Seasonal variation in COVID-19 incidence could impact the trajectory of the pandemic. Using global linelist data on COVID-19 cases reported until 29 th February 2020 and global gridded temperature data, and after adjusting for surveillance capacity and time since first imported case, higher average temperature was strongly associated with lower COVID-19 incidence for temperatures of 1°C and higher. However, temperature explained a relatively modest amount of the total variation in COVID-19 incidence. These preliminary findings support stringent containment efforts in Europe and elsewhere.

The topology of animal transport networks contributes substantially to how fast and to what extent a disease can transmit between animal holdings. Therefore, public authorities in many countries mandate livestock holdings to report all movements of animals. However, the reported data often does not contain information about the exact sequence of transports, making it impossible to assess the effect of truck sharing and truck contamination on disease transmission. The aim of this study was to analyze the topology of the Swiss pig transport network by means of social network analysis and to assess the implications for disease transmission between animal holdings. In particular, we studied how additional information about transport sequences changes the topology of the contact network. The study is based on the official animal movement database in Switzerland and a sample of transport data from one transport company. The results show that the Swiss pig transport network is highly fragmented, which mitigates the risk of a large-scale disease outbreak. By considering the time sequence of transports, we found that even in the worst case, only 0.34% of all farm-pairs were connected within one month. However, both network connectivity and individual connectedness of farms increased if truck sharing and especially truck contamination were considered. Therefore, the extent to which a disease may be transmitted between animal holdings may be underestimated if we only consider data from the official animal movement database. Our results highlight the need for a comprehensive analysis of contacts between farms that includes indirect contacts due to truck sharing and contamination. As the nature of animal transport networks is inherently temporal, we strongly suggest the use of temporal network measures in order to evaluate individual and overall risk of disease transmission through animal transportation.

Seasonal variations in COVID-19 incidence have been suggested as a potentially important factor in the future trajectory of the pandemic. Using global line-list data on COVID-19 cases reported until 17th of March 2020 and global gridded weather data, we assessed the effects of air temperature and relative humidity on the daily incidence of confirmed COVID-19 local cases at the subnational level (first-level administrative divisions). After adjusting for surveillance capacity and time since first imported case, average temperature had a statistically significant, negative association with COVID-19 incidence for temperatures of −15 • C and above. However, temperature only explained a relatively modest amount of the total variation in COVID-19 cases. The effect of relative humidity was not statistically significant. These results suggest that warmer weather may modestly reduce the rate of spread of COVID-19, but anticipation of a substantial decline in transmission due to temperature alone with onset of summer in the northern hemisphere, or in tropical regions, is not warranted by these findings.

BackgroundAfrican horse sickness (AHS) is a major, Culicoides-borne viral disease in equines whose introduction into Europe could have dramatic consequences. The disease is considered to be endemic in sub-Saharan Africa. Recent introductions of other Culicoides-borne viruses (bluetongue and Schmallenberg) into northern Europe have highlighted the risk that AHS may arrive in Europe as well. The aim of our study was to provide a spatiotemporal quantitative risk model of AHS introduction into France. The study focused on two pathways of introduction: the arrival of an infectious host (PW-host) and the arrival of an infectious Culicoides midge via the livestock trade (PW-vector). The risk of introduction was calculated by determining the probability of an infectious animal or vector entering the country and the probability of the virus then becoming established: i.e., the virus’s arrival in France resulting in at least one local equine host being infected by one local vector. This risk was assessed using data from three consecutive years (2010 to 2012) for 22 regions in France.ResultsThe results of the model indicate that the annual risk of AHS being introduced to France is very low but that major spatiotemporal differences exist. For both introduction pathways, risk is higher from July to October and peaks in July. In general, regions with warmer climates are more at risk, as are colder regions with larger equine populations; however, regional variation in animal importation patterns (number and species) also play a major role in determining risk. Despite the low probability that AHSV is present in the EU, intra-EU trade of equines contributes most to the risk of AHSV introduction to France because it involves a large number of horse movements.ConclusionIt is important to address spatiotemporal differences when assessing the risk of ASH introduction and thus also when implementing efficient surveillance efforts. The methods and results of this study may help develop surveillance techniques and other risk reduction measures that will prevent the introduction of AHS or minimize AHS’ potential impact once introduced, both in France and the rest of Europe.Electronic supplementary materialThe online version of this article (doi:10.1186/s12917-015-0435-4) contains supplementary material, which is available to authorized users.

