**Health Seminar Series - Emerging Diseases**

**April 21, 2000**

Session 8 -- "Remote Sensing Technology Applications for Disease
Identification" and

"Antimicrobial Resistance Surveillance and Application of Information
Technology"

Ms. Catherine Angotti introduced the eighth session in the Health
Seminar Series on Emerging Diseases, the sixth of a series of continuing
education programs sponsored by NASA's Occupational Health Program,
Office of Health Affairs (OHA), in cooperation with the Uniformed
Services University of the Health Sciences (USUHS). Ms. Beth McCormick,
Deputy Associate Administrator, Office of Life and Microgravity Sciences
and Applications introduced the speakers for this session: Dr. Compton
Tucker, Senior Earth Scientist, NASA Goddard Space Flight Center; Lt.
Col. Ken Linthicum, Associate Director, Overseas Plans and Operations,
Walter Reed Army Institute of Research; and Dr. Daniel Sahm, Chief
Scientific Officer, MRL Pharmaceutical Services, Inc.

Dr. Tucker reviewed basic and general aspects of remote sensing and how
remote sensing is applied to form time series data sets to study
environmental properties, including possible vectors of diseases or
agricultural pests. Remote sensing involves using instruments that
measure electromagnetic energy. Most information that we perceive is
based upon visual perception. The premise of remote sensing is making
detailed spectral measurements in different parts of the electromagnetic
spectrum and by doing so, tremendously expand information for a variety
of purposes. We perceive energy in the blue, green, and red spectral
regions. There is a much greater breadth of information available to us
from the reflectance or emitted properties of natural materials in terms
of shorter wavelength (e.g., ultraviolet) and longer wavelength (e.g.,
infrared energy) radiation. In remote sensing we want to use a variety
of spectral information sources. In our case, it will be the coupling of
vegetation to ecological or climatic properties related to disease
vectors. Dr. Tucker showed the spectral curve for a radiative black
body. He discussed how we are able to use the reflective properties of
green vegetation to obtain information about this vegetation, and how
this information can be related to disease vectors or disease
situations. Dr. Tucker explained some of the properties of green
vegetation to enable understanding of the basic coupling between the
spectral measurements (remote sensing) and what is actually being
measured. Dr. Tucker showed an aerial photo of the Mt. Hood National
Forest and noted different vegetation areas. This was an example of true
color remote sensing via aerial photograph. Green vegetation has a
higher degree of reflectance in the green region of the spectrum. It is
the complement of absorption (green leaves absorb more in the blue and
red regions). Dr. Tucker showed three different spectral curves---one
for green vegetation, one for clear water, and one for dry bare soil.
Because of the differences, we can distinguish surface properties and
follow those through time. Photosynthesis determines the spectral
properties of a leaf. Photosynthesis is driven by the absorption of
light. The structure of leaves has evolved to achieve photosynthesis,
which includes a high degree of scattering of light within leaves
(necessary to achieve the absorption by the plant elements, which absorb
in the blue region of the spectrum). Liquid water is absorbed in the
near infrared. When all of these properties of scattering and absorption
are combined, we have the spectral properties of green leaves. Various
instruments can exploit these properties. By a remote sensing, we are
able to have an index that is highly related to photosynthesis. This
allows us to get at some underlying properties of vegetation via remote
sensing.

Remote sensing systems started in balloons (with telescopes) during the
Civil War; information was transmitted to the ground via telegraph. In
the 1960's, the U.S. had the Corona System, which used a camera that
dropped film back to Earth. Now we have moved to electro-optical systems
(Landsat, Terra, etc.). These are spacecraft that use digital
instruments and have a digital transmission of data to Earth. Dr. Tucker
showed a series of satellite data in a variety of colors. We are able to
combine the data into seamless data sets with a high degree of
inter-calibration over time. The photosynthesis index has been computed
and different colors have been assigned to it (false color images). We
have a time series of data sets that we can use to look at different
climatic or biological processes. It is imperative to compare satellite
data with actual ground measurements. We can look at the ocean and land
in a variety of ways and have a representation of the entire global
biosphere. These are easy techniques to employ from satellites; this can
be done on a variety of scales. Dr. Tucker showed some examples of
different spatial scales.

