Inferring the potential risks of H7N9 infection by spatiotemporally characterizing bird migration and poultry distribution in eastern China

Background:In view of the rapid geographic spread and the increasing number of confirmed cases of novel influenza A(H7N9)virus infections in eastern China,we developed a diffusion model to spatiotemporally characterize the impacts of bird migration and poultry distribution on the geographic spread of H7N9 infection.Methods:Three types of infection risks were estimated for 12 weeks,from February 4 to April 28,2013,including(i)the risk caused by bird migration,(ii)the risk caused by poultry distribution,and(iii)the integrated risk caused by both bird migration and poultry distribution.To achieve this,we first developed a method for estimating the likelihood of bird migration based on available environmental and meteorological data.Then,we adopted a computational mobility model to estimate poultry distribution based on annual poultry production and consumption of each province/municipality.Finally,the spatiotemporal risk maps were created based on the integrated impacts of both bird migration and poultry distribution.Results:In the study of risk estimation caused by bird migration,the likelihood matrix was estimated based on the 7-day temperature,from February 4 to April 28,2013.It was found the estimated migrant birds mainly appear in the southeastern provinces of Zhejiang,Shanghai and Jiangsu during Weeks 1 to 4,and Week 6,followed by appearing in central eastern provinces of Shandong,Hebei,Beijing,and Tianjin during Weeks 7 to 9,and finally in northeastern provinces of Liaoning,Jilin,and Heilongjiang during Weeks 10 to 12.In the study of risk caused by poultry distribution,poultry distribution matrix was created to show the probability of poultry distribution.In spite of the fact that the majority of the initial infections were reported in Shanghai and Jiangsu,the relative risk of H7N9 infection estimated based on the poultry distribution model predicted that Jiangsu may have a slightly higher likelihood of H7N9 infection than those in Zhejiang and Shanghai,if we only take the probability of poultry distribution into consideration.In the study of integrated risk caused by both bird migration and poultry distribution,the higher risk in southeastern provinces occurred during the first 8 weeks,and that in central eastern provinces appeared during Weeks 8 to 12,and that in northeastern provinces since Week 12.Therefore,it is necessary to regulate the poultry markets as long as the poultry-to-poultry transmission is not so well understood.Conclusion:With reference to the reported infection cases,the demonstrated risk mapping results will provide guidance in active surveillance and control of human H7N9 infections by taking intensive intervention in poultry markets.

Risk assessment of malaria transmission at the border area of China and Myanmar

Background:In order to achieve the goal of malaria elimination,the Chinese government launched the National Malaria Elimination Programme in 2010.However,as a result of increasing cross-border population movements,the risk of imported malaria cases still exists at the border areas of China,resulting in a potential threat of local transmission.The focus of this paper is to assess the Plasmodium vivax incidences in Tengchong,Yunnan Province,at the border areas of China and Myanmar.Methods:Time series of P.vivax incidences in Tengchong from 2006 to 2010 are collected from the web-based China Information System for Disease Control and Prevention,which are further separated into time series of imported and local cases.First,the seasonal and trend decomposition are performed on time series of imported cases using Loess method.Then,the impact of climatic factors on the local transmission of P.vivax is assessed using both linear regression models(LRM)and generalized additive models(GAM).Specifically,the notion of vectorial capacity(VCAP)is used to estimate the transmission potential of P.vivax at different locations,which is calculated based on temperature and rainfall collected from China Meteorological Administration.Results:Comparing with Ruili County,the seasonal pattern of imported cases in Tengchong is different:Tengchong has only one peak,while Ruili has two peaks during each year.This may be due to the different cross-border behaviors of peoples in two locations.The vectorial capacity together with the imported cases and the average humidity,can well explain the local incidences of P.vivax through both LRM and GAM methods.Moreover,the maximum daily temperature is verified to be more suitable to calculate VCAP than the minimal and average temperature in Tengchong County.Conclusion:To achieve malaria elimination in China,the assessment results in this paper will provide further guidance in active surveillance and control of malaria at the border areas of China and Myanmar.

