Leading research to understand, treat, and prevent infectious, immunologic, and allergic diseases

Complexity Explorables | I herd you! This explorable illustrates the mechanism of herd immunity. When an infectious disease spreads in a population, an individual can be protected by a vaccine that delivers immunity. But there's a greater good. Press Play and keep on reading.... This is how it works This explorable is actually a set of four similar explorables, all of which model the spread of a disease in a population with \(N\) individuals. This model is known as the SIS-model, one of the simplest dynamical models for infectious disease dynamics. Vaccination is modelled this way: All individuals can spontaneously decide to vaccinate at a certain rate such that in equilibrium a fraction \(P\) of the population is vaccinated. Both, vaccine uptake and transmissibility of the disease can be controlled with a slider. The system is initially fully susceptible with a few infected individuals randomly scattered into the population. Model 1: The mixed population Model 2: The static network model Model 3: The dynamic network model C.

24999 Modelling of disease spread 24999 Modelling of disease spread The course is free of charge for master and PhD students from the European Union. Student from none EU countries and all professionals must pay tuition fees of 8250 DKK. Course information General course objectives Simulation modeling is a discipline often used in the veterinary field to investigate epidemiological questions. Learning objectives A student who has met the objectives of the course will be able to: Understand basic concepts about RUnderstand the basic principles in simulation modelingConstruct simple deterministic simulation modelsConstruct simple stochastic simulation modelsConstruct simple stochastic and dynamic simulation modelsModel simple mechanisms of disease spread between units (e.g. individuals or populations)Collect the results from the simulations in a proper way and present them visuallyRead and understand more complicated papers using the taught techniques Content Training in deterministic, stochastic and dynamic modeling. Remarks

Chemotherapy, Then and Now Since I am claiming that cancer research is doing something suboptimal, I’m going to have to examine what progress has actually been made in cancer research, and what results it had. Here, I’ll focus on the history of chemotherapy. Early history “A history of Cancer Chemotherapy” is an excellent article that summarizes the early history. Chemotherapy is actually a fairly recent development. The beginnings of modern chemotherapy were in the Chemical Warfare Service during World War II, which studied chemical weapons and discovered the tumor-regressing effects of nitrogen mustards. Sidney Farber, as part of a government drug screening program, and in collaboration with Harriet Kilte, discovered methotrexate and found it effective on children with leukemia. Post-war, Sloane-Kettering hired almost the entire Chemical Warfare Service for a drug development program. In the 1950’s, the CCNSC drug development program was also founded; it was the precursor to the modern pharmaceutical industry.

Counting DNA Nucleotides Figure 1. A 1900 drawing by Edmund Wilson of onion cells at different stages of mitosis. The sample has been dyed, causing chromatin in the cells (which soaks up the dye) to appear in greater contrast to the rest of the cell. Figure 2. A sketch of DNA's primary structure. Making up all living material, the cell is considered to be the building block of life. One class of the macromolecules contained in chromatin are called nucleic acids. The nucleic acid monomer is called a nucleotide and is used as a unit of strand length (abbreviated to nt). For example, Figure 2 shows a strand of deoxyribose nucleic acid (DNA), in which the sugar is called deoxyribose, and the only four choices for nucleobases are molecules called adenine (A), cytosine (C), guanine (G), and thymine (T). For reasons we will soon see, DNA is found in all living organisms on Earth, including bacteria; it is even found in many viruses (which are often considered to be nonliving).

BioWar: Project Overview | Sponsors | Software In trying to prepare for attacks, policy makers need to be able to think through the consequences of their decisions in various situations. Consider, for example, trying to decide if all US citizens should be vaccinated for smallpox. Speculations abound as to the potential devastation that smallpox could wreak. Medical experts, scientists, and policy makers need a way of thinking through the morass of complex interconnections to understand whether different inoculation or containment strategies will be effective. Unfortunately many existing models are quite limited in that they only apply to a single disease, discount factors such as the urban geography which can influence disease spread, or discount how people use their social networks (who is friends with whom) to pass information such as when to go to the doctor to be treated. Version 2.2 had been optimized and tested on 5 cities. "Infectious Disease Modeling Using BioWar" Additional information:

Phillip Stroud, Sara Del Valle, Stephen Sydoriak, Jane Riese and Susan Mniszewski: Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society BARRETT C L, Eubank S G, Smith J P (2005) If Smallpox Strikes Portland, Scientific American 292, March 2005. pp. 41-49. BECKMAN R J, Baggerly K A, and McKay M D (1995) Creating Synthetic Baseline Populations, Transportation Research A 30A(64). pp. 415-429. DUNHAM J B (2005) An Agent-based Spatially Explicit Epidemiological Model in MASON, Journal of Artificial Societies and Social Simulation 9(1)3, EIDELSON B, Lustik I (2004) 'VIR-POX: An Agent-based Analysis of Smallpox Preparedness and Response Policy', Journal of Artificial Societies and Social Simulation, 7(3)6. EUBANK S, Goclu H, Kumar A, Marathe M, Srinivasan A, Totoczkal Z, Wang N (2004) Modelling disease outbreaks in realistic urban social networks, Nature 429, 13 May 2004. pp. 180-184. GERMANN T C, Kadau K, Longini I M, Macken C M (2006) Mitigation strategies for pandemic influenza in the United States, Proc.

Compartmental models in epidemiology - Wikipedia Type of mathematical model used for infectious diseases Compartmental models are a very general modelling technique. They are often applied to the mathematical modelling of infectious diseases. The population is assigned to compartments with labels – for example, S, I, or R, (Susceptible, Infectious, or Recovered). People may progress between compartments. The origin of such models is the early 20th century, with important works being that of Ross[1] in 1916, Ross and Hudson in 1917,[2][3] Kermack and McKendrick in 1927,[4] and Kendall in 1956.[5] The Reed–Frost model was also a significant and widely-overlooked ancestor of modern epidemiological modelling approaches.[6] The models are most often run with ordinary differential equations (which are deterministic), but can also be used with a stochastic (random) framework, which is more realistic but much more complicated to analyze. The SIR model[edit] S: The number of susceptible individuals. I: The number of infectious individuals. . . .

A Comparison between Two Simulation Models for Spread of Foot-and-Mouth Disease Abstract Two widely used simulation models of foot-and-mouth disease (FMD) were used in order to compare the models’ predictions in term of disease spread, consequence, and the ranking of the applied control strategies, and to discuss the effect of the way disease spread is modeled on the predicted outcomes of each model. The DTU-DADS (version 0.100), and ISP (version 2.001.11) were used to simulate a hypothetical spread of FMD in Denmark. Actual herd type, movements, and location data in the period 1st October 2006 and 30th September 2007 was used. Citation: Halasa T, Boklund A, Stockmarr A, Enøe C, Christiansen LE (2014) A Comparison between Two Simulation Models for Spread of Foot-and-Mouth Disease. Editor: Yury E. Received: September 10, 2013; Accepted: February 23, 2014; Published: March 25, 2014 Copyright: © 2014 Halasa et al. Funding: This study was financially supported by the Directorate for Food, Agriculture and Fisheries, Denmark (grant nr. 3304-FVFP-07-782-01). Introduction