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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) 4 Inter J Trop Med Pub Health Vol 3, Issue 1, 2014; 3-11 Bamaiyi et al. 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 5 Inter J Trop Med Pub Health Vol 3, Issue 1, 2014; 3-11 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 6 Inter J Trop Med Pub Health Vol 3, Issue 1, 2014; 3-11 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 7 Inter J Trop Med Pub Health Vol 3, Issue 1, 2014; 3-11 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 8 Inter J Trop Med Pub Health Vol 3, Issue 1, 2014; 3-11 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 Population Population Population Population Population Population Population Population Population Population Count, Count, Count, Count, Count, Count, Count, Count, Count, Count, Population Population Population Population Count, Count, Count, Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Count, Population Population Count, Count, Population Population Count, Count, Population Population Population Count, Count, Count, Population Population Population Population Count, Count, Count, Count, Population Population Population Population Population Population Population Population Population Population Population Count, Population Count, Population Count, Count, Count, Count, Count, Count, Count, Count, Count, Count, Count, Population Population Population Population Population Population Population Population Population Population Population Count, Population Count, Count, Count, Count, Count, Count, Count, Count, Count, Count, Count, S,S,S, Thu S, Fri S, Sat S, Sun 21S, Mon Tue 3Mar Wed 4Mar Mar 56S, 12, Mar 79812, Mar 12, 12, 12, S, Thu S, Fri S, Sat 10 Mar 12, S, Sun 11 Mar 12, Mon 12 Mar 12, S, Tue 13 Wed 14 Mar 12, S, Thu 15 Wed Thu Fri 30 31 1May Jun 12, S, S, Mon Sun Tue Sat Fri 21 28 23 24 25 19 26 20 27 22 29 16 17 18 15 14 12 13 S, Thu Fri 10 May 12, S, Tue Wed 911 May 12, Mon 78S, May 12, S, S, Sat Sun 5S, 6May May 12, 12, S, Fri S, Thu S, Wed 243S, May 12, S, Tue 1S, May 12, S, Mon 30 Apr 12, S, Sun 29 Apr 12, S, Sat 28 Apr 12, S, Fri 27 S, Thu 26 S, Wed 25 Apr 12, S, Tue 24 S, Mon 23 Apr 12, S, Sun 22 Apr 12, S, Fri 16 S, Sat 21 Apr 12, S, Fri 20 S, Thu 19 S, Wed 18 Apr 12, S, Tue 17 S, Mon 16 Apr 12, S, Sun 15 Apr 12, S, Sat 17 Mar 12, S, Sat 14 Apr 12, S, Fri 13 S, Thu 12 S, Wed 11 Apr 12, S, Sun 18 Mar 12, S, Tue 10 Apr 12, Thu Fri Sat Sun Mon 12Tue Mar 3Wed Mar 4Thu Mar 5Mar 6Sun 12, Mar 12, 7Mar 8S, 12, Mar Mar 12, 12, 12, 12, 12, Fri Sat Mon 9Tue 10 Wed Mar 11 Mar 12 13 Mar 14 12, Mar Mar 12, Mar 12, 12, 12, 12, Thu 15 Mar 12, Fri 16 Mar 12, Sat 17 Mar 12, Sun 18 Mar 12, Mon 19 Mar 12, Tue 20 Mar 12, S, Mon 9 Apr 12, Wed 21 Mar 12, Thu 22 Mar 12, S, Mon Fri 19 23 Mar Mar 12, 12, Sat 24 Mar 12, S, Sun 8 Apr 12, Sun 25 Mar 12, Mon 26 Mar 12, Tue 27 Mar 12, S, Sat 7 Apr 12, Wed 28 Mar 12, Thu 29 Mar 12, Fri 30 Mar 12, Sat 31 Mar 12, Sun 1 Apr 12, Mon 2 Apr 12, S, Tue 20 Tue Wed 3 4 Apr Apr 12, 12, Thu 5 Fri 6 Apr 12, Sat 7 Apr 12, Mon Tue Wed 9 10 11 Apr Apr Apr 12, 12, 12, Thu Fri Sat Sun Mon 12 13 14 Apr 15 Apr 16 Apr Apr 12, 12, Apr 12, 12, 12, Tue Wed Thu Fri 17 Sat Sun 18 Mon 19 20 Apr Tue 21 Wed Apr Apr 22 Apr Thu 12, 23 Apr Fri 24 Apr 12, 12, 25 12, 26 Apr 27 Apr 12, Apr 12, Apr Apr 12, 12, 12, 12, 12, Sat Sun Mon Tue 28 Wed 29 Thu 30 Apr Fri 1 Apr Sat Sun 2 May Mon Apr 3 4 12, May Tue May 5 Wed May 12, Thu Mon Wed 6 Thu May Sun Tue 12, Sat Fri 12, 7 May Fri 8 12, 12, May 12, 9 10 May 11 18 25 12 19 26 13 20 27 15 22 29 17 24 31 14 21 28 16 23 30 1 12, May 12, May Jun May May May 12, 12, 12, 12, 12, 12, 12, 12, S, Fri 6 Apr 12, S, Wed 21 Mar 12, S, S, Wed 4 12, Apr 12, 1.28E+08 S,S,S, Thu 2224Mar 1.31E+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 S, 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 S, Mon 2 Sat Mar 12, 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 1.28E+08 1.28E+08 1.28E+08 1.28E+08 1.28E+08 1.28E+08 1.28E+08 1.28E+08 1.28E+08 1.28E+08 1.28E+08 S, Sun 1 Apr 1.28E+08 S, Sun 25 Mar 12, 1.27E+08 1.27E+08 1.27E+08 1.27E+08 1.27E+08 1.26E+08 S, 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 1.31E+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 S1.29E+08 1.31E+08 1.31E+08 1.31E+08 1.25E+08 1.31E+08 1.30E+08 1.25E+08 1.24E+08 1.30E+08 1.30E+08 S, 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 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 1.29E+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 9 Inter J Trop Med Pub Health Vol 3, Issue 1, 2014; 3-11 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. 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