Smart Healthcare Framework for Chikungunya Surveillance Using Machine Learning

Vishal Gupta; Rupinder Kaur  Walia; Sandeep  Singh; Abhishek Gowda  R M; Sumita  Nagah; Ruchika  Rana

Vishal Gupta Associate Professor, CSE Department, MMEC, Maharishi Markandeshwar, Deemed To Be University, Mullana, Ambala, Haryana, India
Rupinder Kaur Walia Associate Professor, CSE Department, MMEC, Maharishi Markandeshwar, Deemed To Be University, Mullana, Ambala, Haryana, India
Sandeep Singh Assistant Professor, Computer Science Engineering, SGT University, Gurugram, India
Abhishek Gowda R M Alliance School of Advance Computing, Alliance University, Bengaluru, India
Sumita Nagah School of Engineering and Technology, CGC University, Mohali, India
Ruchika Rana Assistant Professor, School of Computer Applications, Lovely Professional University Phagwara, Punjab, India

Keywords: Machine Learning, Time Series, Arima Model, Public Health

Abstract

Chikungunya virus (CGN) is a mosquito-borne alphavirus responsible for causing significant outbreaks characterised by fever, severe joint pain, rash, and long-lasting arthralgia, impacting millions worldwide.
Despite its clinical similarity to other arboviral infections, early and accurate diagnosis remains critical for effective patient management and outbreak control. This paper presents a comprehensive review of
CGN’s epidemiology, transmission dynamics, and monitoring challenges. Furthermore, it explores the recent advancements in digital health, technologies specifically the integration of Internet of Things (IoT)
devices and Artificial Intelligence (AI) to enhance real-time detection, monitoring, and epidemiological surveillance of CGN. These innovations offer promising avenues for early warning systems, improved vector
control strategies, and data-driven public health interventions. By harnessing digital tools, healthcare systems can achieve more efficient outbreak responses, reduce disease burden, and better protect
vulnerable populations. This study underscores the imperative for multidisciplinary approaches combining cutting-edge digital technologies to combat emerging arboviral threats like Chikungunya.
Keywords: Machine Learning, Time Series, Arima Model, Public Health

DOI: https://doi.org/10.24321/0019.5138.202667

How to cite this article:
Gupta V, Walia R K, Singh S, Gowda R M A, Nagah S, Rana R. Smart Healthcare Framework
for Chikungunya Surveillance Using MachineLearning. J Commun Dis. 2026;58(3):183-190.

References

Weaver SC, Lecuit M. Chikungunya virus and the global spread of a mosquito-borne disease. New England

Journal of Medicine. 2015 Mar 26;372(13):1231-9. [Google Scholar]

World Health Organization (WHO), “Chikungunya –Epidemiological Update,” WHO, Geneva, Switzerland,

[Online]. Available:WHO Chikungunya Fact Sheet. [Accessed: 20-Jan-2026].

Suhrbier A, Jaffar-Bandjee MC, Gasque P. Arthritogenic alphaviruses—an overview. Nature Reviews

Rheumatology. 2012 Jul;8(7):420-9. [Google Scholar]

Thiberville SD, Moyen N, Dupuis-Maguiraga L, Nougairede A, Gould EA, Roques P, de Lamballerie X.

Chikungunya fever: epidemiology, clinical syndrome,pathogenesis and therapy. Antiviral research. 2013

Sep 1;99(3):345-70. [Google Scholar]

Tsetsarkin KA, Chen R, Leal G, Forrester N, Higgs S, Huang J, Weaver SC. Chikungunya virus emergence

is constrained in Asia by lineage-specific adaptive landscapes. Proceedings of the National Academy

of Sciences. 2011 May 10;108(19):7872-7. [Google Scholar]

Kumari R. The impact of climate change on vector-borne diseases (VBDs): Indian and global perspectives.

[Google Scholar]

Chandra R, Kumar A, Singh RK, Kamal S, Sharma SN, Sharma RS, Srivastava PK. Standardisation of units

for assessment of adult disease vector density under vector control programmes in India.[Google Scholar]

Chadsuthi S, Iamsirithaworn S, Triampo W, Cummings DA. The impact of rainfall and temperature on the

spatial progression of cases during the chikungunya re-emergence in Thailand in 2008–2009. Transactions of the Royal Society of Tropical Medicine and Hygiene.

Feb 1;110(2):125-33. [Google Scholar]

van Laarhoven A, Koeken VA, Dian S, Ganiem AR, van Crevel R. Neuromarker levels also predict mortality

in adult Tuberculous meningitis. Clinical Infectious Diseases. 2018 Aug 1;67(4):642-3. [Google Scholar]

Khongwichit S, Chansaenroj J, Thongmee T, Benjamanukul S, Wanlapakorn N, Chirathaworn C,

Poovorawan Y. Large-scale outbreak of Chikungunya virus infection in Thailand, 2018–2019. PloS one. 2021

Mar 10;16(3):e0247314. [Google Scholar]