ABSTRACT
Objective:To analyze the current situation of public health management and public health services in public hospitals at the second level and above in Anhui Province, for references for the government to formulate relevant policies.Methods:A stratified whole-group random sampling method was used to investigate the current status of public health services provided by 55 public hospitals at the second level and above in Anhui Province. The contents of the questionnaire included the establishment of public health management departments, operation and public health services. Descriptive analysis was conducted for all data, and chi-square test was used for comparison between groups.Results:Of the 55 hospitals, only one tertiary hospital had a separate public health department, while the public health management works of the other hospitals were scattered among the hospital infection department, medical affairs department, preventive health department and so on. 32 hospitals putted the public health services into their performance appraisal. Among the public health services provided by hospitals, the management of infectious disease diagnosis and treatment presented the best performance, as 55 hospitals had established relevant management systems, processes, business training, infectious disease reporting and other information systems. Mental health service presented the poorest performance, as only 36 hospitals had established relevant management systems.Conclusions:The public health management and service level of public hospitals in Anhui Province needed to be further improved. The government authorities should strengthen their top-level design and coordinate the public health department setup and performance evaluation mechanism of public hospitals from the institutional level. On the other hand, the public hospitals should constantly optimize their content of public health services, complement the weaknesses, and effectively improve the level of public health services based on respective conditions of the hospitals.
ABSTRACT
This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Strikingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the basic reproduction number. The model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously inform policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.
