A new study in Nature Communications challenges how scientists have been modeling malaria transmission, offering a simpler and surprisingly more effective way to estimate when and where the disease can spread. Researchers including CEID members Kerri Miazgowicz, Richard Hall, and Courtney Murdock tested whether hourly temperature models, which try to capture the fine-scale ups and downs of daily weather, actually improve predictions about malaria’s thermal limits. They found that a much simpler approach of just using the average daily temperature did a better job of predicting the temperature boundaries for malaria transmission across three major mosquito-parasite combinations.
The team used controlled experiments and existing datasets to compare the two modeling strategies: one based on hourly rate summation (where you add up mosquito and parasite growth rates each hour) and one based on mean daily temperature. Despite the assumption that more detailed hourly models would be more accurate, the researchers showed that these models often overestimated transmission risk at the extremes, especially in hot environments. In contrast, the simpler daily average approach provided a more consistent and biologically realistic picture of when transmission is likely or unlikely.
This has big implications for how we forecast malaria risk under climate change. Many existing models use complex thermal performance curves built from hourly temperature data, which may overcomplicate things and miss key ecological limits. By showing that daily mean temperature can reliably capture the thermal constraints of malaria transmission, the authors make a strong case for shifting toward simpler, more robust models that are easier to apply at large scales and in data-limited regions. This could ultimately help improve how we plan for and respond to malaria outbreaks in a warming world.
Please find the full study here.