The limiting factor for the development of the industry is the difficult and unfavorable epizootic situation with African swine fever (hereinafter - ASF), which can significantly slow down the growth rate of the pig industry. Until now, no effective biological products have been developed that could provide reliable ASF protection for susceptible livestock. According to veterinary instructions, in the event of ASF, all pigs, being in the focus of infection, must be destroyed. Having no deterrent factors and reliable biological products to protect the pig herd, the ASF spread can lead to the loss of pig herds both at individual enterprises, where ASF foci have been identified, and at the regional or even country level. The digital system will make it possible to carry out an objective assessment of potential threats in connection with the directions of slopes and runoffs, the dates of liquid organic fertilizer (hereinafter referred to as LOF) removal to the fields, the migration of wild boars and synanthropic animals, the intensity of precipitation, river flow directions and other factors that can provide ways of spreading the ASF causative agent. As a result, pig enterprises (hereinafter referred to as PEs) can have an opportunity to take additional measures to prevent the spread of infection on their own initiative. Despite the antiepizootic measures taken, it is not possible to prevent the ASF spread as not all existing factors and mechanisms of the ASF spread are taken into account according to the veterinary instructions.
Similar processes of aggravation of the ASF epizootic situation and the tendencies for an increase of this infection are also observed in the world: in Poland there are more than 200 outbreaks, in Romania - more than 700, in the Baltic countries - more than 100 outbreaks, in Ukraine - more than 200 over the past two years [ 7-8].
The aim of this work is to develop a digital control system over African swine fever (ASF) antiepizootic measures
using unmanned aerial systems (hereinafter, UAS).
Methodology. The following problem solving methods were used to develop this digital control system for taking African swine fever (ASF) antiepizootic measures with the help of unmanned aerial systems:
- a method of epizootic analysis of the mechanisms and factors of the ASF spread due to the presence of geographically distributed links of the ASF epizootic chain. The method is based on the data of the information layers of the Digital System and contains a comparative historical description, a comparative geographical description, an epizootic survey, statistical processing of materials [ 1-2].
- a method for creating orthophotomaps of the territory using UAVs, which allows creating a cartographic basis. Aerial photography was carried out using the UAS "Geoscan 201 Agro / geodesy". The UAS "Geoscan 201" with an autopilot and an inertial navigation system was intended for automatic aerial photography.
- a method of vectorization of an orthophotomap for creating maps of the location of PEs, water bodies, federal, regional and intermunicipal roads, borders of fields, settlements. With the help of the orthophotomap there was an opportunity to create vector maps with the exact location of objects and link the necessary data for the subsequent analysis of the ASF epizootic situation.
To create PE location maps, we used a high-resolution orthophotomap obtained by aerial photography. Having the address of the pig farm location, the corresponding addresses were also found on the orthophotomap and digitized. The system database recognizes PE data as polygons. For each PE we created an object information card in the database. The card contains information about the name of the enterprise or an agricultural holding it belongs to, and other characteristics.
We used the method of visual location determination to create maps of the location of water bodies, field boundaries, settlements. As a result of digitization, the System database has the corresponding layers. We used paper maps of regional roads to create federal road maps, regional road maps and intermunicipal ones.
Research results
The digital control system for taking ASF antiepizootic measures using UAS was developed according to the region data with densely planted PEs (about 200 units). The operation principle of the Digital System is based on the general Scheme of the biological cycle of the infectious agent (Fig. 1). It is necessary to take into account all the features of the development of this infection.

Figure: 1. Diagram of the biological development cycle of ASF pathogen in natural and anthropogenic conditions
The developed digital system allows the user to mark the boundaries of the districts of the region automatically and in real time. For the first time now, it is possible to see the location of the PE region and its boundaries of 5 km safe zones on the map. This makes it possible to realize whether there are possible mechanisms and factors of infection transmission at the territory of these five-kilometer zones, how these 5-km safety zones intersect with other PEs located closely to each other and, accordingly, can affect the epizootic situation in the event of infection near these farms ( fig. 2).

