Modeling the impact of surveillance activities combined with physical distancing interventions on COVID-19 epidemics at a local level.

Physical distancing and contact tracing are two key components in controlling the COVID-19 epidemics. Understanding their interaction at local level is important for policymakers. We propose a flexible modeling framework to assess the effect of combining contact tracing with different physical distancing strategies. Using scenario tree analyses, we compute the probability of COVID-19 detection using passive surveillance, with and without contact tracing, in metropolitan Barcelona. The estimates of detection probability and the frequency of daily social contacts are fitted into an age-structured susceptible-exposed-infectious-recovered compartmental model to simulate the epidemics considering different physical distancing scenarios over a period of 26 weeks. With the original Wuhan strain, the probability of detecting an infected individual without implementing physical distancing would have been 0.465, 0.515, 0.617, and 0.665 in designated age groups (0-14, 15-49, 50-64, and >65), respectively. As the physical distancing measures were reinforced and the disease circulation decreased, the interaction between the two interventions resulted in a reduction of the detection probabilities; however, despite this reduction, active contact tracing and isolation remained an effective supplement to physical distancing. If we relied solely on passive surveillance for diagnosing COVID-19, the model required a minimal 50% (95% credible interval, 39-69%) reduction of daily social contacts to keep the infected population under 5%, as compared to the 36% (95% credible interval, 22-56%) reduction with contact tracing systems. The simulation with the B.1.1.7 and B.1.167.2 strains shows similar results. Our simulations showed that a functioning contact tracing program would reduce the need for physical distancing and mitigate the COVID-19 epidemics.

Occurrence and antimicrobial resistance of zoonotic enteropathogens in gulls from southern Europe.

Campylobacter spp. and Salmonella spp. are the two most frequent zoonotic bacteria involved in human enteric infections in the European Union. Both enteropathogens have been isolated from a diversity of wild birds in Northern Europe, but there is limited information about gulls as potential reservoirs in Southern Europe. A broad sampling of fledglings from nine colonies of yellow-legged gull (Larus michahellis, N = 1222) and Audouin's gull (Larus audouinii, N = 563) has been conducted in Spain and Tunisia during the late chick-rearing period. Overall, the occurrence of Campylobacter spp. and Salmonella spp. was 5.2% (93/1785, CI95%: 4.2-6.2%) and 20.8% (371/1785, CI95%: 18.9-22.7%), respectively. The most predominant Campylobacter species was C. jejuni (94.6%). A high diversity of Salmonella serovars was isolated and the most frequent were those also reported in human outbreaks, such as Salmonella Typhimurium. A high proportion of Campylobacter and Salmonella isolates showed resistance to at least one antimicrobial agent (20.2% and 51.5%, respectively), while 19.2% of Salmonella isolates were multidrug-resistant. These results show the relevance of gulls as reservoirs of Campylobacter and Salmonella by maintaining and spreading these bacteria, including resistant and multidrug resistant strains, in the environment. Our results suggest that gulls can serve as sentinel species for antibiotic pressure in the environment.

Effect of soybean expeller supplementation during the final phase of sow gestation on litter birth weight.

AIM: Nutrition plays a key role in the production of pigs, especially in pregnant sows, where modifications in nutritional requirements can affect their productive performance. The aim of this study was to evaluate nutritional supplementation with soybean expeller in sows during the last third of the gestation period and its effect on litter birth weight. MATERIALS AND METHODS: A quasi-experimental study was conducted on a farrow-to-finish farm, where 192 sows were equally assigned to treatment and control groups. Several variables were recorded at both the sow and piglet level. The treatment group consisted of piglets from 95 sows supplemented with soybean expeller during the final phase of gestation (20 days), and the comparison group consisted of piglets from 97 sows fed only with a commercial balanced ration (control group). RESULTS: Soybean expeller supplementation increased individual piglet weight by 190-270 g, and the increased number of live piglets could decrease the weight of each piglet. Moreover, the number of piglets weighing <900 g decreased by 10% as compared to the control group, indicating that supplementation could improve pre-weaning mortality. CONCLUSION: Our results suggest that soybean expeller supplementation in sows during the last third of the gestation period could improve production performance, especially on organic farms.

Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits.

BACKGROUND: The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions. RESULTS: The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun- 2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages. CONCLUSIONS: The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures.

Near Real-Time Monitoring of Clinical Events Detected in Swine Herds in Northeastern Spain.

