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International Journal of Tropical Medicine and Public Health
Original Research Paper
Volume 3, Issue 1, 2014
Crosshouse books
DOI: 10. 25064-43/ijtmph..
ISSN No. 2049-1964
SIMULATION MODEL ON THE POTENTIAL USE OF VIBRIO CHOLERAE AS A BIOLOGICAL
AGENT OF TERROR AND SIMULATED ECONOMIC IMPACT IN NIGERIA
P.H. Bamaiyi
Adamawa State University, Mubi, Nigeria
Email: : [email protected]; [email protected]
Received: DateAug 27, 2012 , Revised and Accepted: Date Dec 01, 2012
ABSTRACT
A simulation model study was carried out on the use of Vibrio cholerae as a bioweapon on the Nigerian population by a hypothetical terrorist group
that starts by infecting people with live cultures of V. cholerae in water and food sources in Kano and then the rest of the country. This study was
carried out to understand the potentials of a bioterrorist on a developing economy like Nigeria. The simulation was carried out for a simulated
period of 3 months using the Spatiotemporal Epidemiological Modeler (STEM) with two scenarios and utilizing available data in the STEM software
and published literature. The first scenario in which there was no intervention against cholera for three months had a total incidence of 7,222,499
and total deaths of 25,300,670. In the second scenario in which intervention started on day 28 of the 3 month period, the incidence was 422,142 and
total deaths were 1,549,427. Economic implications of combating the cholera outbreak of the first scenario were USD2.1 billion and for the second
scenario outbreak USD114.5 million for a total of 538 towns and locations simulated. The difference between the first and second scenarios was
statistically significant (P<0.01). Towns and locations with large and overcrowded populations showed higher incidences, infections, death rates
and economic losses. The case is made for proper preparedness strategies that can neutralize or minimize the effects of a potential bioterrorist
attack on a developing economy like Nigeria which is already overburdened by other economic and developmental challenges.
Running Title: Simulation study of Vibrio cholerae as bioterrorism weapon in Nigeria
Keywords: Simulation, model, bioweapon, Vibrio cholerae, economic impact, terror.
INTRODUCTION
A new area of much concern by public health and epidemiological
experts around the world is the potential use of biological agents as
bioterrorist weapons of mass destruction[1–3]. Bio-threats are real
and the concern about their potentials are valid considering the
history of the use of microorganisms in biowarfare that can be
traced back to some of the earliest times of human
civilization[2,4,5].It is known that terrorists will use any weapon at
their disposal no matter how inhuman to achieve their goals, hence
the legitimate fear about possibility of them turning to bioweapons
for terror [5–8].
The world is currently witnessing the 8th cholera pandemic [9,10]. The
causative agent of cholera, Vibrio cholerae, is described as a category
B bio-agent that requires high preparedness because of the
potentials of being used in biowarfare [4,5,11,12]. The Japanese army
used this bio-agent during the second world war to poison water in
some parts of China and thousands of lives were lost as a result [13].
Many other nations including the United States, The former Soviet
Union, France, Egypt, North Korea and South Africa have been
shown to be engaged directly or indirectlyin carrying out research
on Cholera with attempts at weaponizing V. cholerae[13]. The
organism is a gram negative bacteria with many serotypes but the
most important for humans are 01 and 0139 [14–16]. Cholera is a
zoonotic infection [17,18] and a common epidemic in developing
countries occuring less frequently in the developed world. The
infection hasa high morbidity but low mortality when adequately
handled although mortality can reach 50% or more if not properly
handled[2]. It presents with “rice-water-like” profuse
diarrhoea, weakness, nausea, vomiting, abdominal pain [19,20].One of
the dramatic outbreaks of cholera in recent times was in Haiti where
ithad not been reported for about 100 years then suddenly an
outbreak involving about 121,518 cases with a deaths record of
2,591 as of December 2010 [21].Cholera epidemic was not recognized
in Africa till an epidemic claimed 20,000 lives in 1970 and affected
much of the continent [22]. The disease has now become a reoccurring epidemic in many African countries with varying degrees
of epidemics from time to time and is mainly spread through water
and food routes[23], usually associated with poverty and poor
sanitary practices [10].On the other hand, it can also be transmitted
from person to person through contact [24,25].
In Nigeria, V. choleraeis known to cause epidemics in different parts
of the country when water sources and food sources are
contaminated with viable and optimum doses of the organism
leading to hospitalizations and many deaths[26–28]. The northern part
of the country in recent years has shown more incidences of the
infection with highmorbidity and the infection is shown to occur
more during the rainy season although it can occur at any season of
the year [29]. There is paucity of literature on simulation of the
potential of use of V. cholerae as a bioweapon and the economic
impact oflack of preparedness for such a potential attack given the
dynamic nature global terrorism has attained and the fact that
Nigeria is currently neck deep in fighting terrorism. This study is a
simulation model on the use of V. cholerae as bioweapon in Nigeria
to alert on the need for a preparedness plan to avert devastating
consequences. Time being of essence in biological warfare, there is a
need to adequately prepare and plan in advance to thwart any
Bamaiyi et al.
potential use of bio-agents to unleash destruction of lives and the
economy.
