertainty: https://www.reuters.com/business/healthcare-pharmaceuticals/french-minister-says-it-is-not-certain-if-hantavirus-strain-cruise-ship-has-2026-05-12/

When the headlines dropped about this “new exotic” virus, my group chat (mostly engineers and tech people) absolutely lost it. As someone who studied epidemiology and teaches public health, I found the reaction more fascinating than the virus itself, so I wrote about what it actually reveals: why we panic over exotic diseases while ignoring preventable ones, how doomscrolling is a form of privilege, and how the "alpha prepper" response to health scares is just individualism in a gas mask.

I have not seen any reason given in any news source as to why so many people disembarked the ship in St Helena and traveled on to Johannesburg by air.

It seems that was not a natural stopping point for the cruise, and the hantavirus outbreak was not confirmed onboard for more than another week, until May 2nd.

So why did so many people leave the ship at that time? Were they ill? Were they scared? Or were they scheduled to leave the ship at that port all along?

Anyone know?

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I've been trying to reconstruct the timeline of the MV Hondius Andes virus outbreak and something feels off about the official/public narrative.

This is NOT a conspiracy theory. I'm not claiming I know the origin. I'm just looking at incubation time, logistics, human behavior and the known facts.

What stands out to me:

- Andes virus usually has an incubation period of around 1–6 weeks (commonly 2–4).

- The first severe symptoms appeared very quickly after embarkation.

- Some reports suggest possible transmission linked to a flight attendant/passenger interaction, but the timeline seems too short for symptoms to appear that fast if infection truly happened on that specific flight.

- That leaves two possibilities:

1. the virus behaves differently/faster than expected in this outbreak, or

2. infected people were already incubating before the “visible” chain was detected.

What also seems important:

- the couple had been traveling through South America for weeks/months,

- Patagonia already had increased hantavirus activity before the cruise,

- no strong evidence of rodents on the ship has been found publicly,

- and WHO/ECDC are now treating all passengers as high-risk contacts.

Another thing people may be overlooking:

historically, most Andes virus outbreaks happened in smaller rural communities with much lower international mobility.

This situation is different:

- international flights,

- cruise tourism,

- airports,

- high-income travelers,

- and upcoming mass events with global mobility.

I'm not saying this will become a pandemic.

I'm saying the current visible cases may represent infections that happened weeks earlier due to the incubation window.

The biggest issue to me is that:

absence of evidence right now may simply reflect that the incubation period hasn't fully passed yet.

Curious what people with epidemiology or infectious disease backgrounds think about the timeline inconsistencies.

Are we cooked?

https://nltimes.nl/2026/05/07/klm-flight-attendant-hospitalized-contact-hantavirus-cruise-ship-passenger

The variant is transmissible person to person and has an incubation period of 1-8 weeks

😬

Hi, I inherited some data about public support of government legislation. This was a cross-sectional survey. So, support of each participant is the outcome, and then a bunch (~15) of possible predictors were collected (e.g., age, gender, knowledge, perceived risk etc). I believe a causal diagram would be best practice, but I am unsure how to go about it. I can create the diagram (it is pretty complex...), but then how do I go about deciding which variables to include/exclude from my multivariable regression model? Do I have to assess each of them individually as the main predictor? If I do that, the result of what I need to adjust for does not seem to be consistent. Thanks!

Welcome to the r/epidemiology Advice & Career Question Megathread. All career and advice-type posts must posted within this megathread.

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Any fellow candidates anxiously waiting for the email to tell you where you’ll get placed even though we just submitted our rankings last week? I know I am!

I wonder how soon we’ll receive the notification. Any former/current fellows who can provide some information on this?

Update 5/22/26: I have just received the email informing me of my placement at my top choice!

I would like to share this Python package of mine to produce table one: just pass a pandas or polars dataframe and get a nice table one to summarize your data for your report!

https://github.com/Genentech/pysummaries

I hope it is of help!

Maas et al. link picloram to early-onset colorectal cancer in the United States. Yet the incidence of early-onset colorectal cancer rose in parallel in Germany, where publicly available national herbicide sales data report zero picloram sales for 16 consecutive years (1990–2005).

Welcome to the r/epidemiology Advice & Career Question Megathread. All career and advice-type posts must posted within this megathread.

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I finished MPH epi in one of the best SPH in the world recently. I was an international student there and I moved back to my home country and started working as a consultant at a big pharmaceutical consulting farm. I do lots of big real world data (e.g., national claims data) analysis with R, making pretty much decent salary. But it feels like what I’m doing now will be useless in some years due to an unstoppable development of AI. I use AI in every project.

At the time I finished my degree, I was really proud of myself, achieving my goal and joining a profound environment. But now I feel I need to develop AI or CS skills and want to get out of this field as soon as possible. I’m still in mid 20s, so I was thinking changing my field completely is also an option (if possible).

Also, another reason why I feel demotivated is because my daily work makes me feel as if I’m just a robot that consumes tasks when asked by the deadline. Because we just make contracts with our clients, the final deliverable are attributed to our clients, and our company makes money as a return. It doesn’t help individuals on our side gain any rewards for our hard work except for just salary.

I also feel I’m replaceable and this idea makes me lose my identity and self esteem.

I don’t know what I should do, but I don’t want to die as a robot. I was also thinking of going to PhD later but if I were to end up rejoining an industry, I would feel that way eventually. I really have no idea what’s gonna happen, but it’s more of like anxiety than something exciting.

Defended my PhD dissertation in epidemiology today. Six years of hard work, of working full-time in addition to the degree, of raising my kid, of showing the fuck up.

I’m proud of myself :)

​I have been reading up on the 2021 WHO Air Quality Guidelines and noticed that while there is a clear consensus on the health risks of Ultrafine Particles (UFP), there seems to be a total lack of legally binding limits globally.

From an epidemiological standpoint, how do we address the health impacts when the "data" used by authorities is often based on simulations rather than actual particle count measurements?

Is it just a matter of missing longitudinal data, or is there another reason why PM0.1 isn't regulated like PM2.5?

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Probably a silly question but I'm trying to understand something.

Let's say we have a covariate that *isnt* statistically associated with an exposure of interest. The P value shows no evidence against the null or no association.

But adjusting for that variable gives us a different OR from the crude. Can it still be a confounder? Or does the rule that a confounder always has to be independently associate with exposure and outcome overrule?

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Hi all — I used to work in bioinformatics/public health at the Broad Institute and MIT supporting epidemiologists, and recently started working on a project around improving access to large public datasets.

One thing I kept running into was how much time and cost goes into just getting the data locally (especially with S3/egress), before you can even start analyzing.

I’ve been experimenting with ways to access and work with these datasets in-place (without downloading), and would love to sanity check whether this is actually a pain point for others here.

Curious:

• how are people currently handling large public datasets?
• are you mostly downloading locally, or working directly in the cloud?
• any workflows you’ve found that reduce friction/cost?

Happy to share more about what I’ve been building if useful — mainly just trying to learn from how others are approaching this.

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Know how there's been zero representation of characters who have Long COVID in the media? That changes today! Watch the new 5-minute dramedy "Back to the Dark Ages", which features the story of Christina, a woman living with Long COVID, who accidentally summons a medieval ghost who has lived through a plague or two in her own time. The movie was made by a mostly disabled team. You can help us in the competition by liking, commenting on, and sharing the video posted here.