| 1 | Mapping the viruses belonging to the order Bunyavirales in China显示文摘Background:Viral pathogens belonging to the orderBunyavirales pose a continuous background threat to global health,but the fact remains that they are usually neglected and their distribution is still ambiguously known.We aim to map the geographical distribution ofBunyavirales viruses and assess the environmental suitability and transmission risk of majorBunyavirales viruses in China.Methods:We assembled data on allBunyavirales viruses detected in humans,animals and vectors from multiple sources,to update distribution maps of them across China.In addition,we predicted environmental suitability at the 10 km×10 km pixel level by applying boosted regression tree models for two importantBunyavirales viruses,including Crimean-Congo hemorrhagic fever virus(CCHFV)and Rift Valley fever virus(RVFV).Based on model-projected risks and air travel volume,the imported risk of RVFV was also estimated from its endemic areas to the cities in China.Results:Here we mapped all 89 species ofBunyavirales viruses in China from January 1951 to June 2021.Nineteen viruses were shown to infect humans,including ten species first reported as human infections.A total of 447,848 cases infected withBunyavirales viruses were reported,and hantaviruses,Dabie bandavirus and Crimean-Congo hemorrhagic fever virus(CCHFV)had the severest disease burden.Model-predicted maps showed that Xinjiang and southwestern Yunnan had the highest environmental suitability for CCHFV occurrence,mainly related toHyalomma asiaticum presence,while southern China had the highest environmental suitability for Rift Valley fever virus(RVFV)transmission all year round,mainly driven by livestock density,mean precipitation in the previous month.We further identified three cities including Guangzhou,Beijing and Shanghai,with the highest imported risk of RVFV potentially from Egypt,South Africa,Saudi Arabia and Kenya.Conclusions:A variety ofBunyavirales viruses are widely distributed in China,and the two major neglectedBunyavirales viruses including CCHFV and RVFV,both have the potential for outbreaks in local areas of China.Our study can help to promote the understanding of risk distribution and disease burden ofBunyavirales viruses in China,and the risk maps of CCHFV and RVFV occurrence are crucial to the targeted surveillance and control,especially in seasons and locations at high risk. | Ai-Ying Teng Tian-Le Che An-Ran Zhang Yuan-Yuan Zhang Qiang Xu Tao Wang Yan-Qun Sun Bao-Gui Jiang Chen-Long Lv Jin-Jin Chen Li-Ping Wang Simon I.Hay Wei Liu Li-Qun Fang | 2022 | Infectious Diseases of Poverty2022，11，4： | 0 |
| 2 | Mobile Phone-Based Population Flow Data for the COVID-19 Outbreak in China' Mainland显示文摘Background.Human migration is one of the driving forces for amplifying localized infectious disease outbreaks into widespread epidemics.During the outbreak of COVID-19 in China,the travels of the population from Wuhan have furthered the spread of the virus as the period coincided with the world’s largest population movement to celebrate the Chinese New Year.Methods.We have collected and made public an anonymous and aggregated mobility dataset extracted from mobile phones at the national level,describing the outflows of population travel from Wuhan.We evaluated the correlation between population movements and the virus spread by the dates when the number of diagnosed cases was documented.Results.From Jan 1 to Jan 22 of 2020,a total of 20.2 million movements of at-risk population occurred from Wuhan to other regions in China.A large proportion of these movements occurred within Hubei province(84.5%),and a substantial increase of travels was observed even before the beginning of the official Chinese Spring Festival Travel.The outbound flows from Wuhan before the lockdown were found strongly correlated with the number of diagnosed cases in the destination cities(log-transformed).Conclusions.The regions with the highest volume of receiving at-risk populations were identified.The movements of the at-risk population were strongly associated with the virus spread.These results together with province-by-province reports have been provided to governmental authorities to aid policy decisions at both the state and provincial levels.We believe that the effort in making this data available is extremely important for COVID-19 modelling and prediction. | Xin Lu Jing Tan Ziqiang Cao Yiquan Xiong Shuo Qin Tong Wang Chunrong Liu Shiyao Huang Wei Zhang Laurie B.Marczak Simon I.Hay Lehana Thabane Gordon H.Guyatt Xin Sun | 2021 | Health Data Science2021，，1： | 0 |
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