Evidence map›Paper›PMID 37143866›Full record

ArticlePNAS nexus2023

Global age-structured spatial modeling for emerging infectious diseases like COVID-19.

Yixiong Xiao, Jingbo Zhou, Qu Cheng, Jun Yang, Bin Chen, Tao Zhang, Lei Xu, Bo Xu, Zhehao Ren, Zhaoyang Liu and 15 more

Open access · goldAbstract read
In one paragraph

Article in PNAS nexus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 37% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors at 7 institutions in 2 countries.

Yixiong XiaoBusiness Intelligence Lab, Baidu Research, Beijing 100193, China.
Jingbo ZhouBusiness Intelligence Lab, Baidu Research, Beijing 100193, China.ORCID https://orcid.org/0000-0003-2677-7021
Qu ChengDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID https://orcid.org/0000-0003-3074-9218
Jun YangMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.ORCID https://orcid.org/0000-0003-0824-749X
Bin ChenDivision of Landscape Architecture, The University of Hong Kong, Hong Kong 999007, China.ORCID https://orcid.org/0000-0003-3496-2876
Tao ZhangMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
Lei XuVanke School of Public Health, Tsinghua University, Beijing 100084, China.
Bo XuMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.ORCID https://orcid.org/0000-0002-1920-5829
Zhehao RenMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.ORCID https://orcid.org/0000-0003-2450-3318
Zhaoyang LiuCenter for Statistical Science, Tsinghua University, Beijing 100084, China.
Chong ShenCenter for Statistical Science, Tsinghua University, Beijing 100084, China.
Che WangCenter for Statistical Science, Tsinghua University, Beijing 100084, China.
Han LiuBusiness Intelligence Lab, Baidu Research, Beijing 100193, China.
Xiaoting LiMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
Ruiyun LiSchool of Public Health (SPH), Nanjing Medical University, Nanjing 211166, China.
Le YuMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
Dabo GuanMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.ORCID https://orcid.org/0000-0003-3773-3403
Wusheng ZhangDepartment of Computer Science and Technology, Institute of High Performance Computing, Tsinghua University, Beijing 100084, China.
Jie WangState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
Lin HouCenter for Statistical Science, Tsinghua University, Beijing 100084, China.ORCID https://orcid.org/0000-0002-4283-8501
Ke DengCenter for Statistical Science, Tsinghua University, Beijing 100084, China.ORCID https://orcid.org/0000-0002-4383-8319
Yuqi BaiMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.ORCID https://orcid.org/0000-0002-4630-5147
Bing XuMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.ORCID https://orcid.org/0000-0001-9159-2512
Dejing DouBusiness Intelligence Lab, Baidu Research, Beijing 100193, China.
Peng GongDepartment of Geography and Department of Earth Science, The University of Hong Kong, Hong Kong 999007, China.ORCID https://orcid.org/0000-0003-1513-3765
Tsinghua University · CNBaidu (China) · CNUniversity of Hong Kong · HKChinese Academy of Sciences · CNHuazhong University of Science and Technology · CNNanjing Medical University · CNVanke (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modeling the global dynamics of emerging infectious diseases (EIDs) like COVID-19 can provide important guidance in the preparation and mitigation of pandemic threats. While age-structured transmission models are widely used to simulate the evolution of EIDs, most of these studies focus on the analysis of specific countries and fail to characterize the spatial spread of EIDs across the world. Here, we developed a global pandemic simulator that integrates age-structured disease transmission models across 3,157 cities and explored its usage under several scenarios. We found that without mitigations, EIDs like COVID-19 are highly likely to cause profound global impacts. For pandemics seeded in most cities, the impacts are equally severe by the end of the first year. The result highlights the urgent need for strengthening global infectious disease monitoring capacity to provide early warnings of future outbreaks. Additionally, we found that the global mitigation efforts could be easily hampered if developed countries or countries near the seed origin take no control. The result indicates that successful pandemic mitigations require collective efforts across countries. The role of developed countries is vitally important as their passive responses may significantly impact other countries.

Indexed as

COVID-19emerging infectious diseaseglobal airline network

Identifiers

PMID37143866
PMCPMC10153731
OpenAlexW4366987373

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.