Risk factors for African swine fever incursion in Romanian domestic farms during 2019

African swine fever (ASF) entered Georgia in 2007 and the EU in 2014. In the EU, the virus primarily spread in wild boar (Sus scrofa) in the period from 2014–2018. However, from the summer 2018, numerous domestic pig farms in Romania were affected by ASF. In contrast to the existing knowledge on ASF transmission routes, the understanding of risk factors and the importance of different transmission routes is still limited. In the period from May to September 2019, 655 Romanian pig farms were included in a matched case-control study investigating possible risk factors for ASF incursion in commercial and backyard pig farms. The results showed that close proximity to outbreaks in domestic farms was a risk factor in commercial as well as backyard farms. Furthermore, in backyard farms, herd size, wild boar abundance around the farm, number of domestic outbreaks within 2 km around farms, short distance to wild boar cases and visits of professionals working on farms were statistically significant risk factors. Additionally, growing crops around the farm, which could potentially attract wild boar, and feeding forage from ASF affected areas to the pigs were risk factors for ASF incursion in backyard farms.We acknowledge financial support from EFSA, ANSVSA and from the Danish Veterinary and Food Administration (FVST) as part of the agreement of commissioned work between the Danish Ministry of Food, Agriculture and Fisheries and the University of Copenhagen.Peer reviewe

Risk factors for African swine fever incursion in Romanian domestic farms during 2019

African swine fever (ASF) entered Georgia in 2007 and the EU in 2014. In the EU, the virus primarily spread in wild boar (Sus scrofa) in the period from 2014-2018. However, from the summer 2018, numerous domestic pig farms in Romania were affected by ASF. In contrast to the existing knowledge on ASF transmission routes, the understanding of risk factors and the importance of different transmission routes is still limited. In the period from May to September 2019, 655 Romanian pig farms were included in a matched case-control study investigating possible risk factors for ASF incursion in commercial and backyard pig farms. The results showed that close proximity to outbreaks in domestic farms was a risk factor in commercial as well as backyard farms. Furthermore, in backyard farms, herd size, wild boar abundance around the farm, number of domestic outbreaks within 2 km around farms, short distance to wild boar cases and visits of professionals working on farms were statistically significant risk factors. Additionally, growing crops around the farm, which could potentially attract wild boar, and feeding forage from ASF affected areas to the pigs were risk factors for ASF incursion in backyard farms

A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales.

Disentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other seasonal variables using campylobacteriosis as a case study. Campylobacter infections are found worldwide and are the most common bacterial food-borne disease in developed countries, where they exhibit consistent but country specific seasonality. We developed a novel conditional incidence method, based on classical stratification, exploiting the long term, high-resolution, linkage of approximately one-million campylobacteriosis cases over 20 years in England and Wales with local meteorological datasets from diagnostic laboratory locations. The predicted incidence of campylobacteriosis increased by 1 case per million people for every 5° (Celsius) increase in temperature within the range of 8°-15°. Limited association was observed outside that range. There were strong associations with day-length. Cases tended to increase with relative humidity in the region of 75-80%, while the associations with rainfall and wind-speed were weaker. The approach is able to examine multiple factors and model how complex trends arise, e.g. the consistent steep increase in campylobacteriosis in England and Wales in May-June and its spatial variability. This transparent and straightforward approach leads to accurate predictions without relying on regression models and/or postulating specific parameterisations. A key output of the analysis is a thoroughly phenomenological description of the incidence of the disease conditional on specific local weather factors. The study can be crucially important to infer the elusive mechanism of transmission of campylobacteriosis; for instance, by simulating the conditional incidence for a postulated mechanism and compare it with the phenomenological patterns as benchmark. The findings challenge the assumption, commonly made in statistical models, that the transformed mean rate of infection for diseases like campylobacteriosis is a mere additive and combination of the environmental variables

Illustrative description of the method. Created with BioRender.com.

Illustrative description of the method. Created with BioRender.com.</p

Prediction of seasonal patterns for daily <i>Campylobacter</i> cases as done in Fig 4 for the situation when 2 variables are constant (Weather variables averaged over the past 14 days).

A) Constant relative humidity 76% and day-length 15 hours. B) Constant maximum air temperature 20°C and day-length 15 hours. C) Constant maximum air temperature 20°C and relative humidity 76%. D-E-F) Patterns for daily 14-days rolling mean for maximum air temperature, relative humidity and day-length averaged over 19 years. The shaded area represents the 25% and 75% quantiles. G-H-I) Conditional incidence vs the variable weather factors for the situation corresponding to A) B) and C) respectively.</p

The file contains the following section: Regional structure of UK Health Security Agency, diagnostic laboratories and their catchment areas.

