Preview

Problems of Particularly Dangerous Infections

Advanced search

Modern Information and Molecular Technologies in the Practice of Epidemiological Surveillance of Natural Focal Infections

https://doi.org/10.21055/0370-1069-2025-3-57-67

Abstract

The relevance of the presented work is due to the need to improve epidemiological surveillance of natural focal infections, which pose a serious threat to human health and well-being both in the Russian Federation and around the world. Urbanization and development of the territory of natural foci for economic purposes, disruption of ecosystems as a whole leads to the loss of habitat for numerous species of wild animals and forces them to come into contact with humans and domestic animals, thereby contributing to the spread of diseases among them, as well as the subsequent transformation of natural foci into anthropogenic ones. In this regard, it is important to introduce the latest scientific methods and achievements into the practice of the sanitary and epidemiological service of our country. This study examines modern methods and technological solutions that have great potential for expanding the capabilities of epidemiological surveillance (epidemiological and epizootiological monitoring), as well as creating systems to respond to emerging threats of a sanitary and epidemiological nature, including geographic information systems used for data visualization, analysis of spatial and temporal relationships and forecasting risk areas; remote sensing of the Earth, which allows collecting data on the state of the environment using satellites and unmanned aerial vehicles, which is important for analyzing the circulation of infections; genomic epidemiological surveillance, which allows identifying genetic variants of infectious agents, studying their evolution and determining their epidemiological significance; big data analytics – provides information processing for timely detection of outbreaks of infections; artificial intelligence and machine learning – automate data analysis and improve forecast accuracy; the Internet of Things provides real–time data for continuous monitoring of environmental parameters and human health.

About the Authors

A. N. Kulichenko
Stavropol Research Anti-Plague Institute
Russian Federation

13–15, Sovetskaya St., Stavropol, 355035



S. S. Zavgorodny
Territorial Department of the Rospotrebnadzor Office for the Krasnodar Territory in Vyselkovsky, Ust-Labinsky, Korenovsky, Dinsky districts
Russian Federation

5, Severnaya St., Vyselki village, Vyselkovsky District, Krasnodar Territory, 353101



E. V. Chekhvalova
Sochi Branch of the Center for Hygiene and Epidemiology in the Krasnodar Territory
Russian Federation

27, Sochi, Roz St., Krasnodar, 354000



E. A. Manin
Stavropol Research Anti-Plague Institute
Russian Federation

13–15, Sovetskaya St., Stavropol, 355035



A. S. Volynkina
Stavropol Research Anti-Plague Institute
Russian Federation

13–15, Sovetskaya St., Stavropol, 355035



V. M. Dubyansky
Stavropol Research Anti-Plague Institute
Russian Federation

13–15, Sovetskaya St., Stavropol, 355035



F. V. Logvin
Rostov State Medical University of the Ministry of Health of the Russian Federation
Russian Federation

29, Nakhichevansky Lane, Rostov-on-Don, 344022



L. I. Zhukova
Kuban State Medical University of the Ministry of Health of the Russian Federation
Russian Federation

4, Mitrofan Sedina St., Krasnodar, 350063



References

1. Onishchenko G.G., Popova A.Yu., Toporkov V.P., Smolenskiy V.Yu., Shcherbakova S.A., Kutyrev V.V. [Modern threats and challenges in the field of biological safety and counteraction strategy]. Problemy Osobo Opasnykh Infektsii [Problems of Particularly Dangerous Infections]. 2015; (3):5–9. DOI: 10.21055/0370-1069-2015-3-5-9.

2. Pappaioanou M., Kane T.R. Addressing the urgent health challenges of climate change and ecosystem degradation from a One Health perspective: what can veterinarians contribute? J. Am. Vet. Med. Assoc. 2022; 261(1):49–55. DOI: 10.2460/javma.22.07.0315.

3. Ortiz D.I., Piche-Ovares M., Romero-Vega L.M., Wagman J., Troyo A. The impact of deforestation, urbanization, and changing land use patterns on the ecology of mosquito and tick-borne diseases in Central America. Insects. 2021; 13(1):20. DOI: 10.3390/insects13010020.

4. Shaheen M.N.F. The concept of one health applied to the problem of zoonotic diseases. Rev. Med. Virol. 2022; 32(4):e2326. DOI: 10.1002/rmv.2326.

5. Yeh K.B., Parekh F.K., Tabynov K., Tabynov K., Hewson R., Fair J.M., Essbauer S., Hay J. Operationalizing cooperative research for infectious disease surveillance: lessons learned and ways forward. Front. Public Health. 2021; 9:659695. DOI: 10.3389/fpubh.2021.659695.

