Evaluating the risk of tick‐borne relapsing fever among occupational cavers—Austin,TX, 2017

Tick‐borne relapsing fever (TBRF) is a potentially serious spirochetal infection caused by certain species of Borrelia and acquired through the bite of Ornithodoros ticks. In 2017, Austin Public Health, Austin, TX, identified five cases of febrile illness among employees who worked in caves. A cross‐sectional serosurvey and interview were conducted for 44 employees at eight organizations that conduct cave‐related work. Antibodies against TBRF‐causing Borrelia were detected in the serum of five participants, four of whom reported recent illness. Seropositive employees entered significantly more caves (Median 25 [SD: 15] versus Median 4 [SD: 16], p = 0.04) than seronegative employees. Six caves were entered more frequently by seropositive employees posing a potentially high risk. Several of these caves were in public use areas and were opened for tours. Education of area healthcare providers about TBRF and prevention recommendations for cavers and the public are advised.     

A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data

The early and accurately detection of brucellosis incidence change is of great importance for implementing brucellosis prevention strategic health planning. The present study investigated and compared the performance of the three data mining techniques, random forest (RF), support vector machine (SVM) and multivariate adaptive regression splines (MARSs), in time series modelling and predicting of monthly brucellosis data from 2005 (March/April) to 2017 (February/March) extracted from a national public health surveillance system in Hamadan located in west of Iran. The performances were compared based on the root mean square errors, mean absolute errors, determination coefficient (R²) and intraclass correlation coefficient criteria. Results indicated that the RF model outperformed the SVM and MARS models in modeling used data and it can be utilized successfully utilized to diagnose the behaviour of brucellosis over time. Further research with application of such novel time series models in practice provides the most appropriate method in the control and prevention of future outbreaks for the epidemiologist.