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Paul W. Fenimore

Publications and source records attributed to Paul W. Fenimore.

3 recordsLinked to original sources

A wave-mechanical model of incoherent neutron scattering II. Role of the momentum transfer

We recently introduced a wave-mechanical model for quasi-elastic neutron scattering (QENS) in proteins. We call the model ELM for "Energy Landscape Model". We postulate that the spectrum of the scattered neutrons consists of lines of natural width shifted from the center by fluctuations. ELM is based on two facts: Neutrons are wave packets; proteins have low-lying substates that form the free-energy landscape (FEL). Experiments suggest that the wave packets are a few hundred micrometers long. The interaction between the neutron and a proton in the protein takes place during the transit of the wave packet. The wave packet exerts the force $F(t) = dQ(t)/dt$ on the protein moiety, a part of the protein surrounding the struck proton. $Q(t)$ is the wave vector (momentum) transferred by the neutron wave packet to the proton during the transit. The ensuing energy is stored in the energy landscape and returned to the neutron as the wave packet exits. Kinetic energy thus is changed into potential energy and back. The interaction energy is proportional to $Q$, not to $Q^2$. To develop and check the ELM, we use published work on dehydrated proteins after reversing improper normalizations. In such proteins only vibrations are active and the effects caused by the neutron momentum can be studied undisturbed by external fluctuations. ELM has predictive power. For example it quantitatively predicts the observed inelastic incoherent fraction $S(Q, T)$ over a broad range of temperature and momentum $Q$ with one coefficient if $S(0, T)$ is known.

physics.bio-ph↗

SARS oubreaks in Ontario, Hong Kong and Singapore: the role of diagnosis and isolation as a control mechanism

In this article we use global and regional data from the SARS epidemic in conjunction with a model of susceptible, exposed, infective, diagnosed, and recovered classes of people (``SEIJR'') to extract average properties and rate constants for those populations. The model is fitted to data from the Ontario (Toronto) in Canada, Hong Kong in China and Singapore outbreaks and predictions are made based on various assumptions and observations, including the current effect of isolating individuals diagnosed with SARS. The epidemic dynamics for Hong Kong and Singapore appear to be different from the dynamics in Toronto, Ontario. Toronto shows a very rapid increase in the number of cases between March 31st and April 6th, followed by a {\it significant} slowing in the number of new cases. We explain this as the result of an increase in the diagnostic rate and in the effectiveness of patient isolation after March 26th. Our best estimates are consistent with SARS eventually being contained in Toronto, although the time of containment is sensitive to the parameters in our model. It is shown that despite the empirically modeled heterogeneity in transmission, SARS' average reproductive number is 1.2, a value quite similar to that computed for some strains of influenza \cite{CC2}. Although it would not be surprising to see levels of SARS infection higher than ten per cent in some regions of the world (if unchecked), lack of data and the observed heterogeneity and sensitivity of parameters prevent us from predicting the long-term impact of SARS.

q-bio.OT↗

The Basic Reproductive Number of Ebola and the Effects of Public Health Measures: The Cases of Congo and Uganda

Despite improved control measures, Ebola remains a serious public health risk in African regions where recurrent outbreaks have been observed since the initial epidemic in 1976. Using epidemic modeling and data from two well-documented Ebola outbreaks (Congo 1995 and Uganda 2000), we estimate the number of secondary cases generated by an index case in the absence of control interventions ($R_0$). Our estimate of $R_0$ is 1.83 (SD 0.06) for Congo (1995) and 1.34 (SD 0.03) for Uganda (2000). We model the course of the outbreaks via an SEIR (susceptible-exposed-infectious-removed) epidemic model that includes a smooth transition in the transmission rate after control interventions are put in place. We perform an uncertainty analysis of the basic reproductive number $R_0$ to quantify its sensitivity to other disease-related parameters. We also analyze the sensitivity of the final epidemic size to the time interventions begin and provide a distribution for the final epidemic size. The control measures implemented during these two outbreaks (including education and contact tracing followed by quarantine) reduce the final epidemic size by a factor of 2 relative the final size with a two-week delay in their implementation.

q-bio.OT↗