Detection Dogs Ticino in overview

Detection Dogs Ticino is a Swiss association specialized in the training of operational multi-purpose detection dogs, used for special projects in collaboration with different institutions (police, state and academic). In the past years, we have trained dogs in sectors like, biosecurity, food safety, mold control, forensic electronic devices detection, human remains detection and medical (prostate cancer research).

Our website link:

Executive summary

Our project aims at training detection dogs to find SARS-COV-2 infection in asymptomatic and symptomatic patients. Dogs can operate in both labs or public spaces. After a training of about 6-8 weeks a dog becomes operational and can scan about 2000 scent samples a day.


Dog's ability to detect pathogens has been proven in many cases as described by Alexander A. Aksenov et al.(ref: Cellular Scent of Influenza Virus Infection), with different diseases and all over the world. Malaria, cancer, Clostrydium Difficilis and many other bacterial and viral infections have been studied in relation with the ability of dogs to detect them. The results were almost always very meaningful for the scientists, and simply stunning for the broad public. Considering all these aspects, our inspiration and motivation focuses on the application of sniffing dogs for detection of SARS-Cov-2 infected both asymptomatic and symptomatic patients at early stages of the viral infection.

More information links
About the scent of infections
About malaria detection with dogs
About viruses detection with dogs
About cancer detection with dogs
About the pathway of COVID-19 infection and the disease's progression

What it does

Considering previous work done in this field by professor Steve Lindsay and his team at the Durham University in the UK (ref: Malaria Detection Dogs), T. Craig Angle et al. (ref: Real-Time Detection of a Virus Using Detection Dogs) and V. V. Protoshhak et al. (ref: Prostate cancer and dogs sense of smell: opportunities of noninvasive diagnostics), our project aims at training dogs to detect SARS-COV-2 in both asymptomatic and symptomatic infected humans in the early stages of the infection.

How we built it

Based on our previous working experiences in detection dogs training, our main goals are: 1) Imprinting the new scent into dogs; 2) Teaching them to search for it. In the first phase by considering lineups of jars and secondly by involving real people.

More information links
Rigorous Training of Dogs Leads to High Accuracy in Human Scent Matching-To-Sample Performance

Challenges we ran into

Although some early researches conducted by Sarah Temmam et al. (ref: Absence of SARS-CoV-2 infection in cats and dogs in close contact with a cluster of COVID-19 patients in a veterinary campus) in France on pets (both dogs and cats) show that even if in close contact with their SARS-CoV-2 positive owners, these pets showed almost no signs in positivity neither in symptoms nor in serum antigen analysis. On the other hand Qiang Zhang and his team from Wuhan in China (ref: SARS-CoV-2 neutralizing serum antibodies in cats: a serological investigation), found that a percentage of cats showed serum positive SARS-CoV-2 antibodies. In order to stay on the safe side of matter, our main concern is safety. Thus, dogs, handlers and every assistant involved in the project are able to work in a safe environment, reducing potential risks and avoiding fear of catching the virus.

More information links
SARS-CoV-2 neutralizing serum antibodies in cats: a serological investigation
Absence of SARS-CoV-2 infection in cats and dogs in close contact with a cluster of COVID-19 patients in a veterinary campus
Susceptibility of ferrets, cats, dogs, and other domesticated animals to SARS–coronavirus 2

Accomplishments that we are proud of

Through research and multidisciplinary contacts and contributes, we are now aware that the project's goal is feasible and the main issues of the problem can be approached. In this case, the sniffing dogs need a biological source for odours. From our point of view, we believe that sweat or eventually urine could be the safest odours sources from these patients since they might have a very low virus load, or maybe even none as cited and explained by the virologist Chris Smith (ref: Coronavirus: can sweat transmit the virus? And is breastfeeding safe?). For these aspects we do not take into consideration blood, plasma, saliva or exhalation due to the obvious high infectiveness of this virus. Furthermore, the great advantage of sweat or urine is their easy handling and non invasive collection methods in comparison to the other sources. Hence, based on the following research done by Markus Eickmann et al. (ref: Inactivation of Ebola Virus and Middle East Respiratory Syndrome Coronavirus in Platelet Concentrates and Plasma by Ultraviolet C Light and Methylene Blue Plus Visible Light, Respectively), the UV radiations inactivate viruses. Therefore, a further step on what could be implemented for making the sources even more safer, just after collection and before handling, they could be passed “under” UV radiations by using domestic devices. We are currently investigating the "sweat pathway" as an evidence, because this type of odour is physiologically found on everyone, everyday and all day long, meaning that human being (some more than others) is able to naturally and physiologically "express" this odour during all the time. This is of course less indicated in the case when the evidence is urine.

