Geoscience Reference
In-Depth Information
and centralized networks; only the main populated sites in Africa can assimilate this
model. The downfalls of using the centralized model is that it forces the people in
the rural parts of Africa to travel from their homes to the central labs to receive test-
ing, which make thereby making CD4 testing exclusive to large cites and areas that
support the infrastructure. Also, since CD4 testing is high in demand, the central lab
systems become overloaded, due to frequent testing, therefore creating a need for
more system automation (Willyard
2007
; Robinson et al.
2007
).
The low cost, lowmaintenance, portable CD4 counter with full optical and fluidic
assembly will be housed in a high impact plastic, contain powerful electronic com-
ponents that are user friendly, and will need no computer attachments or additional
software and/or interfaces. The CD4 counter will also include GPS capabilities,
cell phone based data transmission, battery operation, CD4 T-cell single line read-
out, and a gravity-feed low-volume sheath fluid system. These features will allow
the CD4 counter to become very robust and durable, thus having the capability to
adapt to any situation that may present itself in Africa. With the addition of GPS,
other than the ability to track the portable devices, the CD4 Counter will have the
ability to transmit epidemiological data; this feature can provide vital information
on the geographical regions of Africa with higher incidence of HIV/AIDS and
of antiretroviral resistant strains of HIV/AIDS and facilitating prompt and accu-
rate decisions with respect to HIV therapy distribution to highly infected areas
(Willyard
2007
).
11.5 Modeling for Epidemics, Biogenic and Anthropogenic
Disasters
Modeling tools that have been developed to facilitate projections of the impact
of epidemics, storms, flooding, oil/chemical spills, earthquakes and fires have a
long history. Thomson et al. (
2006
) evaluated the utility of environmental mod-
els, incorporating Normalized Difference Vegetation Index (NDVI) and Cold Cloud
Duration (CCD) for prediction of meningitis epidemics in Africa. Early inte-
grated model entrants included the Federal Emergency Management Agency's
(FEMA) Integrated Emergency Management Information System (IEMIS) for
a wide array of disasters (e.g. conflagration, storms, gaseous effluent release,
spills), FEMA's HAZUS-MH (Multi-Hazard) modeling tool for flooding, hur-
ricanes and earthquakes (Porter and Eeri
2009
; Scawthorn et al.
2006
; Tralli,
et al.
2007
; Vickery and Lin
2006
; Vickery and Skerlj
2006
), as well as
ALOHA, CAMEO and MARPLOT, for emergency response to oil and haz-
ardous chemical spills, utilized by the United States Coast Guard (USCG), the US
Environmental Protection Agency (EPA) and the National Oceanic and Atmospheric
Agency (NOAA). (
http://www.epa.gov/emergencies/content/cameo/what.htm
)Two
such specific modeling tools are briefly discussed in the following:
