An accidental international team — and our first epidemic-forecasting paper

Created on May 29, 2026

2026   ·   epidemic-forecasting   surveillance   LLM   collaboration   Cambridge   Tokyo   Beijing   ·   research-journey

I don’t usually write blog posts about how a paper happened. This one is an exception, because this one didn’t happen the way papers usually do.

How it started — a formal swap

Early in the MPhil, I met Minghong through a formal swap between our two colleges. I’m at Wolfson; he’s at Peterhouse. Formal swaps are a Cambridge ritual where colleges trade a handful of students for a black-tie dinner across the dining hall — a low-friction way to meet people outside your own department and your own college.

We ended up at the same table. The conversation kept going after dinner, and over the next few weeks Minghong mentioned, in passing, that his older brother Mingxin Liu was a researcher in the Department of Health Communication at the University of Tokyo’s Graduate School of Medicine. I filed it away.

A Dalian coincidence

Around the same time, I got to know Jingyuan Han — a researcher in the Department of Cancer Prevention and Control at the National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College in Beijing. We weren’t in any of the same labs either. We met the way two Chinese students in different cities do: through somebody who knew somebody.

Somewhere in our first real conversation Jingyuan mentioned his girlfriend was a student at Dalian Medical University.

I’m from Dalian.

That moment landed in the place such moments land. The conversation slowed down for a second, then sped up. We didn’t immediately start talking about science — we talked about Dalian. The seafood. The harbour. Where she studied versus where I grew up. Whether the campus had changed since I was last back.

I think that’s actually when the project started, even though the project didn’t exist yet.

The accidental geometry

After a few more conversations, the three of us — Cambridge → Tokyo → Beijing — realised we were each chasing the same problem from different angles.

Public-health surveillance systems do not speak the same language across borders. Different case definitions. Different rules for what counts as a lab-confirmed case. Different ICD coding habits. Reporting delays, revisions, and backfill. A forecasting model fitted on one system breaks the moment you cross a border, change a coding standard, or pull from a different reporting tier.

Mingxin saw it from the health-communication side at Tokyo — how surveillance protocols shape what the public ends up hearing and trusting. Jingyuan saw it from the cancer prevention and control side at the National Cancer Center, where national surveillance data flows through specific institutional pipes and feeds intervention design. I saw it from the statistical / methodological side at Cambridge — if the underlying protocols differ, your forecasting model needs to know about the protocols.

None of us would have written this paper alone.

EpiMap-LLM

The proposal is straightforward in retrospect: bake the protocol structure into the forecasting model itself. Instead of treating each surveillance stream as a black-box source of case counts, condition the model on the protocol that generated those counts — case definition, reporting cadence, revision pattern, backfill — so it learns what each stream is actually measuring, not just the headline numbers.

We built this as EpiMap-LLM — a parameter-efficient alignment framework that connects numerical time-series representations with a frozen language-model backbone, with only lightweight trainable components on top. Protocol-aware tokens are injected as context, so the model can distinguish protocol-induced fluctuations from genuine epidemiological shifts and transfer across datasets and temporal granularities.

We tested it on two heterogeneous surveillance settings — JHU CSSE COVID-19 (daily) and CDC influenza hospitalization (weekly). EpiMap-LLM consistently improves MAE and RMSE over strong forecasting baselines. The gain is largest where reporting irregularities are worst — exactly the operational regime where forecasts have to be trusted.

The full paper is open access in Frontiers in Public Health, published 29 May 2026. Jingyuan and I are co-first authors.

Read the paper · Frontiers (open access)

What I want to remember

The geometry. Cambridge, Tokyo, Beijing. A college friend’s older brother. A girlfriend at a Dalian medical school. Late-night WeChat threads that turned into shared spreadsheets that turned into a draft. International collaboration sometimes gets described as if it were arranged at the institutional level — MOUs, summer schools, formal exchanges. This wasn’t that. Three sides showed up because three friendships did. The paper followed.

What’s next

Jingyuan and I are starting to look at a follow-up — social media in China and cancer prevention — pulling on his cancer-prevention work at the National Cancer Center and on the communication side that Mingxin works on in Tokyo. It’s a natural extension: surveillance data and public-facing communication are two halves of the same loop. It also connects to my dissertation thread on the digital food environment, where the same question recurs in a different language. When we have something to show, this is where I’ll write about it.

Mingxin, Jingyuan — thank you. This one is special.



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