Warmer weather is impacting global food supply, but not in the way you think

Hotter summers are not just uncomfortable, they also changing the odds of getting sick from food. A new analysis links warmer air, higher moisture, specific dewpoint ranges, and longer daylight to spikes in Salmonella infections across two European countries.

The team showed that risk climbs when average temperatures rise above about 50°F and days stretch past 12 to 15 hours.

Lead author Dr. Laura C. González Villeta of the University of Surrey’s School of Veterinary Medicine led work that tied laboratory confirmed Salmonella cases to local weather conditions using a transparent statistical approach.

The bacteria called Salmonella cause food poisoning that often brings diarrhea, fever, and stomach cramps. Common symptoms usually start within hours to a few days and most people recover without treatment.

Salmonella and the weather

Researchers linked daily case counts from the UK Health Security Agency to detailed weather from the Met Office, then checked the model with national surveillance data from the Netherlands.

They examined 14 factors, including temperature, relative humidity, precipitation, sunshine, and dewpoint temperature.

Instead of forcing a complex equation to fit the data, the group calculated a conditional incidence that directly describes how often cases occur when specific combinations of weather are present.

That method lets you see which trio of conditions lines up with higher infection counts without hiding the signal inside a black box.

The approach showed consistency when moved from one country to another after accounting for reporting differences. That matters because public health tools work best when they travel across borders without losing accuracy.

What the model found

Risk rose when average air temperatures topped roughly 50°F, day length sat between 12 and 15 hours, and dewpoint hovered around the mid 40s to about 50°F. The same pattern appeared regardless of location.

Not every weather knob seemed important. Wind speed, air pressure, and sunshine duration had little or no association with reported cases in this dataset.

Seasonal peaks in late summer were reproduced when the model fed on local weather. That gave officials a way to anticipate risk based on last week’s skies rather than waiting for lab reports to trickle in.

How weather boosts Salmonella

Broader evidence lines up with these findings. A large meta-analysis of 23 Salmonella studies found about a 5 percent rise in risk for every 1.8°F increase in temperature.

Europe’s outbreak history shows why this topic matters. In 2018, nearly one in three foodborne outbreaks in the European Union were caused by Salmonella, according to an EFSA report.

That baseline risk has not disappeared in recent years. Surveillance summaries still identify Salmonella as a leading cause of foodborne outbreaks across the region, with eggs and mixed foods often involved, as noted in EFSA’s public publication.

What this means for public health

“The study highlights how weather plays a significant role in Salmonella outbreaks and provides a valuable tool for predicting future risks and tailoring interventions, particularly in the context of climate change,” said Dr. Villeta. The work is not just about describing patterns. 

Weather aware forecasting can help agencies time inspections, target hygiene messaging, and plan surge capacity when the odds of illness tick up.

Food businesses can also adjust cold chain checks, staff training, and consumer advice when riskier conditions line up.

Individuals are not powerless either. Keep cold foods cold, cook poultry and eggs to safe temperatures, refrigerate leftovers within 2 hours, and wash hands and surfaces, especially when the air is warm and the day runs long.

High-tech forecasting tools

Most forecasting tools lean on regression or time series fits that struggle with tangled, non linear interactions.

The conditional incidence approach shows how combinations of temperature, humidity, and daylight align with cases, which is easier to interpret and to explain.

That clarity helps separate what drives risk from noise. It also allows public health teams to identify factor combinations that do not explain much, saving effort where it will not pay off.

Model outputs replicated real world peaks and mapped risk at fine spatial scales. That is the level where health departments and food producers make day to day choices.

Limits and next steps

The model tracks reported cases, which understate total infections. Even so, de trending and validation across two countries suggest the weather signals are real.

Future tests in different climates and income settings would show how far the method travels. Adding data on livestock density, land use, and consumer behavior could sharpen predictions without losing transparency.

Better early warnings do not replace basic food safety, but they can make those steps more timely. That is useful as warmer summers and longer heat seasons become more common.

The study is published in the Journal of Infection.

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