Health Diagnostics

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.

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.

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.

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.

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.

Catherine Arsenault at the Harvard T.H. Chan School of Public Health in the U.S. and colleagues will measure the effect of the COVID-19 pandemic and associated containment policies such as curfews on the quality of health care in seven countries and the rates of mortality from non-COVID conditions. They have extracted data from health management information systems spanning two years from Ethiopia, Ghana, Haiti, Laos, Mexico, Nepal, and South Africa.

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.

To capture data on AMR indicators- antibiotic prescriptions, drug sensitivities and resistance patterns from the private sector (human and animal clinics and laboratories) to generate evidence that will contribute to improving antibiotic prescription practices, epidemiological surveillance and effective control of AMR in Uganda. 

Carol Hanna of Oregon Health and Science University in the U.S. will develop an imaging platform using Positron Emission Tomography-Computed Tomography (PET-CT) to visualize the movement of sperm in the female reproductive tract to accelerate testing of non-hormonal contraceptive compounds at lower cost. Emerging research is positioned to discover non-hormonal contraceptive candidates with varying mechanisms of action. However, there is currently no simple or effective way to test them in human-relevant models, which impedes their clinical development.

Viviana Gradinaru of the California Institute of Technology in the U.S. will perform imaging-based, high-throughput screens using adeno-associated virus (AAV) delivery vectors to rapidly identify ovary-specific macromolecules that are essential for fertility and could be used to develop non-hormonal contraceptives.