3. Example of COI research using NHI data

In this section, we present example of estimating cost-of-illness research using NHI data. This example titled "Socioeconomic Cost of Allergies" estimates the socioeconomic costs associated with allergic diseases using NHI data in South Korea [14]. In South Korea, all citizens are compulsory subscribers to the NHI scheme, which is a type of social insurance, and all medical institutions or health professionals are required to submit claim data to the NHI to charge the bill for the medical services. In other words, the NHI has medical information on around 50 million South Koreans. We hope this example will be useful for readers to conduct cost-of-illness studies.

#### 3.1 Study design and cost components

The present example adopts the prevalence-based approach, because it is important to take into account both new and existing patients suffering from allergic diseases during certain periods. As well, this example employs the human capital approach as the value assessment method, because it clearly quantifies losses Methodology of Estimating Socioeconomic Burden of Disease Using National Health Insurance… DOI: http://dx.doi.org/10.5772/intechopen.89895

of productivity due to illness based on patients' income levels. Moreover, it is better suited to ensuring the objectivity of analysis results, as it excludes the researcher's bias. As for perspective, this study adopts the societal perspective and estimates both the direct costs paid by the insurer and patients and society-wide losses of productivity.

As the purpose of this study is to estimate the entire scope of the socioeconomic costs generated by allergic diseases in South Korea, this study estimates both the direct and indirect costs. The direct costs are divided into healthcare and nonhealthcare costs, as in previous studies that adopted the societal perspective. The indirect costs involve losses of productivity. More specifically, the direct healthcare costs include the costs incurred by outpatients and hospitalized patients, encompassing covered costs paid by insurer, copayments made by patients, noncovered costs, and prescription costs. The direct non-healthcare costs involve all expenses associated with visiting medical institutions, whether as outpatients or hospitalized patients, and receiving services for the treatment and management of allergic diseases, including the costs of transportation and caregiving. The indirect costs, or losses of productivity, are estimated by defining the number of hospitalization days as the number of working days lost and the amounts of time spent for outpatient visits, as losses of working time. The losses of future income due to the premature deaths are estimated for patients aged 15–69 (patients outside of this age bracket are excluded, as, in accordance with the law, they constitute the nonworking-age population). Due to the absence of objective data, however, intangible costs are not estimated in this example. The figure below summarizes the components of costs estimated in this study (Table 5).

Table 5.

Components of estimated costs in this example.

#### 3.2 Data source and case definition

To analyze the socioeconomic costs due to allergic diseases in South Korea, this study used the 2014 National Patient Sample (NPS) derived from NHI data that were collected by the HIRA. In South Korea, almost all citizens (98% or higher) are compulsory subscribers to the NHI scheme, which is a type of social insurance, and all medical institutions are required to submit claim data to the HIRA to charge the bill for the medical services they provided when patients visited the medical institution. Consequently, the HIRA has medical information on around 50 million South Koreans. The NPS is the data of patients sampled from the large amount of claim data held by the HIRA, and it is an abridged version of claim data that contain 1-year information regarding medical treatments and prescriptions of the sampled patients. The data contain the information of about 1.4 million patients, who represent a sample of 3% of all patients.

The NHI data is administrative data, and the prevalence rate is influenced by the case definition of disease. In other words, prevalence rates can vary dramatically depending on how the cases are defined. Most of previous studies that made use of administrative data including NHI data generally used primary diagnoses to estimate prevalence rates. This approach, however, carries the risk of either underestimating or overestimating the prevalence rates. This study therefore applied more rigorous criteria in defining prevalence. First, it identified and extracted patients whose primary and secondary diagnoses were indicated using the ICD-10 codes for allergic diseases. Of these patients, this study identified those who had been hospitalized or made at least two outpatient visits each for allergic diseases and had been prescribed drugs commonly used to treat allergic diseases (as indicated in their insurance billing records), such as nedocromil sodium, oral steroids, ventolin, and so on. Only patients meeting these rigorous criteria were admitted into this study as patients.

#### 3.3 Estimation methods

The sources of data for each component of the costs estimated in this study including direct and indirect costs—are as follows.

#### 3.3.1 Direct costs

Direct costs refer to the amounts of spending directly related to illness and are divided into healthcare and non-healthcare costs.

Direct healthcare costs are the costs of preventing, treating, or managing illnesses by using medical institutions and include the costs of outpatient services, hospitalization, and medications (prescriptions). The majority of existing studies that estimate direct healthcare costs rely on administrative and official statistics for their estimations. As there is little controversy over the use of administrative and official statistics in estimating the direct healthcare costs of illness, this study, also, uses the NHI data to estimate the direct healthcare costs of allergic diseases. Depending on who pays them, direct healthcare costs can be further broken down into covered costs paid by insurer, copayments made by patients, and non-covered costs also paid by patients. The formula used to estimate the direct healthcare costs is provided below.

$$\text{DHC} = \sum\_{\mathfrak{s}} \sum\_{\mathfrak{y}} \left[ E\_{\text{ity}}(\mathbf{1} + a) + E\_{\text{ay}}(\mathbf{1} + \beta) \right] \tag{1}$$

where DHC = Direct healthcare costs, s = Sex, y = Age, E = Costs, i = Hospitalized patients, o = Outpatients, α = Non-covered cost ratio (hospitalized patients), β = Non-covered cost ratio (outpatients).

