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@ARTICLE{RMAndersonandBTGrenfellandRMMay1984,
  author = {RM Anderson and BT Grenfell and RM May},
  title = {Oscillatory fluctuations in the incidence of infectious disease and the impact of vaccination: time series analysis.},
  journal = {J Hyg (Lond)},
  year = {1984},
  volume = {93},
  pages = {587-608},
  number = {3},
  month = {Dec},
  abstract = {This paper uses the techniques of time series analysis
	 (autocorrelation and spectral analysis) to examine oscillatory secular
	 trends in the incidence of infectious diseases and the impact of mass
	 vaccination programmes on these well-documented phenomena. We focus on
	 three common childhood diseases: pertussis and mumps (using published
	 disease-incidence data for England and Wales) and measles (using data from
	 England and Wales, Scotland, North America and France). Our analysis
	 indicates highly statistically significant seasonal and longer-term
	 cycles in disease incidence in the prevaccination era. In general, the
	 longer-term fluctuations (a 2-year period for measles, 3-year periods for
	 pertussis and mumps) account for most of the cyclical variability in
	 these data, particularly in the highly regular measles series for
	 England and Wales. After vaccination, the periods of the longer-term
	 oscillations tend to increase, an observation which corroborates theoretical
	 predictions. Mass immunization against measles (which reduces epidemic
	 fluctuations) magnifies the relative importance of the seasonal cycles. By
	 contrast, we show that high levels of vaccination against whooping cough
	 in England and Wales appear to have suppressed the annual cycle.},
  keywords = {Adolescent, Adult, Age Factors,
	 Biometry, Chickenpox, Child, Cohort Studies, Communicable Disease
	 Control, Communicable Diseases, Data Interpretation, Ecology, England,
	 Epidemiologic Methods, Great Britain, Humans, Infant, Mathematics,
	 Measles, Models, Mumps, Newborn, Non-U.S. Gov't, Population Dynamics,
	 Preschool, Registries, Research Support, Seasons, Statistical, Time
	 Factors, United States, Vaccination, Wales, Whooping Cough, 6512259},
  owner = {claudia},
}

@BOOK{AndersonRMandMayRMandAndersonB1992,
  title = {Infectious Diseases of Humans: Dynamics and Control},
  publisher = {Oxford University Press},
  year = {1992},
  author = {Anderson, RM and May, RM and Anderson,B},
  owner = {claudia},
}

@ARTICLE{BarrettCLandEubankSGandSmithJP2005,
  author = {Barrett, CL and Eubank, SG and Smith, JP},
  title = {If smallpox strikes Portland...},
  journal = {Scientific American},
  year = {2005},
  volume = {www.sciam.com},
  pages = {41-49},
  owner = {claudia},
}

@ARTICLE{BivandRS2000,
  author = {Bivand, RS},
  title = {Usando a linguagem de análise de dados estatísticos R em arquivos de banco de dados do GRASS 5.0 SIG},
  journal = {Computers \& Geosciences},
  year = {2000},
  volume = {26},
  pages = {1043-1052},
  owner = {claudia},
}

