Communicable diseases have long been recognized as a continuous threat to humans. Hence, understanding the underlying mechanisms by which diseases spread and cause epidemics is key for their control. ...This dissertation is concerned with the development of new mathematical models for the spread of infectious diseases and the effects of Public Health interventions. In Chapter I, a mathematical model for the 2003 Severe Acute Respiratory Syndrome outbreaks in Toronto, Hong Kong and Singapore is developed. In Toronto, our model predicted control in late April by the identification of the nonexponential dynamics in the rate of increase of the number of cases. The reproductive number is estimated to be approximately 1.2. Our model predicts that 20% of the population in Toronto could have been infected without control interventions. In Chapter II, an uncertainty and sensitivity analysis is performed on the basic reproductive number. In Chapter III, a novel mathematical model for Ebola spread is developed. Ebola outbreaks have been observed in African regions since 1976. Our model includes a smooth transition in the transmission rate at the time when interventions are put in place. We evaluate the effects of interventions and estimate the reproductive number. In Chapter IV, Foot-and-Mouth disease (FMD) epidemics are modeled using spatial deterministic epidemic model. FMD is a highly infectious illness of livestock. Our model is compared to its non-spatial counterpart. We assess the effectiveness of the contingency plan implemented during the epidemic and explore the expected impact of a mass vaccination policy depending on when it is implemented. In Chapter V, we analyzed from a network point of view the cumulative and aggregated data generated from the simulated movements of 1600,000 individuals generated by TRANSIMS (Transportation Analysis and Simulation System developed at Los Alamos National Laboratories) during a typical day in Portland, Oregon. The node out-degree, the out-traffic, and the total out-traffic follow power law behavior. The resulting weighted graph is a “small world” and has scaling laws consistent with an underlying hierarchical structure. We also explore the time evolution of the largest connected component and the distribution of the component sizes.
Tuberculosis is a public health problem in Mexico. From 1999 to 2002, we assessed retrospectively the epidemiological, clinical, and treatment characteristics of pulmonary tuberculosis in the ...hospitals of the Mexican Institute of Public Health in the state of Colima (Mexico). We included 184 cases diagnosed with pulmonary tuberculosis. A database containing demographic, epidemiological, and clinical information was constructed and analyzed. We estimate a median patient delay of 83 d and a mean treatment delay of 2.3 d. Of 14 cases suspected for multiresistance and microbiologically assayed, 5 were found to carry a multi-drug-resistant strain. We also found a significant association between a short patient delay and the presence of hemoptysis (p = 0.002) or dyspnea (p<0.001). 86 patients (46.8%) were sputum smear microscopy negative at the end of treatment and 40 (21.7%) completed treatment giving an overall success rate of 68.5%, which compares unfavorably with the World Health Organization target success rate of 85%. Five (2.7%) patients failed treatment, 10 (5.4%) died, 39 (21.2%) interrupted treatment, and 4 (2.2%) transferred to another reporting unit. A 2002 strategic change in drug distribution seemed to prove successful.
Physical Review E 68, 066102 (2003) Large scale simulations of the movements of people in a ``virtual'' city and
their analyses are used to generate new insights into understanding the dynamic
...processes that depend on the interactions between people. Models, based on
these interactions, can be used in optimizing traffic flow, slowing the spread
of infectious diseases or predicting the change in cell phone usage in a
disaster. We analyzed cumulative and aggregated data generated from the
simulated movements of 1.6 million individuals in a computer (pseudo
agent-based) model during a typical day in Portland, Oregon. This city is
mapped into a graph with $181,206$ nodes representing physical locations such
as buildings. Connecting edges model individual's flow between nodes. Edge
weights are constructed from the daily traffic of individuals moving between
locations. The number of edges leaving a node (out-degree), the edge weights
(out-traffic), and the edge-weights per location (total out-traffic) are fitted
well by power law distributions. The power law distributions also fit subgraphs
based on work, school, and social/recreational activities. The resulting
weighted graph is a ``small world'' and has scaling laws consistent with an
underlying hierarchical structure. We also explore the time evolution of the
largest connected component and the distribution of the component sizes. We
observe a strong linear correlation between the out-degree and total
out-traffic distributions and significant levels of clustering. We discuss how
these network features can be used to characterize social networks and their
relationship to dynamic processes.
Journal of Theoretical Biology 229 (2004) 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.
Journal of Theoretical Biology 24,1-8 (2003) 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.
Large scale simulations of the movements of people in a ``virtual'' city and their analyses are used to generate new insights into understanding the dynamic processes that depend on the interactions ...between people. Models, based on these interactions, can be used in optimizing traffic flow, slowing the spread of infectious diseases or predicting the change in cell phone usage in a disaster. We analyzed cumulative and aggregated data generated from the simulated movements of 1.6 million individuals in a computer (pseudo agent-based) model during a typical day in Portland, Oregon. This city is mapped into a graph with \(181,206\) nodes representing physical locations such as buildings. Connecting edges model individual's flow between nodes. Edge weights are constructed from the daily traffic of individuals moving between locations. The number of edges leaving a node (out-degree), the edge weights (out-traffic), and the edge-weights per location (total out-traffic) are fitted well by power law distributions. The power law distributions also fit subgraphs based on work, school, and social/recreational activities. The resulting weighted graph is a ``small world'' and has scaling laws consistent with an underlying hierarchical structure. We also explore the time evolution of the largest connected component and the distribution of the component sizes. We observe a strong linear correlation between the out-degree and total out-traffic distributions and significant levels of clustering. We discuss how these network features can be used to characterize social networks and their relationship to dynamic processes.
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.
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.
Death March of 1918 Chowell, Gerardo; Simonsen, Lone; Viboud, Cécile
Natural history,
09/2017, Letnik:
125, Številka:
8
Magazine Article
...it is plausible that the virus was introduced into Spain by Spanish workers traveling to and from neighboring France in search of temporary employment, given the shortage of young French workers ...during wartime. ...these deaths were concentrated among young adults twenty to forty years of age, in stark contrast to seasonal influenza outbreaks, which primarily kill the youngest and the oldest. Several mechanisms have been proposed to explain the multi-wave pattern of pandemic influenza, including school schedules, weather patterns, incomplete acquired immunity, and details of the social network structure. Cécile Viboud, a senior research scientist at the Fogarty International Center, focuses on modeling the spatial and temporal transmission dynamics of influenza and other acute viral infections, as well as the interface of public health, epidemiology, and evolution.
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