Improving Collaboration and Incorporating Technology to Forecast and Mitigate Infectious Disease Impacts
Kansas City, Mo.: (Feb. 20, 2023) – We have some of the tools to predict the next infectious disease pandemic – but to effectively utilize them, we must better collaborate. This needs to occur across disciplines and sectors from top-down policy makers to bottom-up working level and throughout the global scientific community. Using technologies like artificial intelligence (AI) and machine learning (ML) to analyze and even predict future infectious diseases, we will effectively forecast and mitigate their impact before they can become a pandemic.
“The approaches used to track and predict past disease outbreaks often lagged behind the disease itself, which resulted in high morbidity and mortality in both humans and animals. If we continue to do the same, the next virus may be even worse than COVID-19,” said Kenny Yeh, Senior Director, MRIGlobal. “To more effectively address future diseases, we’re calling on researchers to improve collaboration through the use of AI/ML and other technologies. Doing so can enhance our ability to proactively engage and resolve current and emerging infectious diseases around the world, potentially saving lives.”
In the last decade, cases of vector-borne diseases – those infections transmitted to humans by mosquitoes, ticks, and fleas – have increased by 300% in the Americas. Induced and exacerbated by climate change, changes in temperature, rainfall, and vegetation are helping these vectors and the diseases they carry – like Malaria and Lyme disease – expand their range into entirely new geographic areas. This has resulted in vulnerable human populations, including those who lack protective immunity from these diseases, being impacted by increased morbidity and mortality. Effectively monitoring for and predicting such events is crucial to maintaining global health.
The indicators most often used to monitor such disease outbreaks and identify emerging microbes of major consequence, like increasing trends in positive results from diagnostic tests and patient hospitalization figures, offer limited ability to prevent spread of the disease and spillover into new hosts. Conversely, novel multidisciplinary approaches are now feasible, providing valuable insights into disease predictions through holistic monitoring of micro and macro ecological changes. However, because this data is often imperfect and collected from disparate fields of research, the variables that offer predictive insight can be difficult to track and manage, let alone analyze in real-time.
To effectively assess these relevant variables of impact and address the complexities of today’s global challenges requires a multidisciplinary approach that brings together experts who are versed in multiple technologies and data streams. Innovations in predictive analytics using artificial intelligence (AI) and machine learning (ML) tools can be used. Often referred to together as AI/ML, these technologies involve processes and algorithms that simulate human intelligence, including perception, learning, and problem solving. Using data inputs from various sources, these tools can help predict where the impacts of climate change may be driving emerging infectious diseases, which can help inform the public health response. This collaborative ‘team science’ will enable us to better cooperate, coordinate, and communicate, while staying ahead of future disease outbreaks before they become the next pandemic.
Further, AI/ML (often referred to in a drug development context as in silico drug development) can more rapidly and comprehensively respond than traditional drug development. AI/ML drug development can analyze existing drugs for effectiveness against new disease agent strains, optimize new drugs for desired characteristics such as adsorption, distribution, metabolism, elimination, and toxicity, and develop completely novel treatments such as antibodies and vaccines.
MRIGlobal has had success studying AI to discover drug combinations at unprecedented speeds. Working through a program known as ‘IDentif.AI’ (Identifying Infectious Disease Combination Therapy with Artificial Intelligence), one of our researchers was among a multidisciplinary team of scientists led by Dr. Dean Ho at National University of Singapore who developed a pioneering artificial intelligence (AI) platform to enhance efficiency of drug treatments and thereby reduce time for interrogating combinations and optimizing dosages. This research was a step forward in treating infectious diseases by rapidly developing antivirals.
Additional rationale for this collaborative approach and use of technology is detailed in the paper “Climate change and infectious disease: A prologue on multidisciplinary cooperation and predictive analytics” which was recently published in Frontiers in Public Health at Frontiersin.org, and written by multi-national experts from MRIGlobal, EpiPointe LLC, CIRMF, UKHSA, LANL, and University at Buffalo.
###
ABOUT MRIGLOBAL
MRIGlobal improves the health and safety of people everywhere by addressing some of the world’s greatest threats and complex biological, chemical, and engineering challenges. Founded in 1944 as an independent, not-for-profit organization, MRIGlobal provides customized research and development services to health and defense-focused organizations in need of innovative and multidisciplinary solutions. This includes expertise in clinical research support, infectious disease and biological threat agent detection, global biological engagement, in vitro diagnostics, and laboratory management and operations. To learn more, visit mriglobal.org.