Standardizing scenarios to assess the need to respond to an influenza pandemic.
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ABSTRACT:
SUBMITTER: Meltzer MI
PROVIDER: S-EPMC4481578 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundIndividual-based models can provide the most reliable estimates of the spread of infectious diseases. In the present study, we evaluated the diffusion of pandemic influenza in Italy and the impact of various control measures, coupling a global SEIR model for importation of cases with an individual based model (IBM) describing the Italian epidemic.Methodology/principal findingsWe co-located the Italian population (57 million inhabitants) to households, schools and workplaces and we assigned travel destinations to match the 2001 census data. We considered different R(0 )values (1.4; 1.7; 2), evaluating the impact of control measures (vaccination, antiviral prophylaxis -AVP-, international air travel restrictions and increased social distancing). The administration of two vaccine doses was considered, assuming that first dose would be administered 1-6 months after the first world case, and different values for vaccine effectiveness (VE). With no interventions, importation would occur 37-77 days after the first world case. Air travel restrictions would delay the importation of the pandemic by 7-37 days. With an R(0 )of 1.4 or 1.7, the use of combined measures would reduce clinical attack rates (AR) from 21-31% to 0.3-4%. Assuming an R(0) of 2, the AR would decrease from 38% to 8%, yet only if vaccination were started within 2 months of the first world case, in combination with a 90% reduction in international air traffic, closure of schools/workplaces for 4 weeks and AVP of household and school/work close contacts of clinical cases. Varying VE would not substantially affect the results.ConclusionsThis IBM, which is based on country-specific demographic data, could be suitable for the real-time evaluation of measures to be undertaken in the event of the emergence of a new pandemic influenza virus. All preventive measures considered should be implemented to mitigate the pandemic.
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Project description:The unprecedented global spread of highly pathogenic avian H5N1 influenza viruses within the past ten years and their extreme lethality to poultry and humans has underscored their potential to cause an influenza pandemic. Combating the threat of an impending H5N1 influenza pandemic will require a combination of pharmaceutical and nonpharmaceutical intervention strategies. The emergence of the H1N1 pandemic in 2009 emphasised the unpredictable nature of a pandemic influenza. Undoubtedly, vaccines offer the most viable means to combat a pandemic threat. Current egg-based influenza vaccine manufacturing strategies are unlikely to be able to cater to the huge, rapid global demand because of the anticipated scarcity of embryonated eggs in an avian influenza pandemic and other factors associated with the vaccine production process. Therefore, alternative, egg-independent vaccine manufacturing strategies should be evaluated to supplement the traditional egg-derived influenza vaccine manufacturing. Furthermore, evaluation of dose-sparing strategies that offer protection with a reduced antigen dose will be critical for pandemic influenza preparedness. Development of new antiviral therapeutics and other, nonpharmaceutical intervention strategies will further supplement pandemic preparedness. This review highlights the current status of egg-dependent and egg-independent strategies against an avian influenza pandemic.
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Project description:We here propose to model active and cumulative cases data from COVID-19 by a continuous effective model based on a modified diffusion equation under Lifshitz scaling with a dynamic diffusion coefficient. The proposed model is rich enough to capture different aspects of a complex virus diffusion as humanity has been recently facing. The model being continuous it is bound to be solved analytically and/or numerically. So, we investigate two possible models where the diffusion coefficient associated with possible types of contamination are captured by some specific profiles. The active cases curves here derived were able to successfully describe the pandemic behavior of Germany and Spain. Moreover, we also predict some scenarios for the evolution of COVID-19 in Brazil. Furthermore, we depicted the cumulative cases curves of COVID-19, reproducing the spreading of the pandemic between the cities of São Paulo and São José dos Campos, Brazil. The scenarios also unveil how the lockdown measures can flatten the contamination curves. We can find the best profile of the diffusion coefficient that better fit the real data of pandemic.
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