Excess mortality during the COVID-19 pandemic in low-and lower-middle-income countries: a systematic review and meta-analysis.

BACKGROUND: Although the COVID-19 pandemic claimed a great deal of lives, it is still unclear how it affected mortality in low- and lower-middle-income countries (LLMICs). This review summarized the available literature on excess mortality during the COVID-19 pandemic in LLMICs, including methods, sources of data, and potential contributing factors that might have influenced excess mortality. METHODS: We conducted a systematic review and meta-analysis on excess mortality during the COVID-19 pandemic in LLMICs in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines We searched PubMed, Embase, Web of Science, Cochrane Library, Google Scholar, and Scopus. We included studies published from 2019 onwards with a non-COVID-19 period of at least one year as a comparator. The meta-analysis included studies reporting data on population size, as well as observed and expected deaths. We used the Mantel-Haenszel method to estimate the pooled risk ratio with 95% confidence intervals. The protocol was registered in PROSPERO (ID: CRD42022378267). RESULTS: The review covered 29 countries, with 10 countries included in the meta-analysis. The pooled meta-analysis included 1,405,128,717 individuals, for which 2,152,474 deaths were expected, and 3,555,880 deaths were reported. Calculated excess mortality was 100.3 deaths per 100,000 population per year, with an excess risk of death of 1.65 (95% CI: 1.649, 1.655, p < 0.001). The data sources used in the studies included civil registration systems, surveys, public cemeteries, funeral counts, obituary notifications, burial site imaging, and demographic surveillance systems. The primary techniques used to estimate excess mortality were statistical forecast modelling and geospatial analysis. One out of the 24 studies found higher excess mortality in urban settings. CONCLUSION: Our findings demonstrate that excess mortality in LLMICs during the pandemic was substantial. However, estimates of excess mortality are uncertain due to relatively poor data. Understanding the drivers of excess mortality, will require more research using various techniques and data sources.

Influence of thermal heat load accumulation on daily rumination time of lactating Holstein cows in a zone with temperate climate.

In the future, conflicts between animal welfare and climate change will gradually intensify. In the present study, we investigated the daily rumination time (RT) of lactating Holstein-Friesian cows in a zone with temperate climate and the effects of heat load duration and heat load intensity. Responses of individual cows to heat load were assessed, adjusting for milk yield, lactation number, days in milk as well as reproductive status and season. A total of 27,149 data points from 183 cows in a naturally ventilated barn in Brandenburg, Germany, were collected from June 2015 to May 2017. Ambient temperature and relative humidity were recorded at eight positions inside the barn every 5 min, and the temperature-humidity index (THI) was calculated. Based on THI, the degree of heat load was determined, using critical thresholds of THI = 68, 72, and 80. Daily RT was measured with a microphone-based sensor system (collar) on the cow's neck. The analysis models included autocorrelations in time series as well as individual cow-related effects. With each 5 min exposure to contemporaneous heat load, a decrease of approximately 1.17 min d-1 in RT per cow from non-heat stress to heat stress conditions by exceeding THI ≥68 (p < 0.01). This effect was intensified by exceeding the critical THI thresholds of 68 and 72. As heat load duration and intensity increased, daily RT decreased in comparison to daily RT under non-stress conditions. High-yielding (>38.4 kg milk/day) cows were more influenced in rumination time than low-yielding (≤28.8 kg milk/day) cows. With moderate contemporaneous heat load, RT decreased by 0.14 min d-1 per 5 min in high-yielding cows compared to low-yielding cows under moderate heat load. A decrease of 0.1 min d-1 was found in daily RT of mid-yielding cows. However, the delayed effects of heat load (one to three days after the heat stress event) were associated with days in milk and reproduction status. When the heat load duration lasted for several days, the responses were less pronounced than the impacts of contemporaneous heat load (when the heat stress event lasted for one day). Delayed mild heat load resulted in an increase in RT by 0.13 min d-1 in lactating cows ≤60 DIM. This was also found with delayed moderate heat load. Lactating cows ≤60 DIM showed a rise of 0.09 min d-1 in RT. RT also showed interactions with reproduction status of cows under delayed moderate heat stress. Lactating cows with ≤180 days of pregnancy showed an increase of 0.61 min d-1 in RT. Similarly, cows with >180 days of pregnancy had 0.64 min d-1 more RT compared to non-pregnant cows. Further analysis with higher temporal resolution of RT than data accumulation in 24-h blocks as well as the assessment of the correlation between feed composition, intake and rumination will elucidate the influence of heat load on daily RT.

