Addressing observational biases in data-driven approaches of zoonotic hazard prediction

Post provided by Andrea Tonelli Over the past five decades, more than half of emerging infectious diseases in humans originated from animals, with zoonotic pathogens posing a growing threat to global health. Shifts in land use, climate change, direct use of wildlife and biodiversity loss all influence human exposure to pathogens of wild animals, shaping the likelihood of zoonotic spillover events. In the wake of … Continue reading Addressing observational biases in data-driven approaches of zoonotic hazard prediction

It is only by understanding what causes sampling bias that we can correct it

Post provided by Rob J. Boyd Colleagues and I recently published a paper in MEE, and its title might induce a bit of head scratching: “Using causal diagrams … to correct geographic sampling biases in biodiversity monitoring data” (Boyd et al., 2025). If you’re familiar with causal inference, you might be wondering, “What have causal diagrams got to do with sampling biases?” And if you’re … Continue reading It is only by understanding what causes sampling bias that we can correct it