One example of the coupling between satellite data and the biological
process is desert locust habitat monitoring. Meteosat data is used to
monitor storm systems and occurrences of precipitation. Landsat
Multi-Spectral Scanner data is used to map potential locust breeding
sites (sandy soils and vegetation potential). You have to have satellite
monitoring of a large desert area over time. The desert locust problem
starts with the female depositing eggs in the sandy soil, where they can
remain for up to 15 years. When rain occurs, the eggs hatch and go
through seven developmental stages. They are wingless until the last two
stages, where they have wings and are highly mobile. When the swarms
land, they can defoliate large areas. We want to get early warning of a
locust population so those locusts can be controlled while they are in
their wingless stages. Through the use of time series satellite data, we
can look at the green vegetation development in formerly sandy areas.
Results are transmitted to the desert locust control organization. This
is an example of how satellite data can be used to study an insect for
control purposes. Dr. Tucker explained how time series data is used for
analysis. To be most useful, there must be a long data record and the
data must be inter-calibrated. Dr. Tucker showed some false-color images
of Africa over time. The photosynthesis can be represented as a line
plot. For comparison, an index of stability for photosynthesis for the
Arabian Desert and the Taklamakon Desert in Central Asia were observed.
There was a slope of almost zero over 18 years. This establishes a high
degree of confidence in comparison with previous measurements. Dr.
Tucker showed a recent example from Mozambique depicting the effects of
flooding. There are serious implications for malaria, cholera, etc. From
the time series of data, we are able to look at vegetation anomalies. It
has recently been very wet in southern Africa, and there is now a high
risk of Rift Valley fever outbreak in this area. Vegetation index data
was used as a surrogate for precipitation and satellite-produced maps
can be produced. These are very useful for further investigations in
specified areas for disease outbreaks.

Dr. Linthicum discussed Rift Valley Fever (RVF) disease ecology and
epidemiology. RVF is a viral disease first described in the Rift Valley
in Kenya in 1931. We now know the disease occurs in many habitats in
Kenya (along the coast as well as some of the higher elevations). The
disease is a zoonotic disease. It primarily affects animals and
occasionally causes disease in humans when they are in close contact
with the animals. It results in very widespread livestock losses
(primarily abortions in sheep, cattle, and goats). In humans, it is a
hemorrhagic disease. So far, the disease has only occurred in Africa,
primarily sub-Saharan Africa. However, the few aberrant outbreaks in
Egypt have been very severe. Outbreaks of RVF are known to follow
periods of widespread and heavy rainfall associated with the development
of a strong inter-tropical convergence zone over Eastern Africa. From
1950 to 1982, the four periods of excessive rainfall were related to
outbreaks of RVF. Such heavy rainfall floods mosquito-breeding habitats
in East Africa, known as "dambos." The relationship between dambos,
rainfall, and mosquitoes was unclear until the early 1980's when aerial
reconnaissance and habitat studies were done. During the dry season,
there is no standing water in these habitats. There is a type of
mosquito that lays its eggs in low-lying areas that only flood during
heavy rainy periods. The study found three general types of mosquitoes:
Culex, Anopheles (laying eggs in standing water), and Aedes (laying eggs
on moist soil, remaining in dry soil viable for many years). The virus
was found in all mosquitoes, but the Aedes contained the virus in the
adult and the immature stages---the virus was actually in the eggs. It
is very difficult to get Anopheles mosquitoes infected and they are not
important in the RVF disease transmission. The salivary glands in the
Culex mosquito become infected and this is how the virus is transmitted
to animals and human. Both the salivary glands and the ovaries of Aedes
mosquito become infected and hence the eggs become infected. However,
the Aedes mosquito does not become infected at the levels that the Culex
does and they are not good at transmitting the virus by biting. If a
large Culex population develops, secondary transmission develops. This
affects large populations of animals and humans. Dr. Linthicum described
the transmission cycle of the Culex mosquito.

Dr. Linthicum discussed the RVF remote sensing model that grew out of
the ecological studies. The model is a three-tiered system---NOAA
satellites, Landsat/SPOT satellites, and Synthetic Aperture Radar (SAR)
on P-3 aircraft. The NOAA satellites were used to monitor shifts in
vegetation over time. The Landsat satellites had better spatial
resolution. The SAR had the ability to penetrate cloud cover. Increases
in vegetation index values corresponded to small outbreaks in RVF in
East Africa and corresponding fairly well with rainfall and mosquito
populations. Landsat and SPOT satellites were used to map dambo
habitats. This data allowed estimation of where dambos were occurring.
SAR was used to detect flooding in spite of cloud cover. Each of the
types of radar could distinguish different types of flooding and areas
of emergent vegetation in flooded areas. This created the model that has
been used since the late 1980's.

The current work is involved with RVF disease prediction in a more
global and reliable way. As noted before, all known RVF activity in East
Africa from 1950-1998 followed periods of very high rainfall. NOAA
satellite AVHRR data has been used to detect RVF outbreaks. The El
Nino/Southern Oscillation (ENSO) phenomenon is the principal cause of
global interannual climate variability. The Southern Oscillation Index
(SOI) is the most common index for that phenomenon. Also of great value
are Sea Surface Temperatures (SSTs) anomalies coupled with atmospheric
pressure changes. During an ENSO event, there are shifts in global
precipitation patterns. During ENSO events in East Africa, it tends to
be wet in the December timeframe (normally a dry season). Dr. Linthicum
showed outgoing longwave radiation fluxes during an ENSO. Each of the
RVF outbreaks discussed earlier relates to an El Nino event. However,
there were also ENSO events that did not relate to outbreaks. ENSO
events and SST anomalies were examined and compared with East African
rainfall. Concurrent elevated SSTs in both the Indian Ocean and the
Pacific Ocean related to outbreaks in East Africa. Conversely, a warm
Indian Ocean without an elevated Pacific Ocean temperature did not
relate to a RVF outbreak or excessive rainfall in East Africa. SST is
being related to risk maps for East Africa and South Africa. Pacific and
Indian Ocean SST anomalies, coupled with satellite data, predict RVF two
to six months before outbreak. Forecasting could allow timely
vaccination of domestic animals and use of mosquito control agents
(released upon flooding) to lessen or prevent disease. In looking at the
data from 1950 through 1998, it appears that global warming of the
Earth's oceans does not increase disease transmission in this particular
case. There has been only one large outbreak in the last twenty years.
The cold/warm cycling resulted in rain/drought in East Africa, and this
may be important in the cycle of the infected mosquito eggs. More
information can be found on the Department of Defense Web site:
http://www.geis.ha.osd.mil/rvf/Walter_Reed_Project/rift_valley_fever_monitor.html.

Questions:

JSC: Regarding the aspects of volcanic activity and sensor
abilities---What happens to the deforestation/reforestation cycles? Does
this relate to endemic disease processes, e.g., in rodents or anything
else that may have an effect on humans?

Dr. Tucker: If you are using satellite data and there is a volcanic
eruption like Mt. Pinatubo in 1991, you have to have a complete
stratospheric aerosol radiative transfer correction to compensate for it
and to maintain integrity in the data that you use. Secondly, with
respect to tropical deforestation, or the movement of people into areas
where they formerly were not present (in Africa), this has been
suggested as one of the possible variables associated with Ebola
outbreaks. With Landsat data, we typically produce maps showing
encroachment of humans into tropically forested areas. For Mt. St.
Helens, there are satellite data that shows that reforestation is
occurring. Satellite data is invaluable in all of these areas; it has to
be coupled with work on the ground. There is a lot of work being done
within NASA and in private and university communities.

HQ: Are there any initiatives along the lines of non-vector borne
diseases, e.g. vibrio cholera? Its pandemics may be related to sea
surface temperature. Are there other programs related to non-vector
borne diseases that might be environmentally impacted?

Dr. Tucker: I believe so, although our work focuses on vector-borne
diseases. Other groups are looking at SeaWifs data or other satellite
data, such as MODIS, to look specifically at some non-vector borne
diseases. You actually observe some manifestation of the climate or
physical circumstance directly and have a more specific understanding of
the disease risks.

HQ: Has a multiple regression prediction model been developed for
forecasting RVF?

Dr. Linthicum: Yes, it is a multiple vector phenomena and we do use
multiple regression models. We are trying to develop best-fit models to
maximize the predictive capabilities. It is important to stress that the
Web site model for predicting RVF is as yet untested. We have tested it
and multiple regression models work well for east Africa, but what SSTs
affect other parts of Africa are different; that is where the
investigations are taking us now.

HQ: Is remote sensing sensitive enough to detect the absorption
characteristics of certain chemical compounds during air pollution
episodes?

