1.1 prediction and analysis of COVID-19 epidemic spread based on SIR model
The model is established by differential equations. First Δ t \Delta t Δ Number of new patients in t:
The first item describes the number of uninfected persons transmitted from infected persons to uninfected persons at time t , which is interpreted as the number of uninfected persons contacted by the total number of infected persons at the daily contact rate, and becomes the number of uninfected persons transmitted from infected persons to uninfected persons at time t (therefore, it should be multiplied by s (T) ; The second item describes the infected person cured at the time of T; The third item describes the infected person who died at the time of T.
Remove n from the above expression, divided by Δ t and taking the limit, the following limit equation can be obtained:
It can be used to analyze the change law of each proportional variable with time
Of course, the biggest highlight of this article is that the function of y about s is written according to the limit equation, the derivative of the function and the standing point are determined, and the following laws are determined.
2.Modeling ideas
In our problem, we are facing a non closed dynamic problem, that is, the population of a region is not fixed, and there are inflow and outflow. The specific form is shown in the following figure:
The idea now is that these two quantities are not used as modeling variables, but rather to control the initial values of S and e at each time. Both the input population and the output population include the possibility of S and E, but their proportions are affected by: the degree of control ( whether the 48 hour nucleic acid certificate is required)
Radiological findings from 81 patients with COVID-19 pneumonia inWuhan, China: a descriptive study”
81 patients were admitted from December 20, 2019 to January 23, 2020. There were 42 males and 39 females with an average age of 49.5 years.
CT showed that 81 cases of pulmonary lesions involved an average of 10.5 lung segments, including 2.8 lung segments in subclinical stage, 11.1 lung segments in 1 week, 13.0 lung segments in 1-2 weeks, and 12.1 lung segments in 2-3 weeks.
The subclinical stage (n = 15) mainly showed unilateral and multicentric ground glass shadow (93%).
Within one week after the onset of symptoms (n = 21), the lesions were mostly bilateral and diffuse ground glass shadows (81%).
The symptoms appeared for 1-2 weeks (n = 30), the ground glass shadow continued to decrease (57%), and most of them were consolidation and mixed lesions (40%).
The symptoms appeared for 2-3 weeks (n = 15), and the ground glass shadow was significantly reduced (33%), mainly including consolidation and mixed lesions (53%).
Chest CT of patients with COVID-19 showed pulmonary infiltrative lesions, even in asymptomatic patients.
Within 1-3 weeks after the onset, the lesion rapidly progressed from unilateral and focal lesions to diffuse ground glass shadows in both lungs, and consolidation in the later stage.
The results showed that the virus load in throat swabs and sputum samples reached a peak about 5-6 days after the onset of symptoms, from 104 copies per ml to 107 copies.
This change pattern of viral load is different from that of SARS, which usually reaches its peak around 10 days after the onset of the disease.
The viral load of sputum samples is usually higher than that of laryngeal swabs.
Viral RNA was not detected in urine or stool samples of two patients.
Subsequently, the study group analyzed the respiratory tract samples (1 nasal swab, 67 pharyngeal swabs and 42 sputum) of 80 patients with different infection stages.
The viral load of these samples ranged from 641 copies per ml to 1.34 × 1011 copies, with a median of 7.99 for pharyngeal swab samples × 104, the median of sputum samples was 7.52 × 105。
The only nasal swab was collected 3 days after the onset of the disease, and the viral load was 1.69/ml × 105 copies.
Overall, the viral load in the early stage of onset was high (> 1 × 106 copies).
However, one deceased patient had the highest viral load in sputum samples on day 8 after onset, at 1.34 × 1011 copies.
It is worth noting that two people received positive tests due to their contact history. The results showed that the virus was positive on the day before the onset of the disease, indicating that the infected individuals had been infectious before the onset of symptoms.
Among the 30 pairs of throat swabs and sputum samples available, there was a significant correlation between the viral load of the two samples on days 1-3, 4-7 and 7-14.
Of the 17 confirmed cases of novel coronavirus infection (0-13 days after onset), 9 patients had positive stool tests.
The viral load in feces ranged from 550 copies per ml to 1.21 × 105 copies, lower than the respiratory tract samples, and precautions should be taken when handling fecal samples.
Why should re sampling be conducted for recheck if the initial screening is positive?
The covid-19 nucleic acid test must go through two processes: the first is the first test (primary screening) of the testing institution. If the result is positive, the person will be considered as the primary screening positive person. Step 2: all positive samples of primary screening need to be rechecked by higher qualified institutions to ensure that each result is accurate.
After resampling and repeated testing, it may be determined that the coronavirus nucleic acid is positive, or the retest result may be negative, “suspicious samples” are excluded. Generally, there are two possibilities for suspicious samples: one is that CT is located in the gray area, and the other is that a single target gene of the virus appears positive due to some unexpected circumstances.
The purpose of initiating emergency response in the event of a first screening positive person is to make full use of the golden 4-hour emergency response time after receiving the report, to prevent and control the whole chain accurately and quickly, and to control the epidemic in the bud as much as possible.
