BatResearch - A.Prof. Aaron Irving

@batresearch.bsky.social

Understanding bats through research. Host-Pathogen responses/Comparative Biology/Immunology. opinions my own. A. Prof @ZJE_institute Zhejiang University

In case you missed it: Tom Ksiazek joins TWiV to discuss why rapid, accurate diagnosis is a cornerstone of infection control, and how identifying patients early, can make all the difference.

🦇Through our eLearning platform we are pleased to offer new courses, modules and other educational content that can be purchased and completed at any time. From courses to complete beginners through to others tailored for anyone working with bats. More here: www.bats.org.uk/our-work/tra...

eLearning - Training - Bat Conservation Trust

Through our eLearning platform we are pleased to offer new courses, modules and other educational content that can be purchased and completed at any tim...

bats.org.uk

🚨 The Africa CDC statement on the new #Ebola outbreak in #DRC is concerning. 🔬 Non-Zaire ebolavirus outbreak 🧪 13/20 samples positive ⚠️ 246 suspected cases reported 🕊️ 65 deaths, 4 laboratory workers Urgent action is needed to strengthen detection, containment, and protection of frontline workers.

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Using Serosurveys to Optimize Surveillance for Zoonotic Pathogens. Ecohealth. 2026 Apr 25. doi.org/10.1007/s103... #batpapers #batserology

Using Serosurveys to Optimize Surveillance for Zoonotic Pathogens - EcoHealth

Zoonotic pathogens pose significant risk to human health, with spillover into human populations contributing to chronic disease and epidemics. Despite the widely recognized burden of zoonotic spillover, our ability to identify which animal populations serve as primary reservoirs remains incomplete. This challenge is compounded when prevalence in reservoir populations reaches detectable levels only at specific times of year. In these cases, statistical models designed to predict the timing of peak prevalence could guide field sampling for active infections or predict when spillover risk is likely to be greatest. Thus, we develop a general mathematical model that leverages routinely collected serosurveillance data to optimize sampling for elusive pathogens. Using simulated data, we show that our methodology reliably identifies times when pathogen prevalence is expected to peak. Then, we demonstrate an implementation of our method using previously published surveillance data in straw-colored fruit bats (Eidolon helvum). The generality and simplicity of our methodology make it broadly applicable to a wide range of putative reservoir species where seasonal patterns of birth lead to cyclic, but potentially short-lived, pulses of pathogen prevalence.

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