Removing Reporting Delays and the effect of Incubation Period. Correlations among the weather variables and their distributions. Validation with Agent Based Models. Patterns in conditional incidence according to different weather variables (two weather factors simultaneously). Different Ways to Visualize conditional incidence (three weather factors simultaneously). Patterns in conditional incidence according to different weather variables (four weather factors simultaneously). Patterns in conditional incidence according to maximum air temperature and relative humidity for different periods of the year. Incidence of campylobacteriosis cases when the weather variables are averaged over different time-lags (three weather factors simultaneously). Seasonal patterns for daily Campylobacter cases using only one predictor. Seasonal patterns for daily Campylobacter cases using only two predictors. Predictions using rainfall, instead of relative humidity, as predictor. (PDF)</p

Campylobacteriosis cases per 1, 000, 000 per day conditioned to maximum air temperature, relative humidity and day-length.

As the day-length depends on the time of the year (as well as latitude), each panel broadly correspond to (A) last week of October—middle of February, (B) middle of February—first week of April and middle of September- last week of October (C) first week of April—second-half of May and second-half of July—middle of September (D) second-half of May—second-half of of July 22. Data were averaged over the past 14 days. The shaded area shows the 95% confidence intervals for the Poisson means using the normal approximation (i.e. . Data divided by quantiles.</p

Fig 4 -

A) Reconstruction of the time-series of Campylobacter cases in England and Wales. B) Seasonal patterns for daily Campylobacter cases averaged over 19 years. The shaded area represents the 25% and F quantiles. Weather variables are maximum air temperature, relative humidity and day-length. C-D) Scatter plot and map comparing the reported and predicted daily number of campylobacteriosis per catchment area averaged over the entire 19 years. In D) the red circles represent the reported cases while the blue squares the predictions. Weather variables averaged over the past 14 days. Map reproduced in R [45] using shapefiles availalbe at [46].</p

Campylobacteriosis cases per 1, 000, 000 per day <i>vs</i> (A) maximum air temperature, (B) minimum air temperature (C) relative humidity (D) rainfall (E) mean wind speed and (F) day-length.

Data were averaged over the past number of days represented by the time-lag. The shaded area shows the 95% confidence intervals for the Poisson means using the normal approximation (i.e. . Data divided by quantiles.</p

Evaluating the incidence of pathological complete response in current international rectal cancer practice: the barriers to widespread safe deferral of surgery

This is the peer reviewed version of the following article: , which has been published in final form at https://doi.org/10.1111/codi.14361. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions."Colorectal Disease © 2018 The Association of Coloproctology of Great Britain and Ireland Introduction: The mainstay of management for locally advanced rectal cancer is chemoradiotherapy followed by surgical resection. Following chemoradiotherapy, a complete response may be detected clinically and radiologically (cCR) prior to surgery or pathologically after surgery (pCR). We aim to report the overall complete pathological response (pCR) rate and the reliability of detecting a cCR by conventional pre-operative imaging. Methods: A pre-planned analysis of the European Society of Coloproctology (ESCP) 2017 audit was performed. Patients treated by elective rectal resection were included. A pCR was defined as a ypT0 N0 EMVI negative primary tumour; a partial response represented any regression from baseline staging following chemoradiotherapy. The primary endpoint was the pCR rate. The secondary endpoint was agreement between post-treatment MRI restaging (yMRI) and final pathological staging. Results: Of 2572 patients undergoing rectal cancer surgery in 277 participating centres across 44 countries, 673 (26.2%) underwent chemoradiotherapy and surgery. The pCR rate was 10.3% (67/649), with a partial response in 35.9% (233/649) patients. Comparison of AJCC stage determined by post-treatment yMRI with final pathology showed understaging in 13% (55/429) and overstaging in 34% (148/429). Agreement between yMRI and final pathology for T-stage, N-stage, or AJCC status were each graded as ‘fair’ only (n = 429, Kappa 0.25, 0.26 and 0.35 respectively). Conclusion: The reported pCR rate of 10% highlights the potential for non-operative management in selected cases. The limited strength of agreement between basic conventional post-chemoradiotherapy imaging assessment techniques and pathology suggest alternative markers of response should be considered, in the context of controlled clinical trials