The emergence of COVID-19 in Indonesia: analysis of predictors of infection and mortality using independent and clustered data approaches

Background: Analyses of correlates of SARS-CoV-2 infection or mortality have usually assessed individual predictors. This study aimed to determine if patterns of combined predictors may better identify risk of infection and mortality. Methods: For the period of March 2nd to 10th 2020, the first 9 days of the COVID-19 pandemic in Indonesia, we selected all 18 confirmed cases, of which 6 died, and all 60 suspected cases, of which 1 died; and 28 putatively negative patients with pneumonia and no travel history. We recorded data for travel, contact history, symptoms, haematology, comorbidities, and chest x-ray. Hierarchical cluster analyses (HCA) and principal component analyses (PCA) identified cluster and covariance patterns for symptoms or haematology which were analysed with other predictors of infection or mortality using logistic regression. Results: For univariate analyses, no significant association with infection was seen for fever, cough, dyspnoea, headache, runny nose, sore throat, gastrointestinal complaints (GIC), or haematology. A PCA symptom component for fever, cough, and GIC tended to increase risk of infection (OR 3.41; 95% CI 1.06 - 14; p=0.06), and a haematology component with elevated monocytes decreased risk (OR 0.26; 0.07 - 0.79; 0.027). Multivariate analysis revealed that an HCA cluster of 3-5 symptoms, typically fever, cough, headache, runny nose, sore throat but little dyspnoea and no GIC tended to reduce risk (aOR 0.048; = 45, international travel, contact with COVID-19 patient, and pneumonia. Diabetes and history of contact were associated with higher mortality. Conclusions: Cluster groups and co-variance patterns may be stronger correlates of SARS-CoV-2 infection than individual predictors. Comorbidities may warrant careful attention as would COVID-19 exposure levels.