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

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.

SUBMITTER: Xiao Y

PROVIDER: S-EPMC10153731 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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

Xiao Yixiong Y Zhou Jingbo J Cheng Qu Q Yang Jun J Chen Bin B Zhang Tao T Xu Lei L Xu Bo B Ren Zhehao Z Liu Zhaoyang Z Shen Chong C Wang Che C Liu Han H Li Xiaoting X Li Ruiyun R Yu Le L Guan Dabo D Zhang Wusheng W Wang Jie J Hou Lin L Deng Ke K Bai Yuqi Y Xu Bing B Dou Dejing D Gong Peng P

PNAS nexus 20230425 5

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 model ...[more]

PMID: 37143866

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Project description:AimThis study aimed to explore the spatial and temporal characteristics of emerging airborne viral infectious diseases outbreaks worldwide.Subject and methodsWe conducted a systematic literature review on outbreaks of emerging airborne viral infectious diseases and calculated outbreak number and intensity at the country level. Fisher's exact test was used to compare the viral infectious diseases outbreaks in different income-level regions. To identify the major airborne viral infectious diseases outbreaks, we ranked and extracted the leading viral infectious diseases in outbreak number and intensity in each country by year.ResultsA total of 2505 outbreaks were reported from 1873 to 2021 across 2010 studies. There were 47 countries (47/130, 36.15%) with more frequent emerging airborne viral infectious disease outbreaks (more than nine outbreaks), and these countries mainly distributed in high-income regions (22/47 countries, 46.81%, p < 0.05), especially in Western Europe (14/47 countries, 29.79%, p < 0.05). The number of overall outbreaks was more in the United States and China than in other countries in different years. Outbreaks of measles and influenza are always frequent and intense. Highly pathogenic human coronaviruses infection caused short-term pandemics during which their outbreak number and intensity exceeded other viruses. Rift valley fever outbreaks in the human population are spreading outside of Africa through the flow of goods and travelers.ConclusionCountries in high-income regions reported more emerging airborne viral infectious diseases outbreaks, especially in the Western European region, the United States, and China. It is urgent to strengthen collaborative surveillance of emerging airborne viruses, cross-border flow of goods and travelers, and ecological environment to avoid the spread of viral infectious diseases outbreaks worldwide.Supplementary informationThe online version contains supplementary material available at 10.1007/s10389-023-01850-3.

Project description:IntroductionAs emerging infectious diseases (EIDs) increase, examining the underlying social and environmental conditions that drive EIDs is urgently needed. Ecological niche modeling (ENM) is increasingly employed to predict disease emergence based on the spatial distribution of biotic conditions and interactions, abiotic conditions, and the mobility or dispersal of vector-host species, as well as social factors that modify the host species' spatial distribution. Still, ENM applied to EIDs is relatively new with varying algorithms and data types. We conducted a systematic review (PROSPERO: CRD42021251968) with the research question: What is the state of the science and practice of estimating ecological niches via ENM to predict the emergence and spread of vector-borne and/or zoonotic diseases?MethodsWe searched five research databases and eight widely recognized One Health journals between 1995 and 2020. We screened 383 articles at the abstract level (included if study involved vector-borne or zoonotic disease and applied ENM) and 237 articles at the full-text level (included if study described ENM features and modeling processes). Our objectives were to: (1) describe the growth and distribution of studies across the types of infectious diseases, scientific fields, and geographic regions; (2) evaluate the likely effectiveness of the studies to represent ecological niches based on the biotic, abiotic, and mobility framework; (3) explain some potential pitfalls of ENM algorithms and techniques; and (4) provide specific recommendation for future studies on the analysis of ecological niches to predict EIDs.ResultsWe show that 99% of studies included mobility factors, 90% modeled abiotic factors with more than half in tropical climate zones, 54% modeled biotic conditions and interactions. Of the 121 studies, 7% include only biotic and mobility factors, 45% include only abiotic and mobility factors, and 45% fully integrated the biotic, abiotic, and mobility data. Only 13% of studies included modifying social factors such as land use. A majority of studies (77%) used well-recognized ENM algorithms (MaxEnt and GARP) and model selection procedures. Most studies (90%) reported model validation procedures, but only 7% reported uncertainty analysis.DiscussionOur findings bolster ENM to predict EIDs that can help inform the prevention of outbreaks and future epidemics.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier (CRD42021251968).

Dataset Information

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

Publications

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

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