Author response:
Reviewer #1:
Summary:
The authors aim to predict ecological suitability for the transmission of highly pathogenic avian influenza (HPAI) using ecological niche models. This class of models identify correlations between the locations of species or disease detections and the environment. These correlations are then used to predict habitat suitability (in this work, ecological suitability for disease transmission) in locations where surveillance of the species or disease has not been conducted. The authors fit separate models for HPAI detections in wild birds and farmed birds, for two strains of HPAI (H5N1 and H5Nx) and for two time periods, pre- and post-2020. The authors also validate models fitted to disease occurrence data from pre-2020 using post-2020 occurrence data.
Strengths:
The authors follow the established methods of Dhingra et al., 2016 to provide an updated spatial assessment of HPAI transmission suitability for two time periods, pre- and post-2020. They explore further methods of model cross-validation and consider the diversity of the bird species that HPAI has been detected in.
Weaknesses:
The precise ecological niche that the authors are modelling here is ambiguous: if we treat the transmission of HPAI in the wild bird population and in poultry populations as separate transmission cycles, linked by spillover events, then these transmission cycles are likely to have fundamentally different ecological niches.
We apologise if this aspect was not clear enough in the previous version of our manuscript but our analyses do not treat or make the assumption of distinct transmission cycles between wild and domestic bird species; those transmission cycles being indeed interconnected by frequent spillover events. Yet, we indeed conduct independent ecological niche modelling analyses to estimate both the ecological suitability for the risk of local circulation in domestic birds as well as the ecological suitability for the risk of local circulation in wild birds. This distinction does not imply that the virus circulates exclusively within one of these populations but rather allows us to identify potential differences in the environmental conditions associated with virus occurrences in each context.
Our results indicate that these two ecological niche models capture distinct environmental patterns. Virus occurrences in wild birds were primarily associated with factors such as open water and proximity to urban areas, while occurrences in domestic birds were more strongly linked to variables like poultry density and cultivated vegetation. This finding supports the existence of two distinct ecological niches for the virus, corresponding to virus circulation in wild and domestic bird populations. We thank the Reviewer for their feedback and we will take this opportunity to further clarify this aspect in the text.
While an "index case" in farmed poultry is relevant to the wildlife transmission cycle, further within-farm and farm-to-farm transmission is likely to be contingent on anthropogenic factors, rather than the environment. Similarly, we would expect "index cases" in outbreaks of HPAI in mammals to be relevant to transmission risk in wild birds - this data is not included in this manuscript. Such "index cases" in farmed poultry occur under separate ecological conditions to subsequent transmission in farmed poultry, so should be separated if possible. Some careful editing of the language used in the manuscript may elucidate some of my questions related to model conceptualisation.
We agree, but index cases are particularly difficult to separate from secondary spread in the absence of field investigation. Identification of index cases based on space-time filtering have been previously investigated but are strongly dependent on the quality of the surveillance, i.e. an “apparent” primary case can be a secondary case of previously undetected ones, and constant surveillance quality cannot be assumed to be homogeneous across countries. Our ecological niche modelling approach is based on HPAI cases reported in the EMPRES-i database, which includes all documented outbreaks without distinguishing primary introductions from subsequent farm-to-farm transmissions. Thus, our ecological niche models are trained on confirmed cases that result from a combination of different transmission dynamics, including introduction events in poultry populations (which can be impacted by ecological factors) and persistence within and between poultry populations (which can be impacted by anthropogenic factors).
For clarity, we will revise the manuscript to clarify that, while our study primarily aims to assess the environmental suitability for HPAI occurrences, the dataset does not exclude cases resulting from farm-to-farm spread. This means that our models can capture the environmental variables associated with the risk of cases associated with both primary introductions (e.g., spillover from wild birds) and secondary transmission events within poultry systems, although the latter is also influenced by anthropogenic factors such as biosecurity practices and poultry trade networks. These latter factors are not included in our models, which will be highlighted in the limitations (Discussion section) of the revised manuscript.
In addition, we note the Reviewer's comment regarding the relevance of “index cases” in mammalian outbreaks to understanding the risk of HPAI transmission in wild birds. Although these data are not included in our current study, we will highlight the potential value of incorporating these cases into future models in order to refine risk predictions, provided that they can be identified with some reasonable level of certainty.