BackgroundAnimal health data recorded in free text, such as in necropsy reports, can have valuable information for national surveillance systems. However, these data are rarely utilized because the text format requires labor-intensive classification of records before they can be analyzed with using statistical or other software. In a previous study, we designed a text-mining tool to extract data from text in necropsy reports. In the current study, we used the tool to extract data from the reports from pig and cattle necropsies performed between 2000 and 2011 at the Institute of Animal Pathology (ITPA), University of Bern, Switzerland. We evaluated data quality in terms of credibility, completeness and representativeness of the Swiss pig and cattle populations.ResultsData was easily extracted from necropsy reports. Data quality in terms of completeness and validity varied a lot depending on the type of data reported. Diseases of the gastrointestinal system were reported most frequently (54.6% of pig submissions and 40.8% of cattle submissions). Diseases affecting serous membranes were reported in 16.0% of necropsied pigs and 27.6% of cattle. Respiratory diseases were reported in 18.3% of pigs and 21.6% of cattle submissions.ConclusionsThis study suggests that extracting data from necropsy reports can provide information of value for animal health surveillance. This data has potential value for monitoring endemic disease syndromes in different age and production groups, or for early detection of emerging or re-emerging diseases. The study identified data entry and other errors that could be corrected to improve the quality and validity of the data. Submissions to veterinary diagnostic laboratories have selection biases and these should be considered when designing surveillance systems that include necropsy reports.Electronic supplementary materialThe online version of this article (10.1186/s12917-018-1505-1) contains supplementary material, which is available to authorized users.

Early detection surveillance is used for various purposes, including the early detection of non-communicable diseases (e.g. cancer screening), of unusual increases of disease frequency (e.g. influenza or pertussis outbreaks), and the first occurrence of a disease in a previously free population. This latter purpose is particularly important due to the high consequences and cost of delayed detection of a disease moving to a new population.Quantifying the sensitivity of early detection surveillance allows important aspects of the performance of different systems, approaches and authorities to be evaluated, compared and improved. While quantitative evaluation of the sensitivity of other branches of surveillance has been available for many years, development has lagged in the area of early detection, arguably one of the most important purposes of surveillance. This paper, using mostly animal health examples, develops a simple approach to quantifying the sensitivity of early detection surveillance, in terms of population coverage, temporal coverage and detection sensitivity. This approach is extended to quantify the benefits of risk-based approaches to early detection surveillance. Population-based clinical surveillance (based on either farmers and their veterinarians, or patients and their local health services) provides the best combination of sensitivity, practicality and cost-effectiveness. These systems can be significantly enhanced by removing disincentives to reporting, for instance by implementing effective strategies to improve farmer awareness and engagement with health services and addressing the challenges of well-intentioned disease notification policies that inadvertently impose barriers to reporting. K E Y W O R D Sclinical surveillance, early detection surveillance, quantification, risk-based, sensitivity, syndromic surveillance

There is an extensive list of methods available for the early detection of an epidemic signal in syndromic surveillance data. However, there is no commonly accepted classification system for the statistical methods used for event detection in syndromic surveillance. Comparing and choosing appropriate event detection algorithms is an increasingly challenging task. Although lists of selection criteria, and statistical methods used for signal detection have been reported, selection criteria are rarely linked to a specific set of appropriate statistical methods. The paper presents a practical approach for guiding surveillance practitioners to make an informed choice from among the most popular event detection algorithms based on the intended application of the algorithm. We developed selection criteria by mapping the assumptions and performance characteristics of event detection algorithms directly to important characteristics of the time series used in syndromic surveillance. We also considered types of epidemics that may be expected and other characteristics of the surveillance system. These guidelines will provide decisions makers, data analysts, public health practitioners, and researchers with a comprehensive but practical overview of the domain, which may reduce the technical barriers to the development and implementation of syndromic surveillance systems in animal and human health. The classification scheme was restricted to univariate and temporal methods because they are the most commonly used algorithms in syndromic surveillance.

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