Dr. Tucker: It depends upon the concentration and the absorption
characteristics of whatever you are interested in. It is highly likely
this will not be a technique which can be implemented in the short term
or midterm from space, but there is ground based network of sun
photometers that looks remotely at the sun and derives very specific
characteristics from aerosols. This is the Aeronet Network that is part
of the EOS program at GSFC. They have one or two instruments in urban
areas that are looking to see if this can be done. We should look at
techniques like this, but the jury is still out whether it will be
successful or not.

Dr. Nicogossian: Remote sensing and vector borne disease is a very
interesting proposition, but it is still in the research phases. How
successful might this be in the future, given our ability within the
next 5 to 10 years to develop microsensors that you can actually drop
over a large surface area to get information, model the vector-borne
disease from space, and monitor diseases.

Dr. Tucker: It is very similar to trying to understand the stock market
if you only have 20 years of experience. Longer records are needed. As
the record gets longer, we can get more confidence in some areas. We
have only twenty years of data; we would like to have forty or sixty.
Other satellite missions are coming along that will add capabilities,
e.g., direct measurement of rainfall by a combination of infrared,
microwave, and radar techniques. This work will complement the existing
time history. There are many areas that should be pursued.

Dr. Linthicum: We are still in the research phase in terms of long-term
data sets, climate, and its relation to disease. However, remote sensing
has a well-documented ability to tell us other information about
diseases, e.g., to look at mosquito populations in certain kinds of
habitats. This is well established for certain species. The use of this
type of data for hanta virus surveillance in the U.S. southwest is
promising; Landsat and SPOT data has been used to identify habitats for
new species of chiggers that transmit scrub typhus in Asia.

Dr. Nicogossian: One of the current successes of preventive medicine is
the notion of containment and primary prevention of disease. How much
remote sensing will be relying on ground truth data over the next ten
years, given that robotic sensors are improving? Is it going to be
totally independent?

Dr. Tucker: There is no substitute for doing ground-based work. I would
be leery of curtailing confirmation of the various satellite techniques.
I don't see the need for independent sources of information diminishing.
Confirmation of satellite techniques should continue.

Dr. Linthicum: From a biological point of view, we are a long ways from
having any kind of sensor to understand biological transmission of
diseases. I don't see this coming in the near future. A tremendous
amount needs to be done in disease research, and this is not a
well-funded area.

JSC: You had mentioned the habitat for chiggers and mosquitoes could be
identified with remote sensing. Is that primarily with NVDI or a
combination of techniques?

Dr. Linthicum: To actually identify habitats that have defined small
foci, you need high-resolution instruments (Landsat and SPOT). In the
case of scrub typhus, we are looking at particular kinds of vegetation,
development, and the ability to distinguish grassy areas from tree
areas, etc. This can be done effectively with Landsat imagery. Mapping
of habitats for species of chiggers has been done in Thailand.

Dr. Daniel Sahm discussed the current needs for antimicrobial
surveillance. Currently, there are more questions than answers on
antimicrobial resistance. Even for some of the established resistance
phenotypes, we don't know how extensive resistance is, what factors
contribute to resistance. Also, there are questions about emergent
resistance. The better prepared we are to answer these questions the
more effective we will be in designing therapeutic responses.
Availability of timely and accurate answers will allow avoidance of over
reacting to anecdotal reports, or unique events that can unduly
complicate anti-infective strategies and laboratory testing efforts.
Effective surveillance is a key component of any strategy designed to
answer any of the questions. Surveillance can be considered as two
tiered: monitoring and analyzing microbial behavior; and monitoring and
analyzing human behavior. Dr. Sahm focused on the first of these.
Effective surveillance is the marriage of infectious disease knowledge
and information technology. It includes current knowledge of resistance
trends, rapid detection of new phenomena, and education. Dr. Sahm
discussed the evolution of antimicrobial resistance. There are
pre-existing resistance mechanisms in the microbial world. We have
learned a lot about their genetic capabilities (mutations, gene
transfer, etc.). In addition, antimicrobial use patterns and other
medical practices have impacted how the microbial world will respond.
Dr. Sahm discussed the evolution of resistance and information
technology and what is available for data capture... If we are able to
capture a lot of data and standardize it in the regional database, we
can start to determine trends and patterns in drug resistance. IT has
enhanced the ability to capture larger amounts of data, analyze it, and
communicate the findings. IT and its limitations are not the bottleneck
for more enhanced surveillance. The globe is no longer an impediment in
terms of transfer of information. Storage and manipulation capability is
expanding by orders of magnitude. Where is the resistance data is
generated is in the clinical laboratory. Given the fact that there are
thousands of laboratories distributed worldwide, we have felt that if we
could capture data in a meaningful way, we could provide picture of
resistance profiles...The limitation has been the technical capability
of collecting this kind of information. Dr. Sahm described the TSN
network in the US. Information is collected from hospitals using "pull"
technology. The database is approaching 30 millions susceptibility
results. Data is standardized for quality control and goes into the TSN
database. The object is to have data available in a format that allows
analysis, e.g., of resistance patterns. Using the AHA hospital
categories, there are about 5800 institutions likely to have
laboratories. Using the TSN database, most average, laboratories test
about 15 strains/day against an average of 10 drugs. Annually, 3.2 x
10^8^ organism-drug results are generated. In the past surveillance
studies have involved about 5000 results. Even in some of the larger
centralized studies, only about 0.08% is collected from what is
produced. The TSN database currently captures about 3% of the data. This
is about four orders of magnitude higher than what was done in the early
1990's.