Novel techniques of data mining and time series analyses allow the development of new methods to analyze information relating to the health status of the swine population in near real-time. A swine health monitoring system based on the reporting of clinical events detected at farm level has been in operation in Northeastern Spain since 2012. This initiative was supported by swine stakeholders and veterinary practitioners of the Catalonia, Aragon, and Navarra regions. The system aims to evidence the occurrence of endemic diseases in near real-time by gathering data from practitioners that visited swine farms in these regions. Practitioners volunteered to report data on clinical events detected during their visits using a web application. The system allowed collection, transfer and storage of data on different clinical signs, analysis, and modeling of the diverse clinical events detected, and provision of reproducible reports with updated results. The information enables the industry to quantify the occurrence of endemic diseases on swine farms, better recognize their spatiotemporal distribution, determine factors that influence their presence and take more efficient prevention and control measures at region, county, and farm level. This study assesses the functionality of this monitoring tool by evaluating the target population coverage, the spatiotemporal patterns of clinical signs and presumptive diagnoses reported by practitioners over more than 6 years, and describes the information provided by this system in near real-time. Between January 2012 and March 2018, the system achieved a coverage of 33 of the 62 existing counties in the three study regions. Twenty-five percent of the target swine population farms reported one or more clinical events to the system. During the study period 10,654 clinical events comprising 14,971 clinical signs from 1,693 farms were reported. The most frequent clinical signs detected in these farms were respiratory, followed by digestive, neurological, locomotor, reproductive, and dermatological signs. Respiratory disorders were mainly associated with microorganisms of the porcine respiratory disease complex. Digestive signs were mainly related to colibacilosis and clostridiosis, neurological signs to Glässer's disease and streptococcosis, reproductive signs to PRRS, locomotor to streptococcosis and Glässer's disease, and dermatological signs to exudative epidermitis.

Towards an eco-phylogenetic framework for infectious disease ecology.

Identifying patterns and drivers of infectious disease dynamics across multiple scales is a fundamental challenge for modern science. There is growing awareness that it is necessary to incorporate multi-host and/or multi-parasite interactions to understand and predict current and future disease threats better, and new tools are needed to help address this task. Eco-phylogenetics (phylogenetic community ecology) provides one avenue for exploring multi-host multi-parasite systems, yet the incorporation of eco-phylogenetic concepts and methods into studies of host pathogen dynamics has lagged behind. Eco-phylogenetics is a transformative approach that uses evolutionary history to infer present-day dynamics. Here, we present an eco-phylogenetic framework to reveal insights into parasite communities and infectious disease dynamics across spatial and temporal scales. We illustrate how eco-phylogenetic methods can help untangle the mechanisms of host-parasite dynamics from individual (e.g. co-infection) to landscape scales (e.g. parasite/host community structure). An improved ecological understanding of multi-host and multi-pathogen dynamics across scales will increase our ability to predict disease threats.

Influenza Herd-Level Prevalence and Seasonality in Breed-to-Wean Pig Farms in the Midwestern United States.

Influenza is a costly disease for pig producers and understanding its epidemiology is critical to control it. In this study, we aimed to estimate the herd-level prevalence and seasonality of influenza in breed-to-wean pig farms, evaluate the correlation between influenza herd-level prevalence and meteorological conditions, and characterize influenza genetic diversity over time. A cohort of 34 breed-to-wean farms with monthly influenza status obtained over a 5-year period in piglets prior to wean was selected. A farm was considered positive in a given month if at least one oral fluid tested influenza positive by reverse transcriptase polymerase chain reaction. Influenza seasonality was assessed combining autoregressive integrated moving average (ARIMA) models with trigonometric functions as covariates. Meteorological conditions were gathered from local land-based weather stations, monthly aggregated and correlated with influenza herd-level prevalence. Influenza herd-level prevalence had a median of 28% with a range from 7 to 57% and followed a cyclical pattern with levels increasing during fall, peaking in both early winter (December) and late spring (May), and decreasing in summer. Influenza herd-level prevalence was correlated with mean outdoor air absolute humidity (AH) and temperature. Influenza genetic diversity was substantial over time with influenza isolates belonging to 10 distinct clades from which H1 delta 1 and H1 gamma 1 were the most common. Twenty-one percent of farms had three different clades co-circulating over time, 18% of farms had two clades, and 41% of farms had one clade. In summary, our study showed that influenza had a cyclical pattern explained in part by air AH and temperature changes over time, and highlighted the importance of active surveillance to identify high-risk periods when strategic control measures for influenza could be implemented.