Animal Health Discourse during Ecological Crises in the Media-Lessons Learnt from the Flood in Thessaly from the One Health Perspective.

Due to the increasing risk of extreme events caused by climate change (i.e., floods, fires and hurricanes) or wars, European veterinary public health may need some improvement. Utilizing a mix of qualitative (participatory observation) and quantitative methods (Internet mining), we analyzed the Greek media's responses to the millennial flood in Thessaly (September 2023), focusing on animal health (including wild, companion animals and livestock) and public sentiment towards epizootic/epidemic threats. The study revealed a gap in crisis management plans regarding veterinary-related issues, emphasizing the need for comprehensive emergency response strategies. Our findings show how (i) the lay referral system is projecting the perception of epidemic threats into the population; (ii) the emotional load of images of animal carcasses is misused by media creators aiming for a big audience; and (iii) pets' owners are creating online communities for the searching and treatment of their pets. Our results stress the importance of integrating crisis communication in consecutive phases of the discourse, such as the following: (i) weather change; (ii) acute flood; (iii) recovery; and (iv) outbreaks, into veterinary practices to better prepare for such disasters.

Excess mortality during the COVID-19 pandemic in low-income and lower middle-income countries: protocol for a systematic review and meta-analysis.

INTRODUCTION: The COVID-19 pandemic has been marked by a massive death toll. However, the overall effect of the pandemic, including potential unintended negative impacts of some control measures, on mortality remains poorly understood in low-income and lower middle-income countries (LLMICs). This review aims to summarise the available literature on excess mortality in LLMICs, focusing on the methods and data sources used in estimating excess mortality and the drivers of excess mortality. METHODS AND ANALYSIS: We will review the available literature and report results in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis. Searches will be conducted in PubMed, Embase, Web of Science, Cochrane Library, Google Scholar and Scopus. All published studies that report on the estimates of excess mortality in populations of LLMICs will be included. This will include those with a publication date from 2019 onwards and those with at least a 1-year non-COVID-19 period as the comparator in the estimation of excess mortality during the pandemic. There will be no language restrictions on the search. The meta-analysis will include studies with extractable data on excess mortality, methods, population size, and observed and expected deaths. We will use the Mantel-Haenszel method to estimate the pooled risk ratio with 95% CIs. ETHICS AND DISSEMINATION: As there is no primary data collection, there is no requirement for ethical review. The results will be disseminated through peer-reviewed journal publication and conference presentations. PROSPERO REGISTRATION NUMBER: CRD42022378267.

Leveraging Accelerometer Data for Lameness Detection in Dairy Cows: A Longitudinal Study of Six Farms in Germany.

Lameness in dairy cows poses a significant challenge to improving animal well-being and optimizing economic efficiency in the dairy industry. To address this, employing automated animal surveillance for early lameness detection and prevention through activity sensors proves to be a promising strategy. In this study, we analyzed activity (accelerometer) data and additional cow-individual and farm-related data from a longitudinal study involving 4860 Holstein dairy cows on six farms in Germany during 2015-2016. We designed and investigated various statistical models and chose a logistic regression model with mixed effects capable of detecting lameness with a sensitivity of 77%. Our results demonstrate the potential of automated animal surveillance and hold the promise of significantly improving lameness detection approaches in dairy livestock.