The direct non-healthcare costs are the costs of transportation and caregiving incurred by patients in seeking and receiving the services of medical institutions. This study draws upon the national official statistics data on transportation costs. This data include information on the costs of one-way transportation paid by outpatients and hospitalized patients and estimate the final costs of transportation based on the assumption that some patients would be accompanied by their caregivers. The costs of one-way trips were multiplied by the price-adjusted index and used to estimate the total costs of round-trip transportation. As for the cost of caregiving, this study used the average daily cost of hiring a caregiver, as suggested by the caregivers association. Defining the cost of caregiving as the opportunity cost of caregivers' time during patients' hospitalization, this study applied the average

Methodology of Estimating Socioeconomic Burden of Disease Using National Health Insurance… DOI: http://dx.doi.org/10.5772/intechopen.89895

daily wage for caregivers as the unit cost of caregiving. This unit cost was then multiplied by the number of hospitalization days. The cost of caregiving was also estimated for outpatient visits based on the assumption that each outpatient visit takes up one-third of the caregiver's daily working hours. The formula used to estimate the direct non-healthcare costs is provided below.

$$\text{NHC} = \sum\_{\mathfrak{s}} \sum\_{\mathfrak{y}} \left[ \left( N\_{\text{i}\mathfrak{y}} + N\_{\text{a}\mathfrak{y}} \right) \times \mathbf{C}t \times \mathbf{2} \right] + \sum\_{\mathfrak{s}} \sum\_{\mathfrak{y}} \left[ \left( L\_{\text{i}\mathfrak{y}} \times \mathbf{C}t \right) + \frac{\mathbf{1}}{\mathfrak{s}} \left( N\_{\text{a}\mathfrak{y}} \times \mathbf{C}\_{\mathfrak{c}} \right) \right] \tag{2}$$

where NHC = Direct non-healthcare costs, s = Sex, y = Age, N = Number of visits, i = Inpatients, o = Outpatients, Ct = Cost of transportation, L = Length of stay, Cc = Cost of caregiving.

### 3.3.2 Indirect costs

Indirect costs do not represent actual financial costs paid but the losses of labor and productivity due to illnesses. The indirect costs represent the amounts of working time lost in order to visit and use the services of medical institutions, the loss of future income due to the premature death of patients, and the opportunity cost of caregiving. The opportunity costs so incurred include not only the amount of working time lost but also the amount of leisure time lost. This study draws upon the employment and labor statistics provided by the government in order to estimate the indirect costs. These statistics are part of the official employment and labor statistics that provide information on the average daily and monthly wages, total working hours, and employment rates by sex and age.

The losses of labor (productivity) due to the need for treatment and recovery and losses of future income due to premature death were estimated in the following manner. First, loss of labor can be understood as the opportunity cost of labor incurred by spending time hospitalized or making outpatient visits to medical institutions instead of working. These opportunity costs were thus estimated for the working-age population (ages 15–69). In the case of hospitalized patients, loss of productivity was found by multiplying the daily average wage for each age group by the number of hospitalization days. For outpatients, the daily average wage for each age group was multiplied by the number of outpatient visits made, and the result was divided by 3 (based on the assumption that outpatient visits took up one-third of each patient's daily working hours). The formula used to estimate the losses of productivity is provided below.

$$\text{PL} = \sum\_{\text{s}} \sum\_{\text{y}} \left[ \left( N\_{\text{i}\text{y}} + \frac{1}{3} N\_{\text{a}\text{y}} \right) \times W\_{\text{y}} \times E\_{\text{y}} \right] \tag{3}$$

where PL = Productivity loss, s = Sex, y = Year, N = Number of hospitalization days (visits), i = Inpatients, o = Outpatients, W = Average wage, E = Employment rate.

The loss of future income due to the premature death of patients represents the decrease in expected income that individuals could have earned had they lived to their full life expectancy. To estimate this loss, this study relied upon the raw data for the official statistics on causes of death provided by the government to identify the number of deaths by sex and age and then applied the death rate to the average monthly wage and number of working days for each age group. The loss of future income was again estimated for the working-age population (ages 15–69) only, applying the employment rate of each age group. In order to convert the estimated

loss of future income into a present value, a discount rate was applied, and the sensitivity to changes in the discount rate was checked by applying additional discount rates. The formula used to estimate the loss of future income due to premature death is shown below.

$$\text{LFI} = \sum\_{s} \sum\_{\mathcal{V}} \sum\_{k=1}^{n} \left( N\_{\mathcal{V}} \times \frac{\text{Yy}(t+k) \times \text{Py}(t+k)}{(1+r)^k} \right) \tag{4}$$

where LFI = Loss of future income, s = Sex, y = Age, k = 1,2, … , n (where "n" represents the difference between the life expectancy and actual average age at death for each age cohort), t = Age at death, r = Discount rate, Nsy = Number of premature deaths associated with allergic diseases by sex and age, Ysy(t + k) = Annual average income at t + k by sex and age, Psy(t + k) = Employment rate at t + k by sex and age.


**4. Results and discussions**

**Table 2** displays the decision matrix for the pure aluminum graphene nanoplatelets (GNP's) composites, which contains five alternatives and five parameters (1 & 2). The entropy method was used to calculate the objective weights, which are described in **Table 3**. MATLAB R2020a software was used to create a programme that used the formulae for the methods (EDAS and VIKOR) to determine performance measures. The entropy methods are used to measure the weights of different parameters such as mass, percentage elongation, tensile strength (TS), hardness, and impact strength (IS). The material that is used for fuselage construction is of lightweight and good strength and is more corrosion resistant. An attempt was made by developing an alternative material to serve the purpose.

#### **4.1 Evaluation based on distance from average solution (EDAS) method**

**Table 1** decision matrix displays the average solution (AVj) determined using Eq. (3). Eqs. (4) and (5) were used to quantify positive and negative distances based

#### **Table 2.**

*Decision matrix.*

*Selection of Optimal Material from Stir Cast Aluminum Graphene Nano Platelets… DOI: http://dx.doi.org/10.5772/intechopen.100478*

**Table 4.**

*(PDA) positive distance from average.*

#### **Table 5.**

*(NDA) negative distance from average.*

**Table 6.**

*Normalized positive and negative weighted sums, appraisal scores, and rank of alternatives.*

on the types of beneficial and non-beneficial parameters mentioned in **Tables 4** and **5**. Eqs. (6) and (7) are used to measure the positive and negative weighted sums (7). Eqs. (8) and (9) are used to measure the normalized positive and negative weighted sums (9). Eq. (10) is used to measure the final assessment score (ASi) for all alternatives, as shown in **Table 6**. 5-1-2-3-4 is the ranking of alternative composite materials. The best composite for the fuselage in aircraft applications is pure aluminum reinforced with 0.5wt% GNP, though pure aluminum is the least favored.

## **4.2 VIse Kriterijumska OptimizacijaKompromisno Resenje (VIKOR) method**

The related utility and regret measures, as well as Qi values for the five alternative composite materials and the parameters, are determined using Eqs. (13)–(17), as shown in **Table 7** (taking N = 0.5). **Table 8** shows the best and worst values for the beneficial and non-beneficial criteria measured using Eqs. (11) and (12). The ranks of alternative composite materials are 5-1-2-3-4. The first place winner is pure

**Table 7.**

*Utility and regret measure values and rank of the alternatives.*

**Table 8.** *Best and worst of criterion.*

aluminum reinforced with 0.5 wt% GNPs. VIKOR is more beneficial compared to EDAS, though the results are comparatively same. All the expressions were objective with respective to VIKOR to EDAS.


**5. Vitamin D replacement in diet**

*Vitamin D Deficiency*

indicator of vitamin D status.

population (97.5%) [12].

hypercalciuria.

manner.

**4. Evidence-based review**

specific condition (e.g. epilepsy) were excluded.

made according to evidence-based protocols.

the intestinal calcium absorption to 40% and phosphorus to 80% (**Figure 2**, [10]). Vitamin D is transformed for activation through two hydroxylations in the body. Among the metabolic products or modified versions of vitamin include calcitriol (1,25-dihydroxyvitamin D3); the active form of vitamin D, with a 15-hour half-life; and calcifediol (25-hydroxyvitamin D3) with a 15-day half-life [10]. Vitamin D is bounded to specific receptors located throughout the body. Whilst serum 25(OH) D levels do not indicate the amount of vitamin D stored in body tissues, it is the best

When the level was 30 ng/mL or less, there was a significant decrease in intestinal calcium absorption that was associated with increased parathyroid hormone [10]. Further, there is strong support from reports that maternal vitamin D deficiency leads to overt bone disease (congenital rickets) from before birth to post-natal life [11]. Throughout the world, there has been widespread debate as to the serum 25(OH)D concentrations associated with deficiency (e.g. rickets), for optimal bone health and optimal overall health, and most cut points have not been developed by an agreed mutual scientific process. The US Institute of Medicine concluded that individuals are at risk of vitamin D deficiency at serum 25(OH)D concentrations <30 nmol/L (<12 ng/mL). However, some individuals are potentially at risk for inadequacy even at levels ranging from 30 to 50 nmol/L (12–20 ng/mL). The committee agreed with sufficient levels at ≥50 nmol/L (≥20 ng/mL) and that 50 nmol/L is the serum 25(OH)D level that covers the majority of needs of the

Further, hypovitaminosis D in its mild but especially in severe forms can exacerbate symptomatic hypocalcaemia following intravenous bisphosphonate (zoledronic acid) [13]. Even though this is rare, associated hypocalcaemia may be life-threatening and require immediate resuscitation and evaluation, often requiring hospitalisation to prevent additional morbidity and mortality risk from tetany, refractory hypotension, seizures or arrhythmias. Therefore it makes good sense to

Vitamin D toxicity can occur when blood 25(OH)D levels are 88 ng/mL or greater [15]. Symptoms may include sleepiness, vomiting, weakness, headache, nausea and constipation, and acute toxicity may cause hypercalcaemia and

Whilst there have been several treatment guidelines published, they are consensus-based rather than evidence-based [16–19]. This review seeks to address this issue and identify any gaps in research for vitamin D replacement, listing the types available including dietary, other supplementation and sunlight, for bone health (rickets, osteoporosis) as well as to address safety and efficacy in an evidence-based

Systematic search of MEDLINE, CINAHL and EMBASE for articles published rom September 2015 to August 2019 and the Cochrane Database of Systematic Reviews (most recent issue searched—Issue 2, 2019) was conducted by the author. Randomised controlled trials, meta-analyses and reviews of all aspects of vitamin D supplementation in humans were reviewed. Articles pertaining to osteoporosis in a

All studies were reviewed independently by the author, who recorded individual study results, and an assessment of study quality and treatment conclusions was

optimise vitamin D levels prior to administration of these agents [14].

**98**

Human beings require and source vitamin D from diet, dietary supplements and exposure to sunlight.

Whilst in particular for older people and those with restriction in diet, a diet rich in oily fish (which can theoretically improve 25-OHD levels and prevent vitamin D deficiency), in reality this is a challenge to achieve [24]. Only a few foods are a good source of vitamin D. These include fortified dairy products and breakfast cereals, fatty fish, beef liver and egg yolks.

Apart from the flesh of fatty fish (such as salmon, tuna and mackerel) and fish liver oils, very little foods contain vitamin D. Trace amounts of vitamin D can be found in egg yolks, cheese and beef liver. These foods contain vitamin D3 and its metabolite 25(OH)D3 [25]. Vitamin D2 is contained in some mushrooms [26, 27].

In the standard Western diet, fortified foods provide most of the vitamin D [26]. For example, US milk is fortified with 100 IU/cup. (In Canada, by law its milk is fortified with 35–40 IU/100 mL and its margarine at ≥530 IU/100 g.) However, other dairy products derived from milk, such as ice-cream and cheese, are generally not fortified. Some ready-to-eat cereals for breakfast often contain small amounts of added vitamin D, as do some brands of orange juice and yogurt. Some plant-based alternatives to mild (such as drinks made from oats, almond or soy) are sometimes fortified with vitamin D to the amount (about 100 IU/cup) found in fortified cow's milk. Several countries of the developed world (e.g. United States, Canada and Australia) mandate the fortification of infant formula with vitamin D, 40–100 IU/100 kcal in the United States and 40–80 IU/100 kcal in Canada; however, this is not practical especially in those who breastfeed and those countries which do not mandate fortification of their milk formulas.


**3. The Nigerian scenario**

## **3.1 Index outbreak**

Nigeria experienced an outbreak of HPAI H5N1 virus in both commercial and local poultry populations during January 2006 as an extension of the global outbreaks [7]. The index HPAI-H5N1 was confirmed in Nigeria at a commercial poultry farm in Kaduna State, and by the end of the initial outbreak, over 46,000 poultry

*Viruses and Viral Infections in Developing Countries*

strain [1]. The artificial increased density of individual host pathogens increases spread to other hosts, and the short life cycles of the virus prevent the host from developing immunity against the organism [2]. With high density and short life span, contagiousness is not under selective pressure, and a selection criteria on strains allow strains to be selected mainly on virulence, i.e., harm to the host [2]. In addition, inadequate disinfection measures between batches allow the virus to survive in the environment [2]. Three potential clinical outcomes have been observed with AI infection in birds; these are (a) no clinical signs, (b) mild disease, and (c) severe disease with death [3]. The H5 and H7 subtypes of avian influenza (AI) virus associated with HPAI, which is extremely virulent, cause up to 100% mortality in domestic poultry. The virulence and ability to cause systemic infection have been attributed to the multibasic cleavage motif (minimal consensus sequence of -R-X-K/RR-) in their hemagglutinin molecule, which are recognized by subtilisin-like endoproteases that are virtually present in every tissue of their host and preferentially expressed at the surface of respiratory and gastrointestinal epithelia [4], making them capable of replicating in multiple tissue [5, 6]; hence, lesions are multisystemic (i.e., nervous, circulatory, respiratory, integumentary, musculoskeletal, hemopoietic, gastrointestinal, reproductive systems [7]). This chapter considers avian influenza virus infection with a special X-ray on the Nigerian outbreak scenario, general clinicopathologic features in domestic poultry and factors that support the persistence of the virus in the environment, and, when not adequately

controlled, how these can lead to an endemic and/or a pandemic state.

from LPAI progenitors of the H5 and H7 subtypes [16–18].