@ARTICLE{BBolkerandBGrenfell1995,
  author = {B Bolker and B Grenfell},
  title = {Space, persistence and dynamics of measles epidemics.},
  journal = {Philos Trans R Soc Lond B Biol Sci},
  year = {1995},
  volume = {348},
  pages = {309-20},
  number = {1325},
  month = {May},
  abstract = {This paper explores the relations between persistence and dynamics in
	 measles epidemics. Most current models, including the stochastic
	 seasonally forced and age-structured models examined here, fail to capture
	 simultaneously the observed dynamics and persistence characteristics of
	 epidemics in large urban populations before vaccination. Summary measures
	 of persistence and trienniality allow us to compare epidemics in
	 England, New York and Copenhagen with results of non-spatial and
	 spatial stochastic models. Spatial (metapopulation) structure allow
	 persistence and triennial dynamics to coexist i this class of models. The
	 spatial dynamics of measles, for which detailed spatiotemporal data are
	 available, may serve as a useful test of ideas applicable to other
	 epidemiological and ecological systems with an important spatial component.},
  keywords = {20th Century, Active, Acute Disease, Adolescent, Adult, Algorithms,
	 Antibodies, Baltimore, Biological, Birth Rate, Bordetella pertussis,
	 Child, Cities, Communicable Disease Control, Communicable Diseases,
	 Comparative Study, Competitive Behavior, Computer-Assisted, Cross
	 Reactions, Data Interpretation, Demography, Developing Countries, Disease
	 Outbreaks, Disease Susceptibility, Disease Transmission, Ecology,
	 Emigration and Immigration, England, Epidemiologic Methods, Great
	 Britain, History, Horizontal, Host-Parasite Relations, Humans, Immunity,
	 Immunization Programs, Immunologic Memory, Immunological, Incidence,
	 London, Measles, Measles Vaccine, Measles virus, Models, Monte Carlo
	 Method, Netherlands, New York City, Non-U.S. Gov't, Nonlinear Dynamics,
	 Numerical Analysis, Pertussis Vaccine, Population Density, Population
	 Dynamics, Preschool, Quarantine, Regression Analysis, Research Support,
	 Seasons, Statistical, Stochastic Processes, Theoretical, Time Factors,
	 Urban Population, Vaccination, Viral, Wales, Whooping Cough, 8577828},
  owner = {claudia},
}

@ARTICLE{MargaretLBrandeauandGregorySZaricandAnkeRichter2003,
  author = {Margaret L Brandeau and Gregory S Zaric and Anke Richter},
  title = {Resource allocation for control of infectious diseases in multiple independent populations: beyond cost-effectiveness analysis.},
  journal = {J Health Econ},
  year = {2003},
  volume = {22},
  pages = {575-98},
  number = {4},
  month = {Jul},
  abstract = {Traditional cost-effectiveness analysis (CEA) assumes
	 that program costs and benefits scale linearly with investment-an
	 unrealistic assumption for epidemic control programs. This paper
	 combines epidemic modeling with optimization techniques to determine the
	 optimal allocation of a limited resource for epidemic control among
	 multiple noninteracting populations. We show that the optimal resource
	 allocation depends on many factors including the size of each population,
	 the state of the epidemic in each population before resources are
	 allocated (e.g. infection prevalence and incidence), the length of the
	 time horizon, and prevention program characteristics. We establish
	 conditions that characterize the optimal solution in certain cases.},
  keywords = {Communicable Disease Control, Cost-Benefit Analysis, Disease Outbreaks, Econometric,
	 Health Services Needs and Demand, Humans, Models, Non-P.H.S., Non-U.S.
	 Gov't, P.H.S., Prevalence, Quality-Adjusted Life Years, Research
	 Support, Resource Allocation, U.S. Gov't, World Health, 12842316},
  owner = {claudia},
  pii = {S0167629603000432},
}

@ARTICLE{EACasmanandBFischhoffandCPalmgrenandMJSmallandFWu2000,
  author = {EA Casman and B Fischhoff and C Palmgren and MJ Small and F Wu},
  title = {An integrated risk model of a drinking-water-borne cryptosporidiosis outbreak.},
  journal = {Risk Anal},
  year = {2000},
  volume = {20},
  pages = {495-511},
  number = {4},
  month = {Aug},
  abstract = {A dynamic risk model is developed to track the occurrence and
	 evolution of a drinking-water-borne cryptosporidiosis outbreak. The model
	 characterizes and integrates the various environmental, medical,
	 institutional, and behavioral factors that determine outbreak development and
	 outcome. These include contaminant delivery and detection, water
	 treatment efficiency, the timing of interventions, and the choices
	 that people make when confronted with a known or suspected risk. The
	 model is used to evaluate the efficacy of alternative strategies
	 for improving risk management during an outbreak, and to identify
	 priorities for improvements in the public health system. Modeling results
	 indicate that the greatest opportunity for curtailing a large outbreak
	 is realized by minimizing delays in identifying and correcting a
	 drinking-water problem. If these delays cannot be reduced, then the
	 effectiveness of risk communication in preemptively reaching and
	 persuading target populations to avoid exposure becomes important.},
  keywords = {Biological, Choice Behavior, Communication,
	 Community-Institutional Relations, Cryptosporidiosis, Decision Support
	 Techniques, Disease Outbreaks, Environmental Health, Health Behavior,
	 Health Priorities, Humans, Models, Non-U.S. Gov't, Probability, Public
	 Health Administration, Research Support, Risk Assessment, Systems
	 Integration, Time Factors, Water, Water Purification, Water Supply, 11051073},
  owner = {claudia},
}

