An official website of the United States government
COVID-19 has seriously threatened people’s health and well-being across the globe since it was first reported in Wuhan, China in late 2019. This study investigates the mechanism of COVID-19 transmission in different periods within and between cities in China to better understand the nature of the outbreak. We use Moran’s I, a measure of spatial autocorrelation, to examine the spatial dependency of COVID-19 and a dynamic spatial autoregressive model to explore the transmission mechanism. We find that the spatial dependency of COVID-19 decreased over time and that the transmission of the disease could be divided into three distinct stages: an eruption stage, a stabilization stage, and a declination stage. The infection rate between cities was close to one-third of the infection rate within cities at the eruption stage, while it reduced to zero at the declination stage. We also find that the infection rates within cities at the eruption stage and declination stage were similar. China’s policies for controlling the spread of the epidemic, specifically with respect to limiting inter-city mobility and implementing intra-city travel restrictions (social isolation), were most effective in reducing the viral transmission of COVID-19. The findings from this study indicate that the elimination of inter-city mobility had the largest impact on controlling disease transmission.
more »
« less
Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries
Since its outbreak in December 2019, the novel coronavirus 2019 (COVID-19) has spread to 191 countries and caused millions of deaths. Many countries have experienced multiple epidemic waves and faced containment pressures from both domestic and international transmission. In this study, we conduct a multiscale geographic analysis of the spread of COVID-19 in a policy-influenced dynamic network to quantify COVID-19 importation risk under different policy scenarios using evidence from China. Our spatial dynamic panel data (SDPD) model explicitly distinguishes the effects of travel flows from the effects of transmissibility within cities, across cities, and across national borders. We find that within-city transmission was the dominant transmission mechanism in China at the beginning of the outbreak and that all domestic transmission mechanisms were muted or significantly weakened before importation posed a threat. We identify effective containment policies by matching the change points of domestic and importation transmissibility parameters to the timing of various interventions. Our simulations suggest that importation risk is limited when domestic transmission is under control, but that cumulative cases would have been almost 13 times higher if domestic transmissibility had resurged to its precontainment level after importation and 32 times higher if domestic transmissibility had remained at its precontainment level since the outbreak. Our findings provide practical insights into infectious disease containment and call for collaborative and coordinated global suppression efforts.
more »
« less
- Award ID(s):
- 2027375
- PAR ID:
- 10277453
- Publisher / Repository:
- Proceedings of the National Academy of Sciences
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 118
- Issue:
- 31
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- Article No. e2100201118
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)The global public health community is grappling with COVID-2019, a respiratory disease outbreak caused by a novel coronavirus originating from Wuhan, China in late December 2019. A number of countries implemented citizenship-based travel restrictions in late January and early February as an initial response to the outbreak, barring entry to foreign nationals who had previously been in China to prevent the importation of the virus. By early March, when the World Health Organization (WHO) formally declared the outbreak to be a global pandemic, these countries were still relying on travel restrictions as a means of infection control. These travel restrictions constituted restrictions or outright bans on the entry of foreign nationals with a particular citizenship-such policies tend to be popular with a general public with limited public health knowledge. However, travel restrictions are ineffective as an infection control measure and may do more harm than good, depriving the public of its right to health. Furthermore, travel restrictions implemented under the guise of public health policy have historically been used to target migrants and racial and ethnic minorities, violating their rights to nondiscrimination and equal treatment. As states rush to balance public health with politics in their response to this global pandemic, they are sidelining human rights rather than protecting themmore » « less
-
Abstract ObjectivesThis study assessed changes in behaviors/attitudes related to the COVID-19. With the understanding that behaviors and vaccine decision-making could contribute to global spread of infectious diseases, this study collected several waves of internet-based surveys from individuals in the United States, mainland China, Taiwan, Malaysia, Indonesia, and India. The aims of this study were to (1) characterize the relationship between the epidemiology of disease and changes over time in risk perceptions, knowledge, and attitudes towards hygienic behaviors; (2) examine if risk perceptions affect acceptance of less-than-ideal vaccines; and (3) contrast adherence to public health recommendations across countries which have had different governmental responses to the outbreak. Data descriptionWe conducted cross-sectional online surveys in six countries from March 2020 to April 2021. By the end of June 2021, there will be six waves of surveys for the United States and China, and four waves for the rest of countries. There are common sets of questions for all countries, however, some questions were adapted to reflect local situations and some questions were designed intentionally for specific countries to capture different COVID-19 mitigation actions. Participants were asked about their adherence towards countermeasures, risk perceptions, and acceptance of a hypothetical vaccine for COVID-19.more » « less
-
null (Ed.)Abstract The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse ( https://dataverse.harvard.edu/dataverse/2019ncov ), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.more » « less
-
Lal, Rajnesh (Ed.)In late 2019, the emergence of COVID-19 in Wuhan, China, led to the implementation of stringent measures forming the zero-COVID policy aimed at eliminating transmission. Zero-COVID policy basically aimed at completely eliminating the transmission of COVID-19. However, the relaxation of this policy in late 2022 reportedly resulted in a rapid surge of COVID-19 cases. The aim of this work is to investigate the factors contributing to this outbreak using a new SEIR-type epidemic model with time-dependent level of immunity. Our model incorporates a time-dependent level of immunity considering vaccine doses administered and time-post-vaccination dependent vaccine efficacy. We find that vaccine efficacy plays a significant role in determining the outbreak size and maximum number of daily infected. Additionally, our model considers under-reporting in daily cases and deaths, revealing their combined effects on the outbreak magnitude. We also introduce a novel Physics Informed Neural Networks (PINNs) approach which is extremely useful in estimating critical parameters and helps in evaluating the predictive capability of our model.more » « less
