ANTICIPATING CLIMATE CRISIS THROUGH TELEMEDICINE SERVICES AND HEALTH INFORMATICS THROUGH THE IDENTIFICATION OF RISK LEVEL IN PERUVIAN NORTHWESTERN COAST CITIES

Authors

DOI:

https://doi.org/10.18623/rvd.v23.7865

Keywords:

Flooding, Telemedicine, Ehealth, Nonlinear Systems

Abstract

With the advent of climate phenomena such as “El Niño Costero” for example, solid attention should be pain on those Peruvian coast cities that might experience disasters in the shortest times.Thus, it is relevant to employ all possible methodologies that could be advantageous along period of crisis. In this paper, the case of northwestern coast cities under risk of flooding  is studied  from the opics of applied engineering.. Essentially this paper has focused on the strategies based in telemedicine inside the framwork of eHealth. In this manner the paper has central puropose to provide a formalism that can be associated to computational aspects in order to be incorporated inside systems of telemedicina. Indeed, this paper presents simulations by which one can note the notable characteristics of potential deployed electronic services in periods of natural disasters where cities might be experiencing risk of being vulnerable by extreme conditions of climate. The full study has been applied in Piura city, at the north coast of Perú. 

References

1. Boyd, S., & Chua, L. (1985). Fading memory and the problem of approximating nonlinear operators with Volterra series. IEEE Transactions on Circuits and Systems, CAS-32(11), 1150–1161.

2. Boyd, S., Chua, L. O., & Desoer, C. A. (1984). Analytical foundations of Volterra series. Journal of Mathematical Control and Information, 1, 243–282.

3. Boyd, S. P. (1985). Volterra series: Engineering fundamentals (Doctoral dissertation). Department of Electrical Engineering and Computer Science, University of California, Berkeley.

4. Brockett, R. W. (1977). Convergence of Volterra series on infinite intervals and bilinear approximations. In V. Lakshmikanthan (Ed.), Nonlinear systems and applications (pp. 39–46). Academic.

5. Bullo, F. (2002). Series expansions for analytic systems linear in control. Automatica, 38, 1425–1432.

6. Casti, J. L. (1985). Nonlinear system theory (Vol. 175). Academic.

7. Crouch, P. E., & Collingwood, P. C. (1987). The observation space and realizations of finite Volterra series. SIAM Journal on Control and Optimization, 25(2), 316–333.

8. Gilbert, E. G. (1977). Functional expansions for the response of nonlinear differential systems. IEEE Transactions on Automatic Control, AC-22(6), 909–921.

9. Frame. (1974). Explicit solutions in two species Volterra systems. Journal of Theoretical Biology, 43(1), 73–81.

10. Rugh, W. J. (1981). Nonlinear system theory: The Volterra/Wiener approach. Johns Hopkins University Press.

11. Jing, X. J., Lang, Z. Q., & Billings, S. A. (2008). Magnitude bounds of generalized frequency response functions for nonlinear Volterra systems described by NARX model. Automatica, 44, 838–845.

12. UNICEF Peru. (2017). Humanitarian situation report 11 (26 June 2017). ReliefWeb. https://reliefweb.int/report/peru/unicef-peru-humanitarian-situation-report-11-26-june-2017

13. Zhao, X., Huang, Q., & Zhang, J. (2011). Impact of global climatic change on runoff in the upper reaches of the Yellow River. Second International Conference on Mechanic Automation and Control Engineering, 2219–2222.

14. Wu, E., & Chawla, S. (2007). Spatio-temporal analysis of the relationship between South American precipitation extremes and the El Niño Southern Oscillation. Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), 685–692.

15. He, M.-X., Chen, R., Guan, L., & Fang, M. (2001). The relationship between time series sea surface slope and El Niño event from satellite altimetry: A potential indicator. IGARSS 2001: Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium, 4, 1738–1740.

16. Kamsu, B. (2014). Systemic modeling in telemedicine. European Research in Telemedicine, 3(2), 57–65.

17. Kamsu, B. (2014). An ontological view in telemedicine. European Research in Telemedicine, 3(2), 67–76.

18. Le Guen, Y. (2013). Telemedicine: No specific legal liability regime. European Research in Telemedicine, 2(3–4), 121–123.

19. Littman-Quinn, R. (2011). mHealth applications for telemedicine and public health intervention in Botswana. IST-Africa Conference Proceedings.

20. Nanda, P. (2007). Quality of service in telemedicine. First International Conference on Digital Society (ICDS ’07).

21. Trichilli, H. (2008). Telemedicine in developing countries: Case of Tunisia. 3rd International Conference on Information and Communication Technologies: From Theory to Applications (ICTTA 2008).

22. Kropf, N., et al. (1999). Telemedicine for older adults. Home Health Care Services Quarterly, 17(4).

23. McDonald, E., et al. (2014). Acceptability of telemedicine and other cancer genetic counseling models of service delivery in geographically remote settings. Journal of Genetic Counseling, 23(2), 221–228.

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Published

2026-08-05

How to Cite

Nieto-Chaupis, H. (2026). ANTICIPATING CLIMATE CRISIS THROUGH TELEMEDICINE SERVICES AND HEALTH INFORMATICS THROUGH THE IDENTIFICATION OF RISK LEVEL IN PERUVIAN NORTHWESTERN COAST CITIES. Veredas Do Direito, 23(13), e237865. https://doi.org/10.18623/rvd.v23.7865