RESUMEN
The aim is to explore the development trend of COVID-19 (Corona Virus Disease 2019) and predict the infectivity of 2019-nCoV (2019 Novel Coronavirus), as well as its impact on public health. First, the existing data are analyzed through data pre-processing to extract useful feature factors. Then, the LSTM (Long-Short Term Memory) prediction model in the deep learning algorithm is used to predict the epidemic situation in Hubei Province, outside Hubei nationwide, and the whole country, respectively. Meanwhile, the impact of intervention time changes on the epidemic situation is compared. The results show that the prediction results are almost consistent with the actual values. Specifically, Hubei Province abolishes quarantine restrictions after the Spring Festival holiday, and the first COVID-19 peak is reached in late February, while the second COVID-19 peak has been reached in early March. Finally, the cumulative number of diagnoses reaches 85,000 cases, with an increase of 15,000 cases compared with the nationwide cases outside Hubei under the continuous implementation of prevention and control measures. Under the prediction of the proposed LSTM model, if the nationwide implementation of prevention and control interventions is postponed by 5 days, the epidemic will peak in early March, and the cumulative number of diagnoses will be about 200,000; and if the intervention measures are implemented five days earlier, the epidemic will peak in mid-February, with a cumulative number of diagnoses of approximately 40,000. Meanwhile, the proposed LSTM model predicts the RMSE values of the epidemic situation in Hubei Province, outside Hubei nationwide, and the whole country as 34.63, 75.42, and 50.27, respectively. Under model comparison analysis, the prediction error of the proposed LSTM model is small and has better applicability over similar algorithms. The results show that the LSTM model is effective and has high performance in infectious disease prediction, and the research results can provide scientific and effective references for subsequent related research.
RESUMEN
INTRODUCTION: Vaccination is one of the most effective ways to control the COVID-19 pandemic. However, as the number of people vaccinated against COVID-19 continues to increase, there are more reports on the safety of vaccines. So far, there have been no reported cases of spinal infection associated with COVID-19 vaccination. Recently, we admitted a patient who developed cervical Staphylococcus aureus infection resulting in high paraplegia after receiving the third dose of COVID-19 vaccine when the symptoms of cold did not completely disappear. CASE PRESENTATION: The patient was a 70-year-old man who received the third injection of COVID-19 vaccine when the cold symptoms were not completely gone. On the day after the injection, the patient developed severe neck and shoulder pain, accompanied by numbness and fatigue in the limbs. MRI examination of the cervical spine on day 6 after vaccination showed no obvious signs of infection. The patient had progressive weakness in the extremities. On the ninth day after vaccination, the patient developed paralysis of both lower limbs and significant sensory loss. Cervical abscess and cervical spinal cord injury were considered for cervical CT and MRI examination on the 15th day after vaccination. We used an anterior approach to remove as much of the lesion as possible. Staphylococcus aureus was detected and antibiotic treatment was continued after surgery. The patient's pain symptoms were significantly relieved, which prevented the abscess from further pressing the spinal cord and provided possible conditions for the recovery of neurological function in the later stage. CONCLUSION: This case is the first reported cervical Staphylococcus aureus infection resulting in high paraplegia after receiving the third dose of COVID-19 vaccine with low immunity. This case raises awareness of this rare but potentially life-threatening adverse reaction, and reminds people to hold off when their immune system is weakened.