Comparison of Two Detailed Models of Aedes aegypti Population Dynamics

The success of control programs for mosquito-­borne diseases can be enhanced by crucial information provided by models of the mosquito populations. Models, however, can differ in their structure, complexity, and biological assumptions, and these differences impact their predictions. Unfortunately, it is typically difficult to determine why two complex models make different predictions because we lack structured side-­by-­side comparisons of models using comparable parameterization. Here, we present a detailed comparison of two complex, spatially explicit, stochastic models of the population dynamics of Aedes aegypti, the main vector of dengue, yellow fever, chikungunya, and Zika viruses. Both models describe the mosquito?s biological and ecological characteristics, but differ in complexity and specific assumptions. We compare the predictions of these models in two selected climatic settings: a tropical and weakly seasonal climate in Iquitos, Peru, and a temperate and strongly seasonal climate in Buenos Aires, Argentina. Both models were calibrated to operate at identical average densities in unperturbedconditions in both settings, by adjusting parameters regulating densities in each model (number of larval development sites and amount of nutritional resources). We show that the models differ in their sensitivityto environmental conditions (temperature and rainfall) and trace differences to specific model assumptions.Temporal dynamics of the Ae. aegypti populations predicted by the two models differ more markedly under strongly seasonal Buenos Aires conditions. We use both models to simulate killing of larvae and/or adults with insecticides in selected areas. We show that predictions of population recovery by the models differ substantially, an effect likely related to model assumptions regarding larval development and (director delayed) density dependence. Our methodical comparison provides important guidance for model improvement by identifying key areas of Ae. aegypti ecology that substantially affect model predictions, and revealing the impact of model assumptions on population dynamics predictions in unperturbed and perturbed conditions.Fil: Legros, Mathieu. University of North Carolina; Estados UnidosFil: Otero, Marcelo Javier. Universidad de Buenos Aires; ArgentinaFil: Romeo Aznar, Victoria Teresa. Universidad de Buenos Aires; ArgentinaFil: Solari, Hernan Gustavo. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Gould, Fred. National Institutes of Health; Estados UnidosFil: Lloyd, Alun L.. National Institutes of Health; Estados Unido

Dengue disease, basic reproduction number and control

Dengue is one of the major international public health concerns. Although progress is underway, developing a vaccine against the disease is challenging. Thus, the main approach to fight the disease is vector control. A model for the transmission of Dengue disease is presented. It consists of eight mutually exclusive compartments representing the human and vector dynamics. It also includes a control parameter (insecticide) in order to fight the mosquito. The model presents three possible equilibria: two disease-free equilibria (DFE) and another endemic equilibrium. It has been proved that a DFE is locally asymptotically stable, whenever a certain epidemiological threshold, known as the basic reproduction number, is less than one. We show that if we apply a minimum level of insecticide, it is possible to maintain the basic reproduction number below unity. A case study, using data of the outbreak that occurred in 2009 in Cape Verde, is presented.Comment: This is a preprint of a paper whose final and definitive form has appeared in International Journal of Computer Mathematics (2011), DOI: 10.1080/00207160.2011.55454

Capacity-building activities related to climate change vulnerability and adaptation assessment and economic valuation for Fiji

The Terms of Reference for this work specified three objectives to the Fiji component: Objective 1a: to provide a prototype FIJICLIM model (covered under PICCAP funding) Objective 1b: to provide training and transfer of FIJICLIM Objective 1c: to present and evaluate World Bank study findings and to identify future directions for development and use of FIJICLIM (2-day workshop) Proceedings of the training course and workshop were prepared by the Fiji Department of Environment. The summaries from these proceedings reflect a very high degree of success with the contracted activities

Australian healthcare services and the climate change debate

After years of highly charged political and public debate on tackling climate change, Australia started taxing carbon emissions on 1 July 2012. Under the carbon tax, Australia’s biggest carbon emitting companies will pay a fixed-price levy on their carbon emissions for three years. At the end of this period, the carbon tax will transition to an emissions trading scheme, from 1 July 2015. Healthcare services and hospitals are not directly affected by the carbon tax, as they are not among the biggest polluting companies in Australia. However, they may experience some indirect flow-on costs, in the form of higher energy prices. Though as this Policy Brief will explain, any overall increases in costs for public hospitals are likely to be minimal at most once the compensatory effects of new hospital funding arrangements are taken into account. Nonetheless, with the health sector responsible for 7 per cent of total carbon emissions from buildings in Australia,  there is significant scope for the sector to reduce its carbon footprint through greater energy efficiency measures. Of course, the health sector also has a much broader interest in the climate change debate: the impacts of climate change on human health. Climate experts now agree that the health impacts of climate change, such as the spread of infectious diseases, and illness and fatalities related to severe weather events, are significant, and pose a significant threat for the future. This Policy Brief will explore each of these issues and outline policy options and other initiatives currently in place to address them

On the Dynamics of Dengue Virus type 2 with Residence Times and Vertical Transmission

A two-patch mathematical model of Dengue virus type 2 (DENV-2) that accounts for vectors' vertical transmission and between patches human dispersal is introduced. Dispersal is modeled via a Lagrangian approach. A host-patch residence-times basic reproduction number is derived and conditions under which the disease dies out or persists are established. Analytical and numerical results highlight the role of hosts' dispersal in mitigating or exacerbating disease dynamics. The framework is used to explore dengue dynamics using, as a starting point, the 2002 outbreak in the state of Colima, Mexico

Climate change vulnerability and adaptation assessment for Fiji

All nations, including Fiji, that are signatories to the United Nations Framework Convention on Climate Change(UNFCCC) are obliged to provide National Communications to the Conference of Parties (COP) of the UNFCCC. The COP4 stressed the need for parties to the Convention to take into account the need for establishing implementation strategies for adaptation to climate and sea-level changes. As such, Fiji is required to submit a National Communication document that shall include information on climate change vulnerability and adaptation implementation policies and strategies. The methodology used in this assessment is based on the Intergovernmental Panel on Climate Change (IPCC) technical guidelines (Carter et al, 1994) for assessing climate change impacts and adaptation. Firstly, the present conditions are examined and key sectors identified. Then, future climatic and non-climatic scenarios are used to examine the possible effects of climate and sea-level changes on the various sectors identified. These then form the basis for identifying possible adaptation response measures for endorsement, adoption and implementation by the Fiji government. Because of the many gaps in present knowledge, and the fact that this study is focussed only on Viti Levu, the recommendations in this report should be seen as starting point for an on-going process of vulnerability and adaptation assessment in Fij

Mathematical modeling and computation of dengue fever caused by climate change in Jeju Island

Department of Mathematical SciencesDengue fever which is a vector-borne infectious disease that spreads rapidly in subtropical or tropical countries is rarely recognized as a public health concern in South Korea, especially Jeju Island which is target district for study. However, there is a high possibility that the outbreak of dengue fever occurs in Korea within a few years since global warming is accelerating and the medium mosquitoes for dengue are also inhabit in Korea. The purpose of this study is predicting how many patients would occur when there is an outbreak of dengue fever by using climate change scenario. Based on RCPs provided by Korea Meterological Administration, the parameters related to mosquitoes represented as fitting functions and specific function by using climatic factors such as temperature, precipitation, and relative humidity are formulated. The simulation for deterministic models is performed by using two methods, one of applying climate data of all the four seasons, and the other applying climate data of seasons excluding winter. This study show the relation between climate change and outbreaks of dengue, which could be an important indicator to establish polices to reduce spread of the disease