Influenza viruses are segmented negative-sense single-stranded RNA, belonging to the family *Orthomyxoviridae* [8]. This family is classified into influenza viruses' types A, B, and C, *Thogotovirus*, and *Isavirus* [9]. Only influenza type A viruses infect poultry [8], and avian influenza viruses are known to be a diverse group of viruses in the *Orthomyxoviridae* family, genus *Influenza A virus*, and have been categorized into subtypes based on the two surface glycoproteins, the hemagglutinin (H) and neuraminidase (N) [10]. There are now 18 H and 11 N surface glycoproteins [11]. Previously, HA1-16 and NA1-9 have been identified to occur naturally in avian host, mostly waterfowls where they exist in benign form (low pathogenic) [12]. Subtypes HA 17 and 18 and NA 10 and 11 were identified in bats [13] recently. Avian influenza viruses have also been further classified into two different pathotypes (low pathogenicity [LP] and high pathogenicity [HP]), based on the ability to produce disease and death in the major domestic poultry species, the chicken (*Gallus gallus domesticus*) [14]. HPAI has been shown to be caused in susceptible host species by only the avian influenza viruses of H5 and H7 subtypes, which contain multiple basic amino acids at the cleavage site of the hemagglutinin molecule, [15]. However, not all H5 and H7 avian influenza viruses are highly pathogenic. It has been shown that HPAI viruses emerge in domestic poultry

A typical flow of avian influenza virus in poultry mostly begins with the introduction of the virus to a region or country where the virus has not been known to be present. This introduction has been known to occur by way of contact of resident water fowls and/or backyard poultry with an infected migratory wild bird

**2. Avian influenza transmission**

**2.1 The virus**

**62**

**2.2 Transmission**

[25, 26] had been destroyed. This outbreak brought the Asian strain of highly pathogenic avian influenza (HPAI) H5N1 into Africa for the first time in the beginning of January 2006 [7, 27–29] in Nigeria. During this first incursion of the virus into Nigeria, the country lost excess of 1 million birds of various species [30, 31]. Since its emergence in Africa in 2006, avian influenza viruses of the H5N1 subtype have spread rapidly to poultry farms in several African countries.

This acute, generalized, fatal disease [32] affected free-ranged domestic poultry, backyard poultry, and commercial poultry comprising of chickens, ducks, geese, turkeys, guinea fowls, other gallinaceous birds, and ostriches in the poultry industry of the country. Although elsewhere in the world quails, ratites, passerine bird, flamingos, herons, raptors [32], and mammals have also been found to be susceptible to HPAI, this was not so in Nigeria. HPAI which is known to be caused by avian influenza viruses (AIVs) that are extremely virulent, resulting in up to 100% mortality in domestic chickens [33], was isolated in several outbreaks that occurred in Nigeria.

#### **3.2 Source of the virus**

Several sources have been found to be responsible for the introduction and reintroduction of the virus; these include wild bird sources, trade in poultry [29], and direct source introduction. Following the waves of outbreaks during the 2006–2007 periods, phylogeographic analysis [22] identified the north-central (Katsina, Jigawa, Yobe, Kano, Kaduna, Bauchi, and Gombe) and southwest (Lagos, Ogun, Oyo, Ekiti, and Kwara) regions as the two major sources for the HPAIV in Nigeria [22]. This supports the hypothesis that the introduction of the virus into Nigeria may be by wild birds and trade in poultry [29] and its products. Wild birds are strongly believed to be a major source of the virus to the northeastern states of Jigawa and Yobe, which are home to the Hadejia-Nguru wetlands and are said to be characterized by permanent and seasonal lakes and numerous population of migratory and residential waterfowl [22]. It is also known that this area sustains a large backyard poultry population and the highest concentration of domestic ducks, reared under free-range conditions, providing opportunities for contact between wild birds and backyard poultry [24].

#### **3.3 The poultry structure**

The poultry structure in Nigeria is comprised of two major systems, rural poultry production and commercial poultry production [34], as classified by the Food and Agricultural Organization (FAO) of the United Nation (UN). In the rural poultry production, it is a common practice to keep poultry in free-range, multispecies, multiage holdings with low biosecurity levels, thus exposing them to many at-risk contacts, and they could act as the epidemiologic link between the wild reservoir of AI viruses [22, 35] and industrial poultry. The commercial poultry production can be fully intensive or semi-intensive, depending on the level of automation and husbandry. **Figure 2A**–**D**, gives an insight to the poultry structure practiced in Nigeria.

