How does reviewing the evidence change veterinary surgeons' beliefs regarding the treatment of ovine footrot? A quantitative and qualitative study

Footrot is a widespread, infectious cause of lameness in sheep, with major economic and welfare costs. The aims of this research were: (i) to quantify how veterinary surgeons’ beliefs regarding the efficacy of two treatments for footrot changed following a review of the evidence (ii) to obtain a consensus opinion following group discussions (iii) to capture complementary qualitative data to place their beliefs within a broader clinical context. Grounded in a Bayesian statistical framework, probabilistic elicitation (roulette method) was used to quantify the beliefs of eleven veterinary surgeons during two one-day workshops. There was considerable heterogeneity in veterinary surgeons’ beliefs before they listened to a review of the evidence. After hearing the evidence, seven participants quantifiably changed their beliefs. In particular, two participants who initially believed that foot trimming with topical oxytetracycline was the better treatment, changed to entirely favour systemic and topical oxytetracycline instead. The results suggest that a substantial amount of the variation in beliefs related to differences in veterinary surgeons’ knowledge of the evidence. Although considerable differences in opinion still remained after the evidence review, with several participants having non-overlapping 95% credible intervals, both groups did achieve a consensus opinion. Two key findings from the qualitative data were: (i) veterinary surgeons believed that farmers are unlikely to actively seek advice on lameness, suggesting a proactive veterinary approach is required (ii) more attention could be given to improving the way in which veterinary advice is delivered to farmers. In summary this study has: (i) demonstrated a practical method for probabilistically quantifying how veterinary surgeons’ beliefs change (ii) revealed that the evidence that currently exists is capable of changing veterinary opinion (iii) suggested that improved transfer of research knowledge into veterinary practice is needed (iv) identified some potential obstacles to the implementation of veterinary advice by farmers

An Ultra Deep Field survey with WFIRST

Studying the formation and evolution of galaxies at the earliest cosmic times, and their role in reionization, requires the deepest imaging possible. Ultra-deep surveys like the HUDF and HFF have pushed to mag \mAB$\,\sim\,$30, revealing galaxies at the faint end of the LF to $z$$\,\sim\,$9$\,-\,$11 and constraining their role in reionization. However, a key limitation of these fields is their size, only a few arcminutes (less than a Mpc at these redshifts), too small to probe large-scale environments or clustering properties of these galaxies, crucial for advancing our understanding of reionization. Achieving HUDF-quality depth over areas $\sim$100 times larger becomes possible with a mission like the Wide Field Infrared Survey Telescope (WFIRST), a 2.4-m telescope with similar optical properties to HST, with a field of view of $\sim$1000 arcmin$^2$, $\sim$100$\times$ the area of the HST/ACS HUDF. This whitepaper motivates an Ultra-Deep Field survey with WFIRST, covering $\sim$100$\,-\,$300$\times$ the area of the HUDF, or up to $\sim$1 deg$^2$, to \mAB$\,\sim\,$30, potentially revealing thousands of galaxies and AGN at the faint end of the LF, at or beyond $z$\,$\sim$\,9$\,-\,$10 in the epoch of reionization, and tracing their LSS environments, dramatically increasing the discovery potential at these redshifts. (Note: This paper is a somewhat expanded version of one that was submitted as input to the Astro2020 Decadal Survey, with this version including an Appendix (which exceeded the Astro2020 page limits), describing how the science drivers for a WFIRST Ultra Deep Field might map into a notional observing program, including the filters used and exposure times needed to achieve these depths.

Bordetella pertussis Infection Exacerbates Influenza Virus Infection through Pertussis Toxin-Mediated Suppression of Innate Immunity

Pertussis (whooping cough) is frequently complicated by concomitant infections with respiratory viruses. Here we report the effect of Bordetella pertussis infection on subsequent influenza virus (PR8) infection in mouse models and the role of pertussis toxin (PT) in this effect. BALB/c mice infected with a wild-type strain of B. pertussis (WT) and subsequently (up to 14 days later) infected with PR8 had significantly increased pulmonary viral titers, lung pathology and mortality compared to mice similarly infected with a PT-deficient mutant strain (ΔPT) and PR8. Substitution of WT infection by intranasal treatment with purified active PT was sufficient to replicate the exacerbating effects on PR8 infection in BALB/c and C57/BL6 mice, but the effects of PT were lost when toxin was administered 24 h after virus inoculation. PT had no effect on virus titers in primary cultures of murine tracheal epithelial cells (mTECs) in vitro, suggesting the toxin targets an early immune response to increase viral titers in the mouse model. However, type I interferon responses were not affected by PT. Whole genome microarray analysis of gene expression in lung tissue from PT-treated and control PR8-infected mice at 12 and 36 h post-virus inoculation revealed that PT treatment suppressed numerous genes associated with communication between innate and adaptive immune responses. In mice depleted of alveolar macrophages, increase of pulmonary viral titers by PT treatment was lost. PT also suppressed levels of IL-1β, IL-12, IFN-γ, IL-6, KC, MCP-1 and TNF-α in the airways after PR8 infection. Furthermore PT treatment inhibited early recruitment of neutrophils and NK cells to the airways. Together these findings demonstrate that infection with B. pertussis through PT activity predisposes the host to exacerbated influenza infection by countering protective innate immune responses that control virus titers

An open challenge to advance probabilistic forecasting for dengue epidemics

This is the final version. Available on open access from the National Academy of Sciences via the DOI in this recordData Availability: Data deposition: The data are available at https://github.com/cdcepi/dengue-forecasting-project-2015 (DOI: https://doi.org/10.5281/zenodo.3519270).A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue