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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary 1 Climate Change Challenges OVERVIEW The contributions that comprise this chapter establish the context of workshop discussions and depict the “big picture” within which the papers collected in subsequent chapters are set. The three presenters represented herein—Sir Andrew Haines of the London School of Hygiene and Tropical Medicine; Paul Epstein of Harvard Medical School; and keynote speaker Donald Burke of the University of Pittsburgh—offer varied, and occasionally contrasting, perspectives on what is known, suspected, or unknown regarding the consequences of global climate change for health and, more specifically, for infectious disease emergence. At the same time, each of these contributors observes the accelerating pace of ecological upheaval and emphasizes the inherent complexity of biological responses to climate change and extreme weather events, which frequently involve nonlinear “tipping points.” These characteristics inspire both uncertainty and urgency in the quest to better understand, anticipate, and respond to the potentially wide-ranging health effects of climate change. In the chapter’s first paper, Haines reviews the Intergovernmental Panel on Climate Change’s (IPCC’s) most recent findings on global climate change to date and the panel’s predictive scenarios for the future. He then describes several approaches that have been taken to identify and model the potential health impacts of climate change, along with the methodological challenges presented by such studies. Several examples illustrate how variations in climate—in the form of floods, droughts, and other extreme weather events—influence the range and transmission dynamics of infectious diseases, and therefore suggest potential effects of climate change. Yet, as Haines observes, “in the case of infectious
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary diseases there is still considerable controversy about the degree to which climate change has been responsible for changes in the incidence and distribution of disease. This is due to the potential contribution of other factors, such as changing land-use patterns, human behavior, and methodological issues including the use and analysis of appropriate climate data.” Haines also reviews several efforts to date to estimate the future impact of climate change on infectious diseases, which—although individually problematic—support his overall conclusion that “it is likely that the disease burden as a result of climate change will [increase] substantially over time and will be particularly concentrated in the poorer populations.” Thus, his proposed strategies to address the negative health effects of climate change focus on the poor: first, by improving their access to basic public health services (clean water, sanitation, immunization); second, by providing them with cleaner fuels, which offer both immediate health benefits and long-term protection for the atmosphere. “Infectious diseases are one of a number of categories of health outcomes that are likely to be affected adversely by climate change,” Haines concludes. “Public health policies should take into account the need to adapt to a changing climate, as well as the potential for near-term benefits to health from a range of policies to mitigate climate change.” Haines’s paper is followed by two reprinted pieces, authored (in the first case) and coedited by Epstein (in the second), that illustrate the breadth of biological responses to climate change. The first manuscript is an essay, originally published in the New England Journal of Medicine in October 2005—weeks after Hurricane Katrina devastated the Gulf Coast—that focuses on the wide-ranging health effects of extreme weather. The second, an excerpt from the report Climate Change Futures: Health, Ecological and Economic Dimensions (Center for Health and the Global Environment, 2005), introduces a “multidimensional assessment” incorporating trend analysis, case studies, and scenarios that focus on health, ecological, and economic impacts of climate change. This project, undertaken in collaboration by the Center for Health and the Global Environment at Harvard Medical School, the United Nations Development Programme, and Swiss Re, a global reinsurance company, was designed to assess threats posed by climate change to the institution of insurance, a “time-tested method for adapting to change.” Such threats go far beyond immediate property damage, and indeed even health consequences, to the social and political stability of regions affected by climate disasters (see also Chapter 4). In his workshop presentation, Epstein emphasized the methodologies that make such threat assessments possible. He identified three phenomena that underlie climate- and weather-related changes in disease distribution: Since 1950, nighttime and winter warming have occurred twice as fast as overall global warming.