#### **3.4 Surveillance and outbreaks investigation**

As far back as early February 2004, the veterinarians have been saddled with the responsibility of gearing efforts toward managing HPAIV outbreaks [37] if Nigeria was at risk or eventually experience an outbreak. Prevention strategy document for the emergency preparedness and response plan (EPP) [37] was ready as early as 2005. This included a risk analysis of the 24 wetlands in Nigeria and the two major

**65**

*Features of Highly Pathogenic Avian Influenza (HPAI) H5N1 in Domestic Poultry*

migratory routes of wild birds, evaluation and upgrading of veterinary services, ban on importation of poultry and poultry products, and increased surveillance for the virus [37]. Surveillance and disease reporting included passive and active surveillance and regular disease reporting. The active surveillance component involved epidemiological surveillance of network of 170 points within the country and targeted surveillance of wetlands and farms [37]. Surveillance was carried out between September and November 2005 at the Nguru-Hadejia wetlands [37]. Similarly, active surveillance was carried out in the same period in the high-risk agroecological farming areas and among live bird (poultry) markets, but all these surveillance activities failed to detect H5 or H7 avian influenza virus [37]. Several investigations have been carried out and are available to the public about the HPAI H5N1 outbreaks in Nigeria, including the virological identification and confirmation [26, 28, 37, 38], epidemiology and pathology of early [7] and resurgent outbreaks [39], and molecular characterization [29], including regional mortality and

*(A) Intensive poultry production system in the tropics [36]. (B) A semi-intensive poultry production system in the tropics [36]. (C) A mixed species backyard poultry production system comprising of ostriches, geese, Muscovy, mallard, and Pekin ducks before the introduction of HPAI [36]. (D) An extensively (free-ranging)*

Reintroduction of the virus to Nigeria was first detected in January 2015, when the National Veterinary Research Institute (NVRI), Vom, Nigeria, received some chicken carcasses from the Kano State Ministry of Agriculture [39]. The carcasses were gotten from a backyard-commercial poultry farm and also from a live bird market (LBM) in Kauna and Sabon Gari, Kano State, northwestern Nigeria, respectively. The farm husbandry was mixed poultry species system where different types of chickens of various ages and stages were kept and were experiencing high mortality of 350 birds daily with eventual 100% mortality observed in the older birds (54 weeks) [39]. Also, in a concurrent incidence, unusual high mortality of birds brought from the northern part of Nigeria to two LBMs in Onipanu and Mushin, Lagos State, Southwestern Nigeria, was reported [39]. The specimens were analyzed by RT-PCR and virus isolation in embryonating chicken eggs, and all samples were found to be positive for HPAI (H5N1) subtype [39]. This ended the 9-year silence of the activity of the virus in Nigerian poultry [39]. Again, a strong genetic link between viruses isolated in Nigerian poultry and wild bird from Europe has been established [21, 22, 41, 42], thereby suggesting that wild birds are the major

*DOI: http://dx.doi.org/10.5772/intechopen.86098*

morbidity characteristics [40].

*reared Muscovy/mallard duck flock [36].*

**Figure 2.**

**3.5 Reintroduction of the virus into domestic poultry**

*Features of Highly Pathogenic Avian Influenza (HPAI) H5N1 in Domestic Poultry DOI: http://dx.doi.org/10.5772/intechopen.86098*

#### **Figure 2.**

*Viruses and Viral Infections in Developing Countries*

**3.2 Source of the virus**

wild birds and backyard poultry [24].

**3.4 Surveillance and outbreaks investigation**

**3.3 The poultry structure**

practiced in Nigeria.

[25, 26] had been destroyed. This outbreak brought the Asian strain of highly pathogenic avian influenza (HPAI) H5N1 into Africa for the first time in the beginning of January 2006 [7, 27–29] in Nigeria. During this first incursion of the virus into Nigeria, the country lost excess of 1 million birds of various species [30, 31]. Since its emergence in Africa in 2006, avian influenza viruses of the H5N1 subtype

This acute, generalized, fatal disease [32] affected free-ranged domestic poultry, backyard poultry, and commercial poultry comprising of chickens, ducks, geese, turkeys, guinea fowls, other gallinaceous birds, and ostriches in the poultry industry of the country. Although elsewhere in the world quails, ratites, passerine bird, flamingos, herons, raptors [32], and mammals have also been found to be susceptible to HPAI, this was not so in Nigeria. HPAI which is known to be caused by avian influenza viruses (AIVs) that are extremely virulent, resulting in up to 100% mortality in domestic chickens [33], was isolated in several outbreaks that occurred in Nigeria.

Several sources have been found to be responsible for the introduction and reintroduction of the virus; these include wild bird sources, trade in poultry [29], and direct source introduction. Following the waves of outbreaks during the 2006–2007 periods, phylogeographic analysis [22] identified the north-central (Katsina, Jigawa, Yobe, Kano, Kaduna, Bauchi, and Gombe) and southwest (Lagos, Ogun, Oyo, Ekiti, and Kwara) regions as the two major sources for the HPAIV in Nigeria [22]. This supports the hypothesis that the introduction of the virus into Nigeria may be by wild birds and trade in poultry [29] and its products. Wild birds are strongly believed to be a major source of the virus to the northeastern states of Jigawa and Yobe, which are home to the Hadejia-Nguru wetlands and are said to be characterized by permanent and seasonal lakes and numerous population of migratory and residential waterfowl [22]. It is also known that this area sustains a large backyard poultry population and the highest concentration of domestic ducks, reared under free-range conditions, providing opportunities for contact between

