Climate-sensitive infectious diseases are a public health issue due to population growth and climate change. In addition, as the rural-wildlife interface expands, the frequency of interactions between people, animals, vector-borne diseases and pathogens driving environmental diseases increase (1).
On the other side, the transmission and prevalence of infectious diseases are affected by the changes in climate at both local and global scales. This makes the use of environmental predictors crucial when it comes to estimating the risk of disease (2).
Hence the importance of climate-sensitive infectious disease forecasting systems, which enable us to predict the effects of environmental conditions on disease responses and are in great demand for public health planning and early warning systems (3).
A recent article (4) which reviewed tools used in the literature, identified only 37 software tools fully developed for modelling climate-sensitive infectious diseases. Most tools were created for vector-borne diseases and focused on malaria, dengue virus, West Nile virus and Rift Valley fever. Other tools identified focused on infectious diseases with other types of transmission, such as respiratory, food-borne or water-borne diseases.
These systems are statistical tools and models to predict infectious disease outbreaks and can support public health decision-making processes, providing an opportunity to identify the magnitude of these diseases and quantify them using software packages such as R or Python, with the contribution of different professional perspectives. According to these studies, most of these systems were developed for geographical regions where the infectious disease of interest is currently endemic, and for vector-borne diseases, whilst there is a shortage of tools for respiratory, food-borne and water-borne diseases (4).
However, there can be considerable barriers to the implementation of these systems due to technological gaps, mismatch between outcomes or financial constraints. This is why efforts are being made to provide resources to allow researchers, who are developing these systems, to work closely with software engineers to facilitate the creation of new tools to serve the community at general (5).
References:
- Liu, X; Huang, Y; Xu, X et al. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015.Nat. Sustain. 2020; 3: 564-570
- Myers, MF; Rogers, DJ; Cox, J; Flahault, A; Hay, SI. Forecasting disease risk for increased epidemic preparedness in public health. Adv Parasitol. 2000; 47: 309-330.
- Stewart-Ibarra, AM; Romero, M; Hinds, AQJ et al. Co-developing climate services for public health: stakeholder needs and perceptions for the prevention and control of Aedes-transmitted diseases in the Caribbean. PLoS Negl Trop Dis. 2019; 13e0007772
- Sadie, J; Catherine, A el at. The current landscape of software tools for the climate-sensitive infectious disease modelling community. The Lancet Planetary Health. 2023; 7: E527-E536.
- Neta, G; Pan, W; Ebi, K et al. Advancing climate change health adaptation through implementation science. Lancet Planet Health. 2022; 6: e909-e918
Amor Escoz Roldán
PhD in Education Sciences
Environmental Educator, Environmental Science Specialist