Health and health systems adaptation

Health and health systems adaptation

Rising temperatures and increased precipitation can promote an array of infectious diseases, from vector-borne diseases (such as malaria and dengue), to intestinal infections and diarrhea (such as cholera). Mosquitoes in particular do better in warmer climates, and if current trends continue, an estimated 6.1 to 8.4 billion people will be at risk from malaria and dengue by the end of the century, primarily in the Global South., Another significant threat to health is the increasing frequency and intensity of heat-related disasters such as heatwaves, droughts and wildfires. Urban heat islands, mean that extreme heat events are felt even more profoundly in cities, putting the health of urban populations at risk, disproportionately impacting the poorest communities. Urban heat exposure is on the rise and it’s estimated that the world’s hottest cities will experience heat levels adverse to human health for up to half of the year by 2050.

As the scale, location and nature of these health risks change, it will be important for data collection to keep pace so that impacts on health and health systems can be understood and interventions properly targeted and designed. Collective intelligence initiatives are helping to close these data gaps – by involving communities in monitoring factors that contribute to the spread of climate-related diseases such as mosquito breeding grounds. These efforts often help close the distance between scientific knowledge and public knowledge – creating more locally relevant science and more informed communities. A smaller number of collective intelligence initiatives pair data collection activities with microtasking – using the data generated by communities and heightened community awareness to direct and incentivize activities, such as destroying mosquito breeding grounds. Collective intelligence solutions like this can empower people to use data themselves to prevent the spread of the disease, rather than looking solely towards local governments alone to solve problems. Involving more people in tackling problems themselves is one key way collective intelligence initiatives can help to close the doing gap.

Finding and adopting health solutions that are scalable, locally appropriate and inclusive is also important to tackle the impacts of climate-related health challenges as they start to affect more people globally. Collective intelligence methods, such as crowdsourced open innovation and challenge prizes, are meeting this need by attracting new innovators to work on solutions for urban heat, including those with experience of the issue. The best examples of open innovation set contextual constraints that solutions have to satisfy so they better serve the needs of vulnerable communities. These approaches help to address the diversity gap in climate action – by bringing in diverse voices to find solutions.    

Main collective intelligence methods being used

  • Citizen science for disease surveillance and management

  • Combining citizen-generated data and existing datasets to model disease outbreaks

  • Participatory sensing to measure extreme heat in cities

  • Open innovation for inclusive solutions to extreme heat

Main climate action gaps being addressed

  • Data gaps on impact of health interventions and on accuracy of modeling
  • Doing gap where communities depend on local government for action
  • Diversity gap from solutions being provided by a narrow pool of innovators who are removed from the problem

Citizen science for disease surveillance and management

Mosquitos are the most prevalent vectors of infectious disease. Involving local communities in collecting data about mosquitoes for disease surveillance can enhance the work done by health officials who have typically carried out such tasks – and empower communities to take action themselves.

In Colombia, the Premise tool has been used to support data collection to help prevent Zika outbreaks across three cities: Cali, Cucuta and Sanata Maria. During a pilot phase in 2018, more than 7,000 local volunteers were trained to inspect drains, gardens and other locations for mosquito breeding sites. They submitted their reports and photos on the Premise app using their phones. This data was then verified, aggregated and shared with public health officials so they could intervene before an outbreak occurred. The tasks were co-designed with local health authorities from the outset to ensure that the data could be used by decision makers. The volunteer network ultimately carried out over 108,000 home inspections. They were also trained in how to destroy breeding grounds around their own homes and take steps to keep them mosquito-free. As a result of this citizen-led surveillance and official response there was a 65 percent reduction in breeding sites in the areas that received regular inspections.

DengueChat is a similar  Latin American project that uses citizen science to control disease outbreaks at a hyperlocal level. Like the Premise example, it also uses a digital platform to enable community data collection about mosquito breeding sites. The site also has a community portal where residents learn about disease spread by mosquitoes and effective control measures.

Combining citizen-generated data and existing datasets to model disease outbreaks

Disease modeling enables public health officials and researchers to make predictions about the size, duration and geographical spread of an outbreak. Models are widely used to identify high risk areas, design interventions, set public policy and direct resourcing. However, at the start of new outbreaks or when new diseases emerge there is often a data gap that can make models less accurate. Collective intelligence methods such as citizen science and crowdsourcing are an effective way to fill these data gaps by mobilizing communities to collect disease outbreak data themselves.

The Water-Associated infectious Diseases in India: digital Management tools (WADIM) project is a rare example of disease surveillance for waterborne diseases. It’s an early stage initiative led by Plymouth University and research partners in India. It aims to map community vulnerability and incidence of cholera by crowdsourcing data about sanitary conditions and symptoms of waterborne diseases using a smartphone app. Sanitation surveys are used to validate the citizen-science-based risk maps, and there is a training and stakeholder engagement programme to introduce the app to local residents. In the future, the data will be combined with satellite data about floods and community surveys to improve cholera risk modeling and to build resilience in affected communities.

