Health Technologies

Wellcome announces £22.7 million in funding for projects developing digital tools for climate-sensitive infectious diseases – htn

24 research teams across 12 countries are to receive a share of £22.7 million to develop new digital tools in response to climate-sensitive infectious diseases, following a funding call last year from global charitable foundation Wellcome.

The Digital Technology Development Awards focused on the development of open-source technology and aimed to create tools which use climate data to understand and predict climate-sensitive infectious diseases.

As part of the project, Wellcome is funding 20 new tools across four categories, following a study that identified technology gaps in global preparedness for these diseases.

Modelling tools

In this category, tools in development are to help investigate the impact of climate on infectious disease risks. They are designed to help identify times and locations when risk of disease transmission increases.

  • Arboverse: a data-driven platform and disease modelling to anticipate and mitigate arbovirus emergence. Led by researchers at the University of Texas, Arboverse will create a web platform to process and collate data on arboviruses, vectors and environmental factors such as deforestation, human mobility and climate change, with the aim of of understanding what causes the arbovirus outbreaks.
  • WaterPath: future scenario toolkit for waterborne infectious disease modelling. Led by a team of researchers at Wageningen University in The Netherlands, WaterPath will produce an “open-source modelling toolkit” to calculate the impact of climate change and socio-economic development through the spread of waterborne pathogens.
  • Climate-sensitive vector-borne disease intervention tools. A team at Imperial College London is to develop a flexible and user-friendly interface for policymakers and researchers so that they can explore the impact of environmental change on the spread of vector-borne diseases. The tools will allow users to input disease projections and use simulation to allow investigation of the necessary public health measures to mitigate the effects of climate change.
  • Climate-driven models to predict future risk of arenaviruses. A team at One Health Institute at the University of California is to create a “semi-automated data collection tool for arenaviruses” which will include data on human cases, rodents population and trends in rainfall levels. From this, models will be produced to better understand risks in the future from emission scenarios and local climate projections.
  • Software to estimate the impact of climate variability on infectious disease epidemic risks. Researchers from the University of Warwick are to create an “open-source software for estimating future epidemic risks in different locations.” They will target vector-borne diseases, but the software they are developing has the possibility to be adapted for other pathogens too.

Climate-informed mosquito-borne disease early warning systems

These tools will predict and investigate the risk of transmission from mosquito-borne diseases.

  • Digital technology for storm-related malaria response in Mozambique. At the University of Minnesota, researchers are developing models which will analyse areas in Mozambique where severe weather events will increase malaria risk. The team are also to create a pilot software platform that will predict areas at risk of malaria transmission after weather events, to inform malaria control activities in those areas.
  • CLIMSEDIS: a climate-sensitive disease forecasting tool. A team from the University of Liverpool is investigating if the same climate factors drive multiple climate-sensitive diseases across various countries in the Horn of Africa. They are co-developing, piloting and testing models to forecast changes with diseases with the National Ministries of Health and Agriculture.
  • Mosqlimate: a tool to estimate changes to climate and land use. Researchers at Fundação Getulio Vargas in Brazil are developing this tool to examine how the changes will “affect patterns of transmission for multiple arboviral diseases such as Zika, dengue and Chikungunya.” Another tool in development through the same project, OviCounter, is to be used to help improve mosquito surveillance data.
  • HydroVec: a digital surveillance platform for areas under threat of extreme hydro-meteorological events. Researchers at the Liverpool School of Tropical Medicine are creating a platform called HydroVec which aims to predict the transmission of vector-borne diseases in “areas vulnerable to extreme hydro-meteorological events, such as thunderstorms, hailstorms, tornados and floods.”
  • An open-source framework for Rift Valley Fever forecasting. EcoHealth Alliance in the US  is producing a tool designed to predict the changes of Rift Valley Fever. The tool will “integrate data on African climate, population, livestock and vegetation.” Regional and local groups will be able to insert specific data to their area and control measures to local conditions.
  • DART (Dengue Advanced Readiness Tools): an integrated digital system for dengue outbreak prediction and monitoring. Researchers at the University of Oxford  and Oxford University Clinical Research Unit in Vietnam will develop an automated forecasting system that integrates datasets from across Hanoi and Ho Chi Minh. The team are to build a mobile and desktop application  capable of supporting predictions of dengue within cities; the application will integrate weather and disease forecasts in order to improve understanding of the relationship between them,
  • Climate-driven vector-borne disease risk assessment. A team of researchers from The Cyprus Institute is to build a platform that organises and combines environmental and climate data and up-to-date mosquito surveillance data. The data will be combined with large-scale models to predict changes in the number of mosquitoes in the area and disease outbreaks in eastern Europe and central Asia. The team will create tutorials and educational tools for public health specialists.

