Association between meteorological factors and COVID-19 transmission in low- and middle-income countries: A time-stratified case-crossover study.

BACKGROUND: Evidence is limited regarding the association between meteorological factors and COVID-19 transmission in low- and middle-income countries (LMICs). OBJECTIVE: To investigate the independent and interactive effects of temperature, relative humidity (RH), and ultraviolet (UV) radiation on the spread of COVID-19 in LMICs. METHODS: We collected daily data on COVID-19 confirmed cases, meteorological factors and non-pharmaceutical interventions (NPIs) in 2143 city- and district-level sites from 6 LMICs during 2020. We applied a time-stratified case-crossover design with distributed lag nonlinear model to evaluate the independent and interactive effects of meteorological factors on COVID-19 transmission after controlling NPIs. We generated an overall estimate through pooling site-specific relative risks (RR) using a multivariate meta-regression model. RESULTS: There was a positive, non-linear, association between temperature and COVID-19 confirmed cases in all study sites, while RH and UV showed negative non-linear associations. RR of the 90th percentile temperature (28.1 °C) was 1.14 [95% confidence interval (CI): 1.02, 1.28] compared with the 50th percentile temperature (24.4 °C). RR of the10th percentile UV was 1.41 (95% CI: 1.29, 1.54). High temperature and high RH were associated with increased risks in temperate climate but decreased risks in tropical climate, while UV exhibited a consistent, negative association across climate zones. Temperature, RH, and UV interacted to affect COVID-19 transmission. Temperature and RH also showed higher risks in low NPIs sites. CONCLUSION: Temperature, RH, and UV appeared to independently and interactively affect the transmission of COVID-19 in LMICs but such associations varied with climate zones. Our results suggest that more attention should be paid to meteorological variation when the transmission of COVID-19 is still rampant in LMICs.

The effect of information-driven resource allocation on the propagation of epidemic with incubation period.

In the pandemic of COVID-19, there are exposed individuals who are infected but lack distinct clinical symptoms. In addition, the diffusion of related information drives aware individuals to spontaneously seek resources for protection. The special spreading characteristic and coevolution of different processes may induce unexpected spreading phenomena. Thus we construct a three-layered network framework to explore how information-driven resource allocation affects SEIS (susceptible-exposed-infected-susceptible) epidemic spreading. The analyses utilizing microscopic Markov chain approach reveal that the epidemic threshold depends on the topology structure of epidemic network and the processes of information diffusion and resource allocation. Conducting extensive Monte Carlo simulations, we find some crucial phenomena in the coevolution of information diffusion, resource allocation and epidemic spreading. Firstly, when E-state (exposed state, without symptoms) individuals are infectious, long incubation period results in more E-state individuals than I-state (infected state, with obvious symptoms) individuals. Besides, when E-state individuals have strong or weak infectious capacity, increasing incubation period has an opposite effect on epidemic propagation. Secondly, the short incubation period induces the first-order phase transition. But enhancing the efficacy of resources would convert the phase transition to a second-order type. Finally, comparing the coevolution in networks with different topologies, we find setting the epidemic layer as scale-free network can inhibit the spreading of the epidemic.

Examining key factors of beginner's continuance intention in blended learning in higher education.

With the outbreak of the COVID-19 pandemic, blended learning became exceptionally widespread, especially in higher education. As a result, many college students became beginners in this learning method. To identify key factors that impact beginners' continuance intention in blended learning, this study surveyed 1845 first-year college students at a university in central China in the falls of 2020 and 2021 who used blended learning for the first time. Structural equation modeling was employed to verify a model that integrates intrinsic motivation and academic self-efficacy in the Expectation-Confirmation Model of Information System Continuance. The results show that performance expectancy, intrinsic motivation, and satisfaction significantly impact beginners' continuance intention in blended learning. Moreover, performance expectancy, intrinsic motivation, and confirmation significantly impact beginners' continuance intention through mediating variable satisfaction. Academic self-efficacy does not directly impact college students' continuance intention but indirectly impacts their continuance intention through intrinsic motivation. Finally, this study provides suggestions for educators to improve beginners' blended learning experience thus promoting their continuance intention in blended learning.

COVID-19 contact tracking by group activity trajectory recovery over camera networks.