A more established initiative operating at the global level is the GLOBE Mosquito Habitat Mapper, which asks citizen scientists to record breeding sites and identify the species of mosquito being observed. These observations make it possible to track the range and spread of invasive mosquitoes worldwide. Since 2017, more than 32,000 Mosquito Habitat Mapper observations have been submitted by citizen scientists in 84 countries. All data reported by citizen scientists are publicly available. Scientists are using this data to develop new models about the spread of disease and to recognize larvae and mosquito breeding sites from digital images.

Another example which combines crowdsourced data with other datasets for disease modeling is the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA). This is an open source model which supports malaria forecasting in epidemic-prone regions of Ethiopia. EPIDEMIA uses machine-learning methods with malaria surveillance data and environmental data from Earth-observing satellites to determine the relationships between climate variations and malaria outbreaks.

Participatory sensing to measure extreme heat in cities

The use of low cost sensors by communities and groups of volunteers enables the collection of new data on emerging climate health risks including extreme heat. Although data on surface temperature can be monitored remotely by satellites, it often lacks the fine-grained resolution necessary to understand hyper-local variations – or to measure the impact indoors or on people themselves. This is where collective intelligence initiatives can fill an important data gap.

Urban Heat Islands is a field campaign started by the US National Oceanic and Atmospheric Administration (NOAA) to raise awareness about the many impacts of extreme heat and the factors that may affect the uneven distribution of heat throughout a community. They use low-cost sensors and in-person data collection campaigns to engage volunteers in monitoring how extreme heat is distributed in their neighborhoods. The data on air temperature and humidity are used to create a Heat Vulnerability data dashboard and to design hyper-local contextually appropriate adaptation measures, with the involvement of local residents. In the past, it has been used to develop urban heat action plans and decide on the best placement for resilience hubs to support communities during power outages. Originally developed for implementation in US cities, the method also transferred to Sierra Leone and Brazil in 2023.

Similarly, the VITO project works with local volunteers to map heat stress at a street-by-street level using low-cost sensors. The project started in Johannesburg where 100 local volunteers created a detailed map of six neighborhoods and has also been implemented in the city of Ekurhuleni, South Africa. They aimed to gain more insight into the impact of different factors of spatial elements such as buildings, shade and vegetation on urban heat. The resulting maps were used to demonstrate the disparities between rich, residential neighborhoods and poor townships and shared with local politicians, who are using the data to develop tailored adaptation interventions. In several townships, residents have urged the local government to plant more trees in their neighborhoods and to teach children about global warming and its consequences in school. The same approach was implemented in Niamey, Niger, in March 2023, to map heat stress at the resolution of individual houses and trees. The results will be used to develop an urban climate model which aims to predict the impact of green infrastructure on heat stress.

A less-frequently used technology for measuring the impact of heat stress on urban residents are wearables. This approach has mostly been implemented in Global North contexts where the technology is already relatively widespread, but recently, pilot studies in Kenya and Burkina Faso have looked to test their feasibility in low-resource settings.

Open innovation for inclusive solutions to extreme heat

Collective intelligence methods such as open innovation competitions and challenge prizes can help generate a wider range of solutions, helping to close the diversity gap. They can influence the trajectory of technological development to be more responsible, for example, through setting assessment criteria based on maximizing inclusion and by engaging a wider pool of problem-solvers.

The Global Cooling Prize is a global challenge prize competition to spur the development of more energy-efficient cooling technology. The prize was designed to incentivize the development of an affordable residential cooling technology that would have at least five times less climate impact than current solutions. It attracted applications from 31 countries, and the two winning entries were proposed by teams from China and India who had firsthand experience of the issue. The Global Cooling Prize supported the initial development of inclusive cooling technologies but broader adoption and scaling will require further market incentives through collaboration between innovators, manufacturers, investors and policymakers. A similar initiative, the Million Cool Roofs Challenge, aimed to develop inclusive solutions to improve cooling options for vulnerable communities without economic means to access mechanical cooling during heat stress events. Most finalists were based in Global South countries, with the winning team originating from Indonesia. Overall, the winning team was able to install cool roofs in 15 cities on 70 buildings and has also piloted the solution on rural affordable housing structures, with an aim to update future building specifications to include cool roofs. The team measured and verified indoor air temperature reductions of over 10 degrees Celsius in some of the pilots.

1An urban heat island (UHI) is an urban area that is significantly warmer than its surrounding rural areas due to human activities.

2Important for avoiding maladaptation.