Biostatistics is a branch of statistics that applies statistical methods to a wide range of topics in biology. It encompasses the design of biological experiments, the collection and analysis of data from those experiments and the interpretation of the results.
Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.
An epidemic is the rapid spread of disease to a large number of hosts in a given population within a short period of time. For example, in meningococcal infections, an attack rate in excess of 15 cases per 100,000 people for two consecutive weeks is considered an epidemic.
Epidemiology is the study and analysis of the distribution, patterns and determinants of health and disease conditions in a defined population.
Computational biology refers to the use of data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. An intersection of computer science, biology, and big data, the field also has foundations in applied mathematics, chemistry, and genetics. It differs from biological computing, a subfield of computer science and engineering which uses bioengineering to build computers.
Computational neuroscience is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.
In epidemiology, the basic reproduction number, or basic reproductive number, denoted , of an infection is the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection. The definition assumes that no other individuals are infected or immunized. Some definitions, such as that of the Australian Department of Health, add the absence of "any deliberate intervention in disease transmission". The basic reproduction number is not necessarily the same as the effective reproduction number , which is the number of cases generated in the current state of a population, which does not have to be the uninfected state. is a dimensionless number and not a time rate, which would have units of time−1, or units of time like doubling time.
Mathematical models can project how infectious diseases progress to show the likely outcome of an epidemic and help inform public health and plant health interventions. Models use basic assumptions or collected statistics along with mathematics to find parameters for various infectious diseases and use those parameters to calculate the effects of different interventions, like mass vaccination programs. The modelling can help decide which intervention(s) to avoid and which to trial, or can predict future growth patterns, etc.
Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science that uses advanced computing capabilities to understand and solve complex physical problems. This includes
The Biocomplexity Institute of Virginia Tech is a research organization specializing in bioinformatics, computational biology, and systems biology. The institute has more than 250 personnel, including over 50 tenured and research faculty. Research at the institute involves collaboration in diverse disciplines such as mathematics, computer science, biology, plant pathology, biochemistry, systems biology, statistics, economics, synthetic biology and medicine. The institute develops -omic and bioinformatic tools and databases that can be applied to the study of human, animal and plant diseases as well as the discovery of new vaccine, drug and diagnostic targets.
Health geography is the application of geographical information, perspectives, and methods to the study of health, disease, and health care. Medical geography, a sub-discipline of or sister field of health geography, focuses on understanding spatial patterns of health and disease as related to the natural and social environment. Conventionally, there are two primary areas of research within medical geography: the first deals with the spatial distribution and determinants of morbidity and mortality, while the second deals with health planning, help-seeking behavior, and the provision of health services.
Computational Engineering is an emerging discipline that deals with the development and application of computational models for engineering, known as Computational Engineering Models or CEM. Computational engineering uses computers to solve engineering design problems important to a variety of industries. At this time, various different approaches are summarized under the term Computational Engineering, including using computational geometry and virtual design for engineering tasks, often coupled with a simulation-driven approach In Computational Engineering, algorithms solve mathematical and logical models that describe engineering challenges, sometimes coupled with some aspect of AI, specifically Reinforcement Learning.
Economic epidemiology is a field at the intersection of epidemiology and economics. Its premise is to incorporate incentives for healthy behavior and their attendant behavioral responses into an epidemiological context to better understand how diseases are transmitted. This framework should help improve policy responses to epidemic diseases by giving policymakers and health-care providers clear tools for thinking about how certain actions can influence the spread of disease transmission.
James Robert Thompson was an American mathematician, statistician, and university professor whose most influential work combined applied mathematics and nonparametric statistics with computing technologies to advance the fields of financial engineering and computational finance, model disease progression, assess problems in public health, and optimize quality control in industrial manufacturing.
Neil Morris Ferguson is a British epidemiologist and professor of mathematical biology, who specialises in the patterns of spread of infectious disease in humans and animals. He is the director of the Jameel Institute, and of the MRC Centre for Global Infectious Disease Analysis, and head of the Department of Infectious Disease Epidemiology in the School of Public Health and Vice-Dean for Academic Development in the Faculty of Medicine, all at Imperial College London.
Azra Catherine Hilary Ghani is a British epidemiologist who is a professor of Infectious Disease Epidemiology at Imperial College London. Her research considers the mathematical modelling of infectious diseases, including malaria, bovine spongiform encephalopathy and coronavirus. She has worked with the World Health Organization on their technical strategy for malaria. She is associate director of the MRC Centre for Global Infectious Disease Analysis.
Caitlin M. Rivers is an American epidemiologist who as Senior Scholar at the Johns Hopkins Center for Health Security and assistant professor at the Johns Hopkins Bloomberg School of Public Health, specializing on improving epidemic preparedness. Rivers is currently working on the American response to the COVID-19 pandemic with a focus on the incorporation of infectious disease modeling and forecasting into public health decision making.
Caroline O'Flaherty Buckee is an epidemiologist. She is an associate professor of Epidemiology and is the associate director of the Center for Communicable Disease Dynamics, both at the Harvard T.H. Chan School of Public Health. Buckee is known for her work in digital epidemiology, where mathematical models track mobile and satellite data to understand the transmission of infectious diseases through populations in an effort to understand the spatial dynamics of disease transmission. Her work examines the implications of conducting surveillance and implementing control programs as a way to understand and predict what will happen when dealing with outbreaks of infectious diseases like malaria and COVID-2019.
Sara Yemimah Del Valle is a senior scientist and mathematical epidemiologist at Los Alamos National Laboratory (LANL). At the LANL, Del Valle leads the Fusion Team, where she combines internet data with satellite imagery to forecast disease outbreaks. During the COVID-19 pandemic Del Valle created a computational model that could predict the spread of COVID-19 around the United States.
