ABSTRACT
Background and Objectives: Based on practical services of the Henan Province Telemedicine Center (HTCC), the purpose of this study is to investigate the design, construction, implementation, and application effect of a specific telemedicine system in response to the coronavirus disease 2019 (COVID-19). Methods: Data on COVID-19 cases from December 31, 2019, through October 17, 2022, were collected from official websites. Data and information of telemedicine services related to COVID-19 in HTCC were collected and analyzed, and relevant graphical representations were plotted. Results: All the 147 COVID-19 designated hospitals in the Henan Province were covered by the specific telemedicine system. The cities near to the Hubei Province in the south of Henan tended to be with more COVID-19 cases, where more COVID-19-related telemedicine services were conducted. For the telemedicine system, function modules, including real-time monitoring, command and dispatch, intractable cases transfer, remote guidance, and data sharing, were designed and realized to deal with COVID-19. Through the system, telemedicine services involved COVID-19 such as epidemic surveillance, emergency rescue, case discussion, diagnosis and treatment, remote ward-round, and distance education were performed. During the period between February 2 and March 3, 2020, 646 COVID-19 patients were served by the telemedicine system, with an improvement rate of 73.2%. Conclusions: Telemedicine can improve the diagnosis and treatment of COVID-19 patients, which play a helpful role in curbing the COVID-19 epidemic. Given the current global COVID-19 pandemic and the potential re-emerge of novel zoonotic pathogens in the future, the use of telemedicine would be imperative to fight against the pandemic.
ABSTRACT
BACKGROUND: The coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have been conducted to investigate the spatio-temporal distribution of COVID-19 on nationwide city-level in China. OBJECTIVE: To analyze and visualize the spatiotemporal distribution characteristics and clustering pattern of COVID-19 cases from 362 cities of 31 provinces, municipalities and autonomous regions in mainland China. METHODS: A spatiotemporal statistical analysis of COVID-19 cases was carried out by collecting the confirmed COVID-19 cases in mainland China from January 10, 2020 to October 5, 2020. Methods including statistical charts, hotspot analysis, spatial autocorrelation, and Poisson space-time scan statistic were conducted. RESULTS: The high incidence stage of China's COVID-19 epidemic was from January 17 to February 9, 2020 with daily increase rate greater than 7.5%. The hot spot analysis suggested that the cities including Wuhan, Huangshi, Ezhou, Xiaogan, Jingzhou, Huanggang, Xianning, and Xiantao, were the hot spots with statistical significance. Spatial autocorrelation analysis indicated a moderately correlated pattern of spatial clustering of COVID-19 cases across China in the early phase, with Moran's I statistic reaching maximum value on January 31, at 0.235 (Z = 12.344, P = 0.001), but the spatial correlation gradually decreased later and showed a discrete trend to a random distribution. Considering both space and time, 19 statistically significant clusters were identified. 63.16% of the clusters occurred from January to February. Larger clusters were located in central and southern China. The most likely cluster (RR = 845.01, P < 0.01) included 6 cities in Hubei province with Wuhan as the centre. Overall, the clusters with larger coverage were in the early stage of the epidemic, while it changed to only gather in a specific city in the later period. The pattern and scope of clusters changed and reduced over time in China. CONCLUSIONS: Spatio-temporal cluster detection plays a vital role in the exploration of epidemic evolution and early warning of disease outbreaks and recurrences. This study can provide scientific reference for the allocation of medical resources and monitoring potential rebound of the COVID-19 epidemic in China.