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1.
Sci Rep ; 11(1): 23225, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1545646

ABSTRACT

We have further extended our compartmental model describing the spread of the infection in Italy. As in our previous work, the model assumes that the time evolution of the observable quantities (number of people still positive to the infection, hospitalized and fatalities cases, healed people, and total number of people that has contracted the infection) depends on average parameters, namely people diffusion coefficient, infection cross-section, and population density. The model provides information on the tight relationship between the variation of the reported infection cases and a well-defined observable physical quantity: the average number of people that lie within the daily displacement area of any single person. With respect to our previous paper, we have extended the analyses to several regions in Italy, characterized by different levels of restrictions and we have correlated them to the diffusion coefficient. Furthermore, the model now includes self-consistent evaluation of the reproduction index, effect of immunization due to vaccination, and potential impact of virus variants on the dynamical evolution of the outbreak. The model fits the epidemic data in Italy, and allows us to strictly relate the time evolution of the number of hospitalized cases and fatalities to the change of people mobility, vaccination rate, and appearance of an initial concentration of people positives for new variants of the virus.


Subject(s)
COVID-19/epidemiology , Computer Simulation , Evolution, Molecular , Immobilization/statistics & numerical data , SARS-CoV-2/genetics , Vaccination/statistics & numerical data , Basic Reproduction Number , COVID-19/transmission , COVID-19/virology , Epidemiological Models , Humans , Italy/epidemiology , Population Density , SARS-CoV-2/classification
2.
Technol Cancer Res Treat ; 19: 1533033820974021, 2020.
Article in English | MEDLINE | ID: covidwho-983619

ABSTRACT

PURPOSE: With the widespread prevalence of Corona Virus Disease 2019 (COVID-19), cancer patients are suggested to wear a surgical mask during radiation treatment. In this study, cone beam CT (CBCT) was used to investigate the effect of surgical mask on setup errors in head and neck radiotherapy. METHODS: A total of 91 patients with head and neck tumors were selected. CBCT was performed to localize target volume after patient set up. The images obtained by CBCT before treatment were automatically registered with CT images and manually fine-tuned. The setup errors of patients in 6 directions of Vrt, Lng, Lat, Pitch, Roll and Rotation were recorded. The patients were divided into groups according to whether they wore the surgical mask, the type of immobilization mask used and the location of the isocenter. The setup errors of patients were calculated. A t-test was performed to detect whether it was statistically significant. RESULTS: In the 4 groups, the standard deviation in the directions of Lng and Pitch of the with surgical mask group were all higher than that in the without surgical mask group. In the head-neck-shoulder mask group, the mean in the Lng direction of the with surgical mask group was larger than that of the without surgical mask group. In the lateral isocenter group, the mean in the Lng and Pitch directions of the with surgical mask group were larger than that of the without surgical mask group. The t-test results showed that there was significant difference in the setup error between the 2 groups (p = 0.043 and p = 0.013, respectively) only in the Lng and Pitch directions of the head-neck-shoulder mask group. In addition, the setup error of 6 patients with immobilization open masks exhibited no distinguished difference from that of the patients with regular immobilization masks. CONCLUSION: In the head and neck radiotherapy patients, the setup error was affected by wearing surgical mask. It is recommended that the immobilization open mask should be used when the patient cannot finish the whole treatment with a surgical mask.


Subject(s)
COVID-19/prevention & control , Head and Neck Neoplasms/radiotherapy , Masks , Radiotherapy Setup Errors/statistics & numerical data , Adolescent , Adult , Aged , COVID-19/epidemiology , Case-Control Studies , Cone-Beam Computed Tomography/methods , Female , Head and Neck Neoplasms/epidemiology , Humans , Immobilization/instrumentation , Immobilization/methods , Immobilization/statistics & numerical data , Male , Masks/adverse effects , Masks/statistics & numerical data , Middle Aged , Pandemics , Radiation Oncology/methods , Radiation Oncology/standards , Radiotherapy Planning, Computer-Assisted/adverse effects , Radiotherapy Planning, Computer-Assisted/methods , Radiotherapy Planning, Computer-Assisted/statistics & numerical data , Radiotherapy, Image-Guided/adverse effects , Radiotherapy, Image-Guided/methods , Radiotherapy, Image-Guided/statistics & numerical data , Radiotherapy, Intensity-Modulated/methods , SARS-CoV-2/physiology , Shoulder , Young Adult
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