MATERIALS AND METHODS
The IBM Spatiotemporal Epidemiological Modeler (STEM®) version
1.3.1(released 1st May, 2012 through the Eclipse foundation) was
used for the simulation with a Stochastic SEIR disease model. The
following parameters were used for the disease model:















Time period(TP)= 86400000 ms
Reference population density=100PM/SQKM
Road.Net.Inf.proportion=0.01 fraction per road
Characteristic mixing distance=2.25km
Transmission rate(β)=3.5
Non-linearity coefficient=1.0
Infectious recovery rate(γ)=0.4 PM/TP
Infectious mortality rate(μi)=0.05 PM/TP
Immunity loss rate(σ)=0.5 PM/TP
Incubation period(φ)=0.3 PM/TP
Gain=0.01
Infector=Infects 100 people at Kano city, Nigeria
Inoculator=10 people inoculated at Kano city, Nigeria
Sequencer=March 1st 2012 to May 31st 2012
Scenario solver=RungeKuttaImpl
As Cholera is less easily transmissible from person to person, a
transmission rate of 3.5 in the model was estimated to be the
equivalent of a nationwide deliberate outbreak attack of cholera
from water and food sources around the country, beginning from
Kano and within 3 days extending such a deliberate attack to all the
major cities in Nigeria via contamination of water and food sources
with viable cultures of the V. cholerae. The cost of prophylaxis per
person including all health logistics was estimated as USD 29
(USD20 for prophylactic treatment using doxycycline and 9 USD for
logistics by the health personnel per person) and the cost of
intervention to save lives after infection including hospitalization
cost, oral rehydration therapies, antibiotic therapy and logistics was
estimated at USD93 per person (USD50 for oral rehydration therapy,
USD30 for antibiotic therapy, USD13 for transport, ambulance
services and other logistics) and lives saved. The simulation used the
2006 population of Nigeria and map available for some 538
cities/towns/villages in Nigeria with available geographical and
population information as available in STEM.
Scenario setting:There are two scenarios in this model. In the first
scenario the above parameters were used. The infection was allowed
to spread without any form of intervention either from health
experts or the government for 3 whole months. In the second
scenario there was intervention on day 28 that reduced the
transmission rate nationwide from 3.5 to 0.3 and increase recovery
rate from 0.4 to 0.6 and reduced the infectious mortality rate from
0.05 to 0.01 while other parameters remained constant. The Root
Mean Square difference (RMS) between the scenarios was elucidated
by the analysis scenario comparison of STEM. The population was
assumed to be static during this period of 3 months.
Data analysis:The data for 538 towns in Nigeria were checked for
normality using Kolmogorov-Smirnov test, Shapiro-Wilks test,
Kurtosis test and Skewness test. Thedata generated in CSV files were
analysed using Microsoft Excel 2010 and IBM SPSS version 20. Mean
values for the whole period were taken based on the 538 towns in
the model and charts were plotted. Statistical differences were
elucidated at 99% confidence level.
RESULTS
The overall parameters for the 538 cities/towns/villages in Nigeria
studied are shown in Table 1. The infectionbegins at Kano (Figure 1)
then propagated to other parts of the country. As time increases, the
number of deaths increases but the population count and
susceptible individuals decreases (Figure 2). States and locations
with higher population densities show higher susceptible rates,
exposure rates, infection rates, recovery rates, incidence and deaths
in the first scenario (Figures 3-9). Lagos state had the highest
susceptibles and population count of all the states in the second
scenario (Figure 10 &16) but Kano state had the highest number of
exposed individuals, infectives, recovered, incidence and deaths in
the second scenario (Figure 11-15). On average, USD 3.5 million was
spent on the exposed population and USD7.3million on the infected
population towards combating the infection in each of the locations
in Lagos state than other states in the first scenario. A total sum of
USD 2.1 billion spend nationwide(Table 1, Figures 17 & 18) while in
the second scenario more money was spent in Kano state (USD
623,555 on the exposed population and USD1.2million on the
infected population) to combat the infection in each of the locations
in the state (Figures 19 & 20) and the total nationwideexpenditure
was USD 114.5 million(Table 1). Intervention slowed down
transmission of infection to other locations (Figure 21) and
drastically reduced the average deaths and incidences over time in
the second scenario (Figure 22) making a difference of about 61%
between the 2 (Figure 23). All the parameters between the first
scenario (without intervention) and the second (with intervention
beginning on day 28) were statistically significantly different using
Wilcoxon signed Ranked Test at 99% confidence level.