6. Goodchild M.F. Geographic information systems and science: today and tomorrow. Annals of GIS. 2009; 15(1):3–9. DOI: 10.1080/19475680903250715.

7. Sharygin M.D., Chupina L.B. [Approaches to the study of geographical space-time and problems associated with it]. Geograficheskiy Vestnik [Geographical Bulletin]. 2013; (2):4–8.

8. Popova A.Yu., Kuzkin B.P., Demina Yu.V., Dubyansky V.M., Kulichenko A.N., Maletskaya O.V., Shayakhmetov O.Kh., Semenko O.V., Nazarenko Yu.V., Agapitov D.S., Mezentsev V.M., Kharchenko T.V., Efremenko D.V., Orobey V.G., Klindukhov V.P., Grechanaya T.V., Nikolaevich P.N., Tesheva S.Ch., Rafeenko G.K. [The use of modern information technologies in the practice of sanitary and epidemiological surveillance during the XXII Olympic Winter Games and XI Paralympic Winter Games in Sochi]. Zhurnal Mikrobiologii, Epidemiologii i Immunobiologii [Journal of Microbiology, Epidemiology and Immunobiology]. 2015; (2):113–18.

9. Chekhvalova E.V., Manin E.A., Kulichenko A.N. [Ranking the territory of Sochi by the risk of HFRS infection using the maximum entropy method]. Epidemiologia i Vaktsynoprofilaktika [Epidemiology and Vaccine Prevention]. 2023; 22(6):72–80. DOI: 10.31631/2073-3046-2023-22-6-72-80.

10. Sabins F.F. Jr, Ellis J.M. Remote Sensing: Principles, Interpretation, and Applications. Waveland Press; 2020. 524 p.

11. West H., Quinn N., Horswell M. Remote sensing for drought monitoring & impact assessment: progress, past challenges and future opportunities. Rem. Sens. Environ. 2019; 232:111291. DOI: 10.1016/j.rse.2019.111291.

12. Garni R., Tran A., Guis H., Baldet T., Benallal K., Boubidi S., Harrat Z. Remote sensing, land cover changes, and vector-borne diseases: use of high spatial resolution satellite imagery to map the risk of occurrence of cutaneous leishmaniasis in Ghardaïa, Algeria. Infect. Genet. Evol. 2014; 28:725–34. DOI: 10.1016/j.meegid.2014.09.036.

13. McMahon A. Earth Observation and Mosquito-Borne Diseases: Assessing Environmental Risk Factors for Disease Transmission via Remote Sensing Data. 2021. [Internet]. Available from: https://shareok.org/server/api/core/bitstreams/f61a2e9a-6f6c-430d-94d5-ae689472e3df/content.

14. Cunha H.S., Sclauser B.S., Wildemberg P.F., Fernandes E.A.M., Dos Santos J.A., Lage Md.O., Lorenz C., Barbosa G.L., Quintanilha J.A., Chiaravalloti-Neto F. Water tank and swimming pool detection based on remote sensing and deep learning: relationship with socioeconomic level and applications in dengue control. PLoS One. 2021; 16(12):e0258681. DOI: 10.1371/journal. pone.0258681.

15. Prislegina D.A., Maletskaya O.V., Dubyanskiy V.M., Taran T.V., Platonov A.E. [Tick-borne infections in the south of Russia: modern epidemiological situation, new approach to create “forecasting” and “Explaining” morbidity models (in Astrakhan rickettsiosis fever and Crimean-Congo hemorrhagic fever)]. Infektsiya i Immunitet [Russian Journal of Infection and Immunity]. 2023; 13(3):535–48. DOI: 10.15789/2220-7619-TBI-2036.

16. Dubyansky V.M., Prislegina D.A., Platonov A.E. [“Explanatory” models of tick-borne infections morbidity (on the example of Astrakhan rickettsial and Crimean-Congo hemorrhagic fevers)]. Zhurnal Mikrobiologii, Epidemiologii i Immunobiologii [Journal of Microbiology, Epidemiology and Immunobiology]. 2023; (1):34–45. DOI: 10/36233/0372-9311-344.

17. Dubyansky V.M., Tsapko N.V., Shaposhnikova L.I., Degtyarev D.Yu., Davydova N.A., Ostapovich V.V., Grigoriev M.P., Kulichenko A.N. [Using an unmanned aerial vehicle to improve the effectiveness of monitoring a natural plague focus]. Zdorov’ye Naseleniya i Sreda Obitaniya [Public Health and Life Environment]. 2018; (2):52–6. DOI: 10.35627/2219-5238/2018-299-2-52-56.

18. Badmaev N.B. [Geoinformation Technologies for Recognition of Abandoned Cattle Burial Grounds]. Ulan-Ude; 2017. 164 p.

19. Mochalkin P.A., Mochalkin A.P., Stepanov E.G., Farvazova L.A., Popov N.V. [Using remote sensing methods to assess the potential epidemiological danger of HFRS foci in Ufa]. [PEST Management]. 2016; (1-2):5–9.

20. Ashibokov U.M., Dubyansky V.M., Semenko O.V., Gazieva A.Yu., Belova O.A., Kesyan A.A., Khalidov A.Kh., Vetoshkin A.A., Viktorova N.V., Kulik A.A. [Experience of using the MaxEnt model for ranking the territory of the Caspian sandy natural plague focus (43) by registration risk epizootics]. Problemy Osobo Opasnykh Infektsii [Problems of Particularly Dangerous Infections]. 2024; (1):135–40. DOI: 10.21055/0370-1069-2024-1-135-140.

21. Bonicelli L., Porrello A., Vincenzi S., Ippoliti C., Iapaolo F., Conte A., Calderara S. Spotting virus from satellites: modeling the circulation of West Nile virus through Graph neural networks. IEEE Trans. Geosci. Rem. Sens. 2023; 99:1–11. DOI: 10.1109/TGRS.2023.3293270.

22. European Centre for Disease Prevention and Control. ECDC strategic framework for the integration of molecular and genomic typing into European surveillance and multi-country outbreak investigations – 2019–2021. Stockholm: ECDC; 2019.

23. de Jesus A.C.P., Fonseca P.L.C., Alves H.J., Bonfim D.M., Dutra J.V.R., Moreira F.R.R., de Brito Mendonça C.P.T., Rios J.S.H., do Prado Silva J., Malta F.S.V., Braga-Paz I., de Araújo J.L.F., de Oliveira J.S., de Souza C.S.A., da Silva S.E.B., Chaves D.C.C., da Silva Carvalho R., de Oliveira E.S., de Oliveira Ribeiro M., Arruda M.B., Alvarez P., Moreira R.G., de Souza R.P., Zauli D.A.G., Aguiar R.S. Retrospective epidemiologic and genomic surveillance of arboviruses in 2023 in Brazil reveals high co-circulation of chikungunya and dengue viruses. BMC Med. 2024; 22(1):546. DOI: 10.1186/s12916-024-03737-w.

24. Muthuirulandi Sethuvel D.P., Devanga Ragupathi N.K., Bakthavatchalam Y.D., Vijayakumar S., Varghese R., Shankar C., Jacob J.J., Vasudevan K., Elangovan D., Balaji V. Current strategy for local- to global-level molecular epidemiological characterisation of global antimicrobial resistance surveillance system pathogens. Indian J. Med. Microbiol. 2019; 37(2):147–62. DOI: 10.4103/ijmm.IJMM_19_396.

25. Besser J., Carleton H.A., Gerner-Smidt P., Lindsey R.L., Trees E. Next-generation sequencing technologies and their application to the study and control of bacterial infections. Clin. Microbiol. Infect. 2018; 24(4):335–41. DOI: 10.1016/j.cmi.2017.10.013.

26. D’Addiego J., Shah S., Pektaş A.N., Bağci B.N.K., Öz M., Sebastianelli S., Elaldı N., Allen D.J., Hewson R. Development of targeted whole genome sequencing approaches for Crimean-Congo haemorrhagic fever virus (CCHFV). Virus Res. 2024; 350:199464. DOI: 10.1016/j.virusres.2024.199464.

27. Koskela von Sydow A., Lindqvist C.M., Asghar N., Johansson M., Sundqvist M., Mölling P., Stenmark B. Comparison of SARS-CoV-2 whole genome sequencing using tiled amplicon enrichment and bait hybridization. Sci. Rep. 2023; 13(1):6461. DOI: 10.1038/s41598-023-33168-1.

28. Pérez-Losada M., Arenas M., Castro-Nallar E. Microbial sequence typing in the genomic era. Infect. Genet. Evol. 2018; 63:346–59. DOI: 10.1016/j.meegid.2017.09.022.

