Periodic Reporting for period 1 - DIGDEEP (Digging deeper into genes to track infectious disease outbreaks)
Berichtszeitraum: 2020-09-01 bis 2022-08-31
The DIGDEEP project aims to unravel the transmission dynamics of avian influenza viruses (AIV) at the poultry-wild bird-human interface, by using an original combination of epidemiological and phylodynamic approaches. Through the DIGDEEP project, our core goal is to generate underpinning knowledge needed to develop effective control strategies tailored to the characteristics of AIV evolution and transmission and informing health policy making. In particular, the outcomes of the DIGDEEP project are (i) the inference of key epidemiological parameters of AIV spread, such as the basic reproduction number, (ii) the quantification of spillover events between host species and (iii) the estimation of the number of unreported cases, which are crucial for a well-informed response.
As part of O1, the PI (Claire GUINAT) inferred key epidemiological parameters, including the infectious period and the effective reproduction number Re (i.e. the number of expected secondary infections of an infected individual) based on AIV genome sequences generated during epidemics in Europe (H5N8 subtype, 2016-17) and China (H7N9 subtype, 2013-2017). These parameters are keys to parametrize transmission models to better understand the transmission patterns of infectious diseases, to optimize surveillance and control strategies and to inform public and animal health decision-making. This allowed us to assess potential differences in Re estimates between different types of poultry farms and the possible existence of super-spreaders (ex. farms or wild birds responsible for a larger number of infections than usual), which is important to know where control efforts should be focused. More importantly, this allowed us to address challenging questions that could not be addressed using epidemiological data alone, such as how many infections were not reported (which is important in evaluating surveillance effectiveness) and how many transmission events occurred at the poultry-wild bird-human interfaces (which is important in determining where control efforts should be focused).
Though a number of epidemiological studies have highlighted factors that influence the risk of farms acquiring/spreading AIV infection, this has not been investigated yet using phylodynamic tools. Elucidation of the determinants that drive the spread of AIV in poultry farms and wild birds based on AIV genome sequences was crucial to provide robust evidence for disease prevention. As part of O1, the PI has also measured the association between our estimated key transmission parameters (in particular Re) and various predictor variables (such as trade movements, production type, etc.) collected from the studied European countries during the 2016-17 epidemic. I have extended a phylodynamic model with a generalized linear model. This allowed us to shed light on the factors that impact the AIV transmission dynamics between poultry farms across the studied European countries during the 2016-17 epidemic, which will inform epidemic control and prevention decision.
As part of O2, the PI has reconstructed the epidemic transmission tree (which describes the history of transmission events between infected hosts, in other words, who infected whom) for small clusters of infected poultry farms and wild birds that raise concern in Germany (one of the most affected countries during the 2016-17 epidemic). This was done by applying a transmission network model to the AIV genome sequences generated during the 2016-17 epidemic to pin-point sources with a level of resolution. Valuable insights were gained by the reconstruction of the epidemic transmission tree. In particular, this led to more precise estimates of which farms were more likely at risk of infection to better plan the order of depopulation, which farms were more likely infected by wild birds to better plan the restriction of outdoor access for poultry during high-risk periods, and which farms were more likely super-spreaders to better plan quarantine and transport bans of poultry from these farms.
As part of O3, information obtained through O1 and O2 were integrated, summarized and discussed which was paramount for providing recommendations for efficient and realistic control strategies that minimize the impact of AIV epidemic in Europe and China.
Though a number of epidemiological studies have highlighted factors that influence the risk of farms acquiring/spreading AIV infection, this has not been investigated yet using phylodynamic tools. Elucidation of the determinants that drive the spread of AIV in poultry farms and wild birds based on AIV genome sequences was crucial to provide robust evidence for disease prevention. As part of O1, the PI has also measured the association between our estimated key transmission parameters (in particular Re) and various predictor variables (such as trade movements, production type, etc.) collected from the studied European countries during the 2016-17 epidemic. I have extended a phylodynamic model with a generalized linear model. This allowed us to shed light on the factors that impact the AIV transmission dynamics between poultry farms across the studied European countries during the 2016-17 epidemic, which will inform epidemic control and prevention decision.
As part of O2, the PI has reconstructed the epidemic transmission tree (which describes the history of transmission events between infected hosts, in other words, who infected whom) for small clusters of infected poultry farms and wild birds that raise concern in Germany (one of the most affected countries during the 2016-17 epidemic). This was done by applying a transmission network model to the AIV genome sequences generated during the 2016-17 epidemic to pin-point sources with a level of resolution. Valuable insights were gained by the reconstruction of the epidemic transmission tree. In particular, this led to more precise estimates of which farms were more likely at risk of infection to better plan the order of depopulation, which farms were more likely infected by wild birds to better plan the restriction of outdoor access for poultry during high-risk periods, and which farms were more likely super-spreaders to better plan quarantine and transport bans of poultry from these farms.
As part of O3, information obtained through O1 and O2 were integrated, summarized and discussed which was paramount for providing recommendations for efficient and realistic control strategies that minimize the impact of AIV epidemic in Europe and China.
The DIGDEEP project focuses on avian influenza that represents a tenacious and major public and animal health problem across the world. It certainly warrants attention, given what we have experienced and are experiencing with COVID-19 pandemic. Like COVID-19, avian influenza is a zoonotic disease with a wildlife origin, has the potential for cross-species and rapid transmission, causes major socio-economic burden and implies initial control strategies to contain the outbreaks. Such strategies include quarantining of infected hosts, movement bans in areas at high-risk of infection and reinforced biosecurity. It is important to better understand how infectious diseases, such as avian influenza, emerge and circulate at the animal-human interface in order to better prevent and control future emergence of diseases.