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The Effect of Climate on Infectious Disease

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1 The Effect of Climate on Infectious Disease
What changes will occur epidemiologically? How do we correlate changes in climate to global changes in infectious disease? What technologies and tools are used to analyze, predict, and monitor disease spread? Robin Cochran-Dirksen

2 Kinds of health impacts resulting from climate change:
Direct- result from weather extremes. Consequences from ecological disruption. Consequences resulting from climate-induced economic disruption, e.g. traumatic, infectious, nutritional, psychological Changes in human health as a result of climate change will be indicated by alterations in geographic range and seasonality of infectious diseases. Robin Cochran-Dirksen

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4 Challenges to connecting changes in infectious disease to climate change.
Climate influences are modulated by interactions with other ecological processes, social conditions, and adaptive policies. Climate change is just one environmental change that simultaneously impact human health. For example, it has proven difficult to assess how climate change has influenced recent changes in spread of malaria in African highlands because of inadequate baseline health have not been collected on large enough scale. Also, other environmental changes such as deforestation and land-use patterns are concurrently affecting health. Vector-borne infectious disease is jointly affected by climate conditions, population movement, biodiversity loss (affecting mosquito predators), freshwater configurations… The mortality impact of an acute event such as a heatwave is uncertain because an unkown portion of deaths are in susceptible persons who would have died in the near future anyway.

5 Determinants of vectorborne diseases
Vector survival and reproduction Biting rate Incubation rate Vectors, pathogens, and and hosts optimal range are most affected by temp and precip, sea level elevation, wind, and duration of daylight are important. Robin Cochran-Dirksen

6 The tasks of the researchers
Establishing baseline relationships between weather and health. Seeking evidence of early effects of climate change. Scenario-based predictive models Evaluating adaptation options Estimating the co-incidental benefits and costs of mitigation and adaptation. Do higher ambient temperatures cause more illness? Apparently, they do. Major pathogens that cause gastroenteritis multiply faster in warm weather; monthly salmonella infection counts done in New Zealand correlate clearly to average monthly temps. Collecting observations- there has been data connecting changing patterns of tick-borne encephalitis and cholera. Impacts will be clearest where the exposure-outcome is clearest, the local populations adaptive capacity is weakest, e.g. work hours cannot be changed, air conditioning cannot be installed, shade trees cannot be planted. Some impacts can easily be quantified (deaths resulting from storms), others are not so easy to quantify (health consequences of food insecurity). How steps can be taken to reduce the impact of climate change. Ancillary cost and benefits are important to policy makers, e.g. promotion of public transportation will reduce CO2 emissions, but also improves public health by reducing air pollution, and road traffic injuries, and increases physical activity. Robin Cochran-Dirksen

7 Environmental factors affecting infectious disease
Warmer temps enhance vector breeding and reduce the pathogens maturation time within the vector. Expanded pathogen/vector range introduces the pathogen to populations with little protective immunity. Mosquitoes (Anopheles) prefer warmer temps, which in turn increases pathogens causing malaria (P. falciparum) and dengue, Each viral serotype is sufficiently different that there is no cross-protection and epidemics caused by multiple serotypes (hyperendemicity) can occur. Dengue is transmitted to humans by the Aedes aegypti or more rarely the Aedes albopictus mosquito, which feed during the day. ENSO (El Nino Southern Oscillation), a semi-decadal cycle, influences much of the world’s regional weather patterns, increases water pooling. Rodents proliferate in temperate weather increasing incidence of leptospirosis, tularaemia and viral hemorrhagic diseases. Diarrheal diseases peak duting the rainy seasons: cholera, cryptosporidium, E. coli, giardia, typhoid, and Hep A. Robin Cochran-Dirksen

8 Social factors affecting health
Population density Level of economic development of the country Food availability Pre-existing health status Availability of health care Those most affected by heat extremes are the socially isolated elderly and poor. Populations living at the margins of malaria and dengue habitat, without effective primary health care, will be the most affected if these diseases expand their geographic range. Robin Cochran-Dirksen

9 Historic evidence of the connection of climate to infectious disease
The link of climatic events has been studied for a century in the Punjab. Recent analyses show that malaria epidemic risk increases 5x that year after an El Nino event. Robin Cochran-Dirksen

10 Applying Technologies: Types of Models
Statistical Process Based Landscape-based Models Robin Cochran-Dirksen

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Statistical Models Work by deriving the relationship between current geographic distribution and the current location-specific climatic conditions These models have been applied to climate impacts on malaria, dengue fever and in the U.S., enchephalitis. It describes the climatic influence on the actual distributions of the disease, given prevailing levels of human intervention (disease control, environmental management), by then applying this statistical equation to future climate scenarios, the actual distribution of the disease in the future is estimated, assuming unchanged levels of human intervention within any particular climatic zone. These models have been Robin Cochran-Dirksen

12 Process-based (mathematical) models
Use equations that express the scientifically documented relationship between climatic variables and biological parameters- e.g., vector breeding, parasite incubation rate, In their simplest form, such models express, how a given configuration of climate variables would affect vector and parasite biology, and consequently disease transmission. The malaria model has shown that small temp increases can greatly affect transmission potential- global temp increases of 2-3’C would increase the number of people at risk of malaria around 3-5%, translating into several hundred million, and that the duration of the malaria season would also increase. Robin Cochran-Dirksen

13 Landscape-based Modeling
Combines the first two models with the use of spatial analytical methods, to study the effects of both climatic and other environmental factors (e.g. different vegetation types- often measured, in the model development stage by ground-based or remote sensors). This type of modeling has been applied to estimate how future climate-induced changes in ground cover and surface water in Africa should affect mosquitoes and tsetse flies, and hence, malaria and African sleeping sickness. In 230 the estimated risk of diarrhea will increase by 10% higher in some regions than if no climate change occurred. Robin Cochran-Dirksen

14 Models allow them to create rubrics:
Robin Cochran-Dirksen

15 A Specific Example of Model Application
A study called Regional-scale climate-variability synchrony of cholera epidemics in West Africa was published in 2007 in Infectious Disease Methods: Wavelet and derived analyses of the evolution of the periodic component over time. Robin Cochran-Dirksen

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Wavelet analyses non-technical definition of a wavelet is that it is a wave with an amplitude that starts out at zero, increases, and then decreases back to zero. It can typically be visualized as a "brief oscillation" like one might see recorded by a seismograph or heart monitor. Generally, wavelets are purposefully crafted to have specific properties that make them useful for signal processing. Wavelets can be combined, using a "multiply and sum" technique called convolution, with portions of an unknown signal to extract information from the unknown signal. For example, a wavelet could be created to have a frequency of "Middle C" and a short duration of roughly a 32nd note. If this wavelet were to be convolved at periodic intervals with a signal created from the recording of a song, then the results of these convolutions would be useful for determining when the "Middle C" note was being played in the song. Mathematically the wavelet will resonate if the unknown signal contains information of similar frequency - just as a tuning fork physically resonates with sound waves of its specific tuning frequency. This concept of resonance is at the core of many practical applications of wavelet theory. As wavelets are a mathematical tool they can be used to extract information from many different kinds of data, including - but certainly not limited to - audio signals and images. Sets of wavelets are generally needed to fully analyze data. A set of "complementary" wavelets will deconstruct data without gaps or overlap so that the deconstruction process is mathematically reversible. Thus, sets of complementary wavelets are useful in wavelet based compression/decompression algorithms where it is desirable to recover the original information with minimal loss. A broader and more rigurous definition of a wavelet is that it is a mathematical function used to divide a given function or continuous-time signal into different scale components. Usually one can assign a frequency range to each scale component. Each scale component can then be studied with a resolution that matches its scale. A wavelet transform is the representation of a function by wavelets. The wavelets are scaled and translated copies (known as "daughter wavelets") of a finite-length or fast-decaying oscillating waveform (known as the "mother wavelet"). Wavelet transforms have advantages over traditional Fourier transforms for representing functions that have discontinuities and sharp peaks, and for accurately deconstructing and reconstructing finite, non-periodic and/or non-stationary signals. In formal terms, this representation is a wavelet series representation of a square-integrable function with respect to either a complete, orthonormal set of basis functions, or an overcomplete set of Frame of a vector space (also known as a Riesz basis), for the Hilbert space of square integrable functions. Wavelet transforms are classified into discrete wavelet transforms (DWTs) and continuous wavelet transforms (CWTs). Note that both DWT and CWT are continuous-time (analog) transforms. They can be used to represent continuous-time (analog) signals. CWTs operate over every possible scale and translation whereas DWTs use a specific subset of scale and translation values or representation grid. The word wavelet is due to Morlet and Grossmann in the early 1980s. They used the French word ondelette, meaning "small wave". Soon it was transferred to English by translating "onde" into "wave", giving "wavelet". Robin Cochran-Dirksen

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Regional-scale climate-variability synchrony of cholera epidemics in West Africa Guillaume Constantin de Magny, Jean-François Guégan, Michel Petit, and Bernard Cazelles Methods: The relationship between cholera and climate was explored in Africa, the continent with the most reported cases, by analyzing monthly 20-year cholera time series for five coastal adjoining W African countries. Findings: results suggest regional scale climate variablility influence both the temporal dynamics and the spatial synchrony of cholera epidemics in human populations in the Gulef of Guinea. The findigns of this study will allow public health officials to develop and early warning system that is based on climate data over an extended intertropical littoral (the littoral zone extends from the high water mark, which is rarely inundated, to shoreline areas that are permanently submerged. It always includes the intertidal zone and is often used to mean the same as the intertidal zone) zone. In the future it will be possible to integrate realtime monitoring of oceanic regions, climate variability and epidemiological and demographic population dynamics to prevent cholera outbreaks. Robin Cochran-Dirksen

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Another example A monitoring program over several years in a lake in Austria in order to identify the preferred niche. Findings: they discovered a significant correleation of the frequency of V. cholerae with temperature, zooplankton biomass, and conductivity. Rapid Growth of Planktonic Vibrio cholerae Non-O1/Non-O139 Strains in a Large Alkaline Lake in Austria: Dependence on Temperature and Dissolved Organic Carbon Quality† Alexander K. T. Kirschner,1* Jane Schlesinger,2 Andreas H. Farnleitner,3 Romana Hornek,4 Beate Su¨ß,5 Beate Golda,6 Alois Herzig,6 and Bettina Reitner2‡ Journal of Applied Environmental Microbiology, April 2008 Robin Cochran-Dirksen

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On the left exponential dependence of the growth rate of V. cholerae (mu) on temperature. On the right the seasonal pattern of V. cholerae prevalence, srustacean zooplankton biomass and water temp. Robin Cochran-Dirksen

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The Future Climate Change Global Risks, Challenges & Decisions Conference Held in Copenhagen March, 2009 was attended by more than 2,500 delegates from almost 80 countries. The synthesis report will be published in June and presented to all participants at the UN Climate Change Conference to be held in December Just a few weeks ago this conference was held. And even though mitigation of disease seems complex, sometimes it isn’t. The researcher that first studied the ecology of cholera, Rita Colwell, observed that V. cholerae live on copepods and other plankton and she and her associates deduced that a folded sari cloth of 4 layers can effectively reduce 99% of the cholera bacteria. Robin Cochran-Dirksen

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