29. Tamura K., Peterson D., Peterson N., Stecher G., Nei M., Kumar S. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol. Biol. Evol. 2011; 28(10):2731–9. DOI: 10.1093/molbev/msr121.

30. Bouckaert R., Heled J., Kühnert D., Vaughan T., Wu C.H., Xie D., Suchard M.A., Rambaut A., Drummond A.J. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 2014; 10(4):e1003537. DOI: 10.1371/journal.pcbi.1003537.

31. Jansen van Vuren P., Ladner J.T., Grobbelaar A.A., Wiley M.R., Lovett S., Allam M., Ismail A., le Roux C., Weyer J., Moolla N., Storm N., Kgaladi J., Sanchez-Lockhart M., Conteh O., Palacios G., Paweska J.T. Phylodynamic analysis of Ebola virus disease transmission in Sierra Leone. Viruses. 2019; 11(1):71. DOI: 10.3390/v11010071.

32. Brault A.C., Huang C.Y., Langevin S.A., Kinney R.M., Bowen R.A., Ramey W.N., Panella N.A., Holmes E.C., Powers A.M., Miller B.R. A single positively selected West Nile viral mutation confers increased virogenesis in American crows. Nat. Genet. 2007; 39(9):1162–6. DOI: 10.1038/ng2097.

33. Hill S., Perkins M., von Eije K., editors. Genomic Sequencing of SARS-CoV-2: a guide to implementation for maximum impact on public health. Geneva: World Health Organization; 2021.

34. Giovanetti M., Faria N.R., Lourenço J., Goes de Jesus J., Xavier J., Claro I.M., Kraemer M.U.G., Fonseca V., Dellicour S., Thézé J., da Silva Salles F., Gräf T., Silveira P.P., do Nascimento V.A., Costa de Souza V., de Melo Iani F.C., Castilho-Martins E.A., Cruz L.N., Wallau G., Fabri A., Levy F., Quick J., de Azevedo V., Aguiar R.S., de Oliveira T., Bôtto de Menezes C., da Costa Castilho M., Terra T.M., Souza da Silva M., Bispo de Filippis A.M., Luiz de Abreu A., Oliveira W.K., Croda J., Campelo de Albuquerque C.F., Nunes M.R.T., Sabino E.C., Loman N., Naveca F.G., Pybus O.G., Alcantara L.C. Genomic and epidemiological surveillance of Zika virus in the Amazon region. Cell Rep. 2020; 30(7):2275–83. DOI: 10.1016/j.celrep.2020.01.085.

35. Jin X., Wah B.W., Cheng X., Wang Y. Significance and challenges of big data research, Big Data Res. 2015; 2(2):59–64. DOI: 10.1016/j.bdr.2015.01.006.

36. Fosso Wamba S., Akter S., Edwards A., Chopin G., Gnanzou D. How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 2015; 165:234–46. DOI: 10.1016/j.ijpe.2014.12.031.

37. De Mauro A., Greco M., Grimaldi M. A formal definition of Big Data based on its essential features. Libr. Rev. 2016; 65(3):122– 35. DOI: 10.1108/LR-06-2015-0061.

38. Asokan G.V., Mohammed M.Y. Chapter 16 – harnessing big data to strengthen evidence-informed precise public health response. In: Moustafa A.A., editors. Big Data in Psychiatry & Neurology. Academic Press; 2021. P. 325–37.

39. Asokan G.V., Asokan V. Leveraging “big data” to enhance the effectiveness of “one health” in an era of health informatics. J. Epidemiol. Glob. Health. 2015; 5(4):311–4. DOI: 10.1016/j. jegh.2015.02.001.

40. Althouse B.M., Scarpino S.V., Meyers L.A., Ayers J.W., Bargsten M., Baumbach J., Brownstein J.S., Castro L., Clapham H., Cummings D.A., Del Valle S., Eubank S., Fairchild G., Finelli L., Generous N., George D., Harper D.R., Hébert-Dufresne L., Johansson M.A., Konty K., Lipsitch M., Milinovich G., Miller J.D., Nsoesie E.O., Olson D.R., Paul M., Polgreen P.M., Priedhorsky R., Read J.M., Rodríguez-Barraquer I., Smith D.J., Stefansen C., Swerdlow D.L., Thompson D., Vespignani A., Wesolowski A. Enhancing disease surveillance with novel data streams: challenges and opportunities. EPJ Data Sci. 2015; 4(1):17. DOI: 10.1140/epjds/s13688-015-0054-0.