Figure1: Simulation of infection spreading from Kano to other cities/towns/villages in Nigeria (Red colour)
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Table1: Overall Cholera simulation parameters for 538 Towns/Villages throughout Nigeria
Variables
S1
Minimum
Statistic
4636.2592
Maximum
Statistic
823638.1122
Sum
Statistic
55241422.3785
Mean
Statistic
102679.223752
Std. Error
3382.5746274
Std. Deviation
Statistic
78458.2333290
E1
1319.8042
428391.6673
23351253.6024
43403.817105
1505.8062757
34926.9160741
I1
851.3550
279464.2313
15180266.9271
28216.109530
981.6403214
22768.9774394
R1
659.0708
219007.8227
11857590.1571
22040.130404
768.8356646
17833.0102382
INCIDENCE1
412.9082
131978.2911
7222499.4822
13424.720227
464.2874011
10769.0659501
DEATHS1
1086.5109
508173.1664
25300670.1619
47027.267959
1827.5793079
42390.3858922
POP1
7466.4891
1750501.8336
105630533.0651
196339.280790
6448.2026748
149564.9455635
S2
8552.9983
2185094.7279
126381904.3251
234910.602835
7801.4034678
180952.2038664
E2
.0000
80865.4948
1411123.7957
2622.906683
305.9111572
7095.5563708
I2
.0000
49104.6137
790789.4482
1469.868863
178.9340350
4150.3439884
R2
.0000
44560.3138
798199.8650
1483.642872
170.9649035
3965.5013616
INCIDENCE2
.0000
24222.0863
422142.9369
784.652299
91.6419206
2125.6184955
DEATHS2
.0000
137319.1960
1549427.5660
2879.976888
443.3524309
10283.4829380
POP2
8552.9998
2245847.2399
129382017.4340
240487.021253
8005.4498285
185685.0239042
Econ.E1(USD)
38274.3210
12423358.3524
677186354.4706
1258710.696042
43668.3819946
1012880.5661495
Econ.I1(USD)
79176.0104
25990173.5132
1411764824.2231
2624098.186288
91292.5498861
2117514.9018623
Econ.E2(USD)
.0005
2345099.3497
40922590.0751
76064.293820
8871.4235579
205771.1347537
Econ.I2(USD)
.0007
4566729.0779
73543418.6855
136697.804248
16640.8652568
385981.9909190
1.40E+08
1.20E+08
1.00E+08
8.00E+07
6.00E+07
4.00E+07
2.00E+07
0.00E+00
S
E
I
R
Thu 1 Mar 12
Tue 6 Mar 12
Sun 11 Mar 12
Fri 16 Mar 12
Wed 21 Mar 12
Mon 26 Mar 12
Sat 31 Mar 12
Thu 5 Apr 12
Tue 10 Apr 12
Sun 15 Apr 12
Fri 20 Apr 12
Wed 25 Apr 12
Mon 30 Apr 12
Sat 5 May 12
Thu 10 May 12
Tue 15 May 12
Sun 20 May 12
Fri 25 May 12
Wed 30 May 12
Number of people
Key: S1=First Scenario Susceptible; E1= First Scenario Exposed; I1= First Scenario Infectives; R1= First Scenario Recovered; Incidence1= First
Scenario Incidence; Deaths1= First Scenario Deaths; Pop1= First Scenario Population count; S2=Second Scenario Susceptibles; E2= Second Scenario
Exposed; I2= Second Scenario Infectives; R2= Second Scenario Recovered; Incidence2= Second Scenario Incidence; Deaths2= Second Scenario
Deaths; Pop2= Second Scenario Population count; Econ.E1=First Scenario economic Impact for Exposed; Econ.I1=First Scenario economic Impact
for Infectives; Econ.E2=Second Scenario economic Impact for Exposed; Econ.I2=Second Scenario economic Impact for Infectives
Population Count
Incidence
Disease Deaths
Figure2.Graphical representation of simulated parameters in the first scenario during 3 months
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Bamaiyi et al.
Figure 2. Mean susceptible population in various states in first
scenario
Figure 4. Mean infectives in various states in first scenario
Figure 3. Mean exposed population in various states in first
scenario
Figure 5. Mean recovered people in various states in first
scenario
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Bamaiyi et al.
Figure 6. Mean incidence in various states in first scenario
Figure 7. Mean deaths in various states in first scenario
Figure 8. Mean population count in various states in first
scenario
Figure 9. Mean susceptible population in various states in
second scenario
Figure 10. Mean exposed population in various states in second
scenario
Figure 11. Mean infectives in various states in second scenario
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Bamaiyi et al.
Figure 12. Mean recovered population in various states in
second scenario
Figure 13. Mean incidence in various states in second scenario
Figure 14. Mean deaths in various states in second scenario
Figure 15. Mean population count in various states in second
scenario
Figure 16. Mean economic impact on exposed people in various
states in first scenario
Figure 17. Mean economic impact on infectives in various states
in first scenario
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Bamaiyi et al.
