Strategic Understanding of Symptom Variation and Long-Term Risks: A Data-Driven Perspective

Understanding how individuals respond to infectious exposure and how symptom patterns evolve over time is critical for developing effective long-term management strategies. This study examines data from northern China to analyze symptom variation and the risk of chronic progression associated with delayed response. We apply data-driven models to explore how individual characteristics¡ªsuch as occupation, age, and gender¡ªare associated with different symptom profiles and long-term outcomes. Our findings suggest that individuals engaged in agriculture, animal handling, and related sectors are significantly less likely to experience high-fever symptoms. Additionally, younger individuals and females tend to exhibit higher peak body temperatures during acute phases. Importantly, delays in response management correlate strongly with an increased likelihood of long-term complications, while general supportive actions¡ªeven without specific identification of the underlying cause¡ªcan help mitigate chronic progression. These insights contribute to more effective planning, resource prioritization, and decision-making for better strategic management of complex symptom-based conditions.