Effects of environmental and housing system factors on ammonia and greenhouse gas emissions from cattle barns: A meta-analysis of a global data collation.

This study provides a meta-analysis on the relationships between cattle barn CH4, NH3 and N2O emission rates and their key drivers (i.e., housing type, floor type, environmental conditions). Understanding these relationships is essential to reduce uncertainties in emission inventories and suggest targeted mitigation measures. The total number of daily emission rates included in the analysis was 139 for CH4, 293 for NH3 and 100 for N2O emissions. Emission rates in the database showed a large variation with 45-803.5 g/LU d-1 for CH4, 0.036-146.7 gN LU-1 d-1 for NH3, and 0.002-18 gN LU-1 d-1 for N2O emissions. Despite the high emission variability, significant effects were identified·NH3 showed positive correlation with air temperature; NH3 emissions differed between housing types but not between floor types·NH3 emissions from tied stalls were lower than the ones from cubicle housing regardless of the floor type. Additionally, NH3 emissions from loose housings were lower than the ones from cubicle housing·NH3 and N2O emission rates from temperate wet zones were lower than the ones from temperate dry zones. CH4 emission rates were affected by environmental factors only and not by housing and floor type, showing negative correlation with air temperature and humidity. The factors investigated can be suggested as ancillary variables and descriptors when cattle barn emissions are measured, in order to make best use of emission data. Country-specific data of these key drivers can be included into national inventories to adapt them to different agroecosystems and support targeted policies.

Antimicrobial resistance- and pathogen patterns in the fecal microbiota of sows and their offspring in German commercial pig farms.

Reducing antibiotic use is one of the biggest challenges in pig farming, as antibiotics have been used for years to control typical problems such as newborn or post-weaning diarrhea. The pressure a one health approach has created on animal production regarding antimicrobial resistance is an opportunity to find other strategies against enterobacterial pathogens in suckling and weaned piglets. A farm-specific approach could have a good success due to the individual farm structures in Germany and other countries. In this study, non-metric multidimensional scaling, hierarchical clustering, and latent class analysis were used to determine the impact of antibiotic use on antibiotic resistance patterns and pathogen prevalence in 20 German pig farms. This may help to develop individualized health strategies. 802 fresh fecal samples were collected from sows and piglets from 20 piglet production and rearing farms at different production times (sows antepartum and postpartum, suckling piglets, weaned piglets). In addition, the use of antibiotics was recorded. DNA extracts were subjected to quantitative real-time qPCR with primers specific for antibiotic resistance genes (int1, sul1-3, dfrA1, mcr-1, blaCTX-M), and virulence factors of relevant bacteria (C. difficile, C. perfringens, Salmonella, Escherichia/Shigella/Hafnia, E. coli). Linear and logistic regression models were used to analyze the relationship between different antibiotics and the major genes contributing to the clustering of observations for the different animal groups. Clustering revealed different farm clusters for sows, suckling piglets, and weaned piglets, with the most remarkable diversity in antibiotic use among weaned piglets. Amoxicillin, lincomycin, and enrofloxacin were identified as the most probable cause of increased odds of the presence of relevant antibiotic resistance genes (mcr1, dfrA1, blaCTX-M). Still, direct effects of a specific antibiotic on its associated resistance gene were rare. Enrofloxacin and florfenicol favored the occurrence of C. difficile in sows. The E. coli fimbriae genes were less affected by antibiotic use in sows and piglets, but the F4 fimbriae gene could be associated with the integrase 1 gene in piglets. The results confirm that multidrug-resistant enterobacteria are widespread in German pig farms and give awareness of the impact of current antibiotic use while searching for alternative health strategies.

Assessment of the biochemical methane potential of in-house and outdoor stored pig and dairy cow manure by evaluating chemical composition and storage conditions.