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary The pace of warming in temperate, boreal, and polar latitudes is occurring faster than warming in the tropics. Since the first International Geophysical Year in 1957, when many global measurements were initiated, the world’s oceans have accumulated 22 times the amount of heat that the atmosphere has, accelerating the global hydrological cycle. There are no appropriate, independent controls for the study of global climate change on Earth, and the experiment we are conducting (with an n of 1) cannot be repeated, Epstein observed. Therefore, he explained, a wide range of methodologies must be harnessed to assess changes in biological variables, such as the geographic range and incidence of diseases in relation to changes in temperature and precipitation. Monitoring and mapping produce data that can be integrated into geographic information systems (GISs) to identify and compare physical and biological phenomena. Further, GISs overlay multiple sets of data, providing input for descriptive and mathematical models that project the biological impacts of various climate change scenarios. Models are used for understanding dynamics, for predicting outcomes, and for decision making (Hilborn and Mangel, 1997). In his presentation, Epstein also described methods for analyzing data gathered across scientific disciplines (e.g., diseases affecting a range of taxonomic groups) in order to reveal patterns and emerging trends associated with climate change, calculate rates of change (i.e., in the geographic range, prevalence, and incidence of infectious diseases), and compare observations with predicted outcomes. Researchers also conduct experiments, called “fingerprint” studies, to compare data with model projections (such as those undertaken by the IPCC to analyze climate models and energy fluxes driven by increases in heat-trapping, greenhouse gases). Many of the methodologies used to study the effects of climate change yield correlations, rather than proof of causation, Epstein acknowledged. However, he added, such associations and their plausible mechanisms can be then tested via qualitative, schematic, and quantitative models. Bayesian methods of assessing causality based on prior probabilities and prior knowledge via first physical principles can also be used to analyze the effects of global climate change, he said. Moreover, Epstein asserted, when observational data from multiple sources match model projections (i.e., the findings are internally consistent) and can be explained by plausible biological mechanisms (e.g., changes in observed altitude ranges in tandem with observed temperature changes), the composite pattern warrants further attention. This may take the form of analyzing attribution probabilities for anomalous events; for example, such an assessment indicates that global warming increased the likelihood of the European heat wave of 2003 two- to fourfold (Stott et al., 2004). Extreme conditions that favor infectious disease outbreaks via multiple pathways may be revealed by “cluster analyses” or
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary characterized through the use of “principal component analyses,” which identify spatial and temporal associations among variables. Despite the existence of such methodologies, “our current understanding of the relationships between climate and weather, and epidemic infectious diseases, is insufficient to make credible predictions about future threats posed by infectious diseases under various global change scenarios,” Burke argues in the chapter’s final paper. To support this contention, he presents detailed analyses of the transmission dynamics of two infectious diseases: influenza and dengue fever. These studies reveal that oscillations in disease incidence occur in the absence of seasonal transmission effects; if these patterns coincide with seasonal variation, small changes in transmissibility may, under some circumstances, produce considerable variability from year to year in epidemic disease occurrence (Dushoff et al., 2004). Burke notes approvingly that conclusions by “respected scientific bodies” regarding the probable impact of global climate change on epidemic infectious diseases remain measured since the publication of the landmark report Under the Weather: Climate, Ecosystems, and Infectious Diseases (NRC, 2001) by the interdisciplinary committee that he chaired. “This caution honestly reflects the uncertainties involved, which in turn reflect the difficulty of the underlying scientific problems,” he states. Calling readers’ attention to the recommendations for future research and surveillance made in that report (see also Box SA-3), Burke concludes that “it is safe to say that [these recommendations] continue to