(1) Background: because of close contacts with COVID-19 patients, hospital workers are among the highest risk groups for infection. This study examined the socioeconomic and behavioral correlates of COVID-19 infection among hospital workers in Indonesia, the country hardest-hit by the disease in the Southeast Asia region. (2) Methods: we conducted a cross-sectional study, which collected data from 1397 hospital staff from eight hospitals in the Greater Jakarta area during April–July 2020. The data was collected using an online self-administered questionnaire and Reverse Transcription-Polymerase Chain Reaction (RT-PCR) tests. We employed descriptive statistics and adjusted and unadjusted logistic regressions to analyze the data of hospital workers as well as the subgroups of healthcare and non-healthcare workers. (3) Results: from a total of 1397 hospital staff in the study, 22 (1.6%) were infected. In terms of correlates, being a healthcare worker (adjusted odds ratio (AOR) = 8.31, 95% CI 1.27–54.54) and having a household size of more than five (AOR = 4.09, 1.02–16.43) were significantly associated with a higher risk of infection. On the other hand, those with middle- and upper-expenditure levels were shown to have a lower risk of infection (AOR = 0.06, 0.01–0.66). Behavioral factors associated with COVID-19 infection among healthcare and non-healthcare workers included knowledge of standard personal protective equipment (PPE) (AOR = 0.08, 0.01–0.54) and application of the six-step handwashing technique (AOR = 0.32, 0.12–0.83). (4) Conclusion: among hospital staff, correlates of COVID-19 infection included being a healthcare worker, household size, expenditure level, knowledge and use of PPE, and application of appropriate hand washing techniques.

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Background: In developing countries like Indonesia, limited resources for routine mass Coronavirus Disease 2019 (COVID-19) RT-PCR testing among healthcare workers leave them with a heightened risk of late detection and undetected infection, increasing the spread of the virus. Accessible and accurate methodologies must be developed to identify COVID-19 positive healthcare workers. This study aimed to investigate the application of machine learning classifiers to predict the risk of COVID-19 positivity in high-risk populations where resources are limited and accessibility is desired. Methods: Two sets of models were built: one both trained and tested on data from healthcare workers in Jakarta and Semarang, and one trained on Jakarta healthcare workers and tested on Semarang healthcare workers. Models were assessed by the area under the receiver-operating-characteristic curve (AUC), average precision (AP), and Brier score (BS). Shapley additive explanations (SHAP) were used to analyze feature importance. 5,394 healthcare workers were included in the final dataset for this study. Results: For the full model, the voting classifier composed of random forest and logistic regression was selected as the algorithm of choice and achieved training AUC (mean [Standard Deviation (SD)], 0.832 [0.033]) and AP (mean [SD], 0.476 [0.042]) and was high performing during testing with AUC and AP of 0.753 and 0.504 respectively. A voting classifier composed of a random forest and a XGBoost classifier was best performing during cross-validation for the Jakarta model, with AUC (mean [SD], 0.827 [0.023]), AP (mean [SD], 0.461 [0.025]). The performance when testing on the Semarang healthcare workers was AUC of 0.725 and AP of 0.582. Conclusions: Our models yielded high predictive performance and can be used as an alternate COVID-19 screening methodology for healthcare workers in Indonesia, although the low adoption rate by partner hospitals despite its usefulness is a concern.