Figure 18. Mean economic impact on exposed people in various
states in second scenario
Figure 19. Mean economic impact on infectives in various states
in second scenario
Number of people
Figure 20. Simulated commencement of second scenario after day 28
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79812,
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243S,
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30
Apr
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29
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28
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15
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17
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14
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13
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11
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18
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28
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29
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1.31E+08
1.31E+08
1.31E+08
1.31E+08
1.31E+08
1.31E+08
1.31E+08
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Tue
3 Apr
12,1.27E+08
1.31E+08
1.31E+08
Fri
23
12,
1.31E+08
1.30E+08
1.30E+08
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Mon
2
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12,
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1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
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1.28E+08
1.28E+08
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Sun
1
Apr
1.28E+08
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Mar
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1.27E+08
1.27E+08
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1.27E+08
1.27E+08
1.26E+08
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Sat
31
Mar
1.26E+08
1.26E+08
S,1.30E+08
Mon
26
Mar
12,
1.25E+08
1.31E+08
1.31E+08
1.31E+08
1.31E+08
1.31E+08
1.31E+08
1.31E+08
1.31E+08
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1.31E+08
1.31E+08
1.31E+08
1.31E+08
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1.31E+08
1.31E+08
1.31E+08
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1.30E+08
1.25E+08
1.24E+08
1.30E+08
1.30E+08
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Fri
30
Mar
12,
1.29E+08
1.24E+08
1.29E+08
1.29E+08
1.29E+08
S,1.30E+08
Tue
27
1.29E+08
Mar
12,
1.29E+08
1.29E+08
1.23E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
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1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.29E+08
1.23E+08
S,
Thu
29
1.22E+08
1.22E+08
1.21E+08
1.21E+08
S,1.16E+08
Wed
28
Mar
12,
1.20E+08
1.19E+08
1.18E+08
1.18E+08
1.17E+08
1.15E+08
1.15E+08
1.13E+08
1.12E+08
1.11E+08
1.10E+08
E
I
R
Population Count
Incidence
Disease
Deaths
Disease
Disease
Disease
Incidence,
Incidence,
Disease
Incidence,
Disease
Incidence,
Disease
Incidence,
Disease
Disease
Deaths,
Disease
Deaths,
Disease
Incidence,
Deaths,
Incidence,
Disease
Deaths,
Disease
Deaths,
Sun
Incidence,
Disease
Deaths,
Mon
Incidence,
Disease
Deaths,
Tue
Incidence,
Disease
Deaths,
Fri
Wed
Disease
Incidence,
Deaths,
4Sat
Thu
Disease
Incidence,
Deaths,
Sun
Incidence,
52Disease
6Deaths,
Incidence,
Disease
Deaths,
Tue
Sun
Incidence,
7Disease
8Deaths,
Mon
Incidence,
Disease
Deaths,
Thu
Incidence,
Disease
Deaths,
Fri
Wed
Disease
Incidence,
Deaths,
11
Sat
Thu
Disease
Incidence,
Deaths,
Sun
Fri
Incidence,
12
9
Disease
Deaths,
Sat
Disease
Incidence,
Deaths,
Tue
Sun
Incidence,
14
Disease
16
15
Deaths,
Mon
Incidence,
Disease
Deaths,
Thu
Tue
17
Disease
Incidence,
Deaths,
Fri
Wed
Disease
Incidence,
Deaths,
18
Sat
Thu
Disease
Deaths,
Sun
Fri
Incidence,