The poultry structure in Nigeria is comprised of two major systems, rural poultry production and commercial poultry production [34], as classified by the Food and Agricultural Organization (FAO) of the United Nation (UN). In the rural poultry production, it is a common practice to keep poultry in free-range, multispecies, multiage holdings with low biosecurity levels, thus exposing them to many at-risk contacts, and they could act as the epidemiologic link between the wild reservoir of AI viruses [22, 35] and industrial poultry. The commercial poultry production can be fully intensive or semi-intensive, depending on the level of automation and husbandry. **Figure 2A**–**D**, gives an insight to the poultry structure

As far back as early February 2004, the veterinarians have been saddled with the responsibility of gearing efforts toward managing HPAIV outbreaks [37] if Nigeria was at risk or eventually experience an outbreak. Prevention strategy document for the emergency preparedness and response plan (EPP) [37] was ready as early as 2005. This included a risk analysis of the 24 wetlands in Nigeria and the two major

have spread rapidly to poultry farms in several African countries.

**64**

*(A) Intensive poultry production system in the tropics [36]. (B) A semi-intensive poultry production system in the tropics [36]. (C) A mixed species backyard poultry production system comprising of ostriches, geese, Muscovy, mallard, and Pekin ducks before the introduction of HPAI [36]. (D) An extensively (free-ranging) reared Muscovy/mallard duck flock [36].*

migratory routes of wild birds, evaluation and upgrading of veterinary services, ban on importation of poultry and poultry products, and increased surveillance for the virus [37]. Surveillance and disease reporting included passive and active surveillance and regular disease reporting. The active surveillance component involved epidemiological surveillance of network of 170 points within the country and targeted surveillance of wetlands and farms [37]. Surveillance was carried out between September and November 2005 at the Nguru-Hadejia wetlands [37]. Similarly, active surveillance was carried out in the same period in the high-risk agroecological farming areas and among live bird (poultry) markets, but all these surveillance activities failed to detect H5 or H7 avian influenza virus [37]. Several investigations have been carried out and are available to the public about the HPAI H5N1 outbreaks in Nigeria, including the virological identification and confirmation [26, 28, 37, 38], epidemiology and pathology of early [7] and resurgent outbreaks [39], and molecular characterization [29], including regional mortality and morbidity characteristics [40].

#### **3.5 Reintroduction of the virus into domestic poultry**

Reintroduction of the virus to Nigeria was first detected in January 2015, when the National Veterinary Research Institute (NVRI), Vom, Nigeria, received some chicken carcasses from the Kano State Ministry of Agriculture [39]. The carcasses were gotten from a backyard-commercial poultry farm and also from a live bird market (LBM) in Kauna and Sabon Gari, Kano State, northwestern Nigeria, respectively. The farm husbandry was mixed poultry species system where different types of chickens of various ages and stages were kept and were experiencing high mortality of 350 birds daily with eventual 100% mortality observed in the older birds (54 weeks) [39]. Also, in a concurrent incidence, unusual high mortality of birds brought from the northern part of Nigeria to two LBMs in Onipanu and Mushin, Lagos State, Southwestern Nigeria, was reported [39]. The specimens were analyzed by RT-PCR and virus isolation in embryonating chicken eggs, and all samples were found to be positive for HPAI (H5N1) subtype [39]. This ended the 9-year silence of the activity of the virus in Nigerian poultry [39]. Again, a strong genetic link between viruses isolated in Nigerian poultry and wild bird from Europe has been established [21, 22, 41, 42], thereby suggesting that wild birds are the major

possible source of introduction and reintroduction of HPAI viruses into Nigeria's poultry. On the other hand, rural poultry mainly kept in free-range, multispecies, multiage holdings with low biosecurity levels are exposed to many at-risk contacts and therefore act as the epidemiologic link between the wild reservoir of AI viruses and industrial poultry [22, 35]. The predominant species in the rural poultry sector of Africa is the scavenging indigenous domestic fowl (*Gallus gallus domesticus*) [43], which in most African countries has no regular health control program, may or may not have shelter, and scavenges for most of their nutritional needs [43]. In fact, village chickens have been reported to act as potential reservoirs and carriers of infections to themselves and to the more susceptible exotic breeds in commercial poultry farms [44].


**4. Diagnosis of tuberculous meningitis**

The diagnosis of tuberculous meningitis is often a real challenge for the clinician, based in some cases on the clinical aspects and the changes found in the CSF, without bacteriological confirmation. The insidious onset with persistent symptoms for more than 6 days, the presence of neurological manifestations represented by cranial nerve paralysis or peripheral paralysis and the presence of a moderate inflammatory reaction in the CSF are elements that are suggestive for the diagnosis of TBM.