@BOOK{DaleyDJandGaniJandCanningsC2001,
  title = {Epidemic Modelling : An Introduction},
  publisher = {Cambridge University Press},
  year = {2001},
  author = {Daley, DJ and  Gani, J and Cannings, C},
  owner = {claudia},
}

@BOOK{DiekmannOandHeesterbeekJAP2000,
  title = {Mathematical Epidemiology of Infectious Diseases : Model Building, Analysis and Interpretation},
  publisher = {John Wiley \& Sons},
  year = {2000},
  author = {Diekmann, O and Heesterbeek, JAP},
  owner = {claudia},
}

@ARTICLE{BFinkenstaedtandBGrenfell1998,
  author = {B Finkenstädt and B Grenfell},
  title = {Empirical determinants of measles metapopulation dynamics in {E}ngland and {W}ales.},
  journal = {Proc R Soc Lond B Biol Sci},
  year = {1998},
  volume = {265},
  pages = {211-20},
  number = {1392},
  month = {Feb},
  abstract = {A key issue in metapopulation dynamics is the relative impact of internal patch
	 dynamics and coupling between patches. This problem can be addressed by
	 analysing large spatiotemporal data sets, recording the local and global
	 dynamics of metapopulations. In this paper, we analyse the dynamics of
	 measles meta-populations in a large spatiotemporal case notification
	 data set, collected during the pre-vaccination era in England and
	 Wales. Specifically, we use generalized linear statistical models to
	 quantify the relative importance of local influences (birth rate and
	 population size) and regional coupling on local epidemic dynamics.
	 Apart from the proportional effect of local population size on case
	 totals, the models indicate patterns of local and regional dynamic
	 influences which depend on the current state of epidemics. Birth
	 rate and geographic coupling are not associated with the size of
	 major epidemics. By contrast, minor epidemics--and especially the
	 incidence of local extinction of infection--are influenced both by
	 birth rate and geographical coupling. Birth rate at a lag of four
	 years provides the best fit, reflecting the delayed recruitment of
	 susceptibles to school cohorts. A hierarchical index of spatial coupling
	 to large centres provides the best spatial model. The model also
	 indicates that minor epidemics and extinction patterns are more strongly
	 influenced by this regional effect than the local impact of birth rate.},
  keywords = {Active, Acute Disease, Algorithms,
	 Antibodies, Baltimore, Biological, Birth Rate, Bordetella pertussis,
	 Child, Cities, Communicable Disease Control, Communicable Diseases,
	 Comparative Study, Competitive Behavior, Computer-Assisted, Cross
	 Reactions, Demography, Developing Countries, Disease Outbreaks, Disease
	 Susceptibility, Disease Transmission, Ecology, England, Epidemiologic Methods,
	 Great Britain, Horizontal, Host-Parasite Relations, Humans, Immunity,
	 Immunization Programs, Immunologic Memory, Immunological, Incidence,
	 London, Measles, Measles Vaccine, Measles virus, Models, Monte Carlo
	 Method, Netherlands, New York City, Non-U.S. Gov't, Nonlinear Dynamics,
	 Numerical Analysis, Pertussis Vaccine, Population Density, Population
	 Dynamics, Preschool, Quarantine, Regression Analysis, Research Support,
	 Seasons, Statistical, Stochastic Processes, Theoretical, Time Factors,
	 Urban Population, Vaccination, Viral, Wales, Whooping Cough, 9493407},
  owner = {claudia},
}

