No abstract

This paper takes confirmed cases of COVID-19 from January 20 to March 18, 2020 as the sample set to establish the susceptible-exposed-infected-recovered (SEIR) model. By evaluating effects of different non-pharmaceutical interventions (NPIs), the research expects to provide references to other countries for formulating corresponding policies. This article divides all non-pharmaceutical interventions into three types according to their different roles. The results show that type-A and type-B non-pharmaceutical interventions both can delay the timing of large-scale infections of the susceptible population, timing of the number of exposed individuals to peak, and timing of peaking of the number of infected cases, as well as decrease the peak number of exposed cases. Moreover, type-B non-pharmaceutical interventions have more significant effects on susceptible and exposed populations. Type-C non-pharmaceutical interventions for improving the recovery rate of patients are able to effectively reduce the peak number of patients, greatly decrease the slope of the curve for the number of infected cases, substantially improve the recovery rate, and lower the mortality rate; however, these non-pharmaceutical interventions do not greatly delay the timing of the number of infected cases to peak. And based on the above analysis, we proposed some suggestions.

Objective. The study aimed to explore the efficacy of pulmonary surfactant (PS) combined with Mucosolvan in the diagnosis of meconium aspiration syndrome (MAS) in newborns through ultrasonic images of lung based on machine learning. Methods. 138 cases of infants with MAS were selected as the research subjects and randomly divided into PS group (n = 46), Mucosolvan group (n = 46), and combination group (n = 46). Then, ultrasonic images based on machine learning algorithm were used for examination. On the basis of conventional treatment, the PS group accepted intratracheal PS drip treatment with 100 mg/kg. For the Mucosolvan group, 7.5 mg/kg of Mucosolvan was added with 50 g/L glucose, which was diluted to 3 mL. Then, the mixture was injected intravenously with a micropump for more than 5 min. The combination group received combined treatment of PS and Mucosolvan. If there was no relief or the symptoms aggravated after 12 h of PS treatment, the patient should be treated again. 7.5 mg/kg/d of Mucosolvan was given for 7 days. Mechanical ventilation time, hospitalization time, oxygenation index (OI) before treatment, at 3 d and at 7 d after treatment, and arterial/alveolar oxygen ratio (a/APO2) of the three groups were detected and compared. Besides, in-hospital mortality and complication rate of the three groups were statistically compared. Results. Ultrasonic image edge detection based on machine learning algorithm was more condensed and better than Sobel operator. Compared with the PS group and the Mucosolvan group, treatment efficiency, OI at 3 d and at 7 d after treatment, and a/APO2 of combination group were increased. Mechanical ventilation time and hospitalization time of the combination group were shortened, and mortality rate of the combination group was reduced ( P  < 0.05). Compared with the situation before treatment, OI at 3 d and at 7 d after treatment and a/APO2 of the combination group were increased, and OI at 7 d after treatment and a/APO2 of the PS group and the Mucosolvan group were increased ( P  < 0.05). Curative effect, mechanical ventilation time, hospitalization time, OI before and after treatment, a/APO2, and mortality rate during hospitalization of the PS group and the Mucosolvan group had no significant difference ( P  > 0.05). There was no significant difference in the complications rates in the three groups ( P  > 0.05). Conclusion. Pulmonary ultrasound based on machine learning algorithm can be used in the diagnosis of MAS in neonates. PS combined with Mucosolvan is feasible and safe in treating neonatal MAS and effectively improves the pulmonary oxygenation function. Therefore, it is worthy of clinical application.

The spread of the coronavirus disease 2019 (COVID-19) had resulted in 16 million infected individuals and 640000 deaths across the world as of July 27, 2020. Unfortunately, there is still no sign that the epidemic spread is slowing down. China, as the first country suffering from the widespread outbreak of the epidemic, has effectively contained the spread of the epidemic since March, 2020. Therefore, confirmed cases of COVID-19 from January 20 to March 18, 2020 were taken as the sample set to establish the susceptible-exposed-infected-recovered (SEIR) model. The model was used to analyze changes in the numbers of individuals becoming infected, exposed (latently infected), susceptible, and recovered in the experimental groups taking different non-pharmaceutical interventions (NPIs) and in the control group not taking any NPIs, so as to evaluate effects of different NPIs. By doing so, the research expects to provide references to other countries for formulating corresponding policies. The results show that type-A NPIs for reducing daily contacts with infected and exposed cases and type-B NPIs for decreasing the probability of post-exposure infections both can delay the timing of large-scale infections of the susceptible population, timing of the number of exposed individuals to peak, and timing of peaking of the number of infected cases, as well as decrease the peak number of exposed cases. Moreover, type-B NPIs have more significant effects on susceptible and exposed populations. Type-C NPIs for improving the recovery rate of patients are able to effectively reduce the peak number of patients, greatly decrease the slope of the curve for the number of infected cases, substantially improve the recovery rate, and lower the mortality rate; however, these NPIs do not greatly delay the timing of the number of infected cases to peak. In addition to these, considering effects of different NPIs on the susceptible and exposed populations and in delaying the timing for the number of infected cases to peak, it is found that the government’s organization of medical supply related companies to resume production exerts the best effect. As for reducing the epidemic number of patients in the core epidemic area (CEA, Hubei Province), delivery and putting-into-operation of Leishenshan hospital shows the best effect, followed by dispatching of medical staff to support Wuhan, delivery and putting-into-operation of Huoshenshan hospital, and construction of mobile cabin hospitals.