☆ 4.8 Editorial Material

Predicting a pandemic

NATURE (2022)

An ounce of pandemic prevention is worth a pound of cure

[Anonymous]

LANCET MICROBE (2022)

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Article Biochemistry & Molecular Biology

Identifying and prioritizing potential human-infecting viruses from their genome sequences

Nardus Mollentze et al.

Summary: By developing machine learning models, we can identify candidate zoonotic pathogens based on host range features encoded in viral genomes, providing a rapid and cost-effective approach for evidence-driven virus surveillance and facilitating research on the biological and ecological characteristics of viruses.

PLOS BIOLOGY (2021)

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Article Biochemistry & Molecular Biology

How accurately can we assess zoonotic risk?

Michelle Wille et al.

Summary: Identifying animal reservoirs of zoonotic viruses is crucial in understanding disease emergence. Studies attempting zoonotic risk assessment have increased, but virological data used in these analyses are incomplete and biased. Virus surveillance at the human-animal interface may be more productive than relying on existing virological data.

PLOS BIOLOGY (2021)

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Review Biology

The future of zoonotic risk prediction

Colin J. Carlson et al.

Summary: With the urgency brought by the COVID-19 pandemic, global investment in wildlife virology is expected to increase, leading to the discovery of hundreds of novel viruses through new surveillance programs that could potentially harm humans. Scientists are increasingly looking towards data-driven rubrics and machine learning models to identify animal pathogens that may pose a threat to global health, discussing the prerequisites, impacts, control, and applications of these technologies.

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES (2021)

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Editorial Material Multidisciplinary Sciences