4,533 research outputs found
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Emerging Challenges and Opportunities in Infectious Disease Epidemiology.
Much of the intellectual tradition of modern epidemiology stems from efforts to understand and combat chronic diseases persisting through the 20th century epidemiologic transition of countries such as the United States and United Kingdom. After decades of relative obscurity, infectious disease epidemiology has undergone an intellectual rebirth in recent years amid increasing recognition of the threat posed by both new and familiar pathogens. Here, we review the emerging coalescence of infectious disease epidemiology around a core set of study designs and statistical methods bearing little resemblance to the chronic disease epidemiology toolkit. We offer our outlook on challenges and opportunities facing the field, including the integration of novel molecular and digital information sources into disease surveillance, the assimilation of such data into models of pathogen spread, and the increasing contribution of models to public health practice. We next consider emerging paradigms in causal inference for infectious diseases, ranging from approaches to evaluating vaccines and antimicrobial therapies to the task of ascribing clinical syndromes to etiologic microorganisms, an age-old problem transformed by our increasing ability to characterize human-associated microbiota. These areas represent an increasingly important component of epidemiology training programs for future generations of researchers and practitioners
Urban Cholera transmission hotspots and their implications for Reactive Vaccination: evidence from Bissau city, Guinea Bissau
Use of cholera vaccines in response to epidemics (reactive vaccination) may provide an effective supplement to traditional control measures. In Haiti, reactive vaccination was considered but, until recently, rejected in part due to limited global supply of vaccine. Using Bissau City, Guinea-Bissau as a case study, we explore neighborhood-level transmission dynamics to understand if, with limited vaccine and likely delays, reactive vaccination can significantly change the course of a cholera epidemic
Vaccination in emergencies.
Nongovernmental organisations (NGOs) are the main actors of vaccine delivery during complex humanitarian emergencies such as large population displacements. This paper discusses the use of vaccinations against measles, cholera and meningitis in this context. The role of NGOs in the advocacy for making new and more effective vaccines available to the most vulnerable populations is also emphasised
Some considerations concerning the challenge of incorporating social variables into epidemiological models of infectious disease transmission
Incorporation of ‘social’ variables into epidemiological models remains a challenge. Too much detail and models cease to be useful; too little and the very notion of infection —a highly social process in human populations—may be considered with little reference to the social. The French sociologist Emile Durkheim proposed that the scientific study of society required identification and study of ‘social currents.’ Such ‘currents’ are what we might today describe as ‘emergent properties,’ specifiable variables appertaining to individuals and groups, which represent the perspectives of social actors as they experience the environment in which they live their lives. Here we review the ways in which one particular emergent property, hope, relevant to a range of epidemiological situations, might be used in epidemiological modelling of infectious diseases in human populations. We also indicate how such an approach might be extended to include a range of other potential emergent properties to repre
Modeling the Influence of Environment and Intervention on Cholera in Haiti
We propose a simple model with two infective classes in order to model the
cholera epidemic in Haiti. We include the impact of environmental events
(rainfall, temperature and tidal range) on the epidemic in the Artibonite and
Ouest regions by introducing terms in the transmission rate that vary with
environmental conditions. We fit the model on weekly data from the beginning of
the epidemic until December 2013, including the vaccination programs that were
recently undertaken in the Ouest and Artibonite regions. We then modified these
projections excluding vaccination to assess the programs' effectiveness. Using
real-time daily rainfall, we found lag times between precipitation events and
new cases that range from 3.4 to 8.4 weeks in Artibonite and 5.1 to 7.4 in
Ouest. In addition, it appears that, in the Ouest region, tidal influences play
a significant role in the dynamics of the disease. Intervention efforts of all
types have reduced case numbers in both regions; however, persistent outbreaks
continue. In Ouest, where the population at risk seems particularly besieged
and the overall population is larger, vaccination efforts seem to be taking
hold more slowly than in Artibonite, where a smaller core population was
vaccinated. The models including the vaccination programs predicted that a year
and six months later, the mean number of cases in Artibonite would be reduced
by about two thousand cases, and in Ouest by twenty four hundred cases below
that predicted by the models without vaccination. We also found that
vaccination is best when done in the early spring, and as early as possible in
the epidemic. Comparing vaccination between the first spring and the second,
there is a drop of about 40% in the case reduction due to the vaccine and about
10% per year after that
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Modelling environmentally-mediated infectious diseases of humans: transmission dynamics of schistosomiasis in China.
