Anthropogenic impacts on mosquito populations in North America over the past century.

The recent emergence and spread of vector-borne viruses including Zika, chikungunya and dengue has raised concerns that climate change may cause mosquito vectors of these diseases to expand into more temperate regions. However, the long-term impact of other anthropogenic factors on mosquito abundance and distributions is less studied. Here, we show that anthropogenic chemical use (DDT; dichlorodiphenyltrichloroethane) and increasing urbanization were the strongest drivers of changes in mosquito populations over the last eight decades in areas on both coasts of North America. Mosquito populations have increased as much as tenfold, and mosquito communities have become two- to fourfold richer over the last five decades. These increases are correlated with the decay in residual environmental DDT concentrations and growing human populations, but not with temperature. These results illustrate the far-reaching impacts of multiple anthropogenic disturbances on animal communities and suggest that interactions between land use and chemical use may have unforeseen consequences on ecosystems

Geographic variation in the response of Culex pipiens life history traits to temperature

BackgroundClimate change is predicted to alter the transmission of many vector-borne pathogens. The quantitative impact of climate change is usually estimated by measuring the temperature-performance relationships for a single population of vectors, and then mapping this relationship across a range of temperatures or locations. However, life history traits of different populations often differ significantly. Specifically, performance across a range of temperatures is likely to vary due to local adaptation to temperature and other factors. This variation can cause spatial variation in pathogen transmission and will influence the impact of climate change on the transmission of vector-borne pathogens.MethodsWe quantified variation in life history traits for four populations of Culex pipiens (Linnaeus) mosquitoes. The populations were distributed along altitudinal and latitudinal gradients in the eastern United States that spanned ~3 °C in mean summer temperature, which is similar to the magnitude of global warming expected in the next 3-5 decades. We measured larval and adult survival, development rate, and biting rate at six temperatures between 16 and 35 °C, in a common garden experiment.ResultsTemperature had strong and consistent non-linear effects on all four life history traits for all four populations. Adult female development time decreased monotonically with increasing temperature, with the largest decrease at cold temperatures. Daily juvenile and adult female survival also decreased with increasing temperature, but the largest decrease occurred at higher temperatures. There was significant among-population variation in the thermal response curves for the four life history traits across the four populations, with larval survival, adult survival, and development rate varying up to 45, 79, and 84 % among populations, respectively. However, variation was not correlated with local temperatures and thus did not support the local thermal adaptation hypothesis.ConclusionThese results suggest that the impact of climate change on vector-borne disease will be more variable than previous predictions, and our data provide an estimate of this uncertainty. In addition, the variation among populations that we observed will shape the response of vectors to changing climates

Historical development of the number concept

Thesis (M.A.)--University of Kansas, Mathematics, 1918. ; Includes bibliographical references

Using network theory to identify the causes of disease outbreaks of unknown origin.

The identification of undiagnosed disease outbreaks is critical for mobilizing efforts to prevent widespread transmission of novel virulent pathogens. Recent developments in online surveillance systems allow for the rapid communication of the earliest reports of emerging infectious diseases and tracking of their spread. The efficacy of these programs, however, is inhibited by the anecdotal nature of informal reporting and uncertainty of pathogen identity in the early stages of emergence. We developed theory to connect disease outbreaks of known aetiology in a network using an array of properties including symptoms, seasonality and case-fatality ratio. We tested the method with 125 reports of outbreaks of 10 known infectious diseases causing encephalitis in South Asia, and showed that different diseases frequently form distinct clusters within the networks. The approach correctly identified unknown disease outbreaks with an average sensitivity of 76 per cent and specificity of 88 per cent. Outbreaks of some diseases, such as Nipah virus encephalitis, were well identified (sensitivity = 100%, positive predictive values = 80%), whereas others (e.g. Chandipura encephalitis) were more difficult to distinguish. These results suggest that unknown outbreaks in resource-poor settings could be evaluated in real time, potentially leading to more rapid responses and reducing the risk of an outbreak becoming a pandemic

Quantifying trends in disease impact to produce a consistent and reproducible definition of an emerging infectious disease.

The proper allocation of public health resources for research and control requires quantification of both a disease's current burden and the trend in its impact. Infectious diseases that have been labeled as "emerging infectious diseases" (EIDs) have received heightened scientific and public attention and resources. However, the label 'emerging' is rarely backed by quantitative analysis and is often used subjectively. This can lead to over-allocation of resources to diseases that are incorrectly labelled "emerging," and insufficient allocation of resources to diseases for which evidence of an increasing or high sustained impact is strong. We suggest a simple quantitative approach, segmented regression, to characterize the trends and emergence of diseases. Segmented regression identifies one or more trends in a time series and determines the most statistically parsimonious split(s) (or joinpoints) in the time series. These joinpoints in the time series indicate time points when a change in trend occurred and may identify periods in which drivers of disease impact change. We illustrate the method by analyzing temporal patterns in incidence data for twelve diseases. This approach provides a way to classify a disease as currently emerging, re-emerging, receding, or stable based on temporal trends, as well as to pinpoint the time when the change in these trends happened. We argue that quantitative approaches to defining emergence based on the trend in impact of a disease can, with appropriate context, be used to prioritize resources for research and control. Implementing this more rigorous definition of an EID will require buy-in and enforcement from scientists, policy makers, peer reviewers and journal editors, but has the potential to improve resource allocation for global health

Context-dependent conservation responses to emerging wildlife diseases

Emerging infectious diseases pose an important threat to wildlife. While established protocols exist for combating outbreaks of human and agricultural pathogens, appropriate management actions before, during, and after the invasion of wildlife pathogens have not been developed. We describe stage-specific goals and management actions that minimize disease impacts on wildlife, and the research required to implement them. Before pathogen arrival, reducing the probability of introduction through quarantine and trade restrictions is key because prevention is more cost effective than subsequent responses. On the invasion front, the main goals are limiting pathogen spread and preventing establishment. In locations experiencing an epidemic, management should focus on reducing transmission and disease, and promoting the development of resistance or tolerance. Finally, if pathogen and host populations reach a stable stage, then recovery of host populations in the face of new threats is paramount. Successful management of wildlife disease requires risk-taking, rapid implementation, and an adaptive approach."Funding was provided by the US National Science Foundation (grants EF-0914866, DGE-0741448, DEB-1115069, DEB-1336290) and the National Institutes of Health (grant 1R010AI090159)."https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/14024

Bushmeat Hunting, Deforestation, and Prediction of Zoonotic Disease

Integrating virology, ecology, and other disciplines enhances prediction of new emerging zoonoses

Population-level use of low-oxygen zones of a mesopelagic predator, the northern elephant seal

The 14th Symposium on Polar Science/Ordinary sessions [OB] Polar biology, Wed. 15 Nov. / Entrance Hall (1st floor), National Institute of Polar Researchconference objec

Wild animals in poorer body condition sleep less to forage more

The 14th Symposium on Polar Science/Ordinary sessions [OB] Polar biology, Wed. 15 Nov. / 4F Multipurpose Conference room, National Institute of Polar Researchconference objec