Résumé
This retrospective, observational study was conducted by collecting data from medical records during COVID 19 pandemic from March 2020 till August 2020. This was compared with the data of 2019 during similar months. The impact of COVID 19 on use of preventive and curative paediatric health care service units like outpatient department, casualty, intensive care and immunization clinic were assessed. Data from 2019 to 2020 were compared using standard parametric and nonparametric tests. There was a significant decline in routine OPD (68%) attendance during the COVID 19 period as compared to pre-COVID period. Paediatric ward admissions and PICU admissions were decreased by 55% and 42% respectively. We also observed a significant 43% decline in the number of children attending immunization clinic in the year 2020. The fear of COVID 19 pandemic and the measures taken to control the pandemic has affected the health seeking behaviour of patients. This evaluation of trends in healthcare use may help in planning the delivery of healthcare service delivery in future.
Résumé
@#The management of emerging infectious diseases has always been given a high priority in public health. Identification of the epidemiological characteristics and transmission patterns of emerging infectious diseases is of great significance to contain the disease transmission and reduce the damages to public health and socioeconomic developments. Currently, infectious disease dynamics models are mainly established based on infectious disease surveillance data to predict the epidemiological patterns and trends of emerging infectious diseases; however, many model-based predictions fail to achieve the expected results due to the presence of multiple uncertain factors during the integrated management of infectious diseases. This review describes the basic principles and variables of common infectious disease dynamics models, including the susceptible-infected-recovered ( SIR ) model, susceptible-infected-removed-susceptible ( SIRS ) model, susceptible-exposed-infected-removed ( SEIR ) model and improved SEIR model, compares the advantages and disadvantages of these models, and summarizes the advances of the infectious disease dynamics models in the prediction of trends in incidence of emerging infectious diseases, so as to provide insights into the effective application of infectious disease dynamics models in the management of infectious diseases.
Résumé
Objective To predict the coronavirus disease 2019 (COVID-19) epidemic situation based on the infectious disease dynamics susceptible-exposed-infected-recovered (SEIR) model, so as to provide guidance for effective control of the epidemic. Methods Python crawler automatic update function was used to collect the epidemic data released by the National Health Commission of China. An improved infectious disease dynamics SEIR model, which can automatically correct the COVID-19 basic reproductive number (R0), was constructed to predict the development trend of COVID-19 epidemic in Hubei Province of China and South Korea. Results The peak of the COVID-19 epidemic in Hubei Province of China predicted by the model would appear on Feb. 21, 2020. The number of confirmed COVID-19 cases would be about 50 000 on Feb. 19 and would fall to below 30 000 on Mar. 4, and the epidemic would end on May 10. According to the actual data released by the National Health Commission of China, the peak number of confirmed COVID-19 patients was 53 371. The model predicted that an epidemic peak in South Korea would be on Mar. 7, and would end at the end of April. Conclusion This improved infectious disease dynamics SEIR model established in the early stage of COVID-19 epidemic has achieved relatively accurate prediction. The timely and effective intervention by relevant government departments has significantly affected the development of the epidemic. The epidemic situation in other countries in East Asia, such as South Korea, is still on the rise in March, suggesting that China needs to be on guard against the risk of imported epidemic.