Macroparasites of humans are sensitive to a variety of environmental variables, including temperature, rainfall and hydrology, yet current comprehension of these relationships is limited. Given the incomplete mechanistic understanding of environment-disease interactions, mathematical models that describe them have seldom included the effects of time-varying environmental processes on transmission dynamics and where they have been included, simple generic, periodic functions are usually used. Few examples exist where seasonal forcing functions describe the actual processes underlying the environmental drivers of disease dynamics. Transmission of human schistosomes, which involves multiple environmental stages, offers a model for applying our understanding of the environmental determinants of the viability, longevity, infectivity and mobility of these stages to controlling disease in diverse environments. Here, a mathematical model of schistosomiasis transmission is presented which incorporates the effects of environmental variables on transmission. Model dynamics are explored and several key extensions to the model are proposed
Analysis of Control Measures Used During Cholera Outbreaks Among Internally Displaced Persons
Cholera remains a major public health problem affecting high-risk populations such as camps of internally displaced persons. During a cholera outbreak, it is essential to reduce transmission and minimize new infections. The Miasma theory, host-agent-environment model and Ecosocial theory were utilized for this study. This study was a retrospective comparison to determine whether historical cholera control measures are effective during current cholera outbreaks within camps of internally displaced persons. A quantitative approach ascertained changes in incidence and mortality rates following implementation of primary and/or secondary control measures. Cholera outbreaks were identified from the World Health Organization\u27s (WHO) Disease Outbreak News reports issued between 1996 and 2017. Each reported cholera outbreak was categorized into one of eight outbreak cohorts -- each cohort having the same primary control measure. The WHO Data Repository was used to identify cholera incidence and/or mortalities and the World Bank data set was used for population total to calculate incidence and/or mortality rates for the years prior to and the year of the outbreak to calculate the case percentage change and death percentage change. Analysis of covariance was used to assess statistical significance in rate change within each intervention cohort. No statistical significance was noted within various cholera control intervention. Limitations of this study provide the basis for continued research on this topic; also aligning with the Global Task Force on Cholera to reduce infections by 90% by the year 2030
Modeling and Optimization of Dynamical Systems in Epidemiology using Sparse Grid Interpolation
Infectious diseases pose a perpetual threat across the globe, devastating communities, and straining public health resources to their limit. The ease and speed of modern communications and transportation networks means policy makers are often playing catch-up to nascent epidemics, formulating critical, yet hasty, responses with insufficient, possibly inaccurate, information. In light of these difficulties, it is crucial to first understand the causes of a disease, then to predict its course, and finally to develop ways of controlling it. Mathematical modeling provides a methodical, in silico solution to all of these challenges, as we explore in this work. We accomplish these tasks with the aid of a surrogate modeling technique known as sparse grid interpolation, which approximates dynamical systems using a compact polynomial representation. Our contributions to the disease modeling community are encapsulated in the following endeavors. We first explore transmission and recovery mechanisms for disease eradication, identifying a relationship between the reproductive potential of a disease and the maximum allowable disease burden. We then conduct a comparative computational study to improve simulation fits to existing case data by exploiting the approximation properties of sparse grid interpolants both on the global and local levels. Finally, we solve a joint optimization problem of periodically selecting field sensors and deploying public health interventions to progressively enhance the understanding of a metapopulation-based infectious disease system using a robust model predictive control scheme
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