Temporal Parasitemia Trends Predict Risk and Timing of Experimental Cerebral Malaria in Mice Infected by Plasmodium berghei ANKA

TL;DR

Tracking how quickly malaria parasites multiply in mice can predict which mice will develop severe brain malaria and when symptoms will appear.

Contribution

Using machine learning to predict cerebral malaria risk and onset based on early parasitemia trends in mice.

Findings

Early parasitemia increases of 0.05 or more between day 1 and 3 predicted ECM with 97% sensitivity.

A Random Forest model predicted ECM onset with a mean absolute error of 0.43 days.

64.5% of the mouse cohort developed experimental cerebral malaria.

Abstract

Background: Experimental models using Plasmodium berghei ANKA (PbA)-infected mice have been essential for uncovering cerebral malaria (CM) pathogenesis. However, variability in experimental CM (ECM) incidence, onset, and mortality introduce challenges when analyses rely solely on infection day, which may reflect different disease stages among animals. Methods: We applied machine learning to predict ECM risk and onset in a cohort of 153 C57BL/6, 164 CBA, and 53 Swiss Webster mice. First, we fitted a logistic regression model to estimate the risk of ECM at any day using parasitemia data from day 1 to day 4. Next, we developed and trained a Random Forest Regressor model to predict the exact day of symptom onset. Results: A total of 64.5% of the cohort developed ECM, with onset ranging between 5 and 11 days. Early increases in parasitemia were strong predictors for the development of ECM, with an increase in parasitemia equal to or greater than 0.05 between day 1 and day 3 predicting the development of ECM with 97% sensitivity. The Random Forest model predicted the day of ECM onset with high precision (mean absolute error: 0.43, R2: 0.64). Conclusion: Parasitemia dynamics can effectively identify mice at high risk of ECM, enabling more accurate modeling of early pathological processes and improving the consistency of experimental analyses.

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