Many future tasks remain, however. Most importantly, the estimation of R 0 from early epidemic growth data for a heterogeneously-mixing population is called for. R 0 in the present study can even be interpreted as R 0 for a heterogeneously-mixing population (i.e., the dominant eigenvalue of the nextgeneration matrix), provided that the early growth rate is the same among sub-populations (though it is not the case if the growth rate varies across sub-populations) [34, 35] . Analyzing heterogeneous transmission among approximately-aggregated discrete groups, the estimate of the next-generation matrix would give more detailed insights into the epidemic dynamics, including the most important target host for intervention [36] . As discussed in a modeling study in this special issue of the International Journal of Environmental Research and Public Health [37] , understanding the implications of sexual partnerships and their variations as a function of calendar time as well as infection-age is also of utmost importance. As the next step for a similar estimation framework, methods for estimating robust R 0 and the next-generation matrix from structured data (i.e., data stratified by age-and/or risk-groups) will be useful. Despite the future challenges, I believe the present study at least satisfies a need to offer a likelihood-based approach to estimate R 0 from early epidemic growth data, while being easily tractable and calculable among general epidemiologists. Using Satellite Images of Environmental Changes to Predict Infectious Disease Outbreaks Recent events clearly illustrate a continued vulnerability of large populations to infectious diseases, which is related to our changing human-constructed and natural environments. A single person with multidrug-resistant tuberculosis in 2007 provided a wake-up call to the United States and global public health infrastructure, as the health professionals and the public realized that today’s ease of airline travel can potentially expose hundreds of persons to an untreatable disease associated with an infectious agent. Ease of travel, population increase, population displacement, pollution, agricultural activity, changing socioeconomic structures, and international conflicts worldwide have each contributed to infectious disease events. Today, however, nothing is larger in scale, has more potential for long-term effects, and is more uncertain than the effects of climate change on infectious disease outbreaks, epidemics, and pandemics. We discuss advances in our ability to predict these events and, in particular, the critical role that satellite imaging could play in mounting an effective response. A tmospheric chemists and climate modelers have little doubt that the earth's climate is changing. Concomitant with rising carbon dioxide levels and temperatures, severe weather events are increasing, which can lead to sub-stantial rises in sea level, flooding, increased droughts, and forest fires (1) . In recent decades, infectious diseases have resurged, and previously unrecognized agents of disease have been characterized (2) . Evidence is accruing that these phenomena may in part be linked to environmental change (3) . Several questions have emerged from events that have occurred over the past 20 years: was cryptosporidiosis inevitable in Milwaukee, Wisconsin, USA, in 1993, and was Escherichia coli O157 infection inevitable in Walkerton, Ontario, Canada, in 2000? Both events were preceded by heavy rains; had highly concentrated sources of pathogens in the form of untreated sewage and animal waste, respectively; and had vulnerable infrastructure. Although the situations were perhaps more complex, could we have predicted epidemic cholera in South America in 1991 after a 100-year absence and the emergence of a new strain of potentially pandemic cholera in India in 1992?