Spatio-temporal epidemiology of highly pathogenic avian influenza (subtype H5N1) in poultry in eastern India

Dhingra, Madhur S.; Dissanayake, Ravi; Negi, Ajender Bhagat; Oberoi, M. S.; Castellan, David; Thrusfield, Michael; Linard, Catherine; Gilbert, Marius

doi:10.1016/j.sste.2014.06.003

Cited by 23 publications

(20 citation statements)

References 41 publications

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“…In this study, we apply an increasingly used method for the analysis of complex categorization problems: boosted regression‐tree (BRT) models (a special case of generalized boosted models). Boosted regression‐tree models have been applied to other areas of high economic importance, such as in the fields of epidemiology (Dhingra et al, ; Friedman & Meulman, ) and biosecurity (Catford, Vesk, White, & Wintle, ; Compton, De Winton, Leathwick, & Wadhwa, ), but, to our knowledge, this is the first application to resistance management issues.…”

Section: Introductionmentioning

confidence: 99%

Climate contributes to the evolution of pesticide resistance

Maino

Umina

Hoffmann

2017

Global Ecol Biogeogr

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Aim The evolution of pesticide resistance through space and time is of great economic significance to modern agricultural production systems, and consequently, is often well documented. It can thus be used to dissect the evolutionary and ecological processes that underpin large‐scale evolutionary responses. There are now nearly 600 documented cases of pesticide resistance in arthropod pests. Although the evolution of resistance is often attributed to the persistent use of chemicals for pest suppression, the rate of development of resistance should also depend on other factors, including climatic conditions that influence population size and generation time. Here, we test whether climatic variables are linked to evolution of resistance by examining the spatial pattern of pyrethroid resistance in an important agricultural pest. Location Southern, agricultural regions of Australia. Time period 2007–2015. Major taxa studied The redlegged earth mite, Halotydeus destructor. Methods We quantified patterns of chemical usage based on paddock histories and collated long‐term climatic data. These data were then compared against presence–absence data on resistance using a boosted regression‐tree approach, applied here for the first time to the spatial categorization of pesticide resistance. Results Although chemical usage was a key driver of resistance, our analysis revealed climate‐based signals in the spatial distribution of resistance, linked to regional variation in aridity, temperature seasonality and precipitation patterns. Climatic regions supporting increased voltinism were positively correlated with resistance, in line with expectations that increased voltinism should accelerate evolutionary responses to selection pressures. Main conclusions Our findings suggest that the prediction of rapid evolutionary processes at continental scales, such as pesticide resistance, will be improved through methods that incorporate climate and ecology, in addition to more immediate selection pressures, such as chemical usage. Boosted regression trees present a powerful tool in the management of resistance issues that has hitherto not been used.

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Section: Introductionmentioning

confidence: 99%

Climate contributes to the evolution of pesticide resistance

Maino

Umina

Hoffmann

2017

Global Ecol Biogeogr

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“…The majority of these have been country-level studies in Thailand (Gilbert et al, 2006), Viet Nam (Minh et al, 2009), China (Martin et al, 2011), Bangladesh (Ahmed et al, 2012), Indonesia (Yupiana et al, 2010) and India (Dhingra et al, 2014). Several studies have also been conducted at regional (Adhikari D, 2009; Gilbert et al, 2008; Williams and Peterson, 2009) and continental levels (Hogerwerf et al, 2010; Peterson and Williams, 2008).…”

Section: Introductionmentioning

confidence: 99%

Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation

Dhingra

Artois

Robinson

et al. 2016

eLife

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Global disease suitability models are essential tools to inform surveillance systems and enable early detection. We present the first global suitability model of highly pathogenic avian influenza (HPAI) H5N1 and demonstrate that reliable predictions can be obtained at global scale. Best predictions are obtained using spatial predictor variables describing host distributions, rather than land use or eco-climatic spatial predictor variables, with a strong association with domestic duck and extensively raised chicken densities. Our results also support a more systematic use of spatial cross-validation in large-scale disease suitability modelling compared to standard random cross-validation that can lead to unreliable measure of extrapolation accuracy. A global suitability model of the H5 clade 2.3.4.4 viruses, a group of viruses that recently spread extensively in Asia and the US, shows in comparison a lower spatial extrapolation capacity than the HPAI H5N1 models, with a stronger association with intensively raised chicken densities and anthropogenic factors.DOI: http://dx.doi.org/10.7554/eLife.19571.001

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“…Human population related transmission. ○ Human population density [ 11 , 14 – 16 , 18 , 19 , 44 , 45 ]. …”

Section: Methodsmentioning

confidence: 99%

Evidence for the Convergence Model: The Emergence of Highly Pathogenic Avian Influenza (H5N1) in Viet Nam

et al. 2015

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Building on a series of ground breaking reviews that first defined and drew attention to emerging infectious diseases (EID), the ‘convergence model’ was proposed to explain the multifactorial causality of disease emergence. The model broadly hypothesizes disease emergence is driven by the co-incidence of genetic, physical environmental, ecological, and social factors. We developed and tested a model of the emergence of highly pathogenic avian influenza (HPAI) H5N1 based on suspected convergence factors that are mainly associated with land-use change. Building on previous geospatial statistical studies that identified natural and human risk factors associated with urbanization, we added new factors to test whether causal mechanisms and pathogenic landscapes could be more specifically identified. Our findings suggest that urbanization spatially combines risk factors to produce particular types of peri-urban landscapes with significantly higher HPAI H5N1 emergence risk. The work highlights that peri-urban areas of Viet Nam have higher levels of chicken densities, duck and geese flock size diversities, and fraction of land under rice or aquaculture than rural and urban areas. We also found that land-use diversity, a surrogate measure for potential mixing of host populations and other factors that likely influence viral transmission, significantly improves the model’s predictability. Similarly, landscapes where intensive and extensive forms of poultry production overlap were found at greater risk. These results support the convergence hypothesis in general and demonstrate the potential to improve EID prevention and control by combing geospatial monitoring of these factors along with pathogen surveillance programs.

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Spatio-temporal epidemiology of highly pathogenic avian influenza (subtype H5N1) in poultry in eastern India

Cited by 23 publications

References 41 publications

Climate contributes to the evolution of pesticide resistance

Climate contributes to the evolution of pesticide resistance

Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation

Evidence for the Convergence Model: The Emergence of Highly Pathogenic Avian Influenza (H5N1) in Viet Nam

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