Predictive Model

Climate change increases the suitability of environments to transmit water/air/food/vector-borne or zoonotic diseases. Despite public health/medical advances to reduce transmission, population growth in wildlife areas, unplanned urbanization, and globalization fuel emerging/re-emerging diseases and global spread. Preventing and controlling such diseases and challenging viral hemorrhagic fevers like Ebola, requires a transdisciplinary approach with surveillance, early detection/response, vector control, education, and safe care.

Anne Lee of Brigham and Women's Hospital in the U.S. and Yasir Shafiq of Aga Khan University in Pakistan will develop geospatial models to predict risks of undernutrition among adolescent girls and pregnant and lactating women in settings affected by conflict, climate and COVID-19 to help target interventions. Globally, around 30–40 million pregnant women and 50 million adolescent girls are underweight. Risks of undernutrition have recently been amplified by numerous armed conflicts, climatic shocks such as flooding and the COVID-19 pandemic.

Margaret Kasaro and Soumya Benhabbour of the University of North Carolina at Chapel Hill in the U.S. will evaluate 3D-printed intravaginal ring (IVR) prototypes in Zambia to identify the design most acceptable to women for long-term use against unplanned pregnancy and HIV infection. In Zambia, HIV prevalence remains particularly high among women, and 41% of pregnancies are unplanned. IVRs are an effective, well-tolerated, and women-controlled contraceptive and HIV-preventative; however, their performance has suffered in large-scale clinical trials because of poor adherence.

Sana Syed of the University of Virginia in the U.S. together with Imran Nisar of Aga Khan University in Pakistan will utilize metabolic modeling of patient-derived ‘omics data from pre-existing maternal and pediatric cohorts to identify new biomarkers and therapeutic targets for environmental enteropathy (EE), which is associated with impaired childhood growth and development and vaccine responses.

Penny Moore of the National Institute for Communicable Diseases in South Africa together with Adriana Bonomo of Fiocruz in Brazil will identify solutions for combating new SARS-CoV-2 variants by developing an in vitro assay to predict new variants and identifying broad specificity antibodies for use as new drugs and diagnostics. Despite the success of vaccines and antibody therapies, the continual emergence of new viral variants, which thwart our immune defenses and therapies, remains a major challenge of the pandemic.

Jurriaan de Steenwinkel of the Erasmus Medical Center Rotterdam in the Netherlands together with Eric Nuermberger of Johns Hopkins University in the U.S. will combine expertise to develop a robust, preclinical mouse model of latent tuberculosis (TB) together with a molecular assay for measuring candidate drug activity to boost drug development. Reducing latent TB infections is essential to meet the goal of the World Health Organization’s End TB Strategy but current drugs have limited effect and measuring the activity of candidate compounds in latent infections is challenging.

Lye McKinnon of the University of Manitoba in Canada together with Ali Ssetaala of the Uganda Virus Research Institute in Uganda will determine whether nasal mucosal immune responses induced by COVID-19 vaccines and natural infection can help prevent infection and transmission. Although COVID-19 mRNA vaccines effectively prevent severe disease, they are less effective at preventing transmission, which is critical for protecting vulnerable populations particularly against emerging, highly transmissible variants.

Adriana Bonomo of Fiocruz in Brazil together with Penny Moore of the National Institute for Communicable Diseases in South Africa will identify solutions for combating new SARS-CoV-2 variants by developing an in vitro assay to predict new variants and identifying broad specificity antibodies for use as new drugs and diagnostics. Despite the success of vaccines and antibody therapies, the continual emergence of new viral variants, which thwart our immune defenses and therapies, remains a major challenge of the pandemic.

Nitin Baliga of the Institute for Systems Biology in the U.S. together with Google Applied Science will combine systems biology with machine learning and artificial intelligence to accelerate the discovery of more effective and affordable treatments for tuberculosis. Tuberculosis kills 1.5 million people annually, but developing novel treatments is expensive using current methods and complicated by the different physiological states and sub-populations of the causative Mycobacterium tuberculosis.

GuoXiong Peng, Yuxian Xia, Yueqing Cao and ZhengBo He of Chongqing University in China together with their international partner Raymond J. St. Leger of the University of Maryland in the U.S. will screen mosquitocidal fungal strains from China and abroad for high-yield virulent and stable production strains against larvae and adults, test the safety of the production strains, optimize solid fermentation medium, fermentation process and the components and proportion in the formulation to develop oil-based fungal mosquitocides for outdoor application.