Space Imaging and Prevention of Infectious Disease: Rift Valley Fever

Abstract

Rift Valley fever (RVF) is a mosquito-borne viral disease causing febrile illness and death in domestic livestock (cattle, sheep, goats) and humans. In Africa, RVF erupts following abnormally high rainfall and flooding. Remote sensing surveillance of vegetative growth could provide early warning, weeks to months in advance of RVF emergence, and thus permit intervention strategies to ameliorate and prevent this infectious disease. To act on this advance notice, however, public health officials must quantify the economic cost associated with the disease (in terms of losses to agriculture and international trade as well as human morbidity and mortality) and weigh the averted losses against the diversion of financial and public health resources dedicated to other major ongoing health needs, such as malaria and HIV/AIDS. Other complications include the accuracy of the predictions, the shelf life of vaccines, and the effectiveness of vector control strategies.

9.1 Introduction

Infectious threats to human and agricultural populations are significant and diverse, ranging from emerging diseases to bioterrorism. Health care organizations and agencies routinely balance diverse considerations and competing priorities to best meet health needs. This process is often difficult; to maximize success, planning and decisionmaking must be guided by sound epidemiological data, methods, and practices. From a public health perspective, two maxims often guide approaches: (1) the importance of prevention; and (2) the concept of targeting scarce resources on those parts of a population most active in transmission of infectious disease. From an economic perspective, the loss in productivity due to morbidity or mortality plus the costs associated with treatment almost always outweigh the cost of preventing infection. Assuming that cost-effective interventions capable of reducing the burden of disease exist, resources are focused where they have the greatest impact. We can express these maximums more colloquially: (1) “an ounce of prevention is worth a pound of cure”; and (2) targeting interventions gives the “biggest bang for the buck.”

In general, answering the question “To prevent infection, when and where do we intervene?” is difficult. However, for a large class of infectious diseases, those that are tied closely to climate and the environment, promising approaches have been described. These employ landscape epidemiology—an understanding of the relationships between ecology and infection—to predict the spatial and temporal distribution of pathogens, vectors, and/or hosts. Landscape epidemiology involves the integration of epidemiological data (e.g., collected from active or passive surveillance or field surveys) with climate, environmental, and ecological data (e.g., collected from space-based remote sensors or field measurements) within a geographic information system (GIS) (Clements and Pfeiffer 2009). The availability of massive data sets collected from specialized airborne and satellite sensors, coupled with the proliferation of computer and information technology, has placed the landscape epidemiology approach within the reach of researchers globally. The general idea is that when clear associations exist between variables such as temperature, precipitation, land cover, vector and host abundance, and vector competency, they can be exploited for public health purposes. If the occurrence of such determinants can be forecast prospectively,¹ then so much the better: it opens up the possibility of intervening to prevent disease transmission.

Because the landscape epidemiology approach gathers data about the spatiotemporal determinants of disease emergence and transmission, it provides the potential to cue public health agencies on both when and where to intervene. Studies have identified associations for many diseases, but it is critical to ask whether these studies have been put to use and whether the economic payoff was assessed. There are examples sufficient to answer the former question, but by and large, the literature is silent on the latter. In this chapter, we describe a body of research surrounding Rift Valley fever and see that much has been done. In this case, there is great promise for prospective forecasting of outbreaks, opening the door for public health intervention. However, much remains to be done to quantify the payoff.

9.2 Rift Valley Fever and Remote Sensing Studies

9.2.1 Remote Sensing

Within the context of this chapter, remote sensing entails using sensors to gather information about Earth’s surface or atmosphere from a distance. Data are typically collected from airborne sensors mounted on aircraft or from space-based sensors mounted on satellites. Herbreteau et al. (2005) describe the process of space-based remote sensing as the following four steps: (1) detection and measurement of electromagnetic radiation emitted, radiated, or reflected by objects on (or near) the surface; (2) recording of these data and transmission to ground stations; (3) reception of the data and processing into images; and (4) analysis and interpretation of the images through visual or computerized methods.

Sensors can be active or passive. Active sensors—for example, synthetic aperture radar (SAR)—send pulsed microwave signals to Earth and receive the returned signals. The resulting data are processed into images. SAR can “see” through cloud cover because clouds are transparent to microwave radiation. Since a transmitter on the sensor platform itself illuminates Earth’s surface, SAR sensors can also produce images at nighttime. Passive sensors detect the reflected or emitted electromagnetic radiation from the surface. Reflected sunlight is the most common source of radiation measured by passive sensors. Sensors are often classified according to the region of the electromagnetic spectrum they measure (e.g., visible, infrared, and microwave). Data from remote sensors result from interactions between the primary sources of electromagnetic energy, atmospheric absorption and distortion of the radiation, and characteristics (e.g., sensitivity, spectral resolution) of the sensors. The details are complex and the reader is referred to the literature for details (e.g., Herbreteau et al. 2005).

An important term in remote sensing is resolution, of which there are four basic types. Spatial resolution refers to the area of Earth’s surface corresponding to each pixel of an image. The pixels of high-resolution imagery represent small areas. For example, 10-m spatial resolution means each pixel image represents an area of 10 m². Temporal resolution refers to how often a remote sensor revisits a specified area at the same viewing angle. High-temporal-resolution satellite sensors have short revisit periods. Spectral resolution refers to the number and width of spectral bands (portions of the electromagnetic spectrum) measured by a sensor. High-spectral-resolution images contain many narrow bands.² Finally, radiometric resolution refers to the sensitivity of a remote sensor to variations in the surface reflectance. High-radiometric-resolution sensors are sensitive to small differences in reflectance values of Earth’s surface.

Resolution and other characteristics of remote sensors combine to allow measurement of a wide range of observables of interest. In the case of infectious disease, there are meaningful meteorologic, climatic, and environmental observables that tell us important things about the likelihood of disease emergence and transmission. Beck et al. (2000) have elucidated physical factors that could be used for both infectious disease research and public health applications. Each factor is essentially an environmental variable thought to influence the survival of pathogens, vectors, reservoir species, and hosts. These factors are vegetation or crop type, vegetation green-up, ecotones,³ deforestation, forest patches, flooded forests, general flooding, permanent water, wetlands, soil moisture, canals, human settlements, urban features, ocean color, sea surface temperature, and sea surface height. Precipitation, humidity, and surface temperature were not included in the list because they are difficult to derive from remotely sensed data.

