Herd immunity of Covid 19 in dynamic environments with vaccination: a model based study (preprint)

BackgroundThe first wave of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Canada is entering the last stage, while the development of vaccine is still ongoing. A thorough analysis on the potential effect of restoring to the normal life was needed.MethodsWe used an infectious disease model which optimized for individual immunity to investigate the potential impact of the vaccine on the number of cases, ℛ𝑡, and the duration of the epidemic. We modeled the severity of the cases with three intervention measures and the effect of herd immunity. The combined intervention strategies with the vaccination, speed of vaccination, and the proportion of population pre-vaccinated before reopening were modeled to give an overview of the effect of the vaccination. For each simulation, we set the observation range to be from Feb, 2020 to Oct. 2021, and modeled the number of cases after the first wave, the change of reproduction number (ℛ𝑡), and the proportion of immunized population under the effect of waning immunity.FindingsWe found the proportion of immunized population to reach herd immunity in a dynamic environment to be between 1−1ℛ0and 1−1ℛ02; for Covid-19, the threshold proportion is 64·16%, the final proportion of infections could be up to 87·15% when basic reproduction number (ℛ0) is 2·79. The average number of cases predicted in Canada after the first wave was 285590, 90260, 163057, and 60082 with no intervention, social distancing, quarantining severe cases, and combined strategies; 122261, 89903, 49276, 39856, and 10983 cases with 0·1%, 0·2%, 0·3%, 0·4%, and 1·0% of the population vaccinated per day; 117475, 93502, 91634, 79418, and 8713 cases with 10%, 20%, 30%, 40%, and 50% of population immunized before reopening. Assuming the half-life of the effectivity of the antibody is 48 weeks for symptomatic cases, 24 weeks for asymptomatic cases.Interpretation Neither of these strategies cannot prevent the second wave solely nor together. However, the third wave can be prevented with both social distancing and quarantining severe cases in practice. The speed and timing of vaccination has a direct impact on the reduction of the final number of cases. Unexpectedly, the proportion of population immunized before reopening did not lead to a huge shift of the number of cases after the first wave when the immunized proportion is lower than the critical proportion (49·6%) when social distancing is in practice.

A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients (preprint)

Background: Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment. Methods: : From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n=141; testing: n=62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts. Results: : The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p<0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively. Conclusion: The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.

A fatality data based on an optimized SEIR Model for Epidemic: A study about the testing and quarantining (preprint)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused by a newly discovered coronavirus, has been announced as a global pandemic by WHO on March 12, 2020. We aimed to provide a more accurate, object-oriented SEIR model for Epidemic as well as a thorough analysis for different intervention strategies and critical parameters. Based on the object-oriented mindset in computer science, we assigned properties to every independent patient, which includes age, likelihood of catching the virus, independent mortality rate, social activeness, awareness of the danger of the virus. We also considered macro level parameters including testing rate, positive rate. Based on the fatality data obtained, we estimated the occurrence time of the first case in China (excluding Hubei), Russia, Germany, and the United States using the model. Eight groups of virus-transmission simulation with different testing rate and quarantine ratio were conducted. The mortality data was found to be an effective and crucial parameter while the number cases can be decisive reflecting the severity of the pandemic.

CT features and artificial intelligence quantitative analysis of recovered COVID-19 patients with negative RT-PCR and clinical symptoms (preprint)

Objectives: To evaluate imaging features and performed quantitative analysis for mild novel coronavirus pneumonia (COVID-19) cases ready for discharge.Methods: CT images of 125 patients (16-67 years, 63 males) recovering from COVID-19 were examined. We defined the double-negative period (DNp) as the period between the sampling days of two consecutive negative RT-PCR and three days thereafter. Lesion demonstrations and distributions on CT in DNp (CTDN) were evaluated by radiologists and artificial intelligence (AI) software. Major lesion transformations and the involvement range for patients with follow-up CT were analyzed.Results: Twenty (16.0%) patients exhibited normal CTDN; abnormal CTDN for 105 indicated ground-glass opacity (GGO) (99/125, 79.2%) and fibrosis (56/125, 44.8%) as the most frequent CT findings. Bilateral-lung involvement with mixed or random distribution was most common for GGO on CTDN. Fibrous lesions often affected both lungs, tending to distribute on the subpleura. Follow-up CT showed lesion improvement manifesting as GGO thinning (40/40, 100%), fibrosis reduction (17/26, 65.4%), and consolidation fading (9/11, 81.8%), with or without range reduction. AI analysis showed the highest proportions for right lower lobe involvement (volume, 12.01±35.87cm3; percentage; 1.45±4.58%) and CT-value ranging –570 to –470 HU (volume, 2.93±7.04cm3; percentage, 5.28±6.47%). Among cases with follow-up CT, most of lung lobes and CT-value ranges displayed a significant reduction after DNp.Conclusions: The main CT imaging manifestations were GGO and fibrosis in DNp, which weakened with or without volume reduction. AI analysis results were consistent with imaging features and changes, possibly serving as an objective indicator for disease monitoring and discharge.