41. Zhou X., Lee E.W.J., Wang X., Lin L., Xuan Z., Wu D., Lin H., Shen P. Infectious diseases prevention and control using an integrated health big data system in China. BMC Infect. Dis. 2022; 22(1):344. DOI: 10.1186/s12879-022-07316-3.

42. Bertino E. Data security and privacy: concepts, approaches, and research directions. In: 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC). IEEE; 2016. P. 400–7. DOI: 10.1109/COMPSAC.2016.89.

43. Bezbaruah R., Ghosh M., Kumari S., Nongrang L., Ali S.R., Lahiri M., Waris H., Kakoti B.B. Role of AI and ML in epidemics and pandemics. In: Chavda V., Anand K., Apostolopoulos V., editors. Bioinformatics Tools for Pharmaceutical Drug Product Development. Wiley; 2023. P. 345–69. DOI: 10.1002/9781119865728.ch15.

44. Kohavi R., Provost F. Glossary of terms. Mach. Learn. 1998; 30(2/3):271–4. DOI: 10.1023/A:1017181826899.

45. Wang M., Wang T., Cai P., Chen X. Nanomaterials discovery and design through machine learning. Small Methods. 2019; 3(5):1900025. DOI: 10.1002/smtd.201900025.

46. Singh R., Singh R. Applications of sentiment analysis and machine learning techniques in disease outbreak prediction – A review. Mater. Today Proc. 2023; 81:1006–11. DOI: 10.1016/j. matpr.2021.04.356.

47. Rashidi H.H., Tran N., Albahra S., Dang L.T. Machine learning in health care and laboratory medicine: General overview of supervised learning and Auto-ML. Int. J. Lab. Hematol. 2021; 43(Suppl. 1):15–22. DOI: 10.1111/ijlh.13537.

48. Hoyos W., Aguilar J., Toro M. Dengue models based on machine learning techniques: A systematic literature review. Artif. Intell. Med. 2021; 119:102157. DOI: 10.1016/j.artmed.2021.102157.

49. Raja D.B., Mallol R., Ting C.Y., Kamaludin F., Ahmad R., Ismail S., Jayaraj V.J., Sundram B.M. Artificial intelligence model as predictor for dengue outbreaks. Malaysian J. Public Health Med. 2019; 19(2):103–8. DOI: 10.37268/mjphm/vol.19/no.2/art.176.

50. Zuenkova Yu.A. [Experience and prospects of using digital twins in public health]. Menedzher Zdravookhraneniya [Health Care Manager]. 2022; (6):69–77. DOI: 10.21045/1811-0185-2022-6-69-77.

51. Wanasinghe T.R., Gosine R.G., James L.A., Mann G.K.I., De Silva O., Warrian P.J. The internet of things in the oil and gas industry: a systematic review. IEEE Internet Things J. 2020; 7(9):8654–73. DOI: 10.1109/JIOT.2020.2995617.

52. Aloi G., Caliciuri G., Fortino G., Gravina R., Pace P., Russo W., Savaglio C. Enabling IoT interoperability through opportunistic smartphone-based mobile gateways. J. Netw. Comput. Appl. 2017; 81:74–84. DOI: 10.1016/j.jnca.2016.10.013.

53. Yu X., Zhang S., Guo W., Li B., Yang Y., Xie B., Li K., Zhang L. Recent advances on functional nucleic-acid biosensors. Sensors (Basel). 2021; 21(21):7109. DOI: 10.3390/s21217109.

54. Rahman M.S., Peeri N.C., Shrestha N., Zaki R., Haque U., Hamid S.H.A. Defending against the novel coronavirus (COVID‑19) outbreak: how can the internet of things (IoT) help to save the world? Health Policy Technol. 2020; 9(2):136–8. DOI: 10.1016/j. hlpt.2020.04.005.

55. Sareen S., Sood S.K., Gupta S.K. IoT-based cloud framework to control Ebola virus outbreak. J. Ambient Intell. Humaniz. Comput. 2018; 9(3):459–76. DOI: 10.1007/s12652-016-0427-7.


Review

For citations:


Kulichenko A.N., Zavgorodny S.S., Chekhvalova E.V., Manin E.A., Volynkina A.S., Dubyansky V.M., Logvin F.V., Zhukova L.I. Modern Information and Molecular Technologies in the Practice of Epidemiological Surveillance of Natural Focal Infections. Problems of Particularly Dangerous Infections. 2025;(3):57-67. (In Russ.) https://doi.org/10.21055/0370-1069-2025-3-57-67

Views: 13


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 0370-1069 (Print)
ISSN 2658-719X (Online)