Disease
19
20
Deaths,
Sat
Disease
Deaths,
Tue
Sun
Incidence,
Disease
21
23
22
Deaths,
Mon
Disease
Deaths,
Thu
Tue
24
Disease
Deaths,
Fri
Wed
Disease
Deaths,
25
Sat
Thu
Disease
Deaths,
Sun
Fri
Incidence,
Disease
26
Deaths,
27
Sat
Incidence,
Disease
Deaths,
Tue
Disease
Incidence,
28
Deaths,
30
29
Mon
Incidence,
Disease
Deaths,
Thu
31
Disease
Deaths,
Fri
Wed
Disease
Incidence,
Deaths,
Sat
Disease
Incidence,
Deaths,
Sun
Incidence,
Disease
2Deaths,
Incidence,
Disease
Deaths,
Tue
Disease
Incidence,
4Deaths,
Mon
Disease
Incidence,
Deaths,
Thu
Tue
Disease
Deaths,
Fri
Wed
Disease
Incidence,
Deaths,
Sat
Thu
Disease
Incidence,
Deaths,
Sun
Incidence,
Disease
96Deaths,
10
Sat
Disease
Incidence,
Deaths,
Tue
Sun
Disease
Incidence,
11
Deaths,
12
Mon
Incidence,
Disease
Deaths,
Thu
Tue
14
Disease
Deaths,
Fri
Wed
Disease
Incidence,
Deaths,
15
Sat
Thu
Disease
Incidence,
Deaths,
Sun
Incidence,
Disease
16
Deaths,
17
Sat
Disease
Deaths,
Tue
Sun
Incidence,
Disease
18
Deaths,
19
Mon
Incidence,
Disease
Deaths,
Thu
Tue
21
Disease
Deaths,
Fri
Wed
Disease
Deaths,
22
Sat
Thu
Disease
Incidence,
Deaths,
Sun
Disease
23
Deaths,
24
Sat
Incidence,
Disease
Deaths,
Tue
Sun
Disease
Incidence,
25
Deaths,
26
Disease
Mon
Disease
Incidence,
Deaths,
Incidence,
Thu
28
Incidence,
Deaths,
Fri
Wed
Incidence,
Incidence,
Deaths,
29
Sat
Disease
Thu
Deaths,
Sun
30
Deaths,
Deaths,
Tue
Sun
Deaths,
23Deaths,
Deaths,
Thu
Tue
Fri
Wed
6Sat
Thu
Deaths,
Wed
Mon
Sun
Fri
Thu
Sun
Tue
4
Sat
8Fri
Tue
911
Thu
Sun
10
Tue
Sat
Fri
14
21
17
24
31
15
22
29
18
25
12
19
26
13
20
27
23
30
E,
Wed
28
Mar
12,
E,
Tue
27
E,
Thu
29
E,
Mon
26
Mar
12,
E,
Sun
25
Mar
12,
E,
Fri
30
Mar
12,
E,
Sat
24
Mar
12,
E,
Fri
23
E,
Sat
31
Mar
I,
Wed
28
Mar
12,
E,
Thu
22
I,
Thu
29
R,
R,
I,
Tue
27
E,
Wed
21
Mar
12,
R,
Thu
29
R,
E,
Sun
1
Apr
12,
I,
Fri
30
I,
Mon
26
Mar
12,
R,
E,
Tue
20
E,
Mon
2
Apr
12,
I,
Sun
25
Mar
12,
I,
Sat
31
Mar
R,
Apr
12,
E,
Mon
19
Mar
12,
R,
Wed
28
Mar
12,
I,
Sat
24
Mar
12,
E,
Tue
3
R,
I,
Sun
1
Apr
12,
R,
Tue
27
E,
Sun
I,
18
Fri
Mar
23
Mar
12,
12,
E,
Wed
4
Apr
12,
R,
Mon
26
Mar
R,
Thu
12,
5
Apr
12,
I,
Thu
22
I,
Mon
2
Apr
12,
E,
Sat
17
Mar
12,
Incidence,
Disease
E,
I,
Incidence,
R,E,
I,
Incidence,
R,
Fri
E,
I,Fri
R,
Sat
I,
R,
E,
Sat
Sun
E,
I,
R,
Sun
2Mon
2I,
E,
R,
Mon
Tue
3Mar
E,
I,Tue
R,
Deaths,
3Mar
Wed
E,
I,
Incidence,
R,
Wed
4Mar
Thu
4I,
E,
R,
Mar
Thu
Thu
Fri
E,
I,
R,
Fri
56Mar
Incidence,
Fri
Sat
5Mar
6E,
I,Mon
R,
12,
Sat
12,
Sun
E,
I,
R,
Sun
712,
Mar
9812,
Mar
Mon
710
912,
8Wed
E,
I,Mon
R,
12,
Incidence,
2
1Tue
10
Mar
E,
I,
R,
12,
Tue
10
Thu
Mar
12,
Wed
3Mar
R,
E,
I,
Wed
11
Thu
11
12,
I,
R,
Thu
Mar
12,
Fri
12,
I,
R,
Mar
Fri
12
13
Fri
Sat
12
13
I,
R,
Mar
12,
Sun
Mar
12,
I,
R,
14
16
15
Mon
14
16
Mar
15
Sat
I,
R,
9Mar
Tue
12,
Mar
17
R,
12,
Wed
Mar
R,
Mar
12,
Wed
18
Mar
Tue
12,
R,
10
Thu
Mar
12,
R,
19
Fri
20
12,
Mar
Sat
12,
21
Sun
13
12,
12,
21
Mar
23
22
12,
Mar
24
12,
25
12,
Incidence,
Mar
12,
Mar
Incidence,
I,12,
I,Mar
Tue
Incidence,
I,E,
12,
Wed
I,
Incidence,
R,
Thu
12,
Incidence,
I,E,
R,
Fri
E,
Incidence,
I,Fri
3Apr
Sat
E,
I,
R,
Sat
Sun
R,
E,
Sun
465Sun
Mon
6Apr
E,
I,12,
R,
Mon
Tue
7I,
E,
R,
Tue
712,
Tue
Wed
E,
I,
R,
Wed
8Apr
Thu
8Apr
I,
R,
E,
Thu
1910
Thu
Fri
Apr
I,
R,
E,
Fri
910
12,
Apr
Fri
Sat
911
Apr
10
E,
I,
R,
Sat
312
Sat
Sun
Apr
I,
R,
E,
12,
Sun
11
Apr
13
12
Sun
12,
Mon
11
Apr
13
12
E,
I,
R,
Mon
65
Apr
12,
Tue
14
E,
I,
R,
12,
Tue
14
Wed
7Apr
E,
I,
Incidence,
R,
Apr
Wed
15
Apr
Thu
Apr
15
12,
I,E,
R,
Thu
8Apr
Fri
12,
I,E,
R,
Fri
12,
16
17
Fri
Sat
16
Apr
17
E,
I,
R,
Sat
12,
Sun
Apr
I,E,
R,
12,
Sun
18
20
12,
19
Mon
18
20
19
Apr
E,
I,
R,
Mon
12,
13
Apr
Tue
21
E,
I,Apr
R,
Tue
21
Wed
Apr
E,
Incidence,
I,12,
R,
Wed
12,
22
Apr
Thu
Apr
22
I,
R,
E,
Thu
Apr
12,
Fri
I,R,
E,
Fri
12,
23
24
Fri
Sat
23
24
E,
I,Sat
R,
12,
Sun
