Evaluating the impact of international airline suspensions on the early global spread of COVID-19

Adiga, Aniruddha; Venkatramanan, Srinivasan; Schlitt, James; Peddireddy, Akhil Sai; Dickerman, Allan; Bura, Andrei C.; Warren, Andrew S.; Klahn, Brian; Mao, Chunhong; Xie, Dawen; Machi, Dustin; Raymond, Erin; Meng, F.; Barrow, Golda H.; Mortveit, Henning S.; Chen, Jiangzhuo; Walke, Jim; Goldstein, Joshua; Wilson, Mandy; Orr, Mark; Porebski, P.J.; Telionis, Pyrros A.; Beckman, Richard J.; Hoops, Stefan; Eubank, Stephen; Baek, Young Yun; Lewis, Bryan; Marathe, Madhav V.; Barrett, Chris

doi:10.1101/2020.02.20.20025882

Cited by 44 publications

(37 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We build on our modeling and simulation framework for epidemic spread [3][4][5][6][7][8][9] using an individual level synthetic social contact network 5,10 -which represents each individual in the population along with their demographic attributes (e.g., age, gender, income), and their social interactions. The main steps in the first-principles based construction of synthetic populations and social contact networks are: (i) construct a synthetic population by using US Census and other commercial databases; (ii) assign daily activities to individuals within each household using activity and time-use surveys (American Time Use Survey data and National Household Travel Survey Data); (iii) assign a geo-location to each activity of each person based on data from Dun and BradStreet, land-use, Open Street Maps etc.…”

Section: Methodsmentioning

confidence: 99%

“…This model is age stratified for the following categories i.e. preschool (0-4 years), students (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) adults , older adults (50-64) and seniors (65+) and calibrated for each of the age groups separately. Details on the transition probabilities between health states for each age group and the length of the stay in each health state are shown…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Medical Costs of Keeping the US Economy Open During COVID-19

Chen

Vullikanti

Hoops

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

We use an individual based model and national level epidemic simulations to estimate the medical costs of keeping the US economy open during COVID-19 pandemic under different counterfactual scenarios. We model an unmitigated scenario and 12 mitigation scenarios which differ in compliance behavior to social distancing strategies and to the duration of the stay-home order. Under each scenario we estimate the number of people who are likely to get infected and require medical attention, hospitalization, and ventilators. Given the per capita medical cost for each of these health states, we compute the total medical costs for each scenario and show the tradeoffs between deaths, costs, infections, compliance and the duration of stay-home order. We also consider the hospital bed capacity of each Hospital Referral Region (HRR) in the US to estimate the deficit in beds each HRR will likely encounter given the demand for hospital beds. We consider a case where HRRs share hospital beds among the neighboring HRRs during a surge in demand beyond the available beds and the impact it has in controlling additional deaths.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Medical Costs of Keeping the US Economy Open During COVID-19

Chen

Vullikanti

Hoops

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…the arrival time (T m ) and the infected cases (I m ) in an arbitrary geographical area m. Increasing evidence shows that human mobility determines arrival times 8,15,35 and infected cases when there is only one OL. However, these approaches are not suitable for the presence of multiple OLs because it is unclear how each OL contributes to the arrival time and infected cases in a geographical area.…”

Section: Rcmentioning

confidence: 99%

“…On the other hand, the OL's aggregate mobility outflow has also been a vital predictor for the cumulative number of infections in the destination location 33 , validated by the Wuhan's outflow to each prefecture in mainland China. Despite advances of both approaches and their follow-up methods 8,34,35 , they are more suitable for the early stage of the pandemic of COVID-19 than the late stage when multiple OLs arise, increasing the level of complexity that promotes the needs of new mathematical tools.…”

mentioning

confidence: 99%

Country distancing increase reveals the effectiveness of travel restrictions in stopping COVID-19 transmission

Wang

Gao

2020

Preprint

View full text Add to dashboard Cite

Non-pharmaceutical interventions are the current central strategy to stop transmitting the novel coronavirus disease (COVID-19) globally. Despite remarkably successful approaches in predicting the ongoing pandemic's spatiotemporal patterns, we lack an intrinsic understanding of the travel restrictions' efficiency and effectiveness. We fill this gap by examining the countries' closeness based on disease spread using country distancing that is analogical to the effective resistance in series and parallel circuits and captures the propagation backbone tree from the outbreak locations globally. Our method estimates that 53.6\% of travel restrictions as of June 1, 2020, are ineffective. Our analytical results unveil that the optimal and coordinated travel restrictions postpone per geographical area by 22.56 [95\% credible interval (CI), 18.57 to 26.59] days of the disease's arrival time and protect the world by reducing 1,872,295 (95\% CI, 216,029 to 23,606,312) infected cases till June 1, 2020, which are significantly better than the existing travel restrictions achieving 12.87 (95\% CI, 10.59 to 15.17) days of arrival time delay and 861,867 (95\% CI, 238,250 to 3,879,638) infected cases reduction. Our approach offers a practical guide that indicates when and where to implement travel restrictions, tailed to the real-time national context.

show abstract

“…With appropriate data-sharing policies, these data sources can be used to study social distancing, while also ensuring individual privacy through a combination of anonymization, aggregation and noising techniques that provide the needed privacy guarantees. There have been a number of recent studies along these lines, for example, in China using Baidu data, in the US using mobility data, and at a global scale using airline traffic [5,6,7,8,9,10,11,12,13,14,15] The Google COVID-19 Aggregated Mobility Research Dataset (cf. Appendix A, henceforth called interchangeably mobility map or mobility flows (MF)) provides a global, time-varying anonymized mobility map of flows at a resolution of 5km 2 .…”

Section: Introductionmentioning

confidence: 99%

Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics

Adiga

Wang

Sadilek

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

This work quantifies the impact of interventions to curtail mobility and social interactions in order to control the COVID-19 pandemic. We analyze the change in world-wide mobility at multiple spatio-temporal resolutions -- county, state, country -- using an anonymized aggregate mobility map that captures population flows between geographic cells of size 5 km2. We show that human mobility underwent an abrupt and significant change, partly in response to the interventions, resulting in 87% reduction of international travel and up to 75% reduction of domestic travel. Taking two very different countries sampled from the global spectrum, we observe a maximum reduction of 42% in mobility across different states of the United States (US), and a 68% reduction across the states of India between late March and late April. Since then, there has been an uptick in flows, with the US seeing 53% increase and India up to 38% increase with respect to flows seen during the lockdown. As we overlay this global map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell -- often before stay-at-home orders were issued. Further, in order to understand mixing within a region, we propose a new metric to quantify the effect of social distancing on the basis of mobility. We find that population mixing has decreased considerably as the pandemic has progressed and are able to measure this effect across the world. Finally, we carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. To our knowledge, this work is the first to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.

show abstract

Evaluating the impact of international airline suspensions on the early global spread of COVID-19

Cited by 44 publications

References 8 publications

Medical Costs of Keeping the US Economy Open During COVID-19

Medical Costs of Keeping the US Economy Open During COVID-19

Country distancing increase reveals the effectiveness of travel restrictions in stopping COVID-19 transmission

Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics

Contact Info

Product

Resources

About