Infectious diseases are inherently dynamic; that is, they are continually changing in time and space.  While summary characteristics (e.g. prevalence) may remain stationary over time, this arises from the balance of rates that govern the continual processes of transmission, infection, and recovery (or removal).  More commonly, these dynamic processes generate fluctuations in time and space, and non-linear phenomena that imply that the future state of the system cannot necessarily be predicted with a simple extrapolation from the current state.  These non-linear dynamics present some challenges in terms of estimation and prediction, but may also take advantage of these dynamics to design effective strategies for control.  Mathematical and computational models have been critical tools in exploring the interaction of dynamics and control.  Here we review various applications of disease dynamics to the monitoring and evaluation of infectious disease management. We first discuss the general applications of disease dynamics and then present lessons from specific classes of dynamic phenomena.

Resurgent outbreaks of vaccine-preventable diseases that have previously been controlled or eliminated have been observed in many settings. Reactive vaccination campaigns may successfully control outbreaks but must necessarily be implemented in the face of considerable uncertainty. Real-time surveillance may provide critical information about at-risk population and optimal vaccination targets, but may itself be limited by the specificity of disease confirmation. We propose an integrated modeling approach that synthesizes historical demographic and vaccination data with real-time outbreak surveillance via a dynamic transmission model and an age-specific disease confirmation model. We apply this framework to data from the 1996-1997 measles outbreak in São Paulo, Brazil. To simulate the information available to decision-makers, we truncated the surveillance data to what would have been available at 1 or 2 months prior to the realized interventions. We use the model, fitted to real-time observations, to evaluate the likelihood that candidate age-targeted interventions could control the outbreak. Using only data available prior to the interventions, we estimate that a significant excess of susceptible adults would prevent child-targeted campaigns from controlling the outbreak and that failing to account for age-specific confirmation rates would under-estimate the importance of adult targeted vaccination. Keywords: measles epidemic, Bayesian disease model, outbreak control, vaccine intervention, decision analysis

AbstractI show that this new regulation has failed to achieve the objectives because of n reasons\footnote {The term ``Infrared Dark Cloud" or ``IRDC" typically refers to any cloud which is opaque in the mid-infrared.}--in pa

abstractThis study investigates the impact of beer taxes and a variety of alcohol-control policies on motor vehicle fatality rates. Special attention is paid to omitted variables biases resulting from failing to adequately control for grassroots efforts to reduce drunk driving, the enactment of other laws which simultaneously operate to reduce highway fatalities, and the economic conditions existing at the time the legislation is passed. In the preferred models, most of the regulations have little or no impact on traffic mortality. By contrast, higher beer taxes are associated with reductions in crash deaths and this result is relatively robust across specifications.

PERIOD 8th August 2015 - 24 August 2015 SHIFTERS Washington Carvalho, Alan Coleman, Aida Lopez, Foteini Oikonomou, Rodrigo Pelayo, Dmytro Ragozin, David Schmidt MAIN ISSUES - 5-8 Aug: LA not operated due to power line being down or unstable - 7,12 Aug: No data acquisition due to rain/snow all night - 11-24 Aug: LL Lidar crashed almost every night after a few minutes of data taking. On most nights, it was possible to restart it for a few more brief runs. - LA Lidar PC crashed during data acquisition and connection was impossible. Luckily, NAGIOS took over and automatically closed down Lidar and cover. - 22-24 Aug: LA lidar cover having problems the last three nights of the shift. Running Cover Open/Close would result in being told that the cover was both closed and open, respectively. The solution of running Emergency Stop, Emergency Reset, then powercycling the steering was found to work. EXTRA ACTIVITIES DURING THE SHIFT - RAMAN shots from 1:25am to 1:55am, closing the respective bays SHORT OVERVIEW OF DATA OBTAINED - Figure ... shows the rate of T3 events as a function GPS Second for the duration of the shift. - Figures ... show the zenith, energy, and Xmax distributions of selected events collected during the shift. We have used standard Offline FD and Hybrid reconstruction routines for the event distributions shown here. Fixed calibration constants and parametric models have been used for the description of the atmosphere in the Offline reconstruction. - Figures ... show the FD Event Browser display for events selected events with energy greater than 10^19.2 eV. [image]

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