@ARTICLE{BFinkenstaedtandMKeelingandBGrenfell1998,
  author = {B Finkenstädt and M Keeling and B Grenfell},
  title = {Patterns of density dependence in measles dynamics.},
  journal = {Proc R Soc Lond B Biol Sci},
  year = {1998},
  volume = {265},
  pages = {753-62},
  number = {1398},
  month = {May},
  abstract = {An important question in metapopulation
	 dynamics is the influence of external perturbations on the population's
	 long-term dynamic behaviour. In this paper we address the question of how
	 spatiotemporal variations in demographic parameters affect the dynamics
	 of measles populations in England and Wales. Specifically, we use
	 nonparametric statistical methods to analyse how birth rate and population
	 size modulate the negative density dependence between successive
	 epidemics as well as their periodicity. For the observed spatiotemporal
	 data from 60 cities, and for simulated model data, the demographic
	 variables act as bifurcation parameters on the joint density of the
	 trade-off between successive epidemics. For increasing population size, a
	 transition occurs from an irregular unpredictable pattern in small
	 communities towards a regular, predictable endemic pattern in large places.
	 Variations in the birth rate parameter lead to a bifurcation from annual
	 towards biennial cyclicity in both observed data and model data.},
  keywords = {Active, Acute Disease, Algorithms,
	 Antibodies, Baltimore, Biological, Birth Rate, Bordetella pertussis,
	 Child, Cities, Communicable Disease Control, Communicable Diseases,
	 Comparative Study, Competitive Behavior, Computer-Assisted, Cross
	 Reactions, Demography, Developing Countries, Disease Outbreaks, Disease
	 Susceptibility, Disease Transmission, Ecology, England, Epidemiologic Methods,
	 Great Britain, Horizontal, Host-Parasite Relations, Humans, Immunity,
	 Immunization Programs, Immunologic Memory, Immunological, Incidence,
	 London, Measles, Measles Vaccine, Measles virus, Models, Monte Carlo
	 Method, Netherlands, New York City, Non-U.S. Gov't, Nonlinear Dynamics,
	 Numerical Analysis, Pertussis Vaccine, Population Density, Population
	 Dynamics, Preschool, Quarantine, Regression Analysis, Research Support,
	 Seasons, Statistical, Stochastic Processes, Theoretical, Time Factors,
	 Urban Population, Vaccination, Viral, Wales, Whooping Cough, 9628034},
  owner = {claudia},
}

@ARTICLE{BaerbelFFinkenstaedtandOttarNBjoernstadandBryanTGrenfell2002,
  author = {Bärbel F Finkenstädt and Ottar N Bjørnstad and Bryan T Grenfell},
  title = {A stochastic model for extinction and recurrence of epidemics: estimation and inference for measles outbreaks.},
  journal = {Biostatistics},
  year = {2002},
  volume = {3},
  pages = {493-510},
  number = {4},
  month = {Dec},
  abstract = {Epidemic dynamics pose a great challenge to stochastic modelling because
	 chance events are major determinants of the size and the timing of
	 the outbreak. Reintroduction of the disease through contact with
	 infected individuals from other areas is an important latent stochastic
	 variable. In this study we model these stochastic processes to explain
	 extinction and recurrence of epidemics observed in measles. We develop
	 estimating functions for such a model and apply the methodology to
	 temporal case counts of measles in 60 cities in England and Wales. In
	 order to estimate the unobserved spatial contact process we suggest a
	 method based on stochastic simulation and marginal densities. The
	 estimation results show that it is possible to consider a unified
	 model for the UK cities where the parameters depend on the city size.
	 Stochastic realizations from the dynamic model realistically capture
	 the transitions from an endemic cyclic pattern in large populations
	 to irregular epidemic outbreaks in small human host populations.},
  doi = {10.1093/biostatistics/3.4.493},
  keywords = {Humans, Immunological, Measles, Measles Vaccine, Models,
	 Netherlands, Non-U.S. Gov't, Research Support, Vaccination, 12933594},
  owner = {claudia},
  pii = {3/4/493},
  url = {http://dx.doi.org/10.1093/biostatistics/3.4.493},
}

