Comparison Between the Serological and Molecular Methods for Detection of Toxoplasma Gondii

Toxoplasma gondii is a worldwide distributed protozoan that is a serious subject of troublesome test, especially in immunocompromised patients and in a pregnant woman. The work is a comparative analysis of serological detection (IgG, IgM, and "IgG/IgM") to polymerase chain reaction (PCR) molecular detection by using the simulated dataset of anonymized patient cases (n = 350). The purpose is to determine the diagnostic performance of each technique and suggest an optimal testing strategy. The statistical analysis was performed in Python, including data preprocessing and the calculation of such metrics as precision, recall, and F1-score, to determine the test effectiveness. The visual tools such as bar graph, heat map, and Venn diagram were used to detect the overlaps and gaps in the diagnostic. According to its results, IgG has the best sensitivity but lacks proper specificity whilst IgM has greater precision but a poor recall. Cumulative serological testing improves recall (72%) but increases a probability of false positives. PCR showed the positive association was highest with the symptomatic cases (r = 0.70), confirming its sensitivity in confirming active infections. This paper presents a simulated yet clinically representative framework to assess the multimodal diagnostic performance of two models in the detection of T. gondii, documenting a reproducible analytical pipeline, and suggest a tiered diagnostic workflow, specific to the resource-constrained healthcare environments. The interface between the visual analytics and symptom-stratified interpretation offers a new, data-based approach to the process of diagnostic protocol optimization. These observations facilitate the use of tiers approach, namely, by serological screening and subsequent confirmation by PCR, to better accuracy, clinical value, and cost-effectiveness in toxoplasmosis diagnosis.