Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.
Yilan Ye from Tsinghua University in China will develop a small, self-adhesive menstrual product based on the suction cups of octopuses that can be fixed securely but reversibly inside the vaginal opening to block the flow of blood and enable its convenient disposal. They will design it specifically for women and girls in low- and middle-income countries by ensuring it is low-cost, re-usable, safe to apply, and does not require sanitation facilities. They will experiment with different commercialized, biocompatible thermoplastic polyurethanes (TPUs) as the raw materials to produce the adhesive polymers. They will first test these polymers for their ability to be strongly, reversibly and repeatedly stuck to the surface of porcine livers and hearts as surrogates that mimic the moist and irregular skin surface inside the vagina. Finally, they will develop an inject mold to manufacture a prototype for human testing that also contains a soft valve for convenient release.
Andrew Boulle and colleagues at the Western Cape Government Health Department and the University of Cape Town in South Africa will use a data science approach applied to anonymized COVID-19 health data from the government health department including over one million tests and 60,000 hospital admissions, to study the clinical epidemiology and evolution of a new variant of SARS-CoV-2 that emerged in South Africa and the impact on patients with existing health conditions. They will conduct a case-control study to determine the clinical severity of the variant and use a cross-sectional design to explore the evolution of viral load. They will also analyze the impact of COVID-19 on pregnancy by evaluating birth weight and other birth outcomes, such as still births, and use death registries to determine mortality rates in patients with HIV, TB, and diabetes.
Xiaofan Liu at the City University of Hong Kong in China and colleagues will reconstruct COVID-19 transmission chains between individuals in communities and households using statistical methods applied to existing datasets to more reliably estimate COVID-19 transmission characteristics, such as reproduction rates, that are critical for planning effective control measures. Currently, transmission characteristics are estimated using aggregated-level data, which leads to inaccuracies. Ideally, data on how COVID-19 is transmitted between individuals are needed. They will curate an existing collection of datasets containing over 40,000 COVID-19 cases in five Asian countries with person-to-person transmission evidence to reconstruct transmission chains. They will then apply statistical tests and an analytical methodology called regression analysis to identify the most important transmission risk factors, which may include virus strain, transmission media, population density, and climate conditions.
Luis Felipe Reyes at the Universidad de La Sabana in Colombia and colleagues will develop a standardized strategy for researchers to better utilize the ISARIC-COVID-19 dataset, which consists of over 520,000 hospitalized patients from more than 62 countries, and identify the causes and health impacts of severe complications. The dataset is particularly valuable because it covers varying standards-of-care around the world and could be used to study the geographic and time-based variability of the disease. The team will develop a standardized strategy to reformat and clean the ISARIC-COVID-19 dataset by producing data descriptors and reference codes and use this strategy to identify the risk factors and clinical characteristics of COVID-19 complications, such as cardiovascular complications, which are a major contributor to long-term morbidity and mortality, in order that vulnerable patients can be better treated.
Fernando Bozza at Fiocruz in Brazil and colleagues will quantify the real-world value of COVID-19 vaccines in Brazil for protecting individuals from severe disease and for protecting the entire population from being infected. Knowing how effective vaccination is, and how durable the response in the real world is, particularly in low- and middle-income countries, it is critical for ending the pandemic. They will determine the effectiveness of the vaccine for protecting individuals using an approach called test-negative design together with statistical and machine learning approaches to compare the severity of respiratory disease in COVID-19 patients from 43 hospitals. At the population level, they will perform an ecological study, and use regression analysis accounting for inequities to vaccine access, to measure the effect of vaccinations on COVID-19 cases, hospitalizations, and deaths.
Maria Yury Ichihara and colleagues at the Centre for Data and Knowledge Integration for Health (Cidacs) at Fiocruz in Brazil will create a social disparities index to measure inequalities relevant to the COVID-19 pandemic, such as unequal access to healthcare, to identify regions that are more vulnerable to infection and to better focus prevention efforts. In Brazil, markers of inequality are associated with COVID-19 morbidity and mortality. They will develop the index of available COVID-19 surveillance data, hosted on the Cidacs platform, and build a public data visualization dashboard to share the index and patterns of COVID-19 incidence and mortality with the broader community. This will enable health managers and policymakers to monitor the pandemic situation in the most vulnerable populations and target social and health interventions.
Kirsty Le Doare and colleagues at the MRC/UVRI & LSHTM Uganda Research Unit and Makarere University John's Hopkins University in Uganda will develop a model using data collected in real-time to identify the risk factors for adverse pregnancy and infant outcomes caused by the COVID-19 pandemic that can be used to rapidly inform interventions. Lockdowns can severely impact women giving birth and access to maternal, neonatal, and child healthcare. They will apply a Bayesian multivariate network meta-analysis, (a methodology that simultaneously analyses multiple outcomes and multiple treatments, allowing more studies to contribute towards each outcome and treatment comparison) to electronic medical records, leveraging existing data on the effect of the lockdown on antenatal and delivery services for over 30,000 pregnancies, vaccination data, and information on COVID-19 infection in pregnancy and infancy. They will also build a user-friendly data dashboard to support decision-making on infection prevention and control at the Ministry of Health.
Juliane Foseca de Oliveira and colleagues at Fiocruz in Brazil will develop mathematical and statistical methods to model COVID-19 infection transmission, prevention and control across populations in Brazil to better inform local intervention efforts. Social and economic inequalities are known to shape the spread of diseases, therefore the team will integrate existing health data together with social and economic determinants for 5,570 Brazilian cities, as well as assessing data on the effects of the mitigation strategies and social mobility patterns. These data will be used to develop and apply statistical analyses and nonlinear mathematical modelling to forecast disease evolution and outcomes that consider the specific socio-economic conditions, which influence transmission rates. The results will be presented on a user-friendly surveillance platform that can be used by local governments and communities to identify the most effective control methods for their region.
Dale Barnhart and colleagues at Harvard Medical School in the U.S. and Partners in Health of Haiti, Malawi, Mexico, and Rwanda will determine how the COVID-19 pandemic has impacted health care provision and utilization for patients with HIV, heart disease, and diabetes, and the health outcomes of these patients, in all four countries. They will pool existing electronic medical data on chronic care patients collected from up to 30 health facilities in each country and create a harmonized database to identify the impacts of COVID-19 and any successful strategies used to improve care. They will also develop a predictive model to identify which patient populations are most at risk from care disruption during the pandemic, which can help prioritize clinical and geographic areas that need interventions. Finally, they will develop data visualization tools to facilitate the communication and interpretation of the data by chronic care managers across the four different countries.
Carl Marincowitz and colleagues at the University of Sheffield in the United Kingdom and the University of Cape Town in South Africa will develop a risk assessment tool to help emergency clinicians quickly decide whether a patient with suspected COVID-19 needs emergency care or can be safely treated at home to avoid overburdening hospitals particularly in low- and middle- income countries (LMICs). They will use existing data to which they have access on 50,000 patients with suspected COVID-19 infection who sought emergency care in the United Kingdom, South Africa, and Sudan to develop prediction models for specific COVID-19 related outcomes in all income settings. These prediction models will be used to develop risk stratification tools, which enable providers to identify the right level of care and services for distinct subgroups of patients. These will be developed with input from patient and clinical stakeholders. The team will test the performance of their risk assessment tools for identifying high-risk patients with existing triage methods.