@ARTICLE{KGlassandBTGrenfell2004,
  author = {K Glass and BT Grenfell},
  title = {Waning immunity and subclinical measles infections in {E}ngland.},
  journal = {Vaccine},
  year = {2004},
  volume = {22},
  pages = {4110-6},
  number = {29-30},
  month = {Sep},
  abstract = {Following the introduction of vaccination against
	 measles, levels of clinical infection have dropped markedly. As we move
	 further into the vaccine era, increasingly many individuals owe their
	 measles immunity to vaccination and have had few (if any) exposures
	 to wild virus. A number of recent reports suggest that vaccinated
	 individuals with low levels of immunity may be at risk of subclinical
	 measles infection. We explore the interplay between levels of infection
	 and immunity over time using a mathematical model that simulates
	 infection, waning and boosting of immunity. We focus particularly on the
	 situation in England, where vaccination has been in place since 1966.
	 Simulations of our model demonstrate a rise in the levels of subclinical
	 measles infection over time, and a corresponding rise in clinical
	 measles infections if vaccination levels are too low. We compare the
	 impact of intervention strategies, and find that the rise in cases is
	 most effectively reduced by 'catch-up' vaccination of children. In
	 recent years, vaccination levels in England have dropped from above
	 90\% in the 1990s to 84\% in 2001/2002. We discuss the impact of
	 declining vaccination levels on clinical and subclinical infections.},
  doi = {10.1016/j.vaccine.2004.02.047},
  keywords = {, , 15364464},
  owner = {claudia},
  pii = {S0264410X04003202},
  url = {http://dx.doi.org/10.1016/j.vaccine.2004.02.047},
}

@ARTICLE{BTGrenfellandRMAnderson1985,
  author = {BT Grenfell and RM Anderson},
  title = {The estimation of age-related rates of infection from case notifications and serological data.},
  journal = {J Hyg (Lond)},
  year = {1985},
  volume = {95},
  pages = {419-36},
  number = {2},
  month = {Oct},
  abstract = {The paper describes a maximum-likelihood method
	 for the estimation of age-related changes in the per capita rate of
	 infection, from case notification records or serological data. The methods
	 are applied to records of measles incidence in the UK and USA, for
	 which the estimated rates of infection tend to rise to a maximum
	 value at around 10 years of age and then to decline in the older
	 age-classes. Longer-term and seasonal trends are analysed by reference to
	 changes in the estimated average age at infection; a statistic derived
	 from a knowledge of the age-specific rates of infection. Future data
	 needs in the epidemiological study of directly transmitted viral and
	 bacterial diseases are discussed with reference to the detection
	 and interpretation of age-dependent rates of disease transmission.},
  keywords = {Adolescent, Adult, Age Factors, Chickenpox, Child, Cohort Studies,
	 Data Interpretation, Ecology, England, Epidemiologic Methods, Great
	 Britain, Humans, Infant, Mathematics, Measles, Models, Newborn, Non-U.S.
	 Gov't, Population Dynamics, Preschool, Registries, Research Support,
	 Seasons, Statistical, United States, Vaccination, Wales, 4067297},
  owner = {claudia},
}

