TY - GEN
T1 - Assessing the impact of climate change on patterns of disease outbreaks, livestock health, and antibiotic use in India
AU - Paul, Jonathan
AU - Eskdale, Adam
AU - Eltholth, Mahmoud
AU - Cole, Jennifer
PY - 2013/12/31
Y1 - 2013/12/31
N2 - Understanding the impacts of climate change on patterns of disease outbreaks is critical to safeguarding global food supplies and economies. Informed by ethnographic research with Indian farmers and veterinarians, we identify how spatiotemporal changes in humidity (and, to a lesser extent, Monsoon rainfall) is the key climate variable that relates to important outbreaks of haemorrhagic septicaemia, anthrax, and black quarter across the Indian state of Karnataka. We also identify – surprisingly – temperature change to be negatively correlated with the same diseases, indicating that a cooling (but still hot) climate with wetter, humid conditions is a prime risk factor for future outbreaks. Principal component analyses have revealed the SW Indian Monsoon and winter periods to be the most critically associated with disease outbreaks. We demonstrate an excellent correlation between time series of humidity and those relating to outbreaks of the aforementioned specific livestock diseases. The negative relationship between temperature and these diseases, combined with the positive correlations with rainfall and humidity, allow us to classify climate-associated risk using a combination of gridded meteorological time series and epidemiological outbreak data covering the same region and timespan of 1987–2020. Risk maps were constructed in response to concerns over the growing impact of climate pressures raised by farmers. Informed by their insights, we used current climate data and future climate projections as a risk classification tool to assess how disease risk varies in Karnataka in the present and possible future scenarios. Despite a relatively modest and fragmentary epidemiological dataset, we were able to define statistically significant relationships, as well as especially susceptible or high-risk zones. This methodology can be replicated to investigate other diseases (including in humans and plants) and across other regions, irrespective of scale, as long as the climate and epidemiological datasets are of similar resolution and cover similar time periods.
AB - Understanding the impacts of climate change on patterns of disease outbreaks is critical to safeguarding global food supplies and economies. Informed by ethnographic research with Indian farmers and veterinarians, we identify how spatiotemporal changes in humidity (and, to a lesser extent, Monsoon rainfall) is the key climate variable that relates to important outbreaks of haemorrhagic septicaemia, anthrax, and black quarter across the Indian state of Karnataka. We also identify – surprisingly – temperature change to be negatively correlated with the same diseases, indicating that a cooling (but still hot) climate with wetter, humid conditions is a prime risk factor for future outbreaks. Principal component analyses have revealed the SW Indian Monsoon and winter periods to be the most critically associated with disease outbreaks. We demonstrate an excellent correlation between time series of humidity and those relating to outbreaks of the aforementioned specific livestock diseases. The negative relationship between temperature and these diseases, combined with the positive correlations with rainfall and humidity, allow us to classify climate-associated risk using a combination of gridded meteorological time series and epidemiological outbreak data covering the same region and timespan of 1987–2020. Risk maps were constructed in response to concerns over the growing impact of climate pressures raised by farmers. Informed by their insights, we used current climate data and future climate projections as a risk classification tool to assess how disease risk varies in Karnataka in the present and possible future scenarios. Despite a relatively modest and fragmentary epidemiological dataset, we were able to define statistically significant relationships, as well as especially susceptible or high-risk zones. This methodology can be replicated to investigate other diseases (including in humans and plants) and across other regions, irrespective of scale, as long as the climate and epidemiological datasets are of similar resolution and cover similar time periods.
M3 - Conference contribution
BT - AGU Fall Meeting 2022 abstracts
ER -