Apr
E,
I,
R,
12,
Sun
25
27
12,
26
Mon
25
27
26
Apr
E,
I,28
R,
Mon
12,
20
Apr
Tue
28
Incidence,
R,
E,
I,
Tue
28
Wed
Apr
I,
Incidence,
R,
E,
Wed
12,
29
Apr
Thu
Apr
29
Incidence,
R,
E,
I,
Thu
12,
Fri
E,
I,
R,
Fri
12,
30
1Fri
Sat
30
12E,
I,
R,
Apr
Sat
12,
Sun
Apr
May
E,
I,
R,
12,
Sun
2412,
Disease
3
Incidence,
May
Mon
24Apr
3I,
E,
R,
Mon
12,
27
Apr
Incidence,
Tue
May
Incidence,
5R,
I,E,
Tue
May
512,
May
12,
R,
E,
I,12,
R,
Wed
12,
R,
6I,May
E,
Thu
R,
6Apr
E,
I,
Thu
Wed
Mon
12,
Tue
I,812,
Fri
12,
May
Thu
12,
Sun
Tue
Fri
7825
May
Fri
Sat
7R,
89
E,
Fri
12,
Sat
I,12,
12,
May
9May
11
10
Deaths,
912,
11
10
Fri
1412,
17
24
31
14
21
28
13
20
27
18
25
12
19
26
15
22
29
May
12,
May
5May
12,
Mon
May
May
1Mon
Wed
12,
Fri
May
Jun
12,
12,
112,
7Fri
12,
28
16
Jun
12,1
Mon
3Wed
Mar
4Mar
Mar
Mar
5
6Mar
Mar
12,
7Sat
Mar
8Mar
12,
Mar
Mar
11
Mar
12,
12,
12,
Mar
12
Mar
13
Mar
12,
14
Mar
12,
Mon
15
Mar
12,
16
12,
17
12,
Wed
12,
18
Mar
12,
12,
19
Mar
20
Mar
Mar
12,
21
Mar
Mon
22
12,
23
Mar
24
Wed
12,
12,
25
Mar
12,
12,
Mar
26
Mar
12,
27
Mar
Mar
28
Mar
29
Mar
12,
30
Mon
31
12,
12,
Wed
Mar
12,
Mar
12,
1E,
Apr
12,
2Apr
3R,
12,
Apr
12,
412,
Apr
Mon
5I,
12,
Wed
Apr
7Apr
12,
8Incidence,
12,
Apr
Apr
12,
12,
Mon
Apr
13
12,
14
12,
Wed
Apr
Apr
15
Apr
12,
12,
12,
16
Apr
17
Apr
12,
Apr
12,
18
12,
Mon
19
Apr
20
Apr
12,
21
Wed
Apr
12,
22
Apr
12,
12,
23
Apr
24
12,
Apr
12,
25
Apr
12,
Mon
26
Apr
27
Apr
12,
Apr
Wed
12,
Apr
29
Apr
12,
12,
12,
30
Apr
1Apr
12,
Apr
May
12,
May
May
12,
Mon
Apr
3
12,
May
5Wed
May
12,
6I,Wed
7May
10
17
24
31
13
15
20
22
27
29
12
19
26
11
18
12,
12,
May
May
12,
May
May
12,
May
16
23
30
12,
12,
12,
12,
12,
12,
1.07E+07
9143794.735
8334414.045
8007855.676
7136882.244
6687768.998
6423975.191
5808721.993
E,
I, Thu
R,
Thu
1Mar
1259.6793806
Mar
Mar
12,
12,
100
010
5487153.207
5470597.451
5243777.223
5064743.633
5034736.421
4963961.896
4810265.887
4697133.56
4627483.549
4623372.329
4582633.356
4410056.87
4281046.077
4160539.115
4144137.022
3878915.855
3833441.992
3743602.52
3667089.408
3591622.064
3512978.815
3432007.281
3316307.419
3193223.219
3150382.272
3117700.645
3042448.841
3056794.27
2856391.781
2774812.383
2762828.942
2692632.885
2651242.463
2477272.64
2473993.742
106.9539177
36.60057637
12,
335.755834
194.9204764
Mar
734.3870465
69.17476399
12,
1388.7642411
1450.969028
132.3933929
763.8683972
386.1626841
2852.821304
1478.362284
5583.274719
552.0673842
509.671358
12,
2910.604429
10847.04643
992.9802631
5743.550049
21126.28234
1941.609386
11040.85431
41176.65455
3806.353308
Mar
12,
021233.29043
79157.50105
7379.021138
40920.54095
150062.3902
Mar
14201.45366
29005.95269
03115.223869
12,
279064.3244
27165.42244
76995.6282
142185.2784
50984.50123
499514.323
252915.4683
845681.8928
93241.52527
425319.8293
1323908.103
163808.2915
12,
666553.0983
55228.8969
1890834.377
272200.4253
423464.8376
959163.039
1282507.069
613520.0129
333746.622
1620831.827
833769.6147
1974039.634
1075730.103
2330312.299
1334751.013
1604741.447
1882944.133
2167867.602
2462574.029
12,
2239902.383
12,
1903340.848
599725.4806
1626559.508
1398537.225
2151883.767
12,
2383847.172
12,
1208464.498
513460.6961
1878143.301
2056347.329
12,
1049102.301
1643605.936
1775963.834
12,
414771.2009
913579.8294
370297.2302
1441724.292
1537047.019
798206.6259
329560.5122
1266962.338
1333411.611
293129.8376
1115110.048
1159720.059
698960.704
613718.9839
982799.3741
1011106.182
539746.8181
867224.0185
883687.7577
475347.5669
12,
765986.0408
774001.1114
419138.6346
677172.3355
679223.2117
369992.3694
597048.1959
161666.934
326799.4613
599067.553
530344.9056
525592.2477
469733.1104
463247.2717
288956.122
255715.1074
416264.1783
408758.3135
226482.5141
369024.7191
361046.3672
12,
200644.2587
327301.9172
319200.6469
177924.3462
290377.8075
282411.3731
157773.8728
70406.68932
257724.3177
250052.6015
139970.9198
228800.2622
221521.4278
124199.6438
196344.1338
110248.3579
203161.452
180441.7798
174098.9339
97907.42641
160291.2999
154434.0537
86931.74868
142413.6282
137043.2454
12,
77209.06539
126543.9771
121638.7038
May
68587.44043
112461.4655
107991.4177
60958.61319
99956.28421
95896.52965
30913.955
54175.80589
88849.40283
85180.03796
48145.64463
78988.78141
75675.38089
42798.73203
70227.16036
67239.75937
12,
38048.24761
12,
62442.18039
59754.06318
12,
May
33828.49316
55524.25409
53108.74515
Jun
May
19328.0837
47208.12817
30073.6472
13596.2364
26750.12212
49375.8548
43909.96323
41965.62098
12093.41685
23788.74396
12,
39052.60599
12,
37312.0075
12,
21154.53105
34732.73289
33176.85263
18802.74616
16406.50303
7229.791448
3187.735649
20741.93553
9135.572375
4027.747932
30892.80717
17198.62855
7583.403789
21736.26614
9581.015306
4225.955107
29501.29721
10468.06124
4614.019922
2035.366838
5832.588863
2571.482187
14592.63403
6431.132732
2836.006541
12,
23323.91964
18447.18321
10270.24227
8127.124947
4527.281241
3583.237405
15299.22319
6746.099292
2975.859492
24437.10644
19334.66824
10770.34705
4749.884916
3759.747775
3344.78925
26230.53773
11545.47751
5088.657419
4103.106383
1810.943553
27474.87674
12106.70953
5338.733943
14874.38634
11767.23485
6554.407925
5188.066644
2890.740662
2287.534834
13231.0277
12980.00923
5720.308804
13609.91546
6001.270824
16721.11207
7369.312959
3249.494263
8523.94465
8282.758929
3651.216601
9312.40906
514.2461528
2523.144658
2647.533452
9565.01668
1611.027703
2118164.605
3338.46757
820.873099
4.828103669
1036.167786
12.10245055
53.79388728
2026.452272
26.07340841
3934.821025
213.8950072
7647.153298
108.2208778
14975.53208
420.9165083
827.3552677
1611.237908
104129.3195
5986.034267
190916.8057
20998.35452
11330.73929
543914.3972
800748.0911
1050811.581
37676.70271
64636.45299
1242952.088
105069.8383
1392305.785
233670.7831
161050.5819
1540932.316
1713686.554
431109.5815
323481.8341
1877298.141
2037865.869
2221022.434
556655.6417
700367.2721
2447696.528
862502.9847
2744824.561
1246473.433
1043938.576
3138661.652
3703060.082
1729437.921
1473216.781
4480972.441
501129.6657
591972.2082
1782481.526
1829933.674
616459.4137
1870660.671
1933982.753
561428.9646
1905054.148
1979030.914
463631.6665
1958370.549
1996638.703
2011729.201
2024736.433
2045795.438
260076.3896
2036000.039
230986.5189
2061812.911
2054338.912
204792.1058
182104.9408
2074122.844
2068364.98
143475.4971
2079190.466
127388.4491
2087596.502
2083656.391
113087.5683
100347.3653
2094151.467
2091076.332
89122.43281
79210.68896
2096871.557
2099279.469
62556.92344
2101412.605
55659.04689
2104979.296
2103302.864
49456.08061
43970.27722
2107785.471
2106466.171
39065.49643
34748.73396
2109995.763
2108956.31
27458.60888
2110918.928
24414.01401
2112466.901
2111738.719
21714.16771
2113688.813
17183.61034
2113113.832
15281.45282
2114199.841
2114654.024
10758.33924
2115057.783
2115735.895
2115416.739
8519.052887
2116272.026
7576.366146
2116019.644
2116496.472
2117506.343
2117951.321
2118147.514
2117031.298
2117296.515
2117741.926
2117858.818
2118106.718
6736.968387
2116873.504
2117672.385
2118024.505
5983.687408
2116696.071
2117594.221
2117990.047
2118055.17
1471.868973
649.7626771
4217.836013
1861.901479
2970.690202
1307.964633
578.3404255
4740.791268
3750.643252
2090.710067
1655.742758
922.9324348
730.1709802
5323.724764
2351.279158
1037.682465
2641.520568
1165.521245
2117907.777
2118128.306
2117407.56
2117171.66
2117803.78
2118082.45
Figure 21. Graphical representation of simulated parameters in the second scenario during 3 months
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Bamaiyi et al.
