Human & Public Health
Challenge
Modeling human health and public health risks and benefits is tough and requires specialized skills. You need answers that you can be confident with – when looking to uncover the evidence related to the efficacy and safety of a pharmaceutical product to value its market potential, determine the severity of a vector-borne disease, predict the spread of a pandemic, assess the health risk of an environmental exposure, or analyze the costs and benefits of an intervention.
Without the necessary staff or expertise, it can be a challenge to characterize risks, and determine how to manage them and move forward. EpiX Analytics has the skill and experience to provide unbiased, in depth, and independent analytics related to human health, including public health.
Solution
Quantitative, sound analyses are critical when they can impact the health and safety of our world. Crafting individualized approaches to analyzing these problems requires keen insight and real-world experience.
We favor parsimony and transparency in our approach. Our experience also allows us to balance data gaps with the need for evidence to support decisions, thus avoiding the common trap of “analysis paralysis” when trying to get perfect data. Rather, we focus on applying empirical evidence and quantifying uncertainty rather than using black-box methods and assumptions to cover data gaps.
Our capabilities include quantitative analysis of non-communicable disease attribution and burden, probabilistic models of exposure in the environment, cost-effectiveness of treatments and interventions, modeling probability of success and optimizing products in early drug development, disease spread modeling, and many other types of data analysis. We also provide training when needed so that the outcomes are useable for your projects and programs.
Service Areas
Multiple challenges in this field benefit from advanced modeling techniques, such as developing infectious disease spread models, conducting dose-dependent meta-analysis of carcinogens, performing systematic review of evidence between treatment and health effects, or incorporating imperfect observational evidence into risk assessments. Our analysts are internationally recognized for their expertise in these techniques to answer pragmatic questions of disease risk.
AMR is One Health issue that threatens the effective treatment of disease worldwide. There is uncertainty around the effects of using certain antimicrobials and how that translates to resistance in organisms in our food, environment, and, ultimately, its impact on human health. Through multiple grant-funded research projects, we have engaged with industry and federal agencies to bring together the latest data in cutting-edge risk assessments of AMR in human illness. Our research focuses on addressing uncertainty and data issues, allowing us to deliver informed, accurate conclusions in peer-reviewed scientific articles.
Determining the impact of a chemical or microbial exposure can require specific methods to model sources, interactions with environment, fate and transport, and our own behaviors. Data quality and availability are often a challenge and uncertainty can be high when working to find answers to these kinds of questions. That’s where effective probabilistic modeling comes in. Our systematic and quantitative approach takes uncertainties into account to deliver analysis in a format that supports your needs, whether that be a report detailing our findings, or a tool or model for you to perform your own calculations.
Knowing the probability of success of a compound or a treatment moving through the approval process can speed your R&D activities. We work with companies to identify the probability of favorable outcomes each step of the way. We also conduct PKPD modeling and assist with strategic portfolio planning and modeling of all drugs or compounds a company has in development, incorporating business analytics aspects including competitor and market share information.
Case Studies
Webinars on COVID-19
Beef Risk Assessment