Early warning systems for other types of disease transmission

These tools are designed to predict transmission risk of climate-sensitive infectious diseases that are not mosquito-borne (such as respiratory, foodborne and waterborne diseases), in order to inform interventions.

  • Chol-Out-EWS: an open-source software application for community-level cholera outbreak risk prediction and feedback through early warning systems. Chol-Out-EWS will be an open-source software developed by a team at the International Centre for Diarrhoeal Disease Research in Bangladesh, and will be designed to model and predict the risk of cholera outbreaks in the area. The project will also involve communication of those risks through an early warning system, and support timely control of transmission and disease management in hotspots.
  • A web app for accessible, reproducible, multi-scale regression models for mapping climate-driven infectious diseases. A team at the University of Leicester, UK, are to develop an online app using health and environmental data to make spatially refined predictions of zoonotic and vector-borne diseases. The tool is to be “co-designed with public health bodies within countries with a high burden of these diseases, such as the Nigeria Center for Disease Control and Prevention”.
  • IDExtremes: a modelling tool to predict the probability of infectious disease outbreaks given compound extreme climatic events. Led by researchers at the Barcelona Supercomputing Center, Spain, this project sees the c0-development of an open-source tool from stakeholders in Barbados, South Sudan, Nigeria, Brazil, Bangladesh and Nepal. The tool will allow users to input observed and forecasted meteorological indicators like droughts, floods or heatwaves, and will predict the probability of outbreaks of climate-sensitive infectious diseases in advance.
  • Co-creation of a multi-pathogen predictive dashboard to improve health system responsiveness to climate-sensitive diseases in rural Madagascar. This prediction tool will be co-created with the Ministry of Public Health of Madagascar and the non-governmental organisation Pivot. It will be used to inform the health system and strengthen interventions against local diseases in rural Madagascar, and will focus on malaria, diarrheal disease and acute respiratory infections.
  • FloDisMod: a framework for flood and disease modelling. Led by researchers at the University of Texas, FloDisMod is an open-source software designed to model floods and storms surges in order to better understanding of their impact on mosquito habitat, disease reporting, social vulnerability and health risks. The team also plan to develop an early warning system for the Texas-Mexico border region, and beyond that extend it to soil-borne diseases too.

Foundational tools

In the final category, we have tools designed to support and apply to a variety of “problem areas” such as modelling, data acquisition and data visualisation.

  • Health RADAR: responsible access to data for analysis and research. Researchers at the University of Cape Town in South Africa, are to develop an open-source web-based platform that aims to provide a central data resource for disease and other relevant data, specific to climate-sensitive infectious disease modelling. The platform is to focus on malaria transmission in Botswana, Eswatini, Namibia and South Africa.
  • WADIM: Water-Associated infectious Diseases in India: digital Management tools. Tools will be developed by a team at Plymouth Marine Laboratory to map sanitation conditions and disease spread in India. Data will be collected through an app designed for citizens and primary health workers.
  • CliZod, compiling knowledge: a participatory database of modelling parameters for climate-sensitive zoonotic disease. CliZod will be a searchable, interactive and participatory database of model frameworks for zoonotic diseases, led by a team at Massey University in New Zealand. Researchers will use natural language processing to find relevant information from the literature which will then be inputted into a database. This is intended to overcome the limitations of systematic reviews, which are subject to human bias and time constraints.

Felipe Colón, Wellcome’s Technology Lead, commented: “Digital technologies such as numerical models and early warning systems are some of the most powerful and useful tools available for understanding and potentially mitigating the impacts of climate on infectious diseases.

“The Digital Technologies Development Award was a great opportunity to improve the field of climate-sensitive infectious disease modelling that could inform actions to reduce the global burden of climate-sensitive infectious diseases.”



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