About the Author

Leveraging Diversity and Building Capacity for Sustained Collaboration

By Anuj Mubayi, Aditi Ghosh, and Madhav Marathe

In 2015, the United Nations established a set of 17 global Sustainable Development Goals (SDGs)—to be met by the year 2030—that will ultimately achieve a better and more sustainable future for everyone. However, the ongoing COVID-19 pandemic has complicated many of the goals and highlighted increasing concerns that are related to supply chain disruption, product development, environmental degradation, and inequality, among other topics. It has also amplified the importance of international research and training collaborations. Pandemics and other large systemic events—like financial contagions and the unfolding climate crisis—are universal problems that do not obey national boundaries. Solutions to such problems must therefore be global in nature and require cooperation from the global community.

Earlier this year, the U.S. Centers for Disease Control and Prevention (CDC) launched a national Center for Forecasting and Outbreak Analytics. This center will tackle future health and social problems and include a group of multidisciplinary scientists from academia and the private sector. It will necessitate an influx of strong, well-rounded professionals in science, technology, engineering, and mathematics (STEM) who are trained in mathematics, computational science, and data science; this training will play an important role in the timely achievement of the global SDGs and inspire a workforce that addresses real situations in real time. To help future generations of STEM researchers recognize the usefulness of practical applied mathematics, we must instill cultural values, positive attitudes, and diversity acceptance among new trainees.

As the international community slowly adapts to a “new normal,” the pace of global problem discussions and collaborations should accelerate to deliver much-needed change. Successful networking opportunities and partnerships allow researchers to access additional expertise, gain new perspectives, and build relationships with other individuals in their fields; the latter is crucial for early-stage career development. In short, collaborations across organizational, disciplinary, and cultural boundaries extend the possibilities of discovery [1]. Here we provide several examples of our international research collaborations in the form of capacity building, STEM education, and epidemiological modeling. These endeavors provide different types of global experiences for participants and expose STEM scholars to a more comprehensive worldview.

Collaborative Initiatives in Latin America

We have engaged in multiple research and training efforts with our counterparts in Latin American countries like Colombia, Peru, Ecuador, and Chile. Such collaborations involve students who are studying computational and mathematical methods and working on epidemic modeling. We also partnered with researchers at clinical institutions like the Instituto Nacional de Investigación en Salud Pública-INSPI in Ecuador and the Centro Nacional de Epidemiología, Prevención y Control de Enfermedades del Ministerio de Salud in Peru, where practical experiments for our research projects took place. Our group has even partaken in multiple training series as speakers for tutorials on mathematical epidemiology and at the 2021 Mathematical Congress of the Americas.

Moreover, we recently initiated a global consortium for the mathematical modeling community—the Consortium of Modeling and Computations in Biosocial, Healthcare, and Sustainability Systems (CMCBHS)—along with the Universidad de Medellín, Universidad del Valle, Universidad Nacional de Colombia (all of which are in Colombia), and Illinois State University. CMCBHS activities include multidisciplinary student conferences, research projects of global importance, and mathematical modeling training modules.

An international research and educational training workshop—funded by a Partnerships for Enhanced Engagement in Research (PEER) grant from the U.S. Agency for International Development—took place in El Salvador in January 2017. Anuj Mubayi (fourth from right), Oscar Picardo, and Victor Cuchillac organized the workshop activities, which addressed crime and insecurity in El Savador. The workshop provided professionals and student mentors with a variety of high-impact teaching strategies and activities that prepared them to meet the deliverables of the PEER Project. Photo courtesy of Oscar Picardo.

Several years ago, we participated in a unique international collaboration and research training workshop through a Partnerships for Enhanced Engagement in Research (PEER) grant from the U.S. Agency for International Development to address crime and insecurity in El Salvador, which has one of the worst crime rates in the world. This fact, combined with the country’s under-resourced educational foundation, inspired us to establish a joint workshop to build science and technology innovation capacity in El Salvador while exposing U.S. students to cultural values and resource limitations in a developing country. The workshop introduced participants—including university students, judicial institutions, and STEM teachers from secondary schools in El Salvador and the U.S.—to data science, statistical computing, and the STEM research process (see photo). It also helped create a crime data sciences laboratory to collect, model, and analyze real-time crime and violence data.

Throughout the course of the aforementioned activities, we published a number of peer-reviewed articles with students and researchers from our partner institutes in Latin America. One study—which involved collaborators from the National Institute for Public Health Research of Ecuador, Universidad del Valle in Colombia, and Yachay Tech University in Ecuador—focused on transmission dynamics of leishmaniasis, a neglected tropical disease that poses a daily threat to millions of people around the world [2]. It utilized data from remote areas of Ecuador and ultimately estimated case underreporting in the country to be at least 38 percent. Unlike many mathematical studies, all coauthors were interdisciplinary and contributed to every aspect of the project: study design, data collection, lab experiments, mathematical modeling, and statistical inferences. This unique comprehensive experience improved participants’ cross-cultural awareness.

Collaborative Initiatives in South Asia

We are presently working with the Royal University of Bhutan and Bhutan’s Mongar Regional Referral Hospital on a PEER program for research partnerships in the context of COVID-19 challenges. This project aims to positively impact Bhutan’s education and research efforts while also addressing and learning from problems of global importance to train the new generation of STEM scientists. The collaboration is building a first-of-its-kind partnership between Bhutan and U.S. institutions, enhancing the investigative capacity of Bhutanese scientists, and familiarizing partners with global databases. Modeling exercises often require data-driven research from across the globe, which is crucial in today’s world.