be relevant.” CLIMATE CHANGE, EXTREME EVENTS, AND HUMAN HEALTH Andy Haines, M.B.B.S., M.D. London School of Hygiene and Tropical Medicine Greenhouse gases are now accumulating in the atmosphere at unprecedented rates. The annual growth rate of carbon dioxide (CO2) concentration was highest over the last 10 years since the beginning of continuous direct atmospheric measurements (IPCC, 2007a). The atmospheric concentration of CO2 now greatly exceeds the natural range over the last 650,000 years. CO2 is the most important greenhouse gas produced by humankind and accounts for around 77 percent of the total. The concentrations of all three major greenhouse gases: CO2, methane, and nitrous oxide, are at the highest levels for at least 10,000 years and have resulted in clear changes in the Earth’s climate. Of the last 12 years (1995-2006), 11 are among the 12 warmest years since 1850 when instrumental records began. Over past 100 years (1906-2005), global surface temperature has increased by 0.74°C (90 percent, uncertainty interval 0.56-0.92) with warming faster over land than over oceans. Of course climate will still vary (e.g., in February 2008, NOAA [National Oceanic and Atmospheric Administration] predicted La Niña
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary conditions—the cold phase of the El Niño Southern Oscillation [ENSO]—to continue throughout spring 2008; however, by May La Niña began to transition to more ENSO-neutral conditions [NOAA Climate Prediction Center, 2008]). But after accounting for these known climate fluctuations, the long-term warming trend will continue. Figure 1-1 shows the trends for temperature, global average sea level, and observed decreases in snow and ice. Sea level rise is due to a combination of thermal expansion of the oceans together with increased melting of glaciers and polar ice sheets. Other significant changes in climate include declines in precipitation from 1900 to 2005 in the Sahel, Mediterranean, southern Africa, and parts of southern Asia. More intense and longer droughts have occurred over increasing areas since the 1970s, particularly in the tropics and subtropics. The IPCC concludes that increased drying as a result of temperature increases and reductions in precipitation has contributed to changes in drought. In contrast, precipitation has increased significantly in eastern areas of North and South America, as well as northern Europe and northern and central Asia. There is an apparent increase in intense tropical cyclone activity since 1970 in the North Atlantic. Over the next two decades, warming of about 0.2°C per decade has been projected for a range of emission scenarios according to the IPCC. After that, different scenarios of socioeconomic development and the use of mitigation strategies result in markedly different trajectories for greenhouse gas emissions (see Figure 1-2). The different scenarios result in best estimates for temperature change in 2090-2099 relative to 1980-1999, ranging between 1.8°C for B1 scenario and 4°C for A1F1 scenario, but the likely range of estimates is even wider, extending to 6.4°C for the latter scenario. Regional-scale changes are outlined in Box 1-1. Climate Variability, Climate Change, and Health It has been known for thousands of years, at least since the time of Hippocrates, that climatic variations can influence health, particularly through changes in temperature and precipitation, as well as extreme weather events. Growing scientific consensus about the existence of global climate change has rekindled interest in linkages between climate and health. The potential range of impacts is wide and they have been reviewed extensively (IPCC, 2007b). There are a number of approaches to studying the potential health impacts of climate change. These include studies of the associations between past climate variability and disease; of the associations between trends in climatic variables over recent decades and the epidemiology of diseases; and of the response of vector species to changes in temperature and rainfall. In addition, there have been a number of approaches to modeling the potential future impacts of climate change on health.
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary FIGURE 1-1 Observed changes in (A) global average surface temperature; (B) global average sea level rise from tide gauge (blue) and satellite (red) data; and (C) Northern Hemisphere snow cover for March-April. All changes are relative to corresponding averages for the period 1961-1990. Smoothed curves represent decadal averaged values while circles show yearly values. The shaded areas are the uncertainty intervals estimated from a comprehensive analysis of known uncertainties (A and B) and from the time series (C). SOURCE: Figure SPM.3 in IPCC (2007a).
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary FIGURE 1-2 Multimodel averages and assessed ranges for surface warming (compared to the 1980-1999 base period) for the SRES scenarios A2 (red), A1B (green), and B1 (blue), shown as continuations of the twentieth-century simulation. The latter two scenarios are continued beyond the year 2100 with forcing kept constant (committed climate change as it is defined in Box TS.9 [of IPCC, 2007a]). An additional experiment, in which the forcing is kept at the year 2000 level is also shown (orange). Linear trends from the corresponding control runs have been removed from these time series. Lines show the multimodel means, shading denotes the ±1 standard deviation range. Discontinuities between different periods have no physical meaning and are caused by the fact that the number of models that have run a given scenario is different for each period and scenario (numbers indicated in figure). For the same reason, uncertainty across scenarios should not be interpreted from this figure. SOURCE: Figure TS.32 in IPCC (2007a). The study of potential associations between climate change and health poses a number of methodological challenges including the need to consider confounding factors as possible explanations of apparent associations between climatic variables and health outcomes. Such confounding factors may include changes in resistance to insecticides (in the case of vector-borne diseases); changes in resistance to commonly used drugs for treatment (e.g., in the case of malaria); migration of populations, which may result in the exposure of nonimmune populations to infectious diseases; and changes in the performance of disease surveillance systems over time. Some diseases, such as malaria, exhibit differences in
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary BOX 1-1 Regional-Scale Changes Changes include the following: Warming greatest over land and at most high northern latitudes and least over the Southern Ocean and parts of the North Atlantic Ocean, continuing recent observed trends Contraction of snow cover area, increases in thaw depth over most permafrost regions, and decrease in sea ice extent; in some projections, Arctic late-summer sea ice disappears almost entirely by the latter part of the twenty-first century Very likely increase in frequency of hot extremes, heat waves, and heavy precipitation Likely increase in tropical cyclone intensity; less confidence in global decrease of tropical cyclone numbers Poleward shift of extratropical storm tracks with consequent changes in wind, precipitation, and temperature patterns Very likely precipitation increases in high latitudes and likely decreases in most subtropical land regions, continuing observed recent trends SOURCE: IPCC (2007a). local transmission dynamics that complicate attempts to model the likely effect of climate change. Improvements in public health infrastructure leading to improved adaptation to climate change could, in the future, attenuate relationships between the changing climate and health outcomes. Climate change is likely to be a long-term process that will evolve over decades and centuries while our understanding of the linkages between climate and health is based largely on studies of short-term variability. There are likely to be interactions between climate change and other environmental changes, such as deforestation, growth in global travel, increased local population mobility, and depletion of aquifers in some regions. For example, deforestation may change the distribution of disease vectors as well as contributing to climate change, and migration of populations into formerly forested areas may result in increased exposure to a number of diseases. A study in the Peruvian Amazon suggested that the abundance of the malaria vector Anopheles darlingi was two hundredfold higher in deforested areas than in pristine forest and this could not be attributed solely to increased population density (Vittor et al., 2006). Deforestation may increase malaria risk in the Americas and Africa, but reduce it in Southeast Asia (Guerra et al., 2006). This paper focuses on potential relationships between infectious disease and climate change, but there are a range of other health outcomes such as the
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary effects of heat waves and potential reductions in cold-related deaths, particularly in temperate countries; the direct effect of wind, storms, and floods causing deaths and injuries; and the effect of droughts on malnutrition and food security. Floods and droughts, together with less extreme changes in precipitation, can also have important implications for a range of water-related diseases, as well as impacting health through the effects of increases in malnutrition and consequent reductions in immunity to disease. A summary of the pathways through which climate change may affect human health including infectious diseases is shown in Figure 1-3. Rainfall, Temperature, and Disease Water-related diseases include water-borne diseases due to ingestion of pathogens in contaminated water and water-washed diseases as a result of poor hygiene. A recent global overview of cross-sectional studies based on 36 published reports from low- and middle-income countries between 1954 and 2000 suggests an average of 4 percent increase in diarrhea incidence in children less than 5 years of age per 10 mm decrease in rainfall per month (Lloyd et al., 2007). Currently, more than 2 billion people live in relatively dry regions of the world and are likely to suffer disproportionately from lack of access to clean water. While there have been substantial improvements in the management of diarrheal disease, particularly because of the widespread use of oral rehydration therapy, child mortality remains unacceptably high, particularly in sub-Saharan Africa. Diarrheal diseases claim almost 2 million lives a year in children under 5. However, it is important to recognize that most freshwater is used for irrigation rather than personal consumption; therefore, the relationship between reduced freshwater availability and diarrheal diseases may be indirect. Hand washing with soap has a protective effect against diarrheal disease (Curtis and Cairncross, 2003), and reduced availability and/or increased costs of freshwater may lead to reduced hand washing where rainfall is low. Heavy precipitation may be associated with outbreaks of water-borne diseases, such as cryptosporidiosis (Curriero et al., 2001). Although the global overview referred to above did not find a relationship with temperature, other studies have found associations between higher temperatures and increased episodes of diarrheal diseases in Peru, the Pacific Islands, and Australia (Checkley et al., 2004; McMichael et al., 2003; Singh et al., 2001). The association between sea surface temperatures and cholera transmission has been most convincingly shown in the Bay of Bengal (Colwell, 1996). Time-series analysis of weekly cases of salmonellosis using data from 16 sites in industrialized countries suggested an approximately linear increase in reported cases above the threshold of about 6°C. The study focused on the analysis of sporadic cases only. The association appeared to be particularly evident in the case of Salmonella enteritidis, with a lag of around 1 week between the
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary FIGURE 1-3 Pathways by which climate change may affect human health, including infectious diseases. SOURCE: Reprinted from Haines and Patz (2004) with permission from the American Medical Association. Copyright 2004. All rights reserved; adapted from Patz et al. (2000).
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary increased temperature and the increase in cases, suggesting that the effect may be on the replication of salmonella after food has been prepared (Kovats et al., 2004). One of the best documented examples of the way in which climate can drive the onset of disease is the relationship between the dry northern winds (called the Harmattan) and meningococcal meningitis epidemics in West Africa. The mean weeks of the winter maximum wind speed and of the onset of the epidemic are identical and usually occur between February 7 and 15 (Sultan et al., 2005). Although the causal mechanism is not fully understood, the disease may result from the effects of mucosal drying and abrasion as a result of the strong dust-laden winds. There is some evidence that the geographical distribution of meningococcal meningitis in West Africa has expanded in the recent past, possibly as a result of changes in land use and climate (Molesworth et al., 2003). The El Niño/Southern Oscillation and Health El Niño events are large-scale ocean-atmospheric climate phenomena emanating from the central and east-central equatorial Pacific Ocean that have occurred for thousands of years. A major feature is the upwelling of warm water off the coast of Peru and Ecuador. Associated by distant connections (teleconnections) with climatic changes in Australia, Indonesia, the Pacific highlands, and East Africa, as well as parts of Latin and North America, the El Niño cycle is usually between 3 and 7 years. The El Niño (warm event) is frequently followed by a La Niña (opposite, cold) event. The Southern Oscillation is the name given to the seesaw of air pressure differences between the east and west Pacific, which is associated with the El Niño phenomenon and leads to the full name El Niño/Southern Oscillation (ENSO). Figure 1-4 demonstrates some of the teleconnections associated with El Niño and indicates where health outcomes, such as increased risk of epidemic malaria, are experienced after the onset of an El Niño event (Kovats et al., 2003). ENSO is the most important climatic cycle that contributes to natural disasters. Drought is twice as frequent worldwide in the year after the onset of the El Niño than in other years, particularly in southern Africa and South Asia. In an average El Niño year, around 35 people per 1,000 worldwide are affected by a natural disaster, more that four times that of non-El Niño years based on analysis of data for three decades between 1963 and 1993 (Bouma et al., 1997). The relationship between El Niño and intense rainfall is also strong in many areas, although unlike drought it is not seen on a global scale because flood-related disasters are relatively localized and the risk is increased during both El Niño and La Niña phases in different parts of the world. During an El Niño event, storm activity in parts of the Pacific is increased and decreased in the Atlantic so that hurricanes in the Caribbean and the Gulf of Mexico tend to be less com-
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary tions about the net effects of global warming will require a better understanding of its effects on each particular infectious disease. Influenza and dengue are both globally important infectious diseases, capable of serious epidemic spread. Although both are caused by small enveloped RNA viruses, one (influenza) is a cool season disease, and one (dengue) is a warm season disease. A comparison is instructive. Seasonality of Influenza A comparison of the incidence curves in the United States, Mexico, Colombia, Brazil, and Argentina shows that the timing of annual influenza incidence varies closely with latitude. Epidemic peaks occur in the winter season in high-latitude countries (alternating with the Northern and Southern Hemisphere winters), but no clear annual peak is seen in countries closer to the equator. In addition to seasonal changes in ambient temperature, a variety of explanations have been proposed to account for the strong wintertime seasonality of influenza (for some recent reviews, see Dowell, 2001; Finkelman et al., 2007; Fisman, 2007; Lofgren et al., 2007). One set of explanations focuses on altered host immunity, possibly due to changes in the photoperiod with shortened days in winter or to low vitamin D caused by decreased exposure to sunshine. Another set of explanations focuses on increased human-to-human exposure in winter—for example, increased social contact among children during the typical school season or breathing shared air in confined spaces. Indeed it is possible that more than one factor contributes to the seasonality of influenza. Experimental studies in laboratory animals have been valuable in understanding the effects of altered environmental conditions on influenza transmission from animal to animal. More than 40 years ago, Kilbourne and colleagues, studying influenza transmission in caged mice, showed that low temperature and low relative humidity can increase influenza transmission (Schulman and Kilbourne, 1963). Recent studies by Palase and colleagues have provided even more compelling evidence and are summarized here (Lowen et al., 2007). They studied guinea pigs in a climate-controlled setup in which air flow