More information links
Inactivation of Ebola Virus and Middle East Respiratory Syndrome Coronavirus in Platelet Concentrates and Plasma by Ultraviolet C Light and Methylene Blue Plus Visible Light, Respectively

What we learned

We can only imagine the power of a dog nose. As virologist Chris Smith said "(...) The site in which the virus grows is the respiratory tract so that means the nose and throat to a lower extent and the lungs to a greater extent. That's why coughs and sneezes in this context spreads diseases. As sweat is made in sweat glands and sweat glands make the sweat by filtering the liquid bit, the watery bit, away from blood, they're not in direct contact with the source of the virus - because this virus does not to an appreciable level go round in the blood stream. Therefore, there should be only limited amounts of the virus in sweat for that is not a means of transmission (...)” (ref: Coronavirus: can sweat transmit the virus? And is breastfeeding safe?).

Based on the following research described by Jha et al. (ref: International Journal of Mass Spectrometry, 406 (2016) 35–47) “(...) The focus of the present study is the human body odour recognition by analysis of information about the chemical compounds identified in their gas chromatography-mass spectrometry (GC–MS) chromatogram. The artificial neural network (ANN) technique implemented in the current study, has been comprehensively used for classification and regression tasks in numerous applications. The experimental data set includes intensity characteristics (peak height, peak area, ratio of peak area and height) of several chemical compounds detected in GC–MS chromatogram of twenty odour samples (from four persons), and two non-body odour samples. The raw data set is transformed with logarithmic scaling, principal component analysis (PCA), and kernel principal component analysis (KPCA) in search of the better features by extracting. After preprocessing of data, feed-forward back-propagation neural network (BPNN) technique is used in discrimination of body and non-body odour samples, as well as recognition of body odour to an individual. Although ANN classifier is optimized for the number of neurons, and training algorithms, the classification result is unstable and unsatisfactory (maximum correct classification rate 78% and the minimum correct classification rate 44%). To improve the stability and accuracy of ANN classification results, data fusion approach is attempted. Eight different weighted and unweighted decision schemes of data fusion have been implemented in body odour recognition. Amongst them simple weighted vote (SWV), quadratic best worst weighted vote (QBWWV), and best worst weighted vote (BWWV) outperform with 100% class recognition outcomes, compared with a single ANN classifier (...)“ (ref: GC-MS Characterization of Body Odour for Identification Using Artificial Neural Network Classifiers Fusion, ref: Human body odor discrimination by GC-MS spectra data mining) we could determine that sweat can be characterised by GC-MS, identifying each of its components in both quantitative and qualitative manner. In parallel, these sweat odours emanate Volatile Organic Compounds (VOCs) which patterns are specific to each disease as described in the following research (ref: Real-Time Detection of a Virus Using Detection Dogs). These VOCs patterns are thought the ones to be recognised by the dogs.

As mentioned beforehand, the application of GS-MS characterization of body odour could help understand even better and deeper the specific VOC pattern that might represent the SARS-CoV-2's own "finger(-odour)print".

We are considering this approach, and we will make any scientific partners in this project, aware of these findings and possibilities.

What's next for ddtvsvirus

Find adequate partners in the medical field to sustain us in the sample collection, for which exact methodology and procedures must be defined, in order to start collecting samples for trainings dogs. A starter hypothetic sampling collection methodology will be based on the one already applied for the Malaria detection cited at the beginning. Upon verification of the results, we would like to share our approach all over the world. In conclusion, once this method has been eventually statistically proven, the new applications are very broad. In our opinion, by considering effectiveness and low cost, it will be applicable in several sensitive crowded and open spaces scenario, such as airports, train stations or central parks.

+ 5 more
Share this project: