From dengue fever in Latin America to swine fever in the Netherlands and norovirus in Belgium, the S-I-R model can provide vital lessons for how to prevent diseases spreading. These "removed" individuals no longer contribute to the spread of the disease. These are the people who have had the disease and recovered and are now immune, or those who have died. The third group are euphemistically referred to as the "removed" class. Those who have contracted the disease and are capable of passing it to susceptibles are the "infectives". Everyone is assumed to be born susceptible and capable of being infected. People who have not yet had the disease are labeled "susceptibles". One of the simplest mathematical models of disease spread splits the population into three basic categories according to disease status. As the COVID-19 pandemic escalates, here's a look inside the modeling that experts use to try and stay one step ahead of the virus. Insights from mathematical modeling are vital to ensuring that authorities can prevent as many deaths as possible. With more complex models, we can start to answer questions about how to efficiently allocate limited resources or tease out the consequences of public health interventions, like closing pubs and banning gatherings. With basic mathematical models, researchers can begin to forecast the progression of diseases and understand the effect of interventions on disease spread. As I explore in The Maths of Life and Death, mathematical epidemiology is playing a crucial role in the fight against large-scale infectious diseases such as COVID-19. There is, however, a little known but highly successful field of science working in the background to unpick the mysteries of infectious disease.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |