4,533 research outputs found

    Urban Cholera transmission hotspots and their implications for Reactive Vaccination: evidence from Bissau city, Guinea Bissau

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    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.

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    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

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    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

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    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

    Analysis of Control Measures Used During Cholera Outbreaks Among Internally Displaced Persons

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    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

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    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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