The authors' handling of sampling bias in disease detection data in poultry is possibly inappropriate: one would expect the true spatial distribution of disease surveillance in poultry to be more closely correlated with poultry farming density, in contrast to human population density. This shortcoming in the modelling workflow possibly dilutes a key finding of the Results, that the transmission risk of HPAI in poultry is greatest in areas where poultry farming density is high.
The Reviewer raises a valid point that poultry surveillance efforts can also be considered as correlated with poultry farm density than with human population density. While human population density can serve as a reasonable proxy for surveillance intensity — given that disease detection is often more active in areas with stronger veterinary notification systems — we acknowledge that poultry disease surveillance can also be influenced by the spatial distribution of poultry farms, as high-density poultry areas could be prioritised for monitoring. Please note that in our study, we followed a previously established approach (Dhingra et al. 2016) and weighted pseudo-absence sampling based on human population density to account for general surveillance biases. However, we do not agree with the Reviewer’s point. In fact, assuming a sampling bias correlated with poultry density would result in reducing its effect as a risk factor. The current approach does not.
Reviewer #2:
Summary:
This study aimed to determine which spatial factors (conceived broadly as environmental, agronomic and socio-economic) explain greater avian influenza case numbers reported since 2020 (2020--2022) by comparing similar models built with data from the period 2015--2020. The authors have chosen an environmental niche modelling approach, where detected infections are modelled as a function of spatial covariates extracted at the location of each case. These covariates are available over the entire world so that the predictions can be projected back to space in the form of a continuous map.
Strengths:
The authors use boosted regression trees as the main analytical tool, which always feature among the best-performing models for environmental niche models (also known as habitat suitability models). They run replicate sets of the analysis for each of their model targets (wild/domestic x pathogen variant), which can help produce stable predictions. The authors take steps to ameliorate some forms of expected bias in the detection of cases, such as geographic variation in surveillance efforts, and in general more detections near areas of higher human population density.
Weaknesses:
The study is not altogether coherent with respect to time. Data sets for the response (N5H1 or N5Hx case data in domestic or wild birds) are divided into two periods; 2015-2020, and 2020-2022. Each set is modelled using a common suite of covariates that are not time-varying. That suggests that causation is inferred by virtue of cases being in different geographic areas in those two time periods. Furthermore, important predictors such as chicken density appear to be informed (in the areas of high risk) from census data from before 2010. The possibility for increased surveillance effort *through time* is overlooked, as is the possibility that previously high-burden locations may implement practice changes to reduce vulnerability.
We acknowledge the Reviewer's comments regarding the consistency of time periods in our study. Our approach is to divide the HPAI case data into two time periods (2015-2020 and 2020-2022) and ecological niche models using a common set of covariates that do not explicitly account for temporal variation. We will further clarify these aspects in the revised version of our manuscript:
(1) Our primary objective is to assess changes in ecological suitability over time rather than infer direct causation. By comparing models trained on pre-2020 data with post-2020 occurrences, we evaluate whether pre-2020 environmental conditions can predict recent HPAI suitability. However, we acknowledge that this does not capture dynamic changes in surveillance efforts, biosecurity measures, or host-pathogen interactions over time.
(2) Regarding predictor variables, we used poultry density data from 2015, rather than pre-2010 data. However, this dataset is not based on a single census year; instead, it represents a median estimate derived from subnational poultry census data collected between 2000 and 2019. This median year approach provides a more stable representation of poultry density than any single-year snapshot. Furthermore, while poultry production systems may exhibit some temporal variation, these changes are generally minor compared to the inter-annual variability observed in HPAI occurrence, which is largely driven by epidemic dynamics. Given the current limitations of global poultry data, distinguishing distributions from different years is not feasible with the available GLW dataset. We will clarify these points in the manuscript.
(3) We recognise that increased surveillance efforts and adaptive changes in poultry farming practices could influence the observed HPAI case distribution. While our current models do not incorporate time-varying surveillance intensity or biosecurity policies, we will address this limitation in the Discussion section and suggest that future work integrates dynamic surveillance data to improve risk assessments.