Contact tracking plays an important role in the epidemiological investigation of COVID-19, which can effectively reduce the spread of the epidemic. As an excellent alternative method for contact tracking, mobile phone location-based methods are widely used for locating and tracking contacts. However, current inaccurate positioning algorithms that are widely used in contact tracking lead to the inaccurate follow-up of contacts. Aiming to achieve accurate contact tracking for the COVID-19 contact group, we extend the analysis of the GPS data to combine GPS data with video surveillance data and address a novel task named group activity trajectory recovery. Meanwhile, a new dataset called GATR-GPS is constructed to simulate a realistic scenario of COVID-19 contact tracking, and a coordinated optimization algorithm with a spatio-temporal constraint table is further proposed to realize efficient trajectory recovery of pedestrian trajectories. Extensive experiments on the novel collected dataset and commonly used two existing person re-identification datasets are performed, and the results evidently demonstrate that our method achieves competitive results compared to the state-of-the-art methods.

The impact of asymptomatic individuals on the strength of public health interventions to prevent the second outbreak of COVID-19.

The pandemic of coronavirus disease 2019 (COVID-19) has threatened the social and economic structure all around the world. Generally, COVID-19 has three possible transmission routes, including pre-symptomatic, symptomatic and asymptomatic transmission, among which the last one has brought a severe challenge for the containment of the disease. One core scientific question is to understand the influence of asymptomatic individuals and of the strength of control measures on the evolution of the disease, particularly on a second outbreak of the disease. To explore these issues, we proposed a novel compartmental model that takes the infection of asymptomatic individuals into account. We get the relationship between asymptomatic individuals and critical strength of control measures theoretically. Furthermore, we verify the reliability of our model and the accuracy of the theoretical analysis by using the real confirmed cases of COVID-19 contamination. Our results, showing the importance of the asymptomatic population on the control measures, would provide useful theoretical reference to the policymakers and fuel future studies of COVID-19.

Exploring Educational Planning, Teacher Beliefs, and Teacher Practices During the Pandemic: A Study of Science and Technology-Based Universities in China.

With the spread of the COVID-19 pandemic worldwide, university teachers are coping with and adjusting to online teaching platforms. In this concurrent mixed-methods study, 10 science and technology universities as the research sites were first chosen, and educational planning in these sites during the pandemic was examined; then, eight selected teacher participants in these sites were interviewed to report how their beliefs and practices changed during the pandemic echoing the examined educational planning. The results show that educational planning and policies assisted teachers in accommodating the new demands and changes during the pandemic; teachers' beliefs and practices generally echoed the educational planning and policies, with certain tensions still existing. The discussion part of the study is centered around emergency remote teaching and planning, tensions between teacher beliefs and practices, and the shift from emergency remote teaching to regular, sustainable online schooling. The study provides administrators and teacher educators with insights on how emergency remote teaching can be planned and implemented during an unprecedented time.

Urban Hierarchical Open-up Schemes Based on Fine Regional Epidemic Data for the Lockdown in COVID-19

During the COVID-19 outbreaking, China's lock-down measures have played an outstanding role in epidemic prevention;many other countries have followed similar practices. The policy of social alienation and community containment was executed to reduce civic activities, which brings up numerous economic losses. It has become an urgent task for these countries to open-up, while the epidemic has almost under control. However, it still lacks sufficient literature to set appropriate open-up schemes that strike a balance between open-up risk and lock-down cost. Big data collection and analysis, which play an increasingly important role in urban governance, provide a useful tool for solving the problem. This paper explores the influence of open-up granularity on both the open-up risk and the lock-down cost. It proposes an SEIR-CAL model considering the effect of asymptomatic patients based on propagation dynamics, and offered a model to calculate the lock-down cost based on the lock-down population. A simulation experiment is then carried out based on the mass actual data of Wuhan City to explore the influence of open-up granularity. Finally, this paper proposed the evaluation score (ES) to comprehensively measure schemes with different costs and risks. The experiments suggest that when released under the non-epidemic situation, the open-up scheme with the granularity refined to the block has the optimal ES. Results indicated that the fine-grained open-up scheme could significantly reduce the lock-down cost with a relatively low open-up risk increase.

Aerosol transmission of SARS-CoV-2? Evidence, prevention and control.

As public health teams respond to the pandemic of coronavirus disease 2019 (COVID-19), containment and understanding of the modes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is of utmost importance for policy making. During this time, governmental agencies have been instructing the community on handwashing and physical distancing measures. However, there is no agreement on the role of aerosol transmission for SARS-CoV-2. To this end, we aimed to review the evidence of aerosol transmission of SARS-CoV-2. Several studies support that aerosol transmission of SARS-CoV-2 is plausible, and the plausibility score (weight of combined evidence) is 8 out of 9. Precautionary control strategies should consider aerosol transmission for effective mitigation of SARS-CoV-2.