Triage Modeling for Differential Diagnosis Between COVID-19 and Human Influenza A Pneumonia: Classification and Regression Tree Analysis.

Background: The coronavirus disease 2019 (COVID-19) pandemic has lasted much longer than an influenza season, but the main signs, symptoms, and some imaging findings are similar in COVID-19 and influenza patients. The aim of the current study was to construct an accurate and robust model for initial screening and differential diagnosis of COVID-19 and influenza A. Methods: All patients in the study were diagnosed at Fuyang No. 2 People's Hospital, and they included 151 with COVID-19 and 155 with influenza A. The patients were randomly assigned to training set or a testing set at a 4:1 ratio. Predictor variables were selected based on importance, assessed by random forest algorithms, and analyzed to develop classification and regression tree models. Results: In the optimal model A, the best single predictor of COVID-19 patients was a normal or high level of low-density lipoprotein cholesterol, followed by low level of creatine kinase, then the presence of <3 respiratory symptoms, then a highest temperature on the first day of admission <38°C. In the suboptimal model B, the best single predictor of COVID-19 was a low eosinophil count, then a normal monocyte ratio, then a normal hematocrit value, then a highest temperature on the first day of admission of <37°C, then a complete lack of respiratory symptoms. Conclusions: The two models provide clinicians with a rapid triage tool. The optimal model can be used to developed countries/regions and major hospitals, and the suboptimal model can be used in underdeveloped regions and small hospitals.

Disease Burden Attributable to the First Wave of COVID-19 in China and the Effect of Timing on the Cost-Effectiveness of Movement Restriction Policies.

OBJECTIVES: Movement restriction policies (MRPs) are effective in preventing/delaying COVID-19 transmission but are associated with high societal cost. This study aims to estimate the health burden of the first wave of COVID-19 in China and the cost-effectiveness of early versus late implementation of MRPs to inform preparation for future waves. METHODS: The SEIR (susceptible, exposed, infectious, and recovered) modeling framework was adapted to simulate the health and cost outcomes of initiating MRPs at different times: rapid implementation (January 23, the real-world scenario), delayed by 1 week, delayed by 2 weeks, and delayed by 4 weeks. The end point was set as the day when newly confirmed cases reached zero. Two costing perspectives were adopted: healthcare and societal. Input data were obtained from official statistics and published literature. The primary outcomes were disability-adjusted life-years, cost, and net monetary benefit. Costs were reported in both Chinese renminbi (RMB) and US dollars (USD) at 2019 values. RESULTS: The first wave of COVID-19 in China resulted in 38 348 disability adjusted life-years lost (95% CI 19 417-64 130) and 2639 billion RMB losses (95% CI 1347-4688). The rapid implementation strategy dominated all other delayed strategies. This conclusion was robust to all scenarios tested. At a willingness-to-pay threshold of 70 892 RMB (the national annual GDP per capita) per disability-adjusted life-year saved, the probability for the rapid implementation to be the optimal strategy was 96%. CONCLUSIONS: Early implementation of MRPs in response to COVID-19 reduced both the health burden and societal cost and thus should be used for future waves of COVID-19.