Such factors have demonstrated relevance to a range of human and zoonotic diseases, including cholera (Lobitz et al. 2000), Lyme disease (Kitron et al. 1997; Dister et al. 1997), tickborne encephalitis (Daniel and Kolár 1990; Daniel et al. 1998), Q fever (Tran et al. 2002), dengue fever (Moloney et al. 1998), Sin Nombre virus (Boone et al. 2000), hantavirus pulmonary syndrome (Glass et al. 2000), Rift Valley fever (Linthicum et al. 1987, 1990, 1999; Pope et al. 1992; Anyamba et al. 2009), St. Louis encephalitis (Wagner et al. 1979), and others. Remote sensing has also proved useful in monitoring crop health and detecting nutrient deficiencies, disease, and weed and insect infestations (Hatfield and Pinter 1993). Vegetation indices derived from multispectral imagery are used to monitor the growth response of plants in relation to measured (or predicted) climate variables. Remote sensors can also quantify crop water stress.⁴

9.2.2 Natural History of Rift Valley Fever

Rift Valley fever (RVF) is a mosquito-borne viral disease causing febrile illness in domestic livestock (cattle, sheep, goats) and humans. Outbreaks of RVF are associated with widespread morbidity and mortality in livestock and morbidity in humans. Identified in Kenya in 1930, RVF is often considered a disease primarily of sub-Saharan Africa, though outbreaks have occurred in northern Africa (Egypt) and, recently, the Arabian Peninsula. Table 9.1 contains an illustrative list of outbreaks.

Table 9.1 Some outbreaks of Rift Valley fever in Africa, 1950–2010

In Africa, RVF erupts aperiodically in 7- to 15-year cycles following times of abnormally high rainfall and flooding. RVF virus (RVFV) is spread by mosquitoes to livestock (and also wildlife hosts, though their importance in outbreaks and interepidemic maintenance is unclear). Culex mosquitoes are infected only directly, through feeding on infectious livestock, but floodwater Aedes mosquitoes also can be infected at birth by vertical transmission (i.e., mother-to-offspring passage of RVFV). RVF in livestock causes abortions in pregnant animals and mortality rates as high as 90% in neonates and 30% in adults. In humans, RVF is typically a self-limited febrile disease, though blindness and fatal hemorrhagic fever can result. Epizootics typically precede human disease in pastoral areas.

In times of heavy rainfall in East Africa, geological features knows as dambos flood. As they fill, desiccated floodwater Aedes mosquito eggs rewet and begin to develop. Infected and infectious adult mosquitoes then emerge, carrying viable RVFV, and bite nearby livestock. The livestock amplify the virus and develop high viremia (concentration of virus in their blood), sufficient to infect Aedes, Culex, and possibly other mosquitoes and biting insects. Humans can become infected through the bites of mosquitoes or by handling infected tissues (e.g., disposing of aborted tissues or slaughtering infected animals). The presence of heavy rains typically precedes large epizootics and epidemics, but a clear association between seasonal rainfall, vector abundance, and RVF serological prevalence has been demonstrated in western Africa (Bicout and Sabatier 2004).

9.2.3 Studies of RVF Using Remote Sensing

Roughly two decades of research on RVF provides the foundation for a public health early warning system.⁵ Pope et al. (1992) studied central Kenyan RVF virus vector habitats with Landsat and evaluated their flooding status with airborne imaging radar. Landsat Thematic Mapper (TM; a multispectral imager) data were shown to be effective in identifying dambos in an area north of Nairobi. Positive results were obtained from a test of flood detection in dambos with high- resolution airborne SAR imagery. In the same year, Davies et al. (1992), studying patterns of RVF activity in Zambia, observed that animal serological conversion was associated with changes in vegetation. Both studies used a normalized difference vegetation index (NDVI), derived from data from multispectral remote sensors.⁶ These studies established that NDVI tends to correlate with rainfall and RVF viral activity.

In 1999 Linthicum et al. published a study in which they found that Rift Valley fever outbreaks in East Africa between 1950 and 1998 tended to follow periods of abnormally high rainfall. Accounting for drivers of regional climate (abnormal rainfall in particular), they considered Pacific and Indian Ocean sea surface temperature anomalies in their analysis.⁷ They concluded that a combination of such anomalies in both oceans and NDVI correlate well with RVF outbreaks, several months in advance of observed disease transmission. This study suggested that surveillance of Pacific and Indian Ocean sea surface temperatures and NDVI could support prospective forecasts of RVF activity.

A recent study documented the successful forecast of RVF activity in East Africa (southern Somalia, Kenya, northern Tanzania) based on these methods. Anyamba and coworkers (2009) derived a spatiotemporal RVF risk-mapping model based on climate-related data, forecasting areas of human and animal RVF in the Horn of Africa between December 2006 and May 2007. The forecasts compared favorably with subsequent entomological and epidemiological field observation. Disease was forecast 2–6 weeks in advance in the study region, a time potentially sufficient for prevention activities to be carried out—assuming that resources exist and that health authorities are primed to execute such activities. This is thought to be the first prediction of a RVF outbreak described in the literature.

9.3 Factors in Evaluating the Payoff of Remote Sensing

There is little in the research literature quantifying the economic dimensions of RVF or the cost-effectiveness of RVF interventions, making an investigation of the economic value of remote sensing–based surveillance problematic. In this section we describe some of the factors relevant for assessing the payoff.

9.3.1 Metrics

The studies above suggest that early warning, weeks to months in advance of RVF emergence, may be possible. Viewing such surveillance as a trigger for intervention strategies, payoff can be assessed in terms of losses averted. “Loss” can be measured in terms of reduction of human suffering or in terms of dollars corresponding to economic costs, as described below. Estimating costs associated with human RVF disease could be stated in terms of dollars or in terms of disability-adjusted life years (DALYs) averted. It is known, however, that DALYs capture only a fraction of the total costs associated with human disease. It is clearly important to consider veterinary and human public health costs when estimating the true consequences of RVF. Exclusion of the animal or the human dimension of RVF will lead to undervaluation of the damage. Recognizing this dual-burden nature of zoonoses is fundamental to any comprehensive assessment of RVF or similar disease (Perry and Grace 2009).

