Clinical Characteristics, Viral Load Dynamics, and Laboratory Biomarkers in Patients with Acute Dengue Virus Infection: A Monte Carlo Simulation Study

Abstract

Background: Acute dengue virus infection has a heterogeneous clinical course, from self-limited febrile illness to severe disease with plasma leakage, severe bleeding, shock, or organ involvement. Viral-load kinetics and routine laboratory biomarkers may help characterize disease progression, but measurements are strongly dependent on illness day and patient-level variability. Objective: To demonstrate a transparent Monte Carlo framework for exploring the joint trajectories of viral load, platelet count, white blood cell count, hematocrit, and aspartate aminotransferase (AST), and to illustrate how assumptions affect simulated severe-disease risk. Methods: We generated 10,000 hypothetical records with patient-level heterogeneity, repeated measurements across illness days 1–7, and prespecified probability distributions. A synthetic severe-outcome indicator was generated from a logistic risk function incorporating day-2 biomarkers and selected covariates. We summarized simulated distributions, longitudinal profiles, correlations, a held-out discrimination estimate, and sensitivity scenarios. The model was not calibrated to a patient cohort. Results: The model generated a severe-outcome proportion of 4.1% in the synthetic dataset. Simulated day-2 means were Viral load (log10 copies/mL): 6.20 (SD 0.82) log10 copies/mL; Platelet count: 158.84 (SD 24.56) ×10⁹/L; White blood cell count: 3.98 (SD 0.73) ×10⁹/L; Hematocrit: 40.09 (SD 1.75) %; AST: 52.25 (SD 19.84) U/L. The illustrative hold-out AUC was 0.619. This AUC is a property of the synthetic data-generating process and does not represent clinical predictive performance. Sensitivity scenarios showed that model-implied outcome risk changed when baseline risk and the platelet effect were altered. Conclusion: Monte Carlo simulation can clarify how assumptions and variability influence expected dengue biomarker patterns and can support study planning. These outputs are hypothesis-generating only; clinical inference requires real patient data, calibration, external validation, and a prespecified protocol.

Keywords: Dengue virus; Monte Carlo simulation; viral kinetics; thrombocytopenia; hematocrit; AST; synthetic cohort; sensitivity analysis; uncertainty