Despite a number of successful approaches in predicting the spatiotemporal patterns of the novel coronavirus (COVID-19) pandemic and quantifying the effectiveness of non-pharmaceutical interventions starting from data about the initial outbreak location, we lack an intrinsic understanding as outbreak locations shift and evolve. Here, we fill this gap by developing a country distance approach to capture the pandemic’s propagation backbone tree from a complex airline network with multiple and evolving outbreak locations. We apply this approach, which is analogous to the effective resistance in series and parallel circuits, to examine countries’ closeness regarding disease spreading and evaluate the effectiveness of travel restrictions on delaying infections. In particular, we find that 63.2% of travel restrictions implemented as of 1 June 2020 are ineffective. The remaining percentage postponed the disease arrival time by 18.56 days per geographical area and resulted in a total reduction of 13,186,045 infected cases. Our approach enables us to design optimized and coordinated travel restrictions to extend the delay in arrival time and further reduce more infected cases while preserving air travel.
Evaluating the impact of international airline suspensions on the early global spread of COVID-19
Global airline networks play a key role in the global importation of emerging infectious diseases. Detailed information
on air traffic between international airports has been demonstrated to be useful in retrospectively validating and
prospectively predicting case emergence in other countries. In this paper, we use a well-established metric known as
effective distance on the global air traffic data from IATA to quantify risk of emergence for different countries as a consequence
of direct importation from China, and compare it against arrival times for the first 24 countries. Using this
model trained on official first reports from WHO, we estimate time of arrival (ToA) for all other countries. We then
incorporate data on airline suspensions to recompute the effective distance and assess the effect of such cancellations in
delaying the estimated arrival time for all other countries. Finally we use the infectious disease vulnerability indices to
explain some of the estimated reporting delays.
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- NSF-PAR ID:
- 10213761
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- medRxiv
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
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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.
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BACKGROUND Madagascar is one of the world’s foremost biodiversity hotspots. Its unique assemblage of plants, animals, and fungi—the majority of which evolved on the island and occur nowhere else—is both diverse and threatened. After human arrival, the island’s entire megafauna became extinct, and large portions of the current flora and fauna may be on track for a similar fate. Conditions for the long-term survival of many Malagasy species are not currently met because of multiple anthropogenic threats. ADVANCES We review the extinction risk and threats to biodiversity in Madagascar, using available international assessment data as well as a machine learning analysis to predict the extinction risks and threats to plant species lacking assessments. Our compilation of global International Union for Conservation of Nature (IUCN) Red List assessments shows that overexploitation alongside unsustainable agricultural practices affect 62.1 and 56.8% of vertebrate species, respectively, and each affects nearly 90% of all plant species. Other threats have a relatively minor effect today but are expected to increase in coming decades. Because only one-third (4652) of all Malagasy plant species have been formally assessed, we carried out a neural network analysis to predict the putative status and threats for 5887 unassessed species and to evaluate biases in current assessments. The percentage of plant species currently assessed as under threat is probably representative of actual numbers, except in the case of the ferns and lycophytes, where significantly more species are estimated to be threatened. We find that Madagascar is home to a disproportionately high number of Evolutionarily Distinct and Globally Endangered (EDGE) species. This further highlights the urgency for evidence-based and effective in situ and ex situ conservation. Despite these alarming statistics and trends, we find that 10.4% of Madagascar’s land area is protected and that the network of protected areas (PAs) covers at least part of the range of 97.1% of terrestrial and freshwater vertebrates with known distributions (amphibians, freshwater fishes, reptiles, birds, and mammal species combined) and 67.7% of plant species (for threatened species, the percentages are 97.7% for vertebrates and 79.6% for plants). Complementary to this, ex situ collections hold 18% of vertebrate species and 23% of plant species. Nonetheless, there are still many threatened species that do not occur within PAs and are absent from ex situ collections, including one amphibian, three mammals, and seven reptiles, as well as 559 plants and more yet to be assessed. Based on our updated vegetation map, we find that the current PA network provides good coverage of the major habitats, particularly mangroves, spiny forest, humid forest, and tapia, but subhumid forest and grassland-woodland mosaic have very low areas under protection (5.7 and 1.8% respectively). OUTLOOK Madagascar is among the world’s poorest countries, and its biodiversity is a key resource for the sustainable future and well-being of its citizens. Current threats to Madagascar’s biodiversity are deeply rooted in historical and present social contexts, including widespread inequalities. We therefore propose five opportunities for action to further conservation in a just and equitable way. First, investment in conservation and restoration must be based on evidence and effectiveness and be tailored to meet future challenges through inclusive solutions. Second, expanded biodiversity monitoring, including increased dataset production and availability, is key. Third, improving the effectiveness of existing PAs—for example through community engagement, training, and income opportunities—is more important than creating new ones. Fourth, conservation and restoration should not focus solely on the PA network but should also include the surrounding landscapes and communities. And finally, conservation actions must address the root causes of biodiversity loss, including poverty and food insecurity. In the eyes of much of the world, Madagascar’s biodiversity is a unique global asset that needs saving; in the daily lives of many of the Malagasy people, it is a rapidly diminishing source of the most basic needs for subsistence. Protecting Madagascar’s biodiversity while promoting social development for its people is a matter of the utmost urgency Visual representation of five key opportunities for conserving and restoring Madagascar’s rapidly declining biodiversity identified in this Review. The dashed lines point to representative vegetation types where these recommendations could have tangible effects, but the opportunities are applicable across Madagascar. ILLUSTRATION: INESSA VOETmore » « less
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null (Ed.)Background Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). Objective Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). Methods We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. Results This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Conclusions Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. International Registered Report Identifier (IRRID) DERR1-10.2196/24432more » « less
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From the start, the airline industry has remarkably connected countries all over the world through rapid long-distance transportation, helping people overcome geographic barriers. Consequently, this has ushered in substantial economic growth, both nationally and internationally. The airline industry produces vast amounts of data, capturing a diverse set of information about their operations, including data related to passengers, freight, flights, and much more. Analyzing air travel data can advance the understanding of airline market dynamics, allowing companies to provide customized, efficient, and safe transportation services. Due to big data challenges in such a complex environment, the benefits of drawing insights from the air travel data in the airline industry have not yet been fully explored. This article aims to survey various components and corresponding proposed data analysis methodologies that have been identified as essential to the inner workings of the airline industry. We introduce existing data sources commonly used in the papers surveyed and summarize their availability. Finally, we discuss several potential research directions to better harness airline data in the future. We anticipate this study to be used as a comprehensive reference for both members of the airline industry and academic scholars with an interest in airline research.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1101/2020.02.20.20025882