Biogas production is a suitable option for producing energy from dairy and pig manure types. During manure storage, organic matter degradation results in methane emissions decreasing the potential biogas yield. The present research advances the understanding of the biochemical methane potential (BMP) and the chemical characteristics of manure collected year-round from sequential stages of the liquid manure management chain of commercial dairy cow and pig farms. To this end, manure samples from six livestock farms in Germany were analyzed. The results showed that changes in chemical composition during storage led to a 20.5% decrease in the BMP of dairy manure from the barn to outdoor storage. For fattening pig manure samples, there was a 39.5% decrease in the BMP from intermediate to outdoor storage. An analysis of BMP according to manure age showed that pig manure degrades faster than dairy manure; the importance of promptly feeding manure to the biogas plant in order to avoid significant CH4 emission losses and reduction in energy producing capacity was highlighted. The best BMP predictors for dairy manure were the contents of dry matter, volatile solids and lignin, whereas best BMP predictors for pig manure were dry matter and volatile fatty acid (VFA) content. Prediction models performed well for samples from outdoor storages; refinements for predicting BMP of less aged samples presenting lower chemical variability would be necessary.

Biocide Susceptibility and Antimicrobial Resistance of Escherichia coli Isolated from Swine Feces, Pork Meat and Humans in Germany.

Phenotypic susceptibility testing of Escherichia (E.) coli is an essential tool to gain a better understanding of the potential impact of biocide selection pressure on antimicrobial resistance. We, therefore, determined the biocide and antimicrobial susceptibility of 216 extended-spectrum ß-lactamase-producing (ESBL) and 177 non-ESBL E. coli isolated from swine feces, pork meat, voluntary donors and inpatients and evaluated associations between their susceptibilities. Minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) of benzalkonium chloride, chlorhexidine digluconate (CHG), chlorocresol (PCMC), glutaraldehyde (GDA), isopropanol (IPA), octenidine dihydrochloride and sodium hypochlorite (NaOCl) showed unimodal distributions, indicating the absence of bacterial adaptation to biocides due to the acquisition of resistance mechanisms. Although MIC95 and MBC95 did not vary more than one doubling dilution step between isolates of porcine and human origin, significant differences in MIC and/or MBC distributions were identified for GDA, CHG, IPA, PCMC and NaOCl. Comparing non-ESBL and ESBL E. coli, significantly different MIC and/or MBC distributions were found for PCMC, CHG and GDA. Antimicrobial susceptibility testing revealed the highest frequency of resistant E. coli in the subpopulation isolated from inpatients. We observed significant but weakly positive correlations between biocide MICs and/or MBCs and antimicrobial MICs. In summary, our data indicate a rather moderate effect of biocide use on the susceptibility of E. coli to biocides and antimicrobials.

A Pilot Study on the Urine Proteome of Cats Fed a High-Protein Complete Diet, Supplemented with or without Arginine, Ornithine or Zeolite.

Proteome analyses can be used to detect biomarkers for the healthy and diseased organism. However, data in cats are scarce, and no information is available on the potential impact of nutritional interventions on the feline urine proteome. In the present study, a label-free shotgun proteomics approach was performed to investigate the urinary proteins of four healthy adult cats. Each animal received a high-protein complete diet without (w/o) or with supplements that could affect the protein metabolism: arginine (+100% compared to the arginine concentration in the w/o diet), ornithine (+200% compared to the arginine concentration in the w/o diet) or zeolite (0.375 g/kg body weight/day). Our results demonstrate a huge number of proteins in the urine of cats (516 ± 49, 512 ± 39, 399 ± 149 and 455 ± 134 in the w/o, arginine, ornithine and zeolite group, respectively), which are associated with several biological processes. In addition, up- and downregulated urinary proteins could be detected in the dietary supplementation periods. Overall, the present pilot study provides basic data on the urine proteome of healthy adult cats. With increasing information, the numerousness of urinary proteins implies the potential to identify biomarkers and metabolic pathways in the feline organism.

Environmental factors associated with the prevalence of ESBL/AmpC-producing Escherichia coli in wild boar (Sus scrofa).

