Abstract:
The COVID-19 pandemic led to a surge in interest among scholars and public health professionals in identifying the predictors of health shocks and their transmission in the population. With
temperature increases becoming a persistent climate stress, our aim is to evaluate how temperature
specifically impacts the incidences of contagious disease. Using annual data from 1 AD to 2021
AD on the incidence of contagious disease and temperature anomalies, we apply both parametric
and nonparametric modelling techniques and provide estimates of the contemporaneous, as well as
lagged, effects of temperature anomalies on the spread of contagious diseases. A nonhomogeneous
hidden Markov model is then applied to estimate the time-varying transition probabilities between
hidden states where the transition probabilities are governed by covariates. For all empirical specifications, we find consistent evidence that temperature anomalies have a statistically significant effect
on the incidence of a contagious disease in any given year covered in the sample period. The best fit
model further indicates that the contemporaneous effect of a temperature anomaly on the response
variable is the strongest. As temperature predictions continue to become more accurate, our results
indicate that such information can be used to implement effective public health responses to limit the
spread of contagious diseases. These findings further have implications for designing cost effective
infectious disease control policies for different regions of the world.