From the data in Figure 2, it can be seen that there were 3 ASF foci in the Shebekinsky region, which are highlighted in red. The first focus of infection appeared in the Tyurensky PE. Then 4 months later, another outbreak was detected on December 11, 2017 at the growing and fattening site No. 2 called “Ivica”, which is located very close to the boundaries of the 5-kilometer zone of the first outbreak. On July 16, 2018, the infection spread to the third farm, the Bulanovsky commercial reproducer, which is located in the first threatened zone.
Comparing epizootic maps of the Shebekinsko-Korochansk zone dated September 10, 2017 and December 18, 2017 showed that the threat level increased after the second outbreak of ASF, since in this territory there were already 2 foci infections (11 km distance from each other), with a time lag of 4 months.
Thus, the possibilities of the Digital System does not require additional labor input and time to determine the safety zones around the PE. The System speeds up the process of taking antiepizootic measures. For the first time, it became possible to determine the density of the PE location in a specific territory of the Belgorod region, which contributes to a faster realization of the development intensity of the ASF epizootic process. Looking at Fig. 2, one can see the districts of the region without any PEs, although there are forest belts where natural sources of the ASF causative agent - wild boars - can habitat. The user can do that for the first time in real time. This simplifies the management of the epizootic process and makes it possible to focus on those territories where there is a high density of PEs and the number of livestock. Therefore, more drastic antiepizootic measures should be taken in these regions, regardless of the level of their compartment.
Using the developed module of the digital system, it can be clearly seen that the Prokhorovsky district has the highest density of the location of PEs. Then follow the Rakityansky, Borisovsky and Ivnyansky districts. In these areas, there is a higher probability of ASF occurrence, therefore, it is necessary to take additional antiepizootic measures in order to ensure anti ASF welfare. The smallest number of PEs is located in the Starooskolsky district. They are located at a great distance from the rest of the farms, which will not require the development of additional antiepizootic measures to preserve their well-being. The next developed information layer of the System allows to see other livestock enterprises (dairy farms and poultry farms). It can contribute to the mechanical spread of infection due to close PEs being in the 5-kilometer safety zones (Fig. 3).

The business activity of other livestock enterprises can contribute to the survivance of the pathogen in a given territory. In addition, in these farms there are fields with a forage base for wild boars, as well as open sources of livestock facilities, where biological vectors of ASF can be found. The carriers can be synanthropic animals (rodents, birds), which can affect the intensity of the development of the epizootic process and the spread of infection to other PEs. This makes it possible to realize the risks paying attention to other livestock enterprises and take measures to break the epizootic chain. On the next developed information layer “Agroholdings”, you can see which agricultural holdings this or that PE belongs to (Fig. 4).

These data allow developing anti-epizootic measures in the shortest possible time, stopping the development of the epizootic process within the Agroholding and preventing the spread of infection to neighboring agricultural holdings. For example, the map of Shebekinsky and Belgorodsky districts shows the highest density of communication line intersection between PEs of different agricultural holdings, which can contribute to the ASF spread to the farms of another Agroholding in the event of infection. Agricultural holdings of Alekseevsky and Starooskolsky districts do not have such intersections, so there is no need to ensure the ASF control. This is clearly demonstrated by the data in Fig. 4. Thus, for the first time, it becomes possible to warn the management of a neighboring agricultural holding about the need of taking antiepizootic measures in order to prevent the spread of ASF epizootics, which improves the effectiveness of antiepizootic measures.
The next information layer "Field Map", developed by the digital system, can show in real time what crops are grown in the first and in the second threatened zones, as well as the zone near the PE. This makes it possible to see the PEs, where the food supply for the natural carrier of the infection causative agent - wild boars, is grown close to. Thus, it can help to contaminate the crops that will be given to pigs.
In the Shebekinsky region we can see the lie spot of wild boars in a 5-kilometer safety zone near the PE “Osinovaya Roshcha Fattening Site”, where other symbionts and parasites are located. They can remain in these fields when the wild boars move. For example, flies and ticks are biological and mechanical carriers of ASF. Since these arthropods can enter the PE by air, including birds, the probability of infection among pigs increases. In addition, if ASF wild boars die in these fields, they become a food base for predators, rodents, birds and other synanthropic animals. Thus, migrating, they can introduce infection into these farms.
The next developed information layer is a map of the liquid organic fertilizer removal to fields in the first and the second threatened zones, in which the ASF causative agent may be located as well(Fig. 5). Using the data on the last outbreak in 2018 at the Tambov Bacon PE, the Digital System made it possible to predict the occurrence of an ASF outbreak in the Balanovskiy Reproductor PE, which, most likely, happened due to the LOF removal contaminated with the ASF agent. This situation clearly demonstrates the possibility of the Digital System to manage the epizootic process by identifying a specific source of the infectious agent.