Some of our activities also involve institutions in India, such as the Indian Council of Medical Research’s Rajendra Memorial Research Institute of Medical Sciences (RMRI) and the Sri Satya Satya Institute of Higher Learning (SSSIHL). For example, we partnered with RMRI to conduct research and collect community-level public health data that then informed state health policies [3]. This enterprise also introduced both U.S. and Indian participants to challenges that pertain to the collection of community information in real time under resource-limited conditions and even resulted in a Ph.D. dissertation [4]. In regards to the SSSIHL, research and training activities that are sponsored by the National Science Foundation (NSF) are actively assembling a network of mathematicians that will benefit researchers and educators in both India and the U.S.

Global Initiatives in the U.S.

We are regularly carrying out multiple activities in the U.S. through Illinois State University’s Intercollegiate Biomathematics Alliance, a consortium that promotes research and education in biomathematics and unites global organizations. For example, we invited participants from our international collaborator institutions to partake in the annual Cross-Institutional Undergraduate Research Experience program, International Symposium on Biomathematics and Ecology Education and Research, and various webinars. These multicultural and multinational programs have truly cultivated a community of mathematicians whose skills range from data science to survey data collection.

We are also currently building a unique multidisciplinary and cutting-edge science community via the Pandemic Research for Preparedness and Resilience (PREPARE) initiative to help society address challenges that are related to global pandemics. Such challenges include virtual education, the spread of misinformation through social media, and efficient vaccination distribution. PREPARE aims to construct a roadmap of important research directions and breakthrough solutions for pandemic preparedness and resilience that will eventually provide a blueprint for researchers, funding agencies, and policymakers. NSF’s Expeditions in Computing program is creating another ambitious global initiative—Global Pervasive Computational Epidemiology—to pursue fundamental research and training agendas that will define the future of computational epidemiology and infectious diseases. This program employs artificial intelligence and machine learning techniques to equip the next generation of public health champions with a variety of skills to tackle epidemics and forecast future disease burden in real time.

Other mathematics-based international programs in the U.S. have addressed global aspects (see Figure 1 for some examples). Moreover, the National Institutes of Health’s Fogarty International Center, the Department of Defense, and the CDC have also undertaken a number of endeavors, though many of them focus primarily on clinical and public health fields. In contrast, most mathematics-based efforts have facilitated database sharing, distance education, and computer-mediated communication in order to access a large and diverse amount of data and conduct collaborative research.

Figure 1. Examples of several mathematics-based international programs in the U.S. that have different types of global components.

Next, we plan to introduce carefully selected integrated tools that allow researchers to develop, access, and use models and data. Communities of mathematicians and interdisciplinary scientists—as well as ongoing regular communication channels via online platforms and hands-on field training—will encourage these actions. We therefore hope to create a tailored program that will boost cultural competence and awareness, move students toward higher levels of achievement and self-confidence, and ultimately increase representation in STEM fields.

Given the rapidly changing nature of global health knowledge, we must bring together and train the next generation of data scientists, expert disease modelers, public health emergency responders, and high-quality communicators to meet the needs of modern-day decision-makers. The aforementioned activities and programs have accelerated access to and use of data for public health officials who require local-to-global information to mitigate the social and economic effects of disease threats. Society will not be able to promptly and effectively face subsequent challenges until STEM researchers can proficiently model and forecast public health; address future ecological concerns; and share information in real time to activate governmental, private sector, and public actions in anticipation of both domestic and international threats. David Hilbert, one of the most influential mathematicians of the 19th and 20th centuries, once aptly said that “Mathematics knows no races or geographic boundaries; for mathematics, the cultural world is one country.” With this sentiment in mind, we recognize that the learning process can be tremendously efficient if we make it more active, engaging, and globally diverse.

[1] Leahey, Erin. (2016). From sole investigator to team scientist: Trends in the practice and study of research collaboration. Ann. Rev. Sociol., 42, 81-100.
[2] Morales, D., Paredes, M., Morales-Butler, E.J., Cruz-Aponte, M., Arriola, L., Cevallos, V., …, Mubayi, A. (2019). Data scarcity and ecological complexity: The cutaneous leishmaniasis dynamics in Ecuador. J. Royal Soc. Inter., 16(157), 20190141.
[3] Mubayi, A., Castillo-Chavez, C., Chowell, G., Kribs-Zaleta, C., Siddiqui, N.A., Kumar, N., & Das, P. (2010). Transmission dynamics and underreporting of Kala-azar in the Indian state of Bihar. J. Theor. Biol., 262(1), 77-185.
[4] Thakur, M.A. (2020). Mathematical modeling of systematic treatment implementation and dynamics of neglected tropical diseases: Case studies of visceral leishmaniasis and soil-transmitted helminths (Doctoral dissertation). Available from Arizona State University Electronic Theses and Dissertations.

Anuj Mubayi is an associate director in PRECISIONheor’s Advanced Modeling Group. He is an applied and computational mathematical scientist with more than 10 years of experience working on modeling problems that are of interest to the public health communities, such as the design and evaluation of cost-effective intervention programs in the healthcare sector.  
  Aditi Ghosh is a tenure-track assistant professor at Texas A&M University-Commerce. Her research interests are in mathematical biology and her critical research work involves projects in hepatology. Ghosh helps prepare undergraduate students for cutting-edge, modeling-based interdisciplinary research projects and model competitions, including the SIMIODE Challenge Using Differential Equations Modeling and COMAP’s international contests in modeling. 
  Madhav Marathe is an endowed Distinguished Professor in Biocomplexity, director of the Network Systems Science and Advanced Computing Division in the Biocomplexity Institute, and a tenured professor of computer science at the University of Virginia. His areas of expertise are network science, artificial intelligence, high-performance computing, computational epidemiology, biological and socially coupled systems, and data analytics.