@ARTICLE{BTGrenfellandONBjoernstadandJKappey2001,
  author = {BT Grenfell and ON Bjørnstad and J Kappey},
  title = {Travelling waves and spatial hierarchies in measles epidemics.},
  journal = {Nature},
  year = {2001},
  volume = {414},
  pages = {716-23},
  number = {6865},
  month = {Dec},
  abstract = {Spatio-temporal travelling waves are striking
	 manifestations of predator-prey and host-parasite dynamics. However, few
	 systems are well enough documented both to detect repeated waves
	 and to explain their interaction with spatio-temporal variations in
	 population structure and demography. Here, we demonstrate recurrent
	 epidemic travelling waves in an exhaustive spatio-temporal data set for
	 measles in England and Wales. We use wavelet phase analysis, which
	 allows for dynamical non-stationarity--a complication in interpreting
	 spatio-temporal patterns in these and many other ecological time series. In the
	 pre-vaccination era, conspicuous hierarchical waves of infection moved
	 regionally from large cities to small towns; the introduction of measles
	 vaccination restricted but did not eliminate this hierarchical contagion. A
	 mechanistic stochastic model suggests a dynamical explanation for
	 the waves-spread via infective 'sparks' from large 'core' cities
	 to smaller 'satellite' towns. Thus, the spatial hierarchy of host
	 population structure is a prerequisite for these infection waves.},
  doi = {10.1038/414716a},
  keywords = {Algorithms, Biological, Cities, Disease Outbreaks, England, Epidemiologic Methods,
	 Humans, Measles, Models, Seasons, Urban Population, Wales, 11742373},
  owner = {claudia},
  pii = {414716a},
  url = {http://dx.doi.org/10.1038/414716a},
}

@ARTICLE{EmilyGrover-KopecandMikaKawanoandRobertWKlaverandBennoBlumenthalandPietroCeccatoandStephenJConnor2005,
  author = {Emily Grover-Kopec and Mika Kawano and Robert W Klaver and Benno Blumenthal and Pietro Ceccato and Stephen J Connor},
  title = {An online operational rainfall-monitoring resource for epidemic malaria early warning systems in {A}frica.},
  journal = {Malar J},
  year = {2005},
  volume = {4},
  pages = {6},
  number = {1},
  month = {Jan},
  abstract = {Periodic epidemics of malaria are a major public health problem for many
	 sub-Saharan African countries. Populations in epidemic prone areas
	 have a poorly developed immunity to malaria and the disease remains
	 life threatening to all age groups. The impact of epidemics could be
	 minimized by prediction and improved prevention through timely vector
	 control and deployment of appropriate drugs. Malaria Early Warning
	 Systems are advocated as a means of improving the opportunity for
	 preparedness and timely response. Rainfall is one of the major factors
	 triggering epidemics in warm semi-arid and desert-fringe areas. Explosive
	 epidemics often occur in these regions after excessive rains and,
	 where these follow periods of drought and poor food security, can be
	 especially severe. Consequently, rainfall monitoring forms one of
	 the essential elements for the development of integrated Malaria
	 Early Warning Systems for sub-Saharan Africa, as outlined by the
	 World Health Organization. The Roll Back Malaria Technical Resource
	 Network on Prevention and Control of Epidemics recommended that a
	 simple indicator of changes in epidemic risk in regions of marginal
	 transmission, consisting primarily of rainfall anomaly maps, could provide
	 immediate benefit to early warning efforts. In response to these
	 recommendations, the Famine Early Warning Systems Network produced
	 maps that combine information about dekadal rainfall anomalies, and
	 epidemic malaria risk, available via their Africa Data Dissemination
	 Service. These maps were later made available in a format that is
	 directly compatible with HealthMapper, the mapping and surveillance
	 software developed by the WHO's Communicable Disease Surveillance
	 and Response Department. A new monitoring interface has recently
	 been developed at the International Research Institute for Climate
	 Prediction (IRI) that enables the user to gain a more contextual
	 perspective of the current rainfall estimates by comparing them to
	 previous seasons and climatological averages. These resources are
	 available at no cost to the user and are updated on a routine basis.},
  doi = {10.1186/1475-2875-4-6},
  keywords = {, , 15663795},
  owner = {claudia},
  pii = {1475-2875-4-6},
  url = {http://dx.doi.org/10.1186/1475-2875-4-6},
}