Identifying key bird species and geographical hotspots of avian influenza A(H7N9)virus in China

Background:In China since the first human infection of avian influenza A(H7N9)virus was identified in 2013,it has caused serious public health concerns due to its wide spread and high mortality rate.Evidence shows that bird migration plays an essential role in global spread of avian influenza viruses.Accordingly,in this paper,we aim to identify key bird species and geographical hotspots that are relevant to the transmission of avian influenza A(H7N9)virus in China.Methods:We first conducted phylogenetic analysis on 626 viral sequences of avian influenza A(H7N9)virus isolated in chicken,which were collected from the Global Initiative on Sharing All Influenza Data(GISAID),to reveal geographical spread and molecular evolution of the virus in China.Then,we adopted the cross correlation function(CCF)to explore the relationship between the identified influenza A(H7N9)cases and the spatiotemporal distribution of migratory birds.Here,the spatiotemporal distribution of bird species was generated based on bird observation data collected from China Bird Reports,which consists of 157272 observation records about 1145 bird species.Finally,we employed a kernel density estimator to identify geographical hotspots of bird habitat/stopover that are relevant to the influenza A(H7N9)infections.Results:Phylogenetic analysis reveals the evolutionary and geographical patterns of influenza A(H7N9)infections,where cases in the same or nearby municipality/provinces are clustered together with small evolutionary differences.Moreover,three epidemic waves in chicken along the East Asian-Australasian flyway in China are distinguished from the phylogenetic tree.The CCF analysis identifies possible migratory bird species that are relevant to the influenza A(H7N9)infections in Shanghai,Jiangsu,Zhejiang,Fujian,Jiangxi,and Guangdong in China,where the six municipality/provinces account for 91.2%of the total number of isolated H7N9 cases in chicken in GISAID.Based on the spatial distribution of identified bird species,geographical hotspots are further estimated and illustrated within these typical municipality/provinces.Conclusions:In this paper,we have identified key bird species and geographical hotspots that are relevant to the spread of influenza A(H7N9)virus.The results and findings could provide sentinel signal and evidence for active surveillance,as well as strategic control of influenza A(H7N9)transmission in China.

Inference and prediction of malaria transmission dynamics using time series data

Background:Disease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time series.To evaluate the severity of an epidemic,statistical methods are widely used to forecast the trend,seasonality,and the possible number of infections of a disease.However,most statistical methods are limited in revealing the underlying dynamics of disease transmission,which may be affected by various impact factors,such as environmental,meteorological,and physiological factors.In this study,we focus on investigating malaria transmission dynamics based on time series data.Methods:A data-driven nonlinear stochastic model is proposed to infer and predict the dynamics of malaria transmission based on the time series of prevalence data.Specifically,the dynamics of malaria transmission is modeled based on the notion of vectorial capacity(VCAP)and entomological inoculation rate(EIR).A particle Markov chain Monte Carlo(PMCMC)method is employed to estimate the model parameters.Accordingly,a one-step-ahead prediction method is proposed to project the number of future malaria infections.Finally,two case studies are carried out on the inference and prediction of Plasmodium vivax transmission in Tengchong and Longling,Yunnan province,China.Results:The results show that the trained data-driven stochastic model can well fit the historical time series of P.vivax prevalence data in both counties from 2007 to 2010.Moreover,with well-trained model parameters,the proposed one-step-ahead prediction method can achieve better performances than that of the seasonal autoregressive integrated moving average model with respect to predicting the number of future malaria infections.Conclusions:By involving dynamically changing impact factors,the proposed data-driven model together with the PMCMC method can successfully(i)depict the dynamics of malaria transmission,and(ii)achieve accurate one-step-ahead prediction about malaria infections.Such a data-driven method has the potential to investigate malaria transmission dynamics in other malaria-endemic countries/regions.