Dr. Sahm discussed antimicrobial resistance. Currently, there are more
questions than answers on emerging resistance. Effective surveillance is
a key component of any strategy designed to answer a number of important
questions. In term of drug resistance, surveillance can be considered
two tiered: (1) monitoring and analyzing microbial behavior; and (2)
monitoring and analyzing human behavior. Dr. Sahm focused on the first
tier. You take infectious disease knowledge, which has been growing
tremendously over the past 20-30 years, and couple it with information
technology to achieve "effective" surveillance programs for enhancing
our current knowledge of resistance trends, rapid detection of new
phenomena, education, and basic and clinical research. Dr. Sahm
discussed the evolution of antimicrobial resistance. There are
pre-existing resistance mechanisms in the microbial world; these
organisms have genetic capabilities (mutations, gene transfer, etc.);
and antimicrobial use patterns and other medical practices have impacted
how the microbial world responds. In addition to expanded microbial
knowledge, there has been an evolution in information technology. Dr.
Sahm gave a technological overview of what is available for data
capture, communications, and manipulations (analysis) with respect to
drug resistant surveillance. Trends and patterns in drug resistance can
be revealed. Information technology has enhanced our ability to capture
large volumes of data, analyze it quickly, and communicate the findings.
Dr. Sahm described the TSN database. Most laboratories in the U.S.
(about 5800 institutions) test about 15 strains/day against an average
of 10 drugs. Information technology can easily handle this volume. In
the past surveillance studies have involved one or two hospitals with
about 5000 results captured (0.002% of total available results); even
larger centralized in vitro studies only capture about 0.08% of what is
produced annually. The TSN database currently captures about 3% of the
volume. This is about four orders of magnitude greater than what was
done in the early 1990's.

Dr. Sahm discussed some different perspectives on resistance using this
analysis. The first has to do with the "local nature" of resistance. In
1999, the national average of E. coli resistance to ciprofloxacin was
about 1.5%. This resistance is highest in the South Atlantic Region
(about 2.5%). Florida shows almost 5% resistance and one site in Florida
has resistance approaching 18%. However, without the availability of
more broad-based data, a report of 18% resistance to ciprofloxacin could
be misleading. With a broad-based surveillance approach, that is not the
case. The fact is that this site has a real problem and should be
targeted for strategic studies. No one site, or small group of sites,
should drive our nation's view of resistance to any particular drug.

