Investigating the impact of climate variability and climate change on tick
Gavera, Miriam Fadziso
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The impact of climate variability and climate change on tickborne diseases in Chiredzi District was investigated. Ticks are responsible for several diseases (tickborne diseases) that significantly affect livestock, especially cattle. The treatment of tickborne diseases and the resulting secondary infections result in very high economic losses in cattle production. Ticks attach themselves on the cattle and this causes irritation and this affects the feeding of cattle resulting in reduced milk production and poor meat quality. Several methods have been developed in tick control, however a more climate based method is required especially where climate variability and change are significant. A climate based method if combined with the already present methods like the use of acaricides and vaccines allows better resource use and more efficient disease management. The research used historic climatic data provided by the Zimbabwe Meteorological Services Department (ZMSD). The data included total monthly precipitation, maximum and minimum temperature values. The data was used to construct time series graphs which showed that generally rainfall is on the decrease while temperature has increased for the periods 1965/66 –2007/08 and 1979-2007 respectively. Data on tickborne disease incidence was obtained from the Department of Veterinary Services Zimbabwe (DVSZ) as collected by local farmers at household level. Downscaled climate data from Global climate models were used to make future climatic prediction for the period 2046-2065 using the Climate Change Explorer Tool. Regression analyses as well as RMSE analysis were done to assess model efficiency. The models performed better in predicting minimum and maximum temperatures with R2 values of about 0.80 and 0.6 respectively. However the models were very poor in predicting rainfall values. An epidemic risk model was developed relating temperature and rainfall to disease occurrence incidence. An equation y=0.451x+2.013 was developed where y is the disease incidence and x is percentage epidemic risk which is derived from the equation The equation relates temperature (T) and rainfall (R) data to percentage epidemic risk. From the data given the epidemic risk model was validated for its ability to predict disease occurrence using previously recorded data. The R2 value for observed disease occurrence against the disease occurrence values predicted by the model was R2= 0.589.