Integrated Clinical, Climate, and Environmental Prediction Modeling for Diagnosis of Spotted Fever Group Rickettsioses in northern Tanzania.

Spotted fever group rickettsioses (SFGR) pose a global threat as emerging zoonotic infectious diseases; however, timely and cost-effective diagnostic tools are currently limited. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, the inclusion of location-specific parameters such as climate data may improve predictive ability. To create a prediction model, we used data from 449 patients presenting to two hospitals in northern Tanzania between 2007 to 2008, of which 71 (15.8%) met criteria for acute SFGR based on [≥]4-fold rise in antibody titers between acute and convalescent serum samples. We fit random forest classifiers by incorporating clinical and demographic data from hospitalized febrile participants as well as satellite-derived climate predictors from the Kilimanjaro Region. In cross- validation, a prediction model combining clinical, climate, and environmental predictors (20 predictors total) achieved a statistically non-significant increase in the area under the receiver operating characteristic curve (AUC) compared to clinical predictors alone [AUC: 0.72 (95% CI:0.57-0.86) versus AUC: 0.64 (95% CI:0.48-0.80)]. In conclusion, we derived and internally-validated a diagnostic prediction model for acute SFGR, demonstrating that the inclusion of climate variables alongside clinical variables improved model performance, though this difference was not statistically significant. Novel strategies are needed to improve the diagnosis of acute SFGR, including the identification of diagnostic biomarkers that could enhance clinical prediction models.