If it is clear that both the animal and the human dimensions of cost must be considered in any analysis of costs averted by RVF prevention or mitigation activities, it is also clear that analyses will be contextual. For example, some nations have large livestock industries that will be affected heavily by RVF. Some have the capacity to act on early warning of RVF activity weeks to months in advance by acquiring and/or distributing vaccine and instituting appropriate vaccination and vector control campaigns. Some have robust medical infrastructures that are able to deal with significant human morbidity. Other nations lack such capacities. The payoff of remote sensing in RVF prevention will vary between nations; inequalities can be substantial.

9.3.2 Costs of RVF

A variety of costs can be associated with RVF outbreaks. Recently, the National Agricultural Biosecurity Center (2010) has enumerated a list that includes costs seen by workers and consumers as well as those associated with control efforts, livestock morbidity and mortality, human morbidity and mortality, and international trade disruption.

9.3.2.1 Costs Associated with Control Efforts

There are three general types of intervention that are thought to be effective or partially effective for controlling RVF: vaccination of livestock, vector control, and livestock movement control (Davies and Martin 2003, 2006; Geering et al. 2002). Livestock vaccination is thought to be the most effective means to control RVF.⁸ The most widely available vaccine is based on the modified live Smithburn strain of RVFV (WHO 1983). Although the vaccine is immunogenic, it can injure the fetus and cause abortion in up to 30% of pregnant sheep. A single dose results in long-lasting immunity. Inactivated vaccines often are poorly immunogenic, but they have the advantage of being suitable for use in pregnant animals and conferring maternal immunity (via colostrum) to offspring. An initial booster followed by annual injections is required. New vaccines are being developed but are not available currently (Lubroth et al. 2007).

Vector populations can be controlled via larvicidal treatment of mosquito breeding sites. Effective larvicide products are available commercially, though widespread flooding, typically seen leading up to and during RVF outbreaks, can complicate application. Ultra-low-volume adulticide sprays appear to have limited effect on RVF transmission (though they are used successfully to control transmission other arboviral infections) (Davies and Martin 2003, 2006; Newton and Reiter 1992). Estimated costs of purchasing and applying adult and larval control over large areas have been published recently; the overall costs can be significant (Anyamba et al. 2010).

Although strict control of livestock movement does not appear to affect transmission within outbreak areas, it is thought to be effective in preventing the long-distance translocation of RVF into nonenzootic and nonepizootic areas. The costs associated with sealing of transportation into and out of farms or agricultural areas include the lack of food delivery and milk collection.

There are other costs associated with epidemic and epizootic management. For example, deployment of public health workers to affected areas to conduct rapid assessment of the outbreak, procurement of personal protective equipment (e.g., gloves, masks, goggles, aprons) for farmworkers as well as veterinarians and field workers, and training of public health personnel on hygiene promotion and health education are all common activities in times of RVF transmission (International Federation of Red Cross and Red Crescent Societies 2010).

9.3.2.2 Costs Associated with Livestock Morbidity and Mortality

Livestock abortion and neonatal mortality can result in “lost” generations of animals following severe, widespread outbreaks of RVF. There are costs associated with diagnosing animals as well as disposing of (e.g., via burial or incineration) and replacing dead animals. The effect of RVF infection on animal fertility and on milk production in aborting mothers is unknown.

9.3.2.3 Costs Associated with Human Morbidity and Mortality

RVF morbidity affects people in terms of medical treatment (hospitalizations, outpatient care, self-care), time lost from work, and reduced productivity related to long-term sequelae (e.g., blindness, neurological complications) (WHO 2010). Human populations at highest risk include farm residents and workers, animal health personnel, and abattoir workers.

9.3.2.4 Costs Associated with Regional or International Trade

RVF is an OIE (Office International des Epizooties) List A disease, meaning it has the potential for rapid spread, has potentially serious socioeconomic or public health consequences, and is of major importance in the international trade of animals and animal products (Bram et al. 2002). The presence of any OIE List A disease within a nation presents barriers to trade by providing trading partners a reason to impose embargoes, often compromising agricultural industries in the outbreak nation (Kitching 2000).

For example, in the 1997–1998 outbreak in East Africa, pastoral economies in Somalia suffered an export decline of more than 75%, following a Saudi Arabian embargo on animal products from the Horn of Africa (LeGall 2006). Trade bans in the 2006–2007 outbreak cost Kenya an estimated US$32 million in lost exports to the Gulf (Rich and Wanyioke 2010). Cessation of South Africa wool exports to China occurred in 2010. South Africa is the world’s third-largest producer of wool used to make clothes, and China accounts for about 60% of the wool exports from South Africa (Lourens 2010). In the previous year, South Africa exported US$128 million worth of raw wool to China (Barrie 2010). In addition to embargoes, there are costs associated with establishing disease-free status and renewed trade (e.g., sustained, intensified surveillance; inspection measures at ports) following an outbreak of RVF. In the case of South African wool, China requires a 12-month period following cessation of disease transmission before wool from RVF areas can be allowed into the country.

9.3.2.5 Costs Seen by Consumers and Workers

In the Egyptian outbreak in 1977, losses of cattle and sheep led to shortages of red meat in the Cairo marketplace (Lederberg and Shope 1992, 71). Scarcity of meat and dairy products can lead to price changes as well as pressure to substitute foods for the products made scarce by RVF. In the case of trade embargoes, agricultural industry workers may be laid off or rendered unable to work. For example, the South Africa wool industry is thought to support approximately 18,000 farmers, and the national flock of 14 million sheep produces roughly 50 million kg of wool annually (Laurens 2010). Loss for a year of 60% of exports undoubtedly affects a substantial number of workers in this sizable industry.

