Bacteria found in wastewater may provide clues about community health, including rates of chronic disease, health behaviors and social conditions, according to a recent study from The Ohio State University. The research, published in the journal Environmental Research, expands on applications of wastewater monitoring, which became more widely used during the COVID-19 pandemic. The findings suggest the same approach could offer a practical way to understand broader health patterns across communities.

The 21st-century epidemics of animal-human diseases, or zoonotic pathogens, such as MERS, SARS, and Covid-19, have demonstrated how pathogen spillover from animal to human can pose a global health threat. There is one factor linked with at least 70% of such emerging diseases: wildlife. In recent research published in the journal One Health, “Exploring the Impacts of Wildlife Trade on Zoonotic Spillover and Disease Outbreaks: A Multiple-perspective Study”, researchers from Xi’an Jiaotong-Liverpool University (XJTLU) have discovered that wildlife imports highly influence the possibility of zoonotic emerging infectious disease (EID). This marks the first global measurement of how wildlife trade is linked to outbreaks of animal-borne diseases, across countries and over time.

Understanding the sources and quantities of carbon dioxide (CO₂) released to the atmosphere is central to assessing past emissions and identifying potential reductions. Fossil fuel combustion is the principal source of human-caused CO₂ emissions, alongside emissions from natural gas flaring and venting and the manufacture of cement. Consistent estimates across countries and years help distinguish changes in emissions from changes in data coverage or accounting methods. The CDIAC-FF dataset provides annual estimates reaching back to 1750 and records emissions by fuel type and, for recent decades, economic sector.

Authors Michael Erb and Gregg Marland from the Research Institute for Environment, Energy and Economics at Appalachian State University updated the CDIAC-FF dataset through 2022. The research describes a data product, using United Nations Statistics Division energy data for fuel production, trade and consumption from 1950 onward, with earlier estimates drawn from historical sources. The authors estimate emissions using both a reference approach based on apparent national fuel use and a sectoral approach based on consumption by economic sector. The inventory covers fossil fuel use, natural gas flaring and venting, and cement production, with national and global estimates kept within consistent accounting boundaries over time.

Two new studies independently demonstrated how viruses target and impact the placenta, one by tunneling through its barrier and the other by interfering with the organ’s work nourishing the fetus, to varying degrees of success. In this Q&A, the researchers discuss how infections and treatments affect the placenta differently than other tissues in the body and why it all matters for a child’s development and for women’s health.

Every commute, delivery, and business trip burns fuel—and much of the carbon released into the atmosphere is a byproduct of exactly that movement. Reporting in The Innovation, researchers turn large-scale mobility data into a near-real-time carbon meter. Daily intercity mobility networks, built from mobile-phone signaling data covering 318 million users in China, tracked national CO2 emissions in lockstep (r = 0.89) through the COVID-19 lockdown upheaval of early 2020. Combined with population and location data, machine-learning models predicted city-level emissions across 366 cities with R2 up to 1.00, and generalized to Italy, the US, and Mexico with R2 of 0.91–0.99 across two independent emission inventories. Mobility networks also reveal functional city clusters—regions defined by real movement rather than administrative borders—offering new units for coordinated climate governance.

- A Cambridge team led by Professor Jonathan Heeney has used AI to engineer vaccine antigens for a new candidate bird flu vaccine that outperforms current stockpiled jabs, which are made from single virus strains recommended by the World Health Organisation. 

- The new broad-spectrum vaccine provides complete protection against diverse bird flu variants, trials in animals show.

- The result follows successful animal trials of broad-spectrum vaccines for coronavirus and swine flu, designed using the same platform.

- Earlier this year the team announced that their broad-spectrum coronavirus vaccine had proved safe in human clinical trials.

- The new study provides further evidence that the team’s ‘plug and play’ approach could be used to create safe, highly effective vaccines for a plethora of infectious diseases threatening human health globally, including Ebola and Marburg viruses, amongst some of the most dangerous human pathogens in the world.

- This new class of vaccines moves us closer to a wide variety of broadly protective vaccines in the future - including safer, much more effective seasonal Flu jabs. They not only protect against many variants simultaneously, but may also provide protection against related viruses that have not yet emerged or spilled over into humans.

- The technology could be a game changer for pandemic preparedness, health security and national biosecurity. It could help translate global surveillance data into broad protective immunity, before outbreaks become established epidemics or pandemics.

Hoping to provide much-needed guidance to clinicians treating patients with lung disease and to promote respiratory health, the Vaccine Guidance Review Panel, a multi-medical society effort, announced its endorsement of influenza, RSV and COVID-19 recommendations for adults and children.

In the first weeks of a new outbreak, the case counts reaching public health officials are late, sparse, and noisy—yet containment decisions rest on them. Reporting in The Innovation, researchers in China present TDA-PIDO, an early-warning framework built on topological data analysis (TDA), a family of methods that captures the "shape" of data and is inherently robust to noise. TDA-PIDO converts disease time series into point clouds, extracts topological features, and is trained on simulated epidemics from two noise-induced SIR models rather than scarce real outbreak data. On a real-world test set combining COVID-19 outbreak waves from 18 countries with non-outbreak SARS data from Hong Kong, every metric exceeded the best-performing baseline by more than 10 percentage points. Accuracy remained stable with up to half of the data missing, and an end-to-end deep learning version removed classifier selection.

Determining whether one variable truly causes another – rather than merely correlating with it – usually requires deliberate intervention experiments, which are often impossible in climate systems, neural circuits, or economies. Reporting in The Innovation, researchers led by Professors Wei Lin, Luonan Chen, and Kazuyuki Aihara introduce a theoretical framework called interventional dynamical causality (IntDC) together with a computational criterion, interventional embedding entropy (IEE). Built on delay-embedding state-space reconstruction, IEE quantifies the strength of intervention-level causal effects using observational time-series data alone – no manipulation of the system required. The method accurately removed confounding effects in benchmark simulations and successfully reconstructed the neural connectome of Caenorhabditis elegans, COVID-19 transmission networks in Japan, and circadian gene regulatory networks.