Antimicrobial resistances (AMR) in bacteria, such as ESBL/AmpC-producing E. coli, are a burden to human and animal health. This burden is mainly driven by the consumption and release of antimicrobial substances into the environment. The pollution and contamination of habitats by AMR in bacteria and antimicrobial substances can lead to the transmission of bacterial AMR to wildlife. Therefore, it is necessary to understand the transmission cycle of antibiotics and resistant bacteria between humans, and animals as well as their occurrences in the environment. Environmental factors associated with the occurrence of bacterial AMR in wildlife can lead to a better understanding of the distribution of bacterial AMR in humans and animals using One Health approaches. Here, we analyzed data gathered in the framework of the German zoonoses monitoring program in 2016 and 2020 using spatiotemporal statistics to identify relevant environmental factors (e.g., livestock density, climatic variables, and human density) in association with the spatial distribution of ESBL/AmpC-producing E. coli. For this purpose, we developed a generic data integration and analysis pipeline to link spatially explicit environmental factors to the monitoring data. Finally, we built a binomial generalized linear mixed model (GLMM) to determine the factors associated with the spatial distribution of ESBL/AmpC-producing E. coli. In 2016 and 2020, 807 fecal samples from hunted wild boar (Sus scrofa L.) were randomly taken in 13 federal states and selectively analyzed for ESBL/AmpC-producing E. coli. Forty-eight isolates were identified in 12 German federal states, with an overall prevalence of 6%. We observed an almost three times higher probability of ESBL/AmpC-producing E. coli isolates in wild boar in counties with high cattle densities (OR = 2.57, p ≤ 0.01). Furthermore, we identified a seasonal effect in areas with high precipitation during the off-hunting seasons (OR = 2.78, p = 0.025) and low precipitation throughout the years (OR = 0.42, p = 0.025). However, due to the low amount of identified isolates, confidence intervals were wide, indicating a high level of uncertainty. This suggests that further studies on smaller scales need to be conducted with multiannual data and improved metadata, e.g., on the location, the hunting procedure, and species characteristics to be collected during field sampling.

A Combined Bayesian and Similarity-Based Approach for Predicting E. coli Biofilm Inhibition by Phenolic Natural Compounds.

Screening for biofilm inhibition by purified natural compounds is difficult due to compounds' chemical diversity and limited commercial availability, combined with time- and cost-intensiveness of the laboratory process. In silico prediction of chemical and biological properties of molecules is a widely used technique when experimental data availability is of concern. At the same time, the performance of predictive models directly depends on the amount and quality of experimental data. Driven by the interest in developing a model for prediction of the antibiofilm effect of phenolic natural compounds such as flavonoids, we performed experimental assessment of antibiofilm activity of 320 compounds from this subset of chemicals. The assay was performed once on two Escherichia coli strains on agar in 24-well microtiter plates. The inhibition was assessed visually by detecting morphological changes in macrocolonies. Using the data obtained, we subsequently trained a Bayesian logistic regression model for prediction of biofilm inhibition, which was combined with a similarity-based method in order to increase the overall sensitivity (at the cost of accuracy). The quality of the predictions was subsequently validated by experimental assessment in three independent experiments with two resistant E. coli strains of 23 compounds absent in the initial data set. The validation demonstrated that the model may successfully predict the targeted effect as compared to the baseline accuracy. Using a randomly selected database of commercially available natural phenolics, we obtained approximately 6.0% of active compounds, whereas using our prediction-based substance selection, the percentage of phenolics found to be active increased to 34.8%.

Canine Vaginal Cytology: A Revised Definition of Exfoliated Vaginal Cells.