@BOOK{IshamVandMedleyG1996,
  title = {Models for Infectious Human Diseases : Their Structure and Relation to Data},
  publisher = {Cambridge University Press},
  year = {1996},
  editor = {Isham,V and Medley, G},
  owner = {claudia},
}

@ARTICLE{MJKeelingandBTGrenfell1997,
  author = {MJ Keeling and BT Grenfell},
  title = {Disease extinction and community size: modeling the persistence of measles.},
  journal = {Science},
  year = {1997},
  volume = {275},
  pages = {65-7},
  number = {5296},
  month = {Jan},
  abstract = {A basic issue in ecology
	 is the relation between extinction and population size. One of the
	 clearest manifestations of a population threshold for extinction is the
	 critical community size below which infections like measles do not
	 persist. The current generation of stochastic models overestimates the
	 observed critical community size for measles, generating much less
	 persistence of infection than is observed. The inclusion of a more
	 biologically realistic model for the duration of infection produced a
	 much closer fit to the actual critical community size and explains
	 previously undescribed high-frequency oscillations in measles incidence.},
  keywords = {Active, Acute Disease, Adolescent, Adult, Algorithms,
	 Antibodies, Baltimore, Biological, Birth Rate, Bordetella pertussis,
	 Child, Cities, Communicable Disease Control, Communicable Diseases,
	 Comparative Study, Competitive Behavior, Computer-Assisted, Cross
	 Reactions, Demography, Developing Countries, Disease Outbreaks, Disease
	 Susceptibility, Disease Transmission, Ecology, England, Epidemiologic Methods,
	 Great Britain, Horizontal, Host-Parasite Relations, Humans, Immunity,
	 Immunization Programs, Immunologic Memory, Immunological, Incidence,
	 London, Measles, Measles Vaccine, Measles virus, Models, Monte Carlo
	 Method, Netherlands, New York City, Non-U.S. Gov't, Nonlinear Dynamics,
	 Numerical Analysis, Pertussis Vaccine, Population Density, Population
	 Dynamics, Preschool, Quarantine, Regression Analysis, Research Support,
	 Seasons, Statistical, Stochastic Processes, Theoretical, Time Factors,
	 Urban Population, Vaccination, Viral, Wales, Whooping Cough, 8974392},
  owner = {claudia},
}

@ARTICLE{LaurenAncelMeyersandBabakPourbohloulandMEJNewmanandDanutaMSkowronskiandRobertCBrunham2005,
  author = {Lauren Ancel Meyers and Babak Pourbohloul and M E J Newman and Danuta M Skowronski and Robert C Brunham},
  title = {Network theory and {SARS}: predicting outbreak diversity.},
  journal = {J Theor Biol},
  year = {2005},
  volume = {232},
  pages = {71-81},
  number = {1},
  month = {Jan},
  abstract = {Many infectious diseases spread through populations via the networks formed by
	 physical contacts among individuals. The patterns of these contacts tend
	 to be highly heterogeneous. Traditional "compartmental" modeling
	 in epidemiology, however, assumes that population groups are fully
	 mixed, that is, every individual has an equal chance of spreading
	 the disease to every other. Applications of compartmental models
	 to Severe Acute Respiratory Syndrome (SARS) resulted in estimates
	 of the fundamental quantity called the basic reproductive number
	 R0--the number of new cases of SARS resulting from a single initial
	 case--above one, implying that, without public health intervention, most
	 outbreaks should spark large-scale epidemics. Here we compare these
	 predictions to the early epidemiology of SARS. We apply the methods of
	 contact network epidemiology to illustrate that for a single value
	 of R0, any two outbreaks, even in the same setting, may have very
	 different epidemiological outcomes. We offer quantitative insight into
	 the heterogeneity of SARS outbreaks worldwide, and illustrate the
	 utility of this approach for assessing public health strategies.},
  doi = {10.1016/j.jtbi.2004.07.026},
  keywords = {Adult, Biological, Child, Computer Simulation, Disease Outbreaks, Humans,
	 Models, Non-U.S. Gov't, Public Health, Research Support, Severe Acute
	 Respiratory Syndrome, Stochastic Processes, Urban Health, 15498594},
  owner = {claudia},
  pii = {S0022-5193(04)00351-0},
  url = {http://dx.doi.org/10.1016/j.jtbi.2004.07.026},
}