direction, temperature, and relative humidity could be carefully controlled. Pairs of guinea pigs were placed in adjacent cages, one intentionally inoculated with the H3N2 A/Panama/99 strain influenza virus (a typical strain) and placed in the cage upwind of the other noninfected animal. Four pairs of animals were studied at a time under controlled conditions of temperature (5 to 30°C) and relative humidity (20 to 80 percent). Results are shown in Figure 1-11. Under conditions of high temperature or high relative humidity, none of the four downwind guinea pigs breathing infectious air became infected. In contrast, under conditions of low temperature and/or low relative humidity, four of four downwind animals invariably became infected. Increased viral shedding into respiratory secretions was found in animals held at lower temperatures. The authors concluded that both low temperature and
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary FIGURE 1-11 Transmission of influenza from infected guinea pigs to uninfected exposed guinea pigs under different experimental conditions in which ambient temperature and relative humidity were varied. Each box shows the results of one experimental study in which four guinea pigs were exposed. Results are shown as the number (of these four) that became infected. Boxes are colored, with red indicating that all (4/4) exposed animals became infected, green indicating that no (0/4) exposed animals became infected, and yellow indicating that some but not all (1, 2, or 3 out of 4) became infected. Black indicates that no experiments were conducted for those specific conditions. SOURCE: Based on data provided in Lowen et al. (2007). low relative humidity increased transmission, the temperature effect mediated by increased virus shedding and the relative humidity effect probably by bio-aerosol formation of droplet nuclei. Although these fine experiments do provide strong evidence of the mechanism relating winter to flu, they provide little help in formulating predictive models on which to base future influenza transmission scenarios. How much of a seasonal change in transmissibility (e.g., shedding, droplet nuclei formation) is needed to see the typical seasonal oscillations of influenza? The answer is that it depends on how the magnitude and timing of these seasonal forces interact with other oscillations inherent in the influenza epidemic processes. Dushoff and colleagues recently demonstrated that epidemic systems such as influenza can display intrinsic oscillations without requiring any seasonal effects on transmissibility (Dushoff et al., 2004). Instead they showed that seasonal effects on transmissibility can either resonate with or dampen the intrinsic
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary oscillations of the system, depending on their respective relative frequencies. They formulated a relatively simple model system in which individuals progress from susceptible to infected to recovered or immune and back to susceptible again (S-I-R-S). The incidence of new cases in the system = β × I × S or beta (a measure of transmission) × the number of infected × the number of susceptibles. Even when the transmission variable beta is held constant, the system shows oscillations, and the intrinsic period of the oscillation of incidence is a function of several standard epidemic parameters, including the basic reproductive ratio (R0), the length of infection (L), and the duration of immunity (D). Decreasing R0, prolonging the duration of infection, or lengthening the duration of immunity can all serve to slow the intrinsic periodicity. Importantly, this intrinsic periodicity need not be annual. Dushoff et al. (2004) then asked what happens if seasonality is imposed. That is, instead of being a fixed constant, β is varied sinusoidally (seasonally). How does this exogenous forcing oscillation of transmissibility interact with the intrinsic S-I-R-S oscillation? They found that a very small seasonal oscillation in β (the transmission parameter) could markedly increase the peak-to-trough amplitude of the system. Figure 1-12 illustrates the effects of a small 4 percent change in the amplitude of the seasonal transmission parameter. Random combinations of R0, D, and L were seeded to generate intrinsically oscillating systems. The graph shows the oscillation amplitudes without and with a tiny 4 percent seasonally varying change in transmission (β). There is a strong nonlinear resonance of the effects of the seasonal forcing on oscillation amplitude when the intrinsic oscillation period is also seasonal (i.e., equal to 1). If natural influenza systems follow the same behaviors as this model system, then it may prove difficult indeed to measure seasonal changes in disease incidence as a function of seasonal changes in the transmission parameter β. Regrettably, real-world influenza dynamics are more complex than the simple model of Dushoff et al. One aspect of this complexity is that “seasonal influenza” is in fact not one disease process but three: influenza A/H3, influenza A/H1, and influenza B. All three viruses co-circulate worldwide and contribute to the reported total cases of influenza. Data on influenza isolates in the United States were obtained from the WHO “FluNet” website9 and graphed as a continuous 11-year time series, from 1997 through 2007, as shown in Figure 1-13. It can be seen that A/H1 and A/H3 influenza strains dominate in different years; it is possible that they may reciprocally interfere with each other. Another factor complicating any analysis of the relationships between weather and influenza epidemiology is the fact that the three influenza viruses may well have somewhat different values of transmission parameters (βs); however, this has not been directly measured. 9 See http://gamapserver.who.int/GlobalAtlas/home.asp.
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary FIGURE 1-12 Graph showing the amplitude of oscillations (y axis, peak-trough ratio) as a function of the endogenous oscillation period (x axis) in a stochastic forced S-I-R-S epidemic model for 2,000 sets of randomly chosen parameters. The imposed 1-year seasonal variation in transmission for all instances is set as a sinusoidal curve with only ±4 percent variation. Strong resonance between the endogenous and imposed oscillations occurs when the approximate endogenous period is near 1 year. SOURCE: Dushoff et al. (2004). Epidemics as Partially Decomposable Systems Because dynamical systems, such as epidemics, are often composed of two or more semi-independent but partially interacting dynamical subsets (e.g., environmental conditions, immunity, health systems), it is essential to isolate and analyze these component dynamical subsystems so as to be able to understand the effects of any particular forcing factor (e.g., ambient temperature). This field is still in its infancy, but there are a number of techniques available. My colleague Derek Cummings and I have experimented with various time-series decomposition techniques borrowed from physics to tease apart the component subsystems from long-term records of the waxing and waning of epidemic diseases (Cummings et al., 2004). Working with our colleagues at the Thailand Ministry of Public Health, we reviewed epidemiological records over many years, entered them into digital format, and applied decomposition methods. Some of these
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary FIGURE 1-13 Influenza virus types isolated in the United States between 1997 and 2007. Influenza H3N2 is shown in red, H1N1 in blue, and B in green. Numbers shown are total isolates typed and reported. SOURCE: WHO (2008). time-series decomposition methods include the well-known Fourier decomposition methods, but we also examined various wavelet decomposition methods, and the Empirical Mode Decomposition. Figure 1-14 is just one example of an analysis of longitudinal epidemic time-series data, showing that this is a partially decomposable system. We applied the Empirical Mode Decomposition (Huang et al., 1998) to analyze 15 consecutive years of the incidence of dengue hemorrhagic fever in Bangkok. A major feature of the Empirical Mode Decomposition is that the method identifies component “modes” of differing frequency, from fast to slow, that together contribute to the full tracing of the epidemic time series. Note that in the Empirical Mode Decomposition, the identified modes are not single standing frequencies, but instead are patterns whose frequencies may vary. For dengue in Bangkok, we identified several major component frequency modes, including a slow 3- to 4-year oscillation that we believe is due to changes in host immunity, a clear 1-year annual oscillation that is probably driven by seasonal changes in weather, and a spiky, irregular, rapid (faster-than-annual) oscillation
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary FIGURE 1-14 Multiyear time series of incidence of dengue hemorrhagic fever cases in Bangkok decomposed using the Empirical Mode Decomposition method into three modes of different approximate frequencies. When summed, the three modes add up to the original series. We hypothesize that the modes represent the isolation of different dynamic processes. SOURCE: Adapted with permission from Macmillan Publishers Ltd: Nature, Cummings et al. (2004), copyright 2004. that may represent local and chance events. Our expectation is that decomposition methods such as these will make it possible to eliminate—as “noise”—the changes in incidence contributed by other modes and to focus on a single mode to understand its intrinsic behaviors and responsiveness to external forces. Conclusions In this paper, I have argued that our current understanding of the relationships between climate and weather and epidemic infectious diseases is insufficient to make quantitative predictions about future threats posed by infectious diseases under various global climate change scenarios. Nonetheless, I am confident that new state-of-the-art methods, including computational tools, are now available to apply to these difficult scientific problems. I am hopeful that within a few years it may be possible to robustly predict such risks and take steps to intervene to avert potential crises. It is safe to say that the main “Recommendations for Future Research and Surveillance” of our Under the Weather group continue to be relevant: Research on the linkages between climate and infectious diseases must be strengthened.
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Global Climate Change and Extreme Weather Events: Understanding the Contributions to Infectious Disease Emergence - Workshop Summary Further development of disease transmission models is needed to assess the risks posed by climatic and ecological changes. Epidemiological surveillance programs should be strengthened. Observational, experimental, and modeling activities are all highly interdependent and must progress in a coordinated fashion. Research on climate and infectious disease linkages inherently requires interdisciplinary collaborations. REFERENCES Overview References Center for Health and the Global Environment. 2005. Climate change futures: health, ecological and economic dimensions. Cambridge, MA: Harvard Medical School. Dushoff, J., J. B. Plotkin, S. A. Levin, and D. J. D. Earn. 2004. Dynamical resonance can account for seasonality of influenza epidemics. Proceedings of the National Academy of Sciences 101(48):16915-16916. Hilborn, R., and M. Mangel. 1997. The ecological detective. Princeton, NJ: Princeton University Press. NRC (National Research Council). 2001. Under the weather: climate, ecosystems, and infectious diseases. Washington, DC: National Academy Press. Stott, P. A., D. A. Stone, and M. R. Allen. 2004. Human contribution to the European heatwave of 2003. Nature 432(7017):610-614. Haines References Abeku, T. A., G. J. van Oortmarssen, G. Borsboom, S. J. de Vlas, and J. D. Habbema. 2003. Spatial and temporal variations of malaria epidemic risk in Ethiopia: factors involved and implications. Acta Tropica 87(3):331-340. Abeku, T. A., S. I. Hay, S. Ochola, P. Langi, B. Beard, S. J. de Vlas, and J. Cox. 2004. Malaria epidemic early warning and detection in African highlands. Trends in Parasitology 20(9):400-405. Ahern, M., R. S. Kovats, P. Wilkinson, R. Few, and F. Matthies. 2005. Global health impacts of floods: epidemiologic evidence. Epidemiologic Reviews 27(1):36-46. BCAS/RA/Approtech. 1994. Vulnerability of Bangladesh to climate change and sea level rise: concepts and tools for calculating risk in integrated coastal zone management. Dhaka, Bangladesh: Bangladesh Centre for Advanced Studies (BCAS). Bouma, M. J., R. S. Kovats, S. A. Goubet, J. S. Cox, and A. Haines. 1997. Global assessment of El Niño’s disaster burden. Lancet 350(9089):1435-1438. Cairncross, S. 2003. Water supply and sanitation: some misconceptions. Tropical Medicine and International Health 8(3):193-195. Cairncross, S., and M. Alvarinho. 2006. Floods, hazards and health: responding to present and future risks, edited by R. Few and F. Matthies. London, UK: Earthscan Publications Ltd. Checkley, W., R. H. Gilman, R. E. Black, L. D. Epstein, L. Cabrera, C. R. Sterling, and L. H. Moulton. 2004. Effect of water and sanitation on childhood health in a poor Peruvian peri-urban community. Lancet 363(9403):112-118. Colwell, R. R. 1996. Global climate and infectious disease: the cholera paradigm. Science 274(5295): 2025-2031.
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