Vaginal cytology is an important examination method in the context of gynecological disorders and cycle staging in the bitch. While collection and preparation of samples are easy, the evaluation appears to be challenging. Inconsistent definitions of cell attributes such as size, cornification and the appearance of the nucleus have been published. The aim of the project was to develop a tutorial for vaginal cell determination. To get a deeper insight into the use of cytology in practice, an online survey was distributed to veterinarians interested in small animal reproduction. Participants were asked to define eight cells and answer questions. The agreement of the 16 participants, working in eight different countries, determining the cells was poor (κ = 0.412). Eleven respondents stated that vaginal cytology has a low reliability. Nevertheless, 13 participants use this tool regularly. The tutorial was developed as a flowchart based on the survey results, scientific literature and own measurements. It guides the user systematically through the evaluation of specific cell characteristics. An evaluation of the results of five raters with difference experience levels led to a high agreement (κ = 0.858). Vaginal cytology is a useful diagnostic tool, but it seems helpful to standardize the determination of cell types.

Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning.

BACKGROUND: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. OBJECTIVE: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. METHODS: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) "DeepPavlov," which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. RESULTS: Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (ß=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry. CONCLUSIONS: After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines.

Characterization of the fecal microbiota of sows and their offspring from German commercial pig farms.

Strategies to combat microbiota-associated health problems are of high interest in pig production. Successful intervention strategies with beneficial long-term effects are still missing. Most studies on pig microbiota have been conducted under standardized experimental conditions, but the situation in commercial farms differs dramatically. This study describes the fecal microbiota in German commercial pig farms under practical conditions. The study is part of the larger project "Optibiom" that aims to use bacterial composition and farm metadata to formulate tailor-made solutions for farm-specific health maintenance strategies. Special consideration is given to the sow-piglet relationship. Fecal samples from sows and their piglets were collected at two time points each in 20 different farms (sows ante- and postpartum and piglets before and after weaning). The extracted DNA was sequenced with Illumina 16S rDNA sequencing. For data analysis and visualization, differential abundance analyses, as well as hierarchical clustering and nonmetric multidimensional scaling (NMDS) were performed. A new "family unit" was implemented to compare farms based on the association between the microbiota in sows and their offspring. There are distinct changes in the microbial communities in sows before and after birth as well as in suckling and post-weaning piglets. The suckling pig microbiota is particularly different from all other groups and shows a lower bacterial diversity. While dominant genera in antepartum sows further displace the abundance of non-dominant genera postpartum, the opposite was true for piglets, where non-dominant bacteria in the suckling phase became dominant after weaning. The family unit for sows and their piglets led to separate cluster formation for some farms. The results indicate that the sow-piglet relationship is one driving force for the observed differences of the pig farms. The next step in the analysis will be the combination of metadata (feeding, housing and management practices) to find farm-specific differences that can be exploited to formulate a farm-specific health maintenance strategy.

Trends in interest of COVID-19 on Polish Internet.

INTRODUCTION: This study aims to investigate the social interest in SARS-CoV-2 and COVID-19 in the Internet media during the epidemic in Poland and serves as a complement to longitudinal surveys monitoring public perception in real time. METHODS: We quantitatively analyzed "coronavirus" digital footprints on the Internet, in particular, on Google, Twitter, YouTube, Wikipedia and electronic media represented by EventRegistry, from 15.01 to 07.04.2020 (before and after official introduction of COVID-19 to Poland on 04.03.2020). We present results on trend analysis supported by statistical techniques. RESULTS: We identified six major temporal clusters of the interest on the topic COVID-19: 1) Chinese, 2) Italian, 3) Waiting, 4) Mitigations, 5) Physical distancing and Lockdown, 6) Anti-crisis shield. There was an exponential increase of the interest when the Polish government "declared a war against disease" around 11/12.03.2020 with a massive response activity. Later on, there was a decay in the interest with additional phases related to physical distancing and an anti-crisis legislation act associated with pronounced local peaks. We have found that declarations of response strategies by the Polish prime minister or the minister of health gathered the highest attention of Internet users. So already enacted or in force events do not affect the interest to such an extent. We have observed very weak or even negative correlations between a colloquial search term "antiviral mask" in Google, encyclopaedic definition in Wikipedia "SARS-CoV-2" as well as official COVID-19 incidence series, implying different mechanisms governing the search for knowledge, panic related behaviour and actual burden of COVID-19. CONCLUSIONS: Traditional and social media do not only reflect reality, but also create it. Interest in COVID-19 (measured by topic intensity on the Internet) is unrelated to the officially notified viral pressure (measured by incidence) and probably physical risk of acquiring infection in Poland. As traditional media are ahead of social media in time, we advise to choose traditional news media for a quick dissemination of information, however for a greater impact, social media should be used. Otherwise public information campaigns might have less impact on society than expected.

Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis.

Assessing the well-being of an animal is hindered by the limitations of efficient communication between humans and animals. Instead of direct communication, a variety of parameters are employed to evaluate the well-being of an animal. Especially in the field of biomedical research, scientifically sound tools to assess pain, suffering, and distress for experimental animals are highly demanded due to ethical and legal reasons. For mice, the most commonly used laboratory animals, a valuable tool is the Mouse Grimace Scale (MGS), a coding system for facial expressions of pain in mice. We aim to develop a fully automated system for the surveillance of post-surgical and post-anesthetic effects in mice. Our work introduces a semi-automated pipeline as a first step towards this goal. A new data set of images of black-furred laboratory mice that were moving freely is used and provided. Images were obtained after anesthesia (with isoflurane or ketamine/xylazine combination) and surgery (castration). We deploy two pre-trained state of the art deep convolutional neural network (CNN) architectures (ResNet50 and InceptionV3) and compare to a third CNN architecture without pre-training. Depending on the particular treatment, we achieve an accuracy of up to 99% for the recognition of the absence or presence of post-surgical and/or post-anesthetic effects on the facial expression.

Cow individual activity response to the accumulation of heat load duration.

In the course of predicted climate change, the welfare of dairy cows and heat load to which they are exposed have become increasingly important even under moderate climate conditions. The objective of this study was to investigate the cow individual activity response to heat load in terms of the heat load duration and intensity in lactating, high-yielding Holstein-Friesian cows in a moderate climate zone. The study was conducted from June 2015 to April 2017 in a naturally ventilated barn in Brandenburg, Germany. The determined temperature-humidity index (THI) inside the barn was used to define the heat load. The heat load was characterized by the average daily THI as well as the duration and intensity of the defined THI levels. In addition to the heat load on the measurement day, we studied the cow individual activity response to the heat load accumulated over the three days preceding the measurement day. The activity of the cows (n = 196) was measured by accelerometers and described the resting behavior and the number of steps per cow and day. The analysis models included autocorrelations in time series as well as individual cow factors. An increase in the duration and intensity of heat load on the measurement day led to a decrease in the lying time and an increase in the number of steps. The cows showed a reduced activity response to heat load when there was additional heat load accumulation over the three days preceding the measurement day. The cows in an advanced stage of lactation were more sensitive to heat load than cows in the early lactation stage. Multiparous cows showed less pronounced activity responses than primiparous cows. Heat load accumulation and individual cow-related factors should be considered in prediction models for the sensitive animal-specific recognition of heat load on the basis of activity responses.

Unravelling daily human mobility motifs.

Human mobility is differentiated by time scales. While the mechanism for long time scales has been studied, the underlying mechanism on the daily scale is still unrevealed. Here, we uncover the mechanism responsible for the daily mobility patterns by analysing the temporal and spatial trajectories of thousands of persons as individual networks. Using the concept of motifs from network theory, we find only 17 unique networks are present in daily mobility and they follow simple rules. These networks, called here motifs, are sufficient to capture up to 90 per cent of the population in surveys and mobile phone datasets for different countries. Each individual exhibits a characteristic motif, which seems to be stable over several months. Consequently, daily human mobility can be reproduced by an analytically tractable framework for Markov chains by modelling periods of high-frequency trips followed by periods of lower activity as the key ingredient.