9.4 Threshold for Public Health Response

As we have described above, costs associated with RVF can affect a society in diverse ways. There are preventive measures that are thought to be capable of ameliorating the damage of RVF if applied before virus circulation begins. Remote sensing–based early warning could, in theory, cue quick reaction activities to control the emergence and spread of RVF virus. It is pragmatic to ask: Beyond the simple questions of when and where to intervene, what do governments and public health authorities need to act on early warning forecasts? This is a complex question. Certainly, timely outbreak response requires effective early warning systems, but inertia must be overcome before response activities can be undertaken. Major RVF outbreaks are infrequent but when they occur can lead to substantial diversion of financial and public health resources normally dedicated to other major ongoing health needs, such as HIV/AIDS, TB, malaria, and diarrheal diseases (Breiman et al. 2010). The reticence to doing this is certainly understandable.

Lack of early response cued by remotely sensed RVF surveillance has been a cause of considerable outcry on the part of public and veterinary health authorities. In connection with the 2006–2007 outbreak, for example, an official of the UN Food and Agriculture Organization (FAO) noted,

It is interesting, if rather disheartening, to watch another RVF epizootic emerge and evolve in eastern Africa and to note that it is such a close recapitulation of events that occurred in 1997/8 and decades before. It is a recapitulation not only with respect to disease evolution but also in terms of national and international preparedness—or lack of it. Those who followed ProMED in those days will be aware that the epizootic attracted intense international attention and was closely reported in postings, which contain much useful information. Despite seminal work on developing early warning systems based on remote sensing … it seems that the capacity to respond has not improved greatly in the high-risk countries in Africa. (Dr. Peter Roeder, Animal Production and Health Division, FAO Writing on ProMED, 12 January 2007, archive #20070112.0164)

At the same time, an African news source reported, under the headline “Kenya: NASA Gave Warning Over Deadly Fever,”

The deaths from Rift Valley fever (RVF) could have been avoided if Kenya had heeded a warning by an American body that changing climatic conditions posed a risk. The UN Food and Agricultural Organization (FAO) says the US-based National Aeronautics and Space Administration (NASA) Goddard Space Flight center sounded the alarm way back in September [2006], 2 months before the 1st case was reported in Garissa. However, it is not clear whether the country received the warning or simply ignored it. … The center had warned that rising temperatures accompanied by heavy rains in the Central and Eastern Pacific Ocean and Western Indian Ocean could spark an outbreak of the disease. The warning was contained in FAO’s September [2006] edition of the Emergency Prevention Systems Magazine, Empres Watch. The center had been monitoring climate in East Africa for several years. … “The outbreak of Rift Valley Fever is another example that requires a quick and coordinated response,” said FAO’s New Crisis Management Centre manager, Karin Schwabenbauer … (All Africa newswire, reported on ProMED, 12 January 2007, archive 20070111.0112) ⁹

Despite such political pressure to act, it is important to realize that the situation is often complex. A recent, comprehensive set of case studies of the 2006–2007 outbreak in East Central Africa was published in the American Journal of Tropical Medicine and Hygiene (August 2010), and many of the nuances are described there. For example, current preparations of the Smithburn vaccine have a shelf life of approximately 4 years. Outbreaks in the Horn of Africa region occur aperiodically, with a mean of near 10 years between outbreaks. Veterinary health authorities cannot spend scarce resources on continually replenishing a stock of RVF vaccine when other needs are present continuously. Nor can manufactures maintain large stocks that are likely to expire before sale. Thus, vaccine may not be available at any given time. Nonetheless, waiting until there is a need to manufacture vaccine is problematic (Consultative Group for RVF Decision Support 2010).

Even if vaccine had been available in the Horn region in 2006, effective and safe administration triggered by the early warning described in Anyamba et al. (2009) would have been complicated; by the time the warning was issued, early outbreak areas had already been inundated by rains and were inaccessible. In fact, the Consultative Group for RVF Decision Support (2010) suggests that up to 141 days may be needed between a vaccine order and the successful acquisition of vaccine-associated herd immunity in a hypothetical target population of 100,000 animals. This is much greater than the 2–6 weeks’ (14–42 days’) advance notice permitted by existing early warning systems. Thus, to be actionable to decisionmakers, forecasts may have to provide longer lead times.

Forecasts also have to be accurate in terms of geographic specificity. One recent study demonstrated a range of accuracy in terms of observed human disease in at-risk areas (Anyamba et al. 2010). Comparing the locations of disease outbreaks among humans and the areas deemed at risk based on remote sensing–based forecasting between 2006 and 2008, the researchers found that in eastern Africa (2006–2007), 65% of human case locations were in at-risk areas; in Sudan (2007), 50% of human cases were in such areas; in Madagascar (2007–2008), 23%; and in southern Africa (2007–2008), 20%. Although the study does not estimate positive or negative predictive values for RVF disease, the observations suggest that the existing forecasting algorithm may be better suited for some areas than others. Whether public and veterinary health authorities in the Horn region will be compelled to act based on such performance is unclear. However, as Breiman et al. (2010) observe, “Higher specificity of forecast models will be needed for them to be confidently used to activate action-steps, which require commitment of public health resources, especially when considering how those resources are often limited.”

9.5 Discussion

Increased rainfall causes vegetation growth, which can be measured or inferred from orbiting remote sensors. In conjunction with epidemiological and other field observations, maps of vegetation indices have been used to estimate the occurrence of increasing vector populations and RVF viral activity in East Central Africa. Correlations between viral activity and satellite observations have been established. Correlations are significantly improved by the addition of Pacific and Indian Ocean sea surface temperature anomaly measurements. Such work has provided a strong foundation to forecast RVF emergence before an epidemic or epizootic activity is observed. Recent studies of the 2006–2007 RVF outbreak in East Africa suggest that about 2 months’ advance notice is possible. With further experience and investigation, the accuracy of these forecasts can likely be improved and application can be generalized to additional regions.

How can we elucidate the value of such disease forecasts? Viewing an early warning system as a trigger for intervention, payoff can be assessed in terms of losses averted. Prevention and mitigation activities may be able to reduce the effects of RVF if implemented early. Preventing widespread disease, if possible, may avert a substantial fraction of losses that would occur in the absence of controls. However, it is unclear that 1–2 months is sufficient time to execute effective prevention measures. Quantifying the payoff of early warning is complex, and we are aware of no analyses in the peer-reviewed literature.

Additional studies developing a comprehensive understanding of the use of such forecasts should consider questions such as these:

For a given situation and region, how should decisionmakers balance competing needs of existing health concerns with the transient, multifaceted “one health” needs related to a potential RVF outbreak? What information is actionable? The Consultative Group for RVF Decision Support (2010) has recently described an analytic tool to guide decisionmakers in responding to future RVF emergencies in the greater Horn of Africa. The tool incorporates the concept that actions should be in proportion to an evolving risk profile and remote sensing–based forecasts. This tool will evolve as additional information becomes available; the degree to which it will be adopted and employed is unknown.

Given sufficient warning, to what degree is it possible to prevent RVF? Could preventive vaccination in combination with vector control substantially reduce human and animal RVF infections? The finite shelf life of existing vaccines, as described above, suggests that in many scenarios, vaccination cued by early warning may not be feasible (even if ground conditions are not already complicated by flooding and it is possible to distribute and administer vaccine). Yet in areas where RVF outbreaks are separated by decadal periods, vaccine-induced immunity may be the key to preventing virus emergence and circulation. Vector control can also be problematic, in terms of logistics and costs (Anyamba et al. 2010). For either vaccination or vector control to be an option, resources need to be ready if campaigns are to be administered preemptively. As Jost et al. (2010) observe, “Donors and international organizations must also reevaluate the policies that resulted in the bulk of financial aid being provided to affected countries only after human cases have been documented.” Since livestock outbreaks typically precede human disease, holding back controls until transmission is well established is problematic.

Can appropriate data be collected to assess reduction in economic costs associated with reduced incidence of RVF due to prevention? Rich and Wanyoike (2010) analyze the economic effects of RVF in Kenya during the 2006–2007 outbreak and observe that “downstream impacts can often dwarf the impacts of the disease at the farm level, but public policy tends to concentrate primarily on losses accruing to producers.” Combined epidemic-economic models capable of analyzing the economic benefits of RVF prevention and control activities may be helpful to elucidate the cost-effectiveness of early warning systems in relation to both types of losses.

Until such questions are answered, it is unclear how the payoff of remote sensing–based early warning systems can be assessed. Studies addressing these and related questions would not only suggest ways in which the use and application of RVF forecasting could be optimized, they may also illustrate approaches for other diseases. As alluded to above, modeling and simulation may be important tools for quantifying cost savings from instituting prevention measures early. Modeling of infectious disease outbreaks is recognized as an important tool for understanding the dynamics of the outbreak process, the effects of the disease, and the potential benefits of interventions (Hartley et al. 2011). Epidemic models could be used to drive economic analyses by providing estimates of disease incidence and mortality in both epidemic (short-term) and endemic (long-term) scenarios Gaff et al. (2007), Xue et al. (2012). They may also provide guidance on how quickly interventions are likely to have effect, and how early control measures need to be instituted to be effective (Gaff et al. 2011).

If the surveillance approaches described in this study are ultimately to have a demonstrated payoff, public and agricultural health decisionmakers must come to rely on and trust the products. Surveillance must be accurate year-in and year-out. The surveillance must also be capable of facilitating an effective response and triggering controls that can be executed during the window of opportunity afforded by the early warning. The research described in this chapter suggests that this is possible, though clearly much remains to be done to achieve the goal.

9.Commentary: An Emerging Approach

• Joshua Michaud 

1. International Development Program, Johns Hopkins University School of Advanced International Studies, Washington, DC, USA

   Joshua Michaud

Public health has always faced the classic conundrum of valuing prevention. How do you assign worth when the desirable product of public health activity is the absence of disease in a population? Policymakers and the public in general are much more likely to notice and understand how important public health can be when it fails as opposed to when it works, since the results of failure—the societal and economic toll of disease and mortality—are highly visible and relatively easy to catalog. The value of cases prevented and costs avoided, on the other hand, is harder to appreciate. Often, public health practitioners lament that many proven and inexpensive health interventions are ignored or underutilized because their full benefits are not understood. Although this may be true to an extent, the problem of underutilization also derives from the continuing inability of public health practitioners themselves to fully understand, quantify, and communicate the value of their work. Public health methods for valuation are imperfect, and not used widely. Practitioners are understandably focused on the health consequences of their work and tend not to dwell on its economic value.

This valuation deficit is certainly present in the important area of disease outbreak prevention and response. It is into this “work-in-progress” valuation of disease outbreak information that David Hartley introduces his chapter, “Space Imaging and Prevention of Infectious Disease: Rift Valley Fever.” Hartley’s chapter provides an overview of a promising public health application of remote sensing, and it quickly becomes clear that it is an initiation of a discussion about assigning value to information useful for public health action rather than a proposed set of methodologies for doing so. The chapter proposes, introduces, outlines, and hints at the potential for using evaluation of costs and benefits of predicting and preventing infectious disease using remotely sensed data. Having read the chapter and having some sense of the other kinds of public health applications for which remotely sensed data could be used, I find it hard to argue with the chapter’s primary conclusion—that the potential for linking satellite data with epidemiologic tools to design and implement predictive capabilities for disease outbreaks is excellent. Still, we are very much at the beginning of this conversation and the process of tool development in this area.

In this response to the chapter, my hope is to contribute to the dialog around remotely sensed data and its application to disease outbreak interventions by highlighting a few points from Hartley’s chapter, building on and supplementing these points, and extending the points by outlining an even broader scope of application for this nascent interdisciplinary work. Further, I point out a few limitations of the approach and points for consideration to be kept in mind as we move forward.

9.1.1 9.C.1.Preliminary but Promising Field

The chapter indicates the existing set of methodologies and scientific literature on application of satellite derived data to disease outbreaks remains quite limited. Hartley’s wording when reviewing the literature in the field is indicative: “no studies,” “studies would provide,” “new field,” “literature is silent” and “great promise”; all of these phrases are present because, while there is reason to believe something of value could be extracted here, much more work is needed to understand and place a value on the information being generated.

As both a researcher in the field and a sometime consumer of the kind of predictive data on RVF that has been produced in recent years, through researchers like Hartley and through NASA and the U.S. Department of Defense’s global emerging infection system program, I can personally attest that even the limited, preliminary kinds of predictions that have been made available in recent years have been valuable to U.S. policymakers, let alone those working in Africa and other locations to reduce the effects of RVF outbreaks in the field. The Department of Defense and others have worked with NASA to use remotely sensed data in East Central Africa, helping produce forecasts of likely RVF activity in the area that rely on monitoring the vegetation index to forecast areas where mosquito hatching, and possibly viral transmission, could occur over subsequent weeks and months. The Department of Defense analyses the remotely sensed data and makes intermittent forecasts of RVF transmission risks, making what might have been an academic exercise of providing proof of principle for the predictive capability of these models a true, operational program. I was a consumer of this information as part of my work with the National Center for Medical Intelligence, and I observed first-hand how outbreak risk forecasts for RVF had decisionmaking implications for Defense in regard to its deployments, field exercises, and force health protection efforts.

As Hartley has effectively outlined, RVF makes an excellent case study for this kind of predictive modeling because of the unique relationship between certain environmental variables. Transmission of disease is highly associated with microenvironments, locations where certain combinations of temperature, rainfall, and vegetation in areas with dambos and dambolike terrain together come together with the mosquito vectors and distributions of animal and human hosts to create ideal RVF transmission pockets. Fortuitously, many of the associated environmental variables can be characterized from space using remote sensing. So, as Hartley shows, there is little doubt that through existing technology, remote sensing can be useful in the prediction of RVF disease outbreaks in East Central Africa, and perhaps in other locations as well.

As interesting and compelling as it is to elucidate an epidemiological connection between satellite data and RVF outbreaks, some additional questions for the purposes of understanding the value of information are, “What are the economic implications of generating RVF (and other infectious disease) predictions?” and, “Are gathering and analyzing that information worth the expense?”

In theory, linking the release of a predictive assessment of a future RVF outbreak with the actions of policymakers and farmers in the region should be measurable, along with an assessment of the costs, but in reality, gathering the wide set of information needed to make accurate cost-benefit calculations, especially in an area such as East Central Africa, is tremendously problematic. The cost of the disease is borne most acutely, one could argue, in the economic sense through the loss of income when farmers’ herds become infected. Certainly, the human toll can be significant, but much of the concern around RVF is centered squarely on its agricultural implications. For this reason, the United States is mainly concerned about RVF, I would say, as an agricultural importation threat. The costs of RVF to livestock farming and potential damage are very significant. For this reason, the Department of Agriculture considers RVF one of the most threatening “foreign animal diseases” out there, and that agency has performed some analyses of the potential economic repercussions should RVF be imported into the United States and cases of the disease be found in U.S. livestock. Local farmers in East Africa and other RVF-affected areas are no less cognizant of the potential losses associated with the disease. But, as Hartley touches on and I hope to indicate more fully in the next section, there is more than just the simple accounting of costs of illness and lost income to consider.

9.1.2 9.C.2.Economic Considerations for Prediction and Response

Rather than attempt to outline a full methodology for calculating the cost-benefit of disease prevention through RVF prediction, Hartley’s chapter is only able to sketch how such a calculation might be done. This is mainly a reflection of the lack of prior academic work and methodology relevant to this particular area. Hartley ably reviews several categories of costs and benefits that would have to be included in valuation calculations, which I will not repeat here. Rather, in this section, I would like to highlight additional considerations that would have to be incorporated in a full valuation model for the kind of remotely sensed work that the chapter characterizes. As Hartley partially recognizes, attempting to determine whether RVF surveillance and prediction are worthwhile requires not only understanding the costs of the animal and human disease burden and the expense of the sensing platforms and public health interventions and the like, but also disentangling a larger set of questions about incentives and externalities that are inherent in disease outbreak prediction, detection, and response. The following discussion centers around three public health functions in this area: surveillance (initial detection—or accurate prediction—of an outbreak), reporting (communicating the presence of an outbreak once it is detected), and response (implementing public health actions to stop transmission and reduce cases of the disease). The discussion is not limited to RVF alone, since it draws lessons from other infectious diseases; the points are applicable for RVF, but also more broadly for many kinds of outbreaks.

Surveillance: Is More Information Always Better? It may come as a surprise to learn that in the context of disease outbreaks, more surveillance information is not always better. “Better” here refers to “economically rational” for the actors involved in conducting surveillance. The reason more surveillance might not be better involves the built-in economic disincentives to infectious diseases that potentially leave some people, industries, and countries worse off with more information. Take a Kenyan farmer with livestock at risk of being infected with RVF. Were he to discover that his animals had been infected, many (or all) might be put down in an attempt to control the disease, or access to markets where the farmer might sell his animals or derived products might be restricted. Such actions might lead to a significant loss of income or even destitution that he would wish to avoid. If some of the animals did become infected and the farmer was unaware (either by chance or by choice), then he might still be able to extract some gain from selling the animals and avoid the potential loss of his entire herd and income. Given the choice of knowing or not knowing, he might prefer not knowing—in other words, he is disincentivized to participate fully in surveillance. The same logic goes for a methodology of prediction using satellites or any other tools. An area’s farmers may feel that by knowing about an impending epidemic in advance—one in which their livelihoods are guaranteed to suffer while the benefits of this knowledge are less certain—they could be worse off than not knowing.

Those kinds of disincentives for surveillance are not restricted to individual farmers in developing countries at risk for outbreaks. In the United States, for example, when birds illegally smuggled into the country, some of which might have come from geographic areas endemic for highly pathogenic H5N1 avian influenza (HPAI H5N1, another frightening “zoonotic” disease, or a disease animals that can affect humans), are intercepted, no laboratory testing of the birds is performed prior to culling them. The rationale for this is to avoid having to say that HPAI H5N1 has been found inside the borders of the United States. So far, the HPAI H5N1 virus has not been found in the United States, but detection of the virus would surely have major implications for the poultry and other industries because immediate trade restrictions and possibly panic might ensue. This is a missed surveillance opportunity put in place for economic concerns, and it indicates the power of an economic disincentive for more information about potentially deadly diseases.

