Can naive Bayes classifier predict infection in a close contact of COVID-19? A comparative test for predictability of the predictive model and healthcare workers in Japan

BACKGROUND: Those who are found in close contact with COVID-19 patients and are also negative by polymerase chain reaction (PCR) test may act without waiting for the incubation period to elapse, can become infectious and spread the infection. METHODS: A machine learning model that can evaluate the risk of infection in close contact with COVID-19 patients within the incubation period from the contact status reported from the index case was created using posterior probabilities. To confirm actual predictability, a verification test was conducted on 169 new close contacts, and the machine learning model was compared with four experienced healthcare workers for the predictability. RESULTS: In a verification test, 33 of the 169 contacts were infected with COVID-19 during the incubation period, and 13 of 33 were negative on initial PCR test, after that the disease developed and their PCR test became positive. The machine learning model predicted the eventual infection in 12 of 13 patients who had negative results on the initial PCR test. In the verification test, the sensitivity of the machine learning model was 0.879 and the specificity was 0.588. The mean−standard deviation of the sensitivity and the specificity of the four health care workers was 0.568 (0.230) for sensitivity and 0.689 (0.103) for specificity. CONCLUSION: If it is possible to convey that individual risk of infection, the close contact may take suppressive action during the incubation period regardless of the result of the initial PCR test, thereby preventing secondary spread of infection.