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Background <p>Chikungunya is an arbovirus capable of affecting the musculoskeletal system of infected individuals. Furthermore, it has the potential to progress from the acute to the chronic phase, marked by the prevalence of symptoms of arthralgia. Joint pain compromises the performance of daily activities, including psychological, economic, and physical functioning.</p> Methods <p>Through the use of data science techniques, such as data analysis, evaluation, and visualization, the aim is to understand the influence of pain points on disease progression. Furthermore, we also evaluate artificial intelligence models to calculate the likelihood of patients progressing to a chronic phase.</p> Results <p>The data analysis showed that arthralgia was reported by 97.70% of the sample (339 cases), followed by 74.06% edema (257 cases), 36.31% low back pain (126 cases) and 34.58% myalgia (120 cases), being factors that are related to chronicity. The artificial intelligence models have achieved metrics above 60%, demonstrating potential for estimating the likelihood of a patient’s progression to the chronic phase.</p> Conclusions <p>Based on these estimates, healthcare professionals can adopt preventive measures capable of mitigating the disease’s impacts. Implementing these models in the decision-making process becomes an important ally in the fight against Chikungunya in Brazil, helping to mitigate the social and economic impacts caused by the chronic phase.</p>

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Chronic phase of Chikungunya: understanding the impact of joint pain using data science and artificial intelligence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月20日

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  • Gabriel Masson,
  • Kaio Viana,
  • Sebastião Rogerio,
  • Kayo Henrique de C. Monteiro,
  • Jamile Taniele-Silva,
  • Gabriela Cavalcanti Lima Albuquerque,
  • Moacyr Jesus Barreto de Melo Rêgo,
  • André Machado de Siqueira,
  • Raphael Dourado,
  • Patricia Takako Endo

摘要

Background

Chikungunya is an arbovirus capable of affecting the musculoskeletal system of infected individuals. Furthermore, it has the potential to progress from the acute to the chronic phase, marked by the prevalence of symptoms of arthralgia. Joint pain compromises the performance of daily activities, including psychological, economic, and physical functioning.

Methods

Through the use of data science techniques, such as data analysis, evaluation, and visualization, the aim is to understand the influence of pain points on disease progression. Furthermore, we also evaluate artificial intelligence models to calculate the likelihood of patients progressing to a chronic phase.

Results

The data analysis showed that arthralgia was reported by 97.70% of the sample (339 cases), followed by 74.06% edema (257 cases), 36.31% low back pain (126 cases) and 34.58% myalgia (120 cases), being factors that are related to chronicity. The artificial intelligence models have achieved metrics above 60%, demonstrating potential for estimating the likelihood of a patient’s progression to the chronic phase.

Conclusions

Based on these estimates, healthcare professionals can adopt preventive measures capable of mitigating the disease’s impacts. Implementing these models in the decision-making process becomes an important ally in the fight against Chikungunya in Brazil, helping to mitigate the social and economic impacts caused by the chronic phase.