In another example, there were similar difficulties in surveillance for bovine spongiform encephalopathy (BSE, or “mad cow disease”) in the United Kingdom, since farmers had little incentive to report suspected cases in their herds. In fact, the United Kingdom’s BSE inquiry report stated, “one reason why BSE was not picked up at a very early stage by the system was the lack of incentive for farmers to refer an isolated case of an unrecognized disease in their herd for laboratory investigation. Indeed, there was a positive disincentive, namely the cost of a post-mortem examination” (UK Government 2000). This obstacle often appears in the context of zoonotic infections that affect agricultural livestock because there are potentially large economic losses from the culling of sick and potentially sick animals. Such culling and destroying of livestock and the associated trade restrictions and lack of access to markets that usually coincide with an outbreak response can serve as a powerful disincentive for individuals (and sometimes whole towns or industries) from participating in disease surveillance. This disincentive for good surveillance information exists whether the surveillance is performed through diagnostic tests or through application of remote sensing data in a predictive climate-based model.

Reporting: Is Being Completely Transparent Always Rational? In a similar vein, there is commonly a disincentive to be fully open and honest about reporting detected outbreaks. Farmers who know their flocks are ill may avoid saying anything for fear of losing their income. Countries wishing to avoid economic damage sometimes downplay or fail to report disease outbreaks. In areas affected by HPAI H5N1, for example, poultry farmers are often reluctant to report cases of dead birds to health authorities for fear that officials will rob them of their livelihoods (and important sources of food) by culling their flocks. As one Nigerian poultry farmer stated, “If the government isn’t able to compensate me [sufficiently], why should I bother to report if my birds become sick? Wouldn’t I be better off just taking my chances?” (Bellagio Meeting 2006).

This reporting disincentive also plays out along international trade routes, motivating obfuscation by governments. A great hindrance to transparency and early disease detection internationally is the cost that an affected country faces when the rest of the world finds out about the outbreak. On learning about an outbreak, many times neighboring countries close borders, trading partners restrict or stop imports, and travel and tourism cease. These actions have real and sometimes very damaging effects on important industries or economic sectors within a country, and therefore there is a strong incentive for underreporting or not reporting at all (Cash and Narasimhan 2000). Economic costs can be significant, in particular if the infectious disease is linked to the agricultural export sector, as RVF often is.

There are many examples of this kind of negative trade consequence from reporting a disease outbreak. Some of the more commonly cited figures include the 1991 cholera epidemic in Peru, which is estimated to have cost the country more than $1.5 billion in lost exports and tourism (Knobler et al. 2006), and India’s 1994 outbreak of suspected plague, which likely cost the country an estimated $1.7 billion (WHO 2005). Thailand initially denied it had H5N1 avian influenza in its chickens, and Indonesia delayed reporting its first bird outbreaks of H5N1 (CNN 2004). Burma failed to report its first H5N1 bird cases when they occurred in 2004 (Beyrer 2006). China has reportedly covered up H5N1 outbreaks in its flocks multiple times. In 2006, the World Health Organization (WHO) accused the Chinese Ministry of Agriculture of “selectively reporting” outbreaks of H5N1 in its chickens and refusing to send samples from infected birds out for testing. At that time, the chief WHO representative in China stated, “It’s so sad that we haven’t got that [outbreak] information or those [H5N1] viruses from the Ministry of Agriculture … it’s really beyond comprehension to us” (CBC 2006). Once reporting of these bird outbreaks does occur, the economic consequences can be very painful. When Thailand’s troubles with bird flu became known, the resulting collapse in poultry exports cost it some $1 billion (Economist 2006). When Vietnam first reported the presence of H5N1 (the virus and the culling wiped out 17% of the country’s chickens in 2004), the outbreak and subsequent trade bans resulted in a loss of more than $83 million for this developing nation (Vietnam News Brief Service 2004). In 2003, when another pathogenic avian influenza subtype (H7N7) was found in poultry in the Netherlands, Belgium, and Germany, 28 million birds were culled and restrictions on trade in both poultry and swine (Dutch pigs were found to harbor evidence of infection) were enforced (Kimball 2005).

Response: Can Anything Be Done About It? Even when a disease outbreak can be detected early and reporting does occur, there might exist a gap between what should be done to implement an ideal public health response, and what can be done given what a country, region, or local community can do or is willing to do. Information that is not “actionable” may not be valuable. It does no good for a country to know where and when an outbreak is occurring if it does not possess the ability, or the willingness, to respond. In such a case, the information would have been generated just for information’s sake, not for policy action. Again, such a restriction on the value of outbreak information applies equally to confirmation in the form of a diagnostic test result, or a trusted prediction based on satellite data.

Therefore the links among surveillance, reporting, and response capacity are critical, and we should not emphasize more and better data when the relevant actors cannot implement or improve policies with that information. One of these activities without the others provides limited or no benefit; all must be provided. Clearly, disease outbreaks are prone to collective action problems, since “rational” action by individuals and governments protecting their own economic interests can lead to overall irrational outcomes, such as worse outbreaks, greater health consequences, and more interruptions of trade and economic activity. Valuing the information contained in the prediction of RVF outbreaks through remote-sensing data would have to take into consideration these characteristics. Could an accurate RVF outbreak prediction actually make farmers in the targeted area worse off economically because of preemptive trade bans or other damaging actions? Is this risk worth it if the outbreak likely cannot be contained, given weak public health capacity? What are the optimal outcomes for all parties involved, economically speaking? Hartley hints at these complications, but it is worthwhile to highlight them more clearly.

9.1.3 9.C.3.Broader Potential for Environmental Observation and Disease Prediction

Although Hartley’s chapter focuses on RVF, a subtext here is that similar techniques and methodologies could perhaps be applied to other infectious disease threats. Certainly the literature is already relatively rich with studies examining the relationship between environmental variables and disease epidemics (Kelly-Hope and Thomson 2008; Harvell et al. 2002).WHO in 2005 identified 14 infectious diseases it classified as potential candidates for environmentally based “early warning systems,” a list that includes RVF, malaria, dengue fever, cholera, meningococcal meningitis, and influenza (WHO 2005). All of the 14 diseases are affected to some extent by the environment, but each to a unique extent, such that variables strongly associated with one may not be associated with others. In addition, many factors besides the environment must be taken into consideration when judging the transmission of these pathogens—everything from geographic variations in endemicity to human and vector behavior, to varied and changing control measures, to dynamic immune states, and other measures that may be unknown or not measurable.