Thus, the operation principle of the Digital System using the developed information layers allows both predicting the spread of infection from an epizootic focus and offering the most effective antiepizootic measures aimed at breaking the epizootic chain and preventing the spread of infection, which gives the System an opportunity to manage the epizootic process.
Thus, the developed digital system for managing the epizootic situation is based on the created mathematical model of the ASF spread. The model takes into account the multifactorial threats of the occurrence and migration of the ASF virus and presents itself as a formula. The developed digital system makes it possible to reduce the time for making decisions and to develop anti-epizootic measures in time.
R E F E R E N C E S
1. Anderson R. Infectious human diseases. Dynamics and control / R. Anderson, R. May. - Moscow: World, 2004 - 783 p.
2. Algorithm of actions of the state veterinary service of municipalities of the constituent entity of the Russian Federation in the event of African swine fever, St. Petersburg, 2013.
3. Kazakova A.S. Test system for express diagnostics of African swine fever by immunoblotting using recombinant protein P30. Veterinary Medicine, 2014. No. 9 P.52-55
4. Kapustin S.I. The system of antiepizootic measures for African swine fever for pig farms in the Voronezh region / S. I. Kapustin, I. T. Shaposhnikov, A. V. Aristov, B. V. Romashov, O. A. Manzhurina // Voronezh State Agrarian University n.a. Emperor Peter I. Voronezh; 2015.
5. Methodical recommendations. African swine fever / S. I. Prudnikov, A. S. Donchenko, T. M. Prudnikova.-Novosibirsk, 2009.-27 p.
6. Ahmed J. A. H., Aksin M., Seitzer U. 2007Current status of ticks in Asia. Parasitol. Res. 101(Suppl. 1), 159–162 (doi:10.1007/s00436-007-0696-3)
7. Anderson E. C., Hutchings G. H., Mukarati N., Wilkinson P. J. 1998African swine fever virus infection of the bushpig (Potamochoerus porcus) and its significance in the epidemiology of the disease. Vet. Microbiol. 62, 1–15 (doi:10.1016/S0378-1135(98)00187-4)
8. Boinas F. S., Hutchings G. H., Dixon L. K., Wilkinson P. J. 2004Characterization of pathogenic and non-pathogenic African swine fever virus isolates from Ornithodoros erraticus inhabiting pig premises in Portugal. J. Gen. Virol. 85, 2177–2187 (doi:10.1099/vir.0.80058-0)
9. Boshoff C. I., Bastos A. D. S., Gerber L. J., Vosloo W. 2007Genetic characterisation of African swine fever viruses from outbreaks in southern Africa (1973–1999). Vet. Microbiol. 121, 45–55 (doi:10.1016/j.vetmic.2006.11.007)
10. Lubisi B. A., Bastos A. D. S., Dwarka R. M., Vosloo W. 2005Molecular epidemiology of African swine fever in East Africa. Arch. Virol. 150, 2439–2452 (doi:10.1007/s00705-005-0602-1)
11. Lubisi B. A., Bastos A. D. S., Dwarka R. M., Vosloo W. 2007Intra-genotypic resolution of African swine fever viruses from an East African domestic pig cycle: a combined p72-CVR approach. Virus Genes35, 729–735 (doi:10.1007/s11262-007-0148-2)
12. Lucchini V., Meijaard E., Diong C. H., Groves C. P., Randi E. 2005New phylogenetic perspectives among species of South-east Asian wild pig (Sus sp.) based on mtDNA sequences and morphometric data. J. Zool. 266, 25–35 (doi:10.1017/S0952836905006588)
Nel P., Righarts M. 2008Natural disasters and the risk of violent civil conflict. Int. Stud. Quart. 52, 159–185 (doi:10.1111/j.1468-2478.2007.00495.x)