The second perspective has to do with the perception of risk. There has
been a lot of discussion about anti-microbial resistance. For
Salmonella, it has been proposed that most of the resistance comes from
drugs in animal feeds. However, the database shows that resistance is
very uncommon in Salmonella. It is not spread person-to-person; it is a
food-borne disease. A pathogen that has a high resistance rate is
Shigella. This is probably the result of use of antibody use in humans.
Another aspect is tracking multiple drug resistance. One of the
organisms that highlight this is Enterococcal faecalis and E. faecium.
One species over the other is gaining resistance. It is important to be
able to track what each species is doing. What is going on with these
organisms can be monitored over time. E. faecium expresses multiple drug
resistance. Also, drug resistance is having an effect on the ecology of
these organisms in a relatively short period of time. Another aspect of
timeliness is publications of results of surveillance studies. The
turnaround time between initiation of a study and publication of study
results is about 2 years. Over this time, for example, ampicillin and
vancomycin resistance jumped significantly. Another aspect is
"pan-resistant" strains---resistant to all antibiotics in use. In 1999,
about 31% of the strains of E. faecium were resistant to all four
agents. Another large problem is the growing resistance of MRSA. There
was nearly a 7% rise over the time from data collection to publication.
For S. aureus, about 28% are resistant to three or more drugs. We are
seeing that one species can jump from zero to multiple drug resistance.
Multiple drug resistance is also a concern for S. pneumoniae. Looking at
comparative frequencies of multiple resistant strains by species, we can
see that not all species of clinically relevant bacteria are the same.

Currently there are many more questions than answers regarding
antimicrobial resistance and much of our current thinking has been based
on limited data. More thorough surveillance and analysis of
antimicrobial susceptibility data will play a key role in finding
answers to these questions. The current and expanding capacities of
information technology provide us with tools never before available to
examine microbial resistance patterns and other phenomena on a local,
national, and global level. These capabilities are also fully applicable
to examining the human side of resistance. The timely availability of
these data along with sophisticated data management and analytical
capabilities will provide more timely and extensive understanding of
current and future issues surrounding antimicrobial resistance.

Questions:

HQ: It appears that the biggest threat seems to come from human
behavior, e.g., the overuse of certain antibiotics. Is this correct?

Dr. Sahm: We tend to blame it on overuse, but analysis of the data shows
that it is a much more complex issue. For example, the organism that
causes strep throat has seen more antibiotics since 1943 than probably
any other organism, and yet there has not been any penicillin resistance
to date. It is also the genetic capabilities and physiology of the
organism. Every organism is different. Multiple drug resistance of
microbacterium tuberculosis has largely been controlled through public
health measures rather than drug prescriptions.

Dr. Nicogossian: In complex diseases, we require complex therapy and
this is where resistance usually develops. Non-compliance is something
we need to tackle. How does one deal with secondary infection as a
result of immune system problems or non-compliance? How do you make sure
that you can impose compliance in those infections that have spread and
become resistant?

Dr. Sahm: It is a very complex situation. There are two very broad
strategies: (1) appropriate drug use and (2) following strict infection
control guidelines. The latter can break the chain of transmission in a
hospital setting. Also, in a hospital setting you can ensure that the
antimicrobials are administered properly and taken full dose. In the
outpatient setting, it is much more difficult. There is no infection
control happening. What is happening instead of full compliance is more
potent doses, so that you don't have to depend on the patient for
multiple doses. Unfortunately, the inpatient and outpatient populations
are becoming mixed. It becomes less certain whether a person has a
community-acquired microorganism or one acquired in the hospital. Drug
compliance is very difficult in the outpatient setting; that is why more
potent, easier to take microbials are being prescribed. The infection
control issue is very complex. This is complicated by the fact that
managed health has given infection control short shrift in terms of what
it takes to manage these kinds of infections. The bacteria respond to
environmental opportunities and will spread.

Dr. Nicogossian: A piecemeal approach to the problem, e.g. to get the
patient out of the hospital and back home, might have been a
contributing factor to increased resistance. We are losing ground with
the increasing resistance to antimicrobials. Aren't we basically in a
crisis mode?

Dr. Sahm: There are still a lot of organisms that are susceptible. The
crisis mode is that it takes about ten years to get a drug from an idea
to the patient. There are certain organisms that are in crisis mode,
e.g., MRSA. Also, the pharmaceutical companies need to sell their drugs
to recoup the cost of development, and we can't realistically ask them
to put a drug on the shelf and not use it unless absolutely necessary.

HQ: Another factor may be the lack of a public health infrastructure.

Dr. Sahm: Some of the policies for what drugs a hospital will and won't
be reimbursed for are sub-optimal therapy. Some of the new potent drugs
are not covered.

HQ: What is your view on the amount spent on antimicrobial drug
development versus how much is spent on microbiology itself.

Dr. Sahm: There are never enough funds for microbiology. We should never
become complacent again about infectious diseases. We need to continue
monitoring even when we think we have things under control. There is
inadequate funding for that; however, there are dozens of biotech
companies trying to identify new targets.