Sub-theme leader

Hiroshi Nishiura (Kyoto University School of Public Health)


Due to the Climate Change Adaptation Act, it is important to establish an evidence-based regional climate change adaptation plan as well as to understand the spatial impact of climate change accurately and robustly. Although the issue of climate change has been an ongoing discussion for a long time, a robust forecasting system that comprehensively quantifies changes in health risks has not yet been established. The purpose of this study is to develop a statistical modeling project focusing on health risks (infectious diseases and heat strokes). Specifically, we will develop a non-linear model that will quantify health risks by utilizing the underlying mechanisms, such as changes in natural ecosystems. In addition, we will implement a prediction model to evaluate the effect of climate change and build a system to evaluate the potential effect of the adaptation, such as changes in land use. It is expected that the relationship between climate change and the environment in a broad sense (including location and land use) and the natural ecosystem and the collapse of human homeostasis will become clear. In addition, the effect of climate change on the incidence of disease will be clearly understood. Finally, we will implement a model that predicts the epidemic risks of vector-borne infection and heatstroke by using meteorological data or information on the geographic environment. We will visualize the relationship between climate change and human infectious diseases by using spatial information, and use our estimation for robust regional planning.

Modeling the risk of the incidence of infection based on information regarding environmental conditions and land use


  • Provide dengue and malaria epidemic forecasting based on long-term observational data of temperature
  • Quantify the effects of temperature and humidity on human dehydration and discomfort by using observational data and information about land use, and implement a high spatial resolution heatstroke prediction model
  • Achieve a precise quantification of health risks (visualization of health risks) at the spatial level

Research target and plan

  • Research focusing on temperature and vector-borne infection
    Our original risk evaluation (modeling study) of mosquito-borne diseases (dengue and malaria) and tick-borne diseases (febrile thrombocytopenia syndrome)
  • Prediction modeling of heatstroke using temperature, humidity, radiant heat, and land use as predictors
    Significantly improve scientific understanding of the relationship between heatstroke and meteorological data such as temperature and precipitation by using theoretical epidemiological research methods
  • Visualize health risks and high-resolution spatial environment information using big data, including land use
    Precisely understand the heterogeneous and spatial change of health risks by using 1 km × 1 km mesh and town-level maps and geographical information systems

Expected strategies

  • Analyze the incidence of infection and heatstroke outbreak and organize the outbreak polices
  • Cool city, lifestyle change, community building for heatstroke vulnerable people
  • Maintain environments that vector insects cannot easily inhabit, improve quarantine systems, and establish support systems for vulnerable people such as the elderly
  • Monitor the formulation of adaptation measures in geographically local areas and provide forecasts of the effects