From bad to worse: airline boarding changes in response to COVID-19.

Airlines have introduced a back-to-front boarding process in response to the COVID-19 pandemic. It is motivated by the desire to reduce passengers' likelihood of passing close to seated passengers when they take their seats. However, our prior work on the risk of Ebola spread in aeroplanes suggested that the driving force for increased exposure to infection transmission risk is the clustering of passengers while waiting for others to stow their luggage and take their seats. In this work, we examine whether the new boarding processes lead to increased or decreased risk of infection spread. We also study the reasons behind the risk differences associated with different boarding processes. We accomplish this by simulating the new boarding processes using pedestrian dynamics and compare them against alternatives. Our results show that back-to-front boarding roughly doubles the infection exposure compared with random boarding. It also increases exposure by around 50% compared to a typical boarding process prior to the outbreak of COVID-19. While keeping middle seats empty yields a substantial reduction in exposure, our results show that the different boarding processes have similar relative strengths in this case as with middle seats occupied. We show that the increased exposure arises from the proximity between passengers moving in the aisle and while seated. Such exposure can be reduced significantly by prohibiting the use of overhead bins to stow luggage. Our results suggest that the new boarding procedures increase the risk of exposure to COVID-19 compared with prior ones and are substantially worse than a random boarding process.

Phylogeography of H5N1 avian influenza virus in Indonesia.

Highly pathogenic avian influenza (HPAI) viruses of the H5N1 subtype are a major concern to human and animal health in Indonesia. This study aimed to characterize transmission dynamics of H5N1 over time using novel Bayesian phylogeography methods to identify factors which have influenced the spread of H5N1 in Indonesia. We used publicly available hemagglutinin sequence data sampled between 2003 and 2016 to model ancestral state reconstruction of HPAI H5N1 evolution. We found strong support for H5N1 transmission routes between provinces in Java Island and inter-island transmissions, such as between Nusa Tenggara and Kalimantan Islands, not previously described. The spread is consistent with wild bird flyways and poultry trading routes. H5N1 migration was associated with the regions of high chicken densities and low human development indices. These results can be used to inform more targeted planning of H5N1 control and prevention activities in Indonesia.

State-level zoonotic disease surveillance in the United States.

Most emerging infectious diseases are zoonotic, yet recent commissions have highlighted deficiencies in their surveillance. We conducted a survey to understand the needs of state agencies for zoonotic disease surveillance. The findings will hopefully support the development of biomedical informatics applications that can link animal and human data for surveillance.

A qualitative study of state-level zoonotic disease surveillance in new England.

Zoonotic diseases are infectious diseases transmittable between animals and humans and outbreaks of these diseases in animals can signify that humans are also infected (or vice versa). Thus, communication between animal and human health agencies is critical for surveillance. Understanding how these agencies conduct surveillance and share information is important for the development of successful automated zoonotic monitoring systems. Individual interviews were conducted with 13 professionals who perform animal or human zoonotic disease surveillance in one of the New England states. Questions centred on existing surveillance methods, collaborations between animal and human health agencies, and technological and data needs. The results showed that agencies routinely communicate over suspected zoonotic disease cases, yet there are barriers preventing automated electronic linking of health data of animals and humans. These include technological barriers and barriers due to sensitivity and confidentiality of information. Addressing these will facilitate the development of electronic systems for integrating animal and human zoonotic disease surveillance data.