A unified global genotyping framework of dengue virus serotype-1 for a stratified coordinated surveillance strategy of dengue epidemics

Background: Dengue is the fastest spreading arboviral disease, posing great challenges on global public health. A reproduceable and comparable global genotyping framework for contextualizing spatiotemporal epidemiological data of dengue virus (DENV) is essential for research studies and collaborative surveillance.Methods: Targeting DENV-1 spreading prominently in recent decades, by reconciling all qualified complete E gene sequences of 5003 DENV-1 strains with epidemiological information from 78 epidemic countries/areas ranging from 1944 to 2018, we established and characterized a unified global high-resolution genotyping framework using phylogenetics, population genetics, phylogeography, and phylodynamics.Results: The defined framework was discriminated with three hierarchical layers of genotype, subgenotype and clade with respective mean pairwise distances 2-6%, 0.8-2%, and ≤ 0.8%. The global epidemic patterns of DENV-1 showed strong geographic constraints representing stratified spatial-genetic epidemic pairs of Continent-Genotype, Region-Subgenotype and Nation-Clade, thereby identifying 12 epidemic regions which prospectively facilitates the region-based coordination. The increasing cross-transmission trends were also demonstrated. The traditional endemic countries such as Thailand, Vietnam and Indonesia displayed as persisting dominant source centers, while the emerging epidemic countries such as China, Australia, and the USA, where dengue outbreaks were frequently triggered by importation, showed a growing trend of DENV-1 diffusion. The probably hidden epidemics were found especially in Africa and India. Then, our framework can be utilized in an accurate stratified coordinated surveillance based on the defined viral population compositions. Thereby it is prospectively valuable for further hampering the ongoing transition process of epidemic to endemic, addressing the issue of inadequate monitoring, and warning us to be concerned about the cross-national, cross-regional, and cross-continental diffusions of dengue, which can potentially trigger large epidemics.Conclusions: The framework and its utilization in quantitatively assessing DENV-1 epidemics has laid a foundation and re-unveiled the urgency for establishing a stratified coordinated surveillance platform for blocking global spreading of dengue. This framework is also expected to bridge classical DENV-1 genotyping with genomic epidemiology and risk modeling. We will promote it to the public and update it periodically.

Accessing the syndemic of COVID-19 and malaria in tervention in Africa

Background:The pandemic of the coronavirus disease 2019(COVID-19)has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases,such as malaria in sub-Saharan Africa.The aim of this study is to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in Africa.Methods:We present a data-driven method to quantify the extent to which the COVID-19 pandemic,as well as various non-pharmaceutical interventions(NPIs),could lead to the change of malaria transmission potential in 2020.First,we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of reported COVID-19 cases.Then,we simulate the epidemic dynamics of COVID-19 under two groups of NPIs:(1)contact restriction and social distanci ng,and(2)early ide ratification and isolation of cases.Based on the simulated epidemic curves,we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets(ITNs).Finally,by treating the total number of ITNs available in each country in 2020,we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity.Results:We con duct case studies in four malaria-endemic coun tries,Ethiopia,Nigeria,Tanza nia,and Zambia,in Africa.The epidemiological parameters(i.e.;the basic reproduction number R°and the duration of infection D1)of COVID-19 in each country are estimated as follows:Ethiopia(Rq=1.57,D1=5.32),Nigeria(Ro=2.18,D1=6.58),Tanzania(Ro=2.47,D1=6.01),and Zambia(R0=2.12,D1=6.96).Based on the estimated epidemiological parameters,the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented,the better the epidemic is controlled.Moreover,the effect of combined NPIs is better than contact restriction and social di st a ncing only.By treating the total number of ITNs available in each country in 2020 as a baseline,our results show that even with stringent NPIs,malaria transmission potential will remain higher than expected in the second half of 2020.Conclusions:By quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmission potential,this study provides a way tojointly address the syndemic between COVID-19 and malaria in malariaendemic countries in Africa.The results suggest that the early intervention of COVID-19 can effectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential.

Research priorities in modeling the transmission risks of H7N9 bird flu

The epidemic of H7N9 bird flu in eastern China in early 2013 has caused much attention from researchers as well as public health workers.The issue on modeling the transmission risks is very interesting topic.In this article,this issue is debated and discussed in order to promote further researches on prediction and prevention of avian influenza viruses supported by better interdisciplinary datasets from the surveillance and response system.