In the case of RVF, the disease’s very direct link to the environmental conditions that favor mosquito breeding in dambos (precipitation, temperature, and other factors that can be measured through satellite monitoring) make it a good candidate for forecasting. For other diseases, environmental variables serve as drivers of disease transmission but are only relatively minor contributors to the overall set of factors that determine when and where disease outbreaks emerge and spread. Thus, among those infectious diseases linked to environmental factors, RVF in parts of East Africa may in fact be the lowest-hanging fruit of remote sensing–based disease prediction. Extending the prediction technique beyond RVF, while possible and worth pursuing, might be more involved, less accurate, and potentially more costly.

Dengue is sometimes referenced as a disease that might be predicted based on environmental factors. Just as in the case of RVF, breeding and activity of mosquito vectors are influenced by temperature and rainfall, but complications in the ecology of dengue transmission make it a bit more unpredictable. This is especially true in the case of the dreaded and explosive “urban” dengue outbreaks, because the drivers of these types of epidemics, which are becoming more common in many cities of tropical developing countries, are heavily based on human behavior rather than on strictly environmental factors. Urban dwellers who leave open containers of water or fail to clear stagnant puddles create accommodating habitats for mosquito breeding whether it has rained recently or not. The complexities of human immunity to dengue’s multiple serotypes are not fully understood, also making clear prediction more difficult. These additional factors have made dengue a more difficult target for environmental modeling and linking to remotely sensed data. Other diseases bring their own complications: the link between the environment and plague, for example, is moderated through the activities not only of the vector that transmits the bacterium, but also the rodent hosts of that vector; this and plague’s highly focal natures takes prediction from environmental observations several steps further away from a direct causality.

Perhaps the biggest prize (and the one with the largest potential benefit) in the outbreak prediction field is malaria. This mosquito-borne parasitic infection is highly endemic in many countries around the globe and remains one of the leading killers of children in low-income countries. Currently, the prediction of malaria outbreaks through use of environmental variables is fraught with complications and confounders. Nonclimatic factors such as population immunity levels, nutrition status of a population, the state of control measures at local levels, the use of antimalarial drugs, and the pattern of drug resistance in circulating malaria strains strongly influence the environment-malaria link. These difficulties have not prevented research and development of climate-based malaria predictions, however. In fact, multiple studies of the relationship between climate factors such as El Niño–Southern Oscillation (ENSO) cycles or changes in temperature or rainfall and malaria transmission have shown there can be a relationship (Ebi 2009; Githeko and Ndegwa 2001), but to put it kindly, the evidence is mixed and highly contingent on the specific circumstances, location, and time.

In the background of those analyses of climatic variables and disease we have the looming shadow of global climate change and its possible effects on infectious disease. If prediction can be somewhat successful on the small scale of weeks and months, how successful can we be on a longer scale? Can we predict disease transmission patterns years in advance once we know what the climate will look like in the future? Current conventional wisdom in public health holds that many diseases that had previously been circumscribed to poor tropical areas of the world, driven by an ever-warming climate, will expand their reach into geographical areas and populations where they had previously not been found; malaria is typically held up as a prime example. But the link between climatic variables and disease transmission actually becomes more tenuous the larger the geographic and temporal scale over which one attempts to predict (Lafferty 2009). It is precisely because the nonclimate variables associated with transmission become so heterogeneous and difficult to model over large areas and longtime scales that the probability of accurate prediction becomes very small, and adherence to simple cause and effect becomes problematic.

Several examples indicate how far we have to go to make long-term predictions of disease transmission based on climate. A recent article by Gething et al. (2010) in Nature deftly points out the problems with blindly ascribing increases in malaria with climate change by comparing the best estimates for the effect size of climate change on malaria transmission compared with the effect sizes of different control and treatment measures. The authors’ bottom line is that a warming planet over a long time frame has the potential to affect transmission, but the effects of available control measures and treatments dwarf the effects predicted from climate. In other words, climate effects are drowned out by control effects (not to mention other nonmeasurable effects, such as general development), and predictions are often based on the erroneous assumption that control and treatment measures and technologies won’t change over the large time scales in these analyses. In another example, researchers in Australia concluded that as parts of that country become drier with climate change, the risk of dengue might actually increase as people hoard water in water tanks that would increase mosquito breeding (Kearney et al. 2009). This is a counterintuitive result, as one might assume that mosquito activity would most likely decrease—and disease transmission with it—as the environment becomes drier. Finally, it is worthwhile to note that two adjacent areas with equivalent climates can have dramatically different transmission patterns for some diseases that have been linked to climate. Researchers have examined the areas that straddle the U.S.-Mexico border and found that between 1980 and 1999 there were more than 62,000 reported cases of dengue on the Mexican side of the border (likely an underestimate), while on the U.S. side there were just 64 cases. The difference is mostly explained through differences in living standards between the United States and Mexico (Brunkard et al. 2007). So, making the link between observed information and disease is not linear and highly determined—many disease processes are complex and resist simple cause-and-effect explanations.

9.1.4 9.C.4.Concluding Remarks

Hartley’s chapter is a valuable review of the possibilities and the obstacles of making predictions about infectious disease outbreaks using climate observations. Certainly, as the chapter indicates, there is reason to believe that real associations have been discovered and that true predictive associations can be made between earth observations and disease transmission. As this commentary has attempted to indicate, the strength of these associations is highly dependent on the disease in question, the geographic and temporal scales involved, and the available data and understanding of disease processes. What is lacking, as is made abundantly clear in the chapter, is proven methodologies or sets of tools that can be applied to valuing the predictive disease work and that take into consideration the complications and externalities associated with transmission of diseases like RVF. Collection of better data along with the development of more robust epidemiologic and economic models of disease prediction would go a long way to bringing us closer to understanding, and ultimately assigning the proper value to, these efforts.