Landscape mapping of software tools for climate- sensitive infectious disease modelling

The report identifies 37 unique software tools for climate-sensitive infectious disease modelling, finding a heavy bias toward vector-borne diseases, particularly malaria. It highlights significant gaps in operationalization, accessibility, and geographic representation, noting that most tools are developed by North American and European institutions despite being intended for use in the Global South. Expert interviews reveal critical barriers to implementation, including poor data sharing between climate and health sectors, lack of political mandates, and a mismatch between complex model outputs and the needs of public health decision-makers.

• A literature review of approximately 9,500 papers identified only 37 unique software tools that meet the criteria of incorporating both climate and epidemiological data to predict disease risk, indicating that few studies progress from evidence of climate-health linkages to operational decision support tools.
• There is a strong imbalance in disease focus; 81.1% of identified tools focus on vector-borne diseases, with 53.3% of those specifically targeting malaria. There is a notable shortage of tools for respiratory, foodborne, and waterborne diseases, and a total absence of tools for soilborne diseases.
• While over half of the tools (n=20) were described as operationalized, only 25% were freely available online and only 25% featured interfaces legible to policy and regional decision-makers.
• Tool creation is disproportionately concentrated in the Global North, with 38% of tool-creators based in the USA and UK alone, despite many tools being designed for use in the Global South.
• Most tools are implemented in regions where the target disease is already endemic, and few provide forecasts beyond a seasonal climate outlook. There is a lack of tools for 'zones of emergence' where climate change and demographic shifts may increase future transmission risk.
• The R programming language is the most common development tool, used in 29.7% of the tools, followed by Python, C++, MatLab, and FORTRAN90.
• Inter-sectoral data sharing is a major barrier; while climate datasets are generally available, health datasets are often politically and ethically sensitive, and many public health and climate sectors lack a formal mandate to focus on climate impacts on health.
• Expert interviews identified key barriers to the use of models as decision-support tools, including poor data quality, miscommunication between researchers and decision-makers, lack of technical expertise, and insufficient long-term funding for maintenance.

APA

Ibarra, A. M. S., Ryan, S. J., Lowe, R., Lippi, C. A., Diaz, A., Dunbar, W., Johnson, S., & Grover, S. (n.d.). Landscape mapping of software tools for climate- sensitive infectious disease modelling. Inter-American Institute for Global Change Research. http://iai.int/admin/site/sites/default/files/Landscape_mapping_of_software_tools_for_climate_sensitvie_infectious_disease_modelling_0.pdf

Chicago

Ibarra, Anna M. Stewart, Sadie J. Ryan, Rachel Lowe, Catherine A. Lippi, Avriel Diaz, Willy Dunbar, Simon Johnson, and Shruti Grover. Landscape mapping of software tools for climate- sensitive infectious disease modelling. Inter-American Institute for Global Change Research, n.d. http://iai.int/admin/site/sites/default/files/Landscape_mapping_of_software_tools_for_climate_sensitvie_infectious_disease_modelling_0.pdf.

Wikipedia

{{cite report |last1=Ibarra |first1=Anna M. Stewart |last2=Ryan |first2=Sadie J. |last3=Lowe |first3=Rachel |last4=Lippi |first4=Catherine A. |last5=Diaz |first5=Avriel |last6=Dunbar |first6=Willy |last7=Johnson |first7=Simon |last8=Grover |first8=Shruti |title=Landscape mapping of software tools for climate- sensitive infectious disease modelling |publisher=Inter-American Institute for Global Change Research |url=http://iai.int/admin/site/sites/default/files/Landscape_mapping_of_software_tools_for_climate_sensitvie_infectious_disease_modelling_0.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{ibarrandlandscape, author = {Ibarra, Anna M. Stewart and Ryan, Sadie J. and Lowe, Rachel and Lippi, Catherine A. and Diaz, Avriel and Dunbar, Willy and Johnson, Simon and Grover, Shruti}, title = {{Landscape mapping of software tools for climate- sensitive infectious disease modelling}}, institution = {Inter-American Institute for Global Change Research}, url = {http://iai.int/admin/site/sites/default/files/Landscape_mapping_of_software_tools_for_climate_sensitvie_infectious_disease_modelling_0.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }