Poultry farm distribution models developed along a gradient of intensification.

Efficient planning of measures limiting epidemic spread requires information on farm locations and sizes (number of animals per farm). However, such data are rarely available. The intensification process which is operating in most low- and middle-income countries (LMICs), comes together with a spatial clustering of farms, a characteristic epidemiological models are sensitive to. We developed farm distribution models predicting both the location and the number of animals per farm, while accounting for the spatial clustering of farms in data-poor countries, using poultry production as an example. We selected four countries, Nigeria, Thailand, Argentina and Belgium, along a gradient of intensification expressed by the per capita Gross Domestic Product (GDP). First, we investigated the distribution of chicken farms along the spectrum of intensification. Second, we built farm distribution models (FDM) based on censuses of commercial farms of each of the four countries, using point pattern and random forest models. As an external validation, we predicted farm locations and sizes in Bangladesh. The number of chicken per farm increased gradually in line with the gradient of GDP per capita in the following order: Nigeria, Thailand, Argentina and Belgium. Interestingly, we did not find such a gradient for farm clustering. Our modelling procedure could only partly reproduce the observed datasets in each of the four sample countries in internal validation. However, in the external validation, the clustering of farms could not be reproduced and the spatial predictors poorly explained the number and location of farms and farm sizes in Bangladesh. Further improvements of the methodology should explore other covariates of the intensity of farms and farm sizes, as well as improvements of the methodology. Structural transformation, economic development and environmental conditions are essential characteristics to consider for an extrapolation of our FDM procedure, as generalisation appeared challenging. We believe the FDM procedure could ultimately be used as a predictive tool in data-poor countries.

Global trends in antimicrobial resistance in animals in low- and middle-income countries.

The global scale-up in demand for animal protein is the most notable dietary trend of our time. Antimicrobial consumption in animals is threefold that of humans and has enabled large-scale animal protein production. The consequences for the development of antimicrobial resistance in animals have received comparatively less attention than in humans. We analyzed 901 point prevalence surveys of pathogens in developing countries to map resistance in animals. China and India represented the largest hotspots of resistance, with new hotspots emerging in Brazil and Kenya. From 2000 to 2018, the proportion of antimicrobials showing resistance above 50% increased from 0.15 to 0.41 in chickens and from 0.13 to 0.34 in pigs. Escalating resistance in animals is anticipated to have important consequences for animal health and, eventually, for human health.

Global trends in antimicrobial use in food animals.

Demand for animal protein for human consumption is rising globally at an unprecedented rate. Modern animal production practices are associated with regular use of antimicrobials, potentially increasing selection pressure on bacteria to become resistant. Despite the significant potential consequences for antimicrobial resistance, there has been no quantitative measurement of global antimicrobial consumption by livestock. We address this gap by using Bayesian statistical models combining maps of livestock densities, economic projections of demand for meat products, and current estimates of antimicrobial consumption in high-income countries to map antimicrobial use in food animals for 2010 and 2030. We estimate that the global average annual consumption of antimicrobials per kilogram of animal produced was 45 mg⋅kg(-1), 148 mg⋅kg(-1), and 172 mg⋅kg(-1) for cattle, chicken, and pigs, respectively. Starting from this baseline, we estimate that between 2010 and 2030, the global consumption of antimicrobials will increase by 67%, from 63,151 ± 1,560 tons to 105,596 ± 3,605 tons. Up to a third of the increase in consumption in livestock between 2010 and 2030 is imputable to shifting production practices in middle-income countries where extensive farming systems will be replaced by large-scale intensive farming operations that routinely use antimicrobials in subtherapeutic doses. For Brazil, Russia, India, China, and South Africa, the increase in antimicrobial consumption will be 99%, up to seven times the projected population growth in this group of countries. Better understanding of the consequences of the uninhibited growth in veterinary antimicrobial consumption is needed to assess its potential effects on animal and human health.