Figure22: Root mean difference between the 2 scenarios simulated
DISCUSSION
Cholera epidemics are common in Nigeria and occur periodically
depending on environmental sanitation and population density
which may be influenced by the financial status of the community as
the infection is often associated with endemic poverty and low social
status [21,28,29]. The rate of spread of infection depends on many
factors which include the disposal of faecal wasteof infected
individuals, water and food sources and the general sanitation of the
environment in a location [10,22,30,31].
A deliberate bioterrorist attack on Nigeria using V.
choleraewhichbegan from the northern state of Kano in this
simulation model affected the entire country both without an
intervention and with an intervention that took 28 days to begin
fully.There were case fatalities all over the country with densely
populated areas showing higher incidences of the infection. Many
deaths occurred in the first place of major attack (Kano) and densely
populated locations like Lagos. Overcrowding is a favourable setting
for the outbreak of cholera epidemics [24,25,32]. There is a complex
epidemiological relationship between cholera outbreaks and the
environment and human-to-human as well as environment-tohuman pathways are both involved in the transmission[31].
The rapid progression that characterized the spread of the infection
underscores the importance of preparedness and rapid response to
cholera. A good example of rapid progression of cholera is in Goma,
Democratic Republic of the Congowhere an estimated 58,000-80,000
cholera cases and 23,800 deaths occurred within a month [32,33]. If
Cholera is not promptly controlled in the event of it being used in
biowarfare then massive losses of lives within a matter of hours and
days could result[9,20,34].
In an outbreak of cholera infection, the locations near the source of
the outbreakusually experience more infections and casualties
[9,27,35,36]. This is so because cholera is usually transmitted
through contaminated water sources and also contaminated food
sources. Food and water source contamination may come from
improper disposal of human waste materials near water
sources[37,38].
Colossal economic losses to the nation can result in the absence of
timely intervention program in addition to many deaths. After 3
months of a free spread of the infection within the population it has
reached every town and village because of the deliberate and natural
spread of the infection costing a massive USD 2 billion to finally
control in a nation with the highest population in Africa[39]. This
unfortunate scenario can be avoided considering that much less
economic losses were recorded in the second scenario where
intervention commenced on the 28th day after the initial infections.
Based on this,it is safe to assume that had intervention began on the
first day of infection, much less economic losses would have been
recorded. The ideal situation which may be difficult to achieve in a
developing country is one in which preventive measures are taken to
ensure that there is no cholera infection in the population. This may
be possible through vaccination programs in infection prone areas
and economic development and infrastructural development to
ensure that people drink portable water and eat hygienic food all
over the country. More to this will be adequate biosecurity measures
and surveillance that can thwart potential bioterrorist attacks.
The intervention program in our simulation model drastically
reduced the death rates, infection rates, incidence rates and
economic losses due to cholera. This reiterates the importance of
preparedness and rapid response to bioterrorist attacks in order to
minimize its deleterious effects on a nation [12,15,40,41].In order to
win in biowarfare with minimal casualties, a nation must be
adequately prepared in advance for such potentials attacks.
Conclusion
Preparedness is the watch word against potential bioterrorist
attacks. Many lives will be saved if there are adequate plans on
ground against a potential V. cholerae biowarfare. Massive economic
losses that could render worse the economy of a third world country
like Nigeria can be avoided with careful scientific planning that has
public health as a major priority. Provision of prophylactic drugs like
doxycycline and vaccines in all major hospitals and dispensaries
across the country in addition to oral rehydration therapy such as
lactated ringers solution, will be a good bio-counterterrorism
strategy. There is need to overhaul the whole health care delivery
system in the country to make it more pro-active to face adequately
every health challenge. Adequate economic empowerment of the
population could greatly help in averting widespread impacts of the
infection in a populous country with high rate of poverty and a
history of poor sanitation practices that favours a rapid spread of
cholera infection.
ACKNOWLEDGEMENT
The author wish to thank Dr Sampath Kameshwaran of the analytics
and optimization group of IBM-research, Bangalore, India for his
help on the use of the STEM software and Mrs Jane Banda Chisanga
of the Faculty of Medicine, Universiti Putra Malaysia for proof
reading the work.
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