ABSTRACT
There is a lack of COVID-19 adjusted case fatality risk (aCFR) estimates and information on states with high aCFR. State-specific aCFRs were estimated, using 13-day lag for fatality. To estimate country-level aCFR, state estimates were meta-analysed. Multiple correspondence analyses (MCA), followed by univariable logistic regression, were conducted to understand the association between aCFR and geodemographic, health and social indicators. Based on health indicators, states likely to report a higher aCFR were identified. Using random- and fixed-effects models, the aCFRs in India were 1.42 (95% CI 1.19 - 1.70) and 2.97 (95% CI 2.94 - 3.00), respectively. The aCFR was grouped with the incidence of diabetes, hypertension, cardiovascular diseases and acute respiratory infections in the first and second dimensions of MCA. The current study demonstrated the value of using meta-analysis to estimate aCFR. To decrease COVID-19 associated fatalities, states estimated to have a high aCFR must take steps to reduce co-morbidities. Article Summary LineMeta-analysis and the COVID-19 adjusted case fatality risks (aCFRs) in India are reported and states likely to report a higher aCFR have been identified.
Subject(s)
COVID-19ABSTRACT
Background: The first case of COVID-19 atypical pneumonia was reported in Wuhan, China on December 1, 2019. Since then, at least 33 other countries have been affected and there is a possibility of a global outbreak. A tremendous amount of effort has been made to understand its transmission dynamics; however, the temporal and spatial transmission heterogeneity and changing epidemiology have been mostly ignored. The epidemic mechanism of COVID-19 remains largely unclear. Methods: Epidemiological data on COVID-19 in China and daily population movement data from Wuhan to other cities were obtained and analyzed. To describe the transmission dynamics of COVID-19 at different spatio-temporal scales, we used a three-stage continuous-time Susceptible-Exposed-Infectious-Recovered (SEIR) meta-population model based on the characteristics and transmission dynamics of each stage: 1) local epidemic from December 1, 2019 to January 9, 2020; 2) long-distance spread due to the Spring Festival travel rush from January 10 to 22, 2020; and 3) intra-provincial transmission from January 23, 2020 when travel restrictions were imposed. Together with the basic reproduction number (R_0) for mathematical modelling, we also considered the variation in infectivity and introduced the controlled reproduction number (R_c) by assuming that exposed individuals to be infectious; we then simulated the future spread of COVID across Wuhan and all the provinces in mainland China. In addition, we built a novel source tracing algorithm to infer the initial exposed number of individuals in Wuhan on January 10, 2020, to estimate the number of infections early during this epidemic. Findings: The spatial patterns of disease spread were heterogeneous. The estimated controlled reproduction number (R_c) in the neighboring provinces of Hubei province were relatively large, and the nationwide reproduction number (except for Hubei) ranged from 0.98 to 2.74 with an average of 1.79 (95% CI 1.77-1.80). Infectivity was significantly greater for exposed than infectious individuals, and exposed individuals were predicted to have become the major source of infection after January 23. For the epidemic process, most provinces reached their epidemic peak before February 10, 2020. It is expected that the maximum number of infections will be approached by the end of March. The final infectious size is estimated to be about 58,000 for Wuhan, 20,800 for the rest of Hubei province, and 17,000 for the other provinces in mainland China. Moreover, the estimated number of the exposed individuals is much greater than the officially reported number of infectious individuals in Wuhan on January 10, 2020. Interpretation: The transmission dynamics of COVID-19 have been changing over time and were heterogeneous across regions. There was a substantial underestimation of the number of exposed individuals in Wuhan early in the epidemic, and the Spring Festival travel rush played an important role in enhancing and accelerating the spread of COVID-19. However, China's unprecedented large-scale travel restrictions quickly reduced R_c. The next challenge for the control of COVID-19 will be the second great population movement brought by removing these travel restrictions.
Subject(s)
COVID-19 , PneumoniaABSTRACT
COVID-19 has become a pandemic. The influence of meteorological factors on the transmission and spread of COVID-19 if of interest. This study sought to examine the associations of daily average temperature (AT) and relative humidity (ARH) with the daily count of COVID-19 cases in 30 Chinese provinces (in Hubei from December 1, 2019 to February 11, 2020 and in other provinces from January 20, 2020 to Februarys 11, 2020). A Generalized Additive Model (GAM) was fitted to quantify the province-specific associations between meteorological variables and the daily cases of COVID-19 during the study periods. In the model, the 14-day exponential moving averages (EMAs) of AT and ARH, and their interaction were included with time trend and health-seeking behavior adjusted. Their spatial distributions were visualized. AT and ARH showed significantly negative associations with COVID-19 with a significant interaction between them (0.04, 95% confidence interval: 0.004-0.07) in Hubei. Every 1{degrees}C increase in the AT led to a decrease in the daily confirmed cases by 36% to 57% when ARH was in the range from 67% to 85.5%. Every 1% increase in ARH led to a decrease in the daily confirmed cases by 11% to 22% when AT was in the range from 5.04{degrees}C to 8.2{degrees}C. However, these associations were not consistent throughout Mainland China.