Travel behavior adjustment based epidemic spreading model and prediction for COVID-19

Owing to the continuous variant of the COVID-19 virus, the present epidemic may persist for a long time, and each breakout displays strongly region/time-dependent characteristics. Predicting each specific burst is the basic task for the corresponding strategies. However, the refinement of prevention and control measures usually means the limitation of the existing records of the evolution of the spread, which leads to a special difficulty in making predictions. Taking into account the interdependence of people' s travel behaviors and the epidemic spreading, we propose a modified logistic model to mimic the COVID-19 epidemic spreading, in order to predict the evolutionary behaviors for a specific bursting in a megacity with limited epidemic related records. It continuously reproduced the COVID-19 infected records in Shanghai, China in the period from March 1 to June 28, 2022. From December 7, 2022 when Mainland China adopted new detailed prevention and control measures, the COVID-19 epidemic broke out nationwide, and the infected people themselves took "ibuprofen” widely to relieve the symptoms of fever. A reasonable assumption is that the total number of searches for the word "ibuprofen” is a good representation of the number of infected people. By using the number of searching for the word "ibuprofen” provided on Baidu, a famous searching platform in Mainland China, we estimate the parameters in the modified logistic model and predict subsequently the epidemic spreading behavior in Shanghai, China starting from December 1, 2022. This situation lasted for 72 days. The number of the infected people increased exponentially in the period from the beginning to the 24th day, reached a summit on the 31st day, and decreased exponentially in the period from the 38th day to the end. Within the two weeks centered at the summit, the increasing and decreasing speeds are both significantly small, but the increased number of infected people each day was significantly large. The characteristic for this prediction matches very well with that for the number of metro passengers in Shanghai. It is suggested that the relevant departments should establish a monitoring system composed of some communities, hospitals, etc. according to the sampling principle in statistics to provide reliable prediction records for researchers. © 2023 Chinese Physical Society.

[Expressions of MPV, P-LCR and NLR in patients with novel coronavirus disease 2019].

To provide new ideas for clinical diagnosis and treatment of coronavirus disease 2019 (COVID-19), this study explore the expression level and prognostic value of platelet parameters in mild, moderate and severe COVID-19. This is a retrospective analysis. From January to May 2020, a total of 69 patients who were diagnosed with COVID-19 in the Third Central Hospital and the Jinnan Hospital (both situated in Tianjin) were enrolled in the disease group. According to the severity, these patients were divided into mild group (15 cases), moderate group (46 cases), and severe group (8 cases). In the same period, 70 non-infected patients were enrolled in control group. The level of white blood cell count (WBC), absolute neutrophil count (NEU#), absolute lymphocyte count (LY#), neutrophil-lymphocyte ratio (NLR), red blood cell count (RBC), hemoglobin (Hb), platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), and platelet-large contrast ratio (P-LCR) before and after treatment were analyzed. Binary logistic regression analysis is used to establish a mathematical model of the relationship between these indexes and the outcome of severe COVID-19 patients. The receiver operating characteristic(ROC) curve is used to further explore the prognosis value of MPV, P-LCR, NLR separately and jointly in COVID-19 patients. Compare to the control group, WBC and NE# increase (Z=-5.63, P<0.01;Z=-9.19,P<0.01) and LY# decrease (Z=-9.34, P<0.01) in the severe group; NLR increase with the aggravation of the disease, there is significant difference between groups (Z=17.61, P<0.01); PLT, PDW, MPV and P-LCR decrease with the aggravation of the disease, there is significant difference between groups (Z=9.47, P<0.01; Z=11.41, P<0.01; Z =16.76, P<0.01; Z=13.97, P<0.01). Binary logistic regression analysis shows MPV, P-LCR and NLR have predictive value for severe COVID-19 patients. There is a negative correlation between MPV, P-LCR and severe COVID-19 patients (OR=1.004, P=0.034; OR=1.097, P=0.046). There is a positive correlation between NLR and severe COVID-19 patients (OR=1.052, P=0.016). MPV and P-LCR of patients with good prognosis after treatment were significantly higher than those before treatment (Z=-6.47, P<0.01; Z=-5.36, P<0.01). NLR was significantly lower than that before treatment (Z=-8.13, P<0.01). MPV and P-LCR in poor prognosis group were significantly lower than those before treatment (Z=-9.46, P<0.01; Z=-6.81, P<0.01). NLR was significantly higher than that before treatment (Z=-3.24, P<0.01). There were significant differences between good and poor prognosis groups before and after treatment in MPV, P-LCR and NLR (P<0.01). Combination of these three indexes, ROC shows the AUC is 0.931, the sensitivity is 91.5%, the specificity is 94.1%, the positive predictive value is 88.9%, and the negative predictive value is 87.4%, which is better than any of these indexes separately. Changes in these parameters are closely related to clinical stage of COVID-19 patients. MPV, P-LCR and NLR are of great value in the prediction and prognosis of severe COVID-19 patients.