@ARTICLE{YingcunXiaandOttarNBjoernstadandBryanTGrenfell2004,
  author = {Yingcun Xia and Ottar N Bjørnstad and Bryan T Grenfell},
  title = {Measles metapopulation dynamics: a gravity model for epidemiological coupling and dynamics.},
  journal = {Am Nat},
  year = {2004},
  volume = {164},
  pages = {267-81},
  number = {2},
  month = {Aug},
  abstract = {Infectious diseases provide a particularly clear illustration
	 of the spatiotemporal underpinnings of consumer-resource dynamics.
	 The paradigm is provided by extremely contagious, acute, immunizing
	 childhood infections. Partially synchronized, unstable oscillations are
	 punctuated by local extinctions. This, in turn, can result in spatial
	 differentiation in the timing of epidemics and, depending on the nature of
	 spatial contagion, may result in traveling waves. Measles epidemics are
	 one of a few systems documented well enough to reveal all of these
	 properties and how they are affected by spatiotemporal variations in
	 population structure and demography. On the basis of a gravity coupling
	 model and a time series susceptible-infected-recovered (TSIR) model
	 for local dynamics, we propose a metapopulation model for regional
	 measles dynamics. The model can capture all the major spatiotemporal
	 properties in prevaccination epidemics of measles in England and Wales.},
  doi = {10.1086/422341},
  keywords = {Humans, Immunological, Measles, Measles Vaccine, Models,
	 Netherlands, Non-U.S. Gov't, Research Support, Vaccination, 15278849},
  owner = {claudia},
  pii = {AN30242},
  url = {http://dx.doi.org/10.1086/422341},
}

@ARTICLE{GregorySZaricandMargaretLBrandeau2002,
  author = {Gregory S Zaric and Margaret L Brandeau},
  title = {Dynamic resource allocation for epidemic control in multiple populations.},
  journal = {IMA J Math Appl Med Biol},
  year = {2002},
  volume = {19},
  pages = {235-55},
  number = {4},
  month = {Dec},
  abstract = {We develop a dynamic resource allocation
	 model in which a limited budget for epidemic control is allocated
	 over multiple time periods to interventions that affect multiple
	 populations. For certain special cases with two time periods, multiple
	 independent populations, and a linear relationship between investment in a
	 prevention programme and the resulting change in risky behaviour, we
	 demonstrate that the optimal solution involves investing in each period as
	 much as possible in some of the populations and nothing in all the
	 other populations. We present heuristic algorithms for solving the
	 general problem, and present numerical results. Our computational
	 analyses suggest that good allocations can be made based on some fairly
	 simple heuristics. Our analyses also suggest that allowing for some
	 reallocation of resources over the time horizon of the problem, rather
	 than allocating resources just once at the beginning of the time
	 horizon, can lead to significant increases in health benefits. Allowing
	 for reallocation of funds may generate more health benefits than
	 use of a sophisticated model for one-time allocation of resources.},
  keywords = {Biological, Communicable Disease
	 Control, Computer-Assisted, Cost-Benefit Analysis, Disease Outbreaks,
	 Econometric, Economic, Health Care Rationing, Health Services Needs
	 and Demand, Humans, Models, Non-P.H.S., Non-U.S. Gov't, Numerical
	 Analysis, P.H.S., Prevalence, Quality-Adjusted Life Years, Research
	 Support, Resource Allocation, U.S. Gov't, World Health, 12828363},
  owner = {claudia},
}