Since novel coronavirus is transmitted through the respiratory tract, it has different clinical significance to collect samples from different parts. In addition to the well-known nasal swabs and pharyngeal swabs, sputum, blood and anal swabs can also be collected:
Nasal swabs and pharyngeal swabs – upper respiratory tract specimens
Sputum – lower respiratory tract specimen
Anal swab – alimentary tract specimen
Blood – antibody test specimen
The viral load of upper respiratory tract specimens (nasopharyngeal swabs and oropharyngeal swabs) is high in the first 1-3 days of clinical symptoms, and begins to slowly decrease after a week. The viral load of lower respiratory tract specimens (sputum) usually reaches a peak within 2 weeks after the onset of the disease, and the viral load is higher than that of upper respiratory tract specimens. The viral load of stool samples usually reaches a peak in 2-3 weeks after the onset of the disease, and the overall viral load is lower than that of sputum samples, The antibody in the serum can be detected after one week, and it can also determine whether there has been infection in the past.
Therefore, in the recent cases, it is mentioned that the patient has infection symptoms, but the respiratory tract specimen is negative for coronavirus nucleic acid, multiple collection can be carried out at multiple points and locations.
For example, the New England Journal of Medicine recently published an article describing the relationship between the viral load of nasal swabs and pharyngeal swabs and the disease progression, revealing the dynamic changes of the viral load of novel coronavirus after the onset of symptoms. In the early stage of COVID-19, the viral load was high and then gradually decreased.
Why is there an acid positive condition in the antigen negative nucleus?
twenty-three thousand one hundred and fifty-one trillion and six hundred and fifty-two billion three hundred and forty-six million five hundred and twenty-three thousand four hundred and thirty-six
A review on lancet in February 2022 indicated that antigen detection and RNA detection occur within 0-7 days of infection. The sensitivity of antigen detection is far lower than that of RNA detection. Antigen detection can only detect 105-106 copies, while RNA detection can reach 102-103 copies. Antibody detection usually occurs 7-14 days after infection and includes IgG and IgM.
It can be seen that when the viral load is small, the antigen cannot be detected, but it can be detected by high-sensitivity RNA detection. This is the reason why the antigen is positive. It also explains why the CDC recommends that antigen and RNA detection complement each other.
How long will there be a positive to negative change?
seventy-seven thousand four hundred and fifty-one trillion and six hundred and fifty-two billion three hundred and forty-six million five hundred and twenty-three thousand seven hundred and twenty-one ertain, but it will turn negative within one week.
The influencing factors of negative conversion are complex, including the autoimmune system, whether there are basic diseases, and the vaccine situation. Among them, the immune system is like the bodyguard of the human body. When the immunity is strong, the virus is weak, and it may recover quickly. Maybe after a sleep, it will turn positive to negative; When the immunity is weak, the virus is strong and needs a period of time to recover.
It is precisely because of the complex tug of war between the virus and the immune system that regular intensive sampling and detection are needed to monitor the progress of the disease in real time.
Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19
Objective: to determine the prognostic value of quantification of well ventilated lungs obtained on baseline chest CT in patients with covid-19 pneumonia。
Methods: open source software (% s-wall and absolute volume, vol-wall) and CT were used for visual quantification (% v-wall) and quantitative analysis (% s-wall and absolute volume, vol-wall) of well ventilated lungs. Clinical parameters included demographics, comorbidity, symptoms and duration of symptoms, oxygen saturation, and laboratory values. Logistic regression analysis was used to evaluate the relationship between clinical parameters, CT indicators and patient prognosis (ICU admission / death and no ICU admission / death). The model performance is determined by calculating the area under the receiver operating characteristic curve (AUC).
Results: 236 patients (59 / 123, 25%; median age, 68 years) were included in this study. Compared with the clinical model containing only clinical parameters (AUC, 0.83), all three quantitative models showed higher diagnostic performance (AUC of 0.86 for all models).
Conclusion: in patients with confirmed covid-19 pneumonia, visualization or software quantification of CT lung abnormalities is a predictor of ICU admission or death
Background:
CT quantitative display of well ventilated lungs is helpful to evaluate the filling of alveoli during ventilation or predict the prognosis of patients with acute respiratory distress syndrome
Method:
Open source 3D slicer software (version 4.10.2, https://www.slicer.org )。 B40f kernel is used to realize automatic segmentation and analysis of lung parenchymal histogram. When the lung segmentation effect is not ideal, the user uses manual tools to correct the lung contour
Filtering of data:
Patients were divided into two groups:Patients admitted to ICU or died (ICU / death) and patients discharged without admission to ICU. The time between CT and ICU admission or death was recorded
result:
The results of this study suggest that the proportion of well ventilated lungs assessed by chest CT obtained in the emergency department is associated with a better prognosis in patients with covid-19 pneumonia, but not with other clinical parameters
Discussion:
Obesity has been described as a common comorbidity in hospitalized patients with H1N1 influenza infection. Previous observations suggest that covid-19 may have a more severe course in obese patients. Therefore, CT evaluation of adipose tissue may be an objective marker of obesity and has prognostic significance.
