Dynamic maps allow us to investigate processes, such as disease dynamics, rather than simply recognize static patterns: it is useful for modeling disease transmission, place of exposure and probabilities of hospitalization.
By modeling disease transmission by age category and risk grouping, we were able to estimate the effect of herd immunity, which we know from actual vaccine rollouts can be substantial (37, 38 ).
The novel mechanistic approach we employed for estimating effects of density and landscape on transmission is a powerful complement to typical data-fitting approaches for modelling disease transmission.
Compartment modeling has been widely used in modeling infectious disease transmission [ 2, 4, 20, 34].
In contrast, stochastic agent based simulations or network-based models of disease transmission directly model the distribution of cases.
My interests include infectious diseases, outbreak and anomaly detection, using surveillance data as epidemic intelligence, and mathematical modeling of disease transmission.
Mathematical modeling of disease transmission has provided quantitative predictions for health policy, facilitating the evaluation of epidemiological outcomes and the cost-effectiveness of interventions.
Turning to the modeling of disease transmission dynamics, we assess whether accounting for daily changes in contacts better accounts for the variation in new cases.
Mathematical modeling for disease transmission in host population is of great practical value in predicting and controlling disease spread (West Nile virus in North America in the 1990s, Avian influenza worldwide in the 2000s, SARS in Asia in 2003, etc).
Funded by the National Institute of General Medical Sciences in the National Institutes of Health, MIDAS has two missions, says its director, Jim Anderson: first, to advance the science of modeling infectious disease transmission, and second, to be ready in the event of a national emergency.
As with most models for disease transmission and control, our model is based on the simple susceptible-infected-recovered (SIR) model [22].