In TBM, the following changes in the cerebrospinal fluid are characteristic:


*2.5. Migration Path of Droughts*

The center of mass used to study the migration of matter and energy is an important method to study the geographical distribution [26]. In this study, the centroid model was used to study the spatiotemporal migration characteristics of soil dryness, and the distance of centroid movement reflects the spatial difference of the differentiation degree of SSMI change. Further, we used the migration of drought centers to describe the spatiotemporal evolution of soil drought. Initially, we used the statistical analysis box (mean center tool) of ArcGIS 10.0 software to obtain the spatial centroid of the SSMI drought index and plotted the centroid migration of two extreme soil drought events to describe the spatiotemporal evolution of soil drought better. The drought centers were then connected to record the track, path length, direction, and velocity characteristics of the droughts.


**5.3 Monosomy 7/deletion 7q**

Chromosome 7 anomalies comports del(7q), monsomy 7 (−7/del(7q)), or more rarely t(1;7), are second in frequencies after del(5q) (10%) and they have a poor Prognostic value on overall survival and risk of transformation.

Prognostic values can be possibly distinct according to regions of deletion. In fact, many and different minimal regions of deletion have been noted in 7q35–36.

Monosomy 7 can change constitutional bone marrow failures syndromes (FA, Down syndrome) or AA, or arise after radiation or toxic exposure. It is the most frequent alteration in childhood MDS where it is often associated with a degree of myeloproliferation.

Now it s known that G-CSF treatment may select a − 7 clone, and that 7q is a genetically instable region.

Patients with −7/del(7q) are characterized by neutrophil functional impairment, they are exposed to severe infections. Those patient is very have poor response to intensive chemotherapy but respond better to hypomethylating agents [14, 15].


**3. The Mathematical Model for Optimal Allocation of BESS**

#### *3.1. Objective Function*

After the DC locking fault occurs in the AC/DC hybrid system, when the system optimizes the configuration of BESS, it should also have a certain economy while eliminating the power flow over-limit on the sensitive and vulnerable lines. Therefore, the multi-objective function of the optimal configuration of BESS in the AC/DC hybrid system is as follows:

$$\max \Gamma = \sum\_{k=1}^{K} \left| \frac{\Delta P'}{D\_{k-A} \cdot P\_{\text{dc}-\text{max}}} \right| \tag{11}$$

$$\text{minG}\_{inv} = \frac{r(1+r)^y}{(1+r)^y - 1} \cdot (c\_1 \cdot P\_\varepsilon + c\_2 \cdot E\_\varepsilon) \tag{12}$$

where Γ is the sum of the improved power flow exceeding risk index of sensitive vulnerable lines after DC blocking fault; *Dk*−*A* is the LODF; *P*dc−max is the thermal stability limit value of DC line; *Ginv* is the annual investment cost of BESS; *c*1, *c*2 are the unit power cost and capacity cost of BESS; *Pe*, *Ee* are the rated power and rated capacity of the BESS, respectively; *r* is the annual interest rate of the fund; *Y* is the life cycle of BESS; *K* is the sensitive vulnerability line.

### *3.2. Constraint*

(1) Power balance constraints

$$\sum\_{m=1}^{M} P\_{G,m} + \sum\_{n=1}^{N} P\_{L,n} + \sum\_{s=1}^{S} P\_{B,s} = 0 \tag{13}$$

where *PG*,*m*, *PL*,*n*, *PB*,*s* are, respectively, the output of generator *m*, the required power of load *n*, and the charging and discharging power of BESS s; *S* is the quantity of configured BESS in the system; *M* and *N* are the number of generators and the number of loads in the system.

(2) Line loss constraint

$$P\_{\mathbb{R}}^{\prime} \mathcal{U}\_{\mathbb{R}}^{2} \geq \, \mathcal{R}\_{\mathbb{R}} \cdot \left(P\_{\mathbb{R}}^{\prime, 2} + \mathcal{Q}\_{\mathbb{R}}^{\prime, 2}\right) \tag{14}$$

where *P R*, *Q R* are the active power and reactive power transmitted by the receiving end of the *R* line, respectively; *RR* is the resistance of the *R* line; *UR* is the voltage amplitude of the receiving terminal node of the Rth line.

(3) Generator power constraint

$$
\overline{P}\_{G,m} \le P\_{G,m} \le \underline{P}\_{G,m} \tag{15}
$$

where *PG*,*m*, *PG*,*m* are the upper and lower limits of generator output, respectively. (4) Line power constraint

$$
\overline{P}\_{L,m} \le P\_{L,m} \le \underline{P}\_{L,m} \tag{16}
$$

where *PL*,*m*, *PL*,*m* are the upper and lower limits of the active power of the transmission line.

(5) Capacity constraints of BESS

$$S\_{\min} \le S\_r \le S\_{\max} \tag{17}$$

where *S*min, *S*max are the minimum and maximum capacities of BESS, respectively. (6) Power constraint of BESS charge and discharge

$$\begin{cases} -P\_{r,\text{max}} \le P\_r^c \le 0\\ 0 \le P\_r^d \le P\_{r,\text{max}} \end{cases} \tag{18}$$

where *Pr*,max is the maximum value of BESS discharge power; *Pc r* , *Pd r* are the charge power and discharge power of the BESS system, respectively.

#### **4. Model-Solving Method**

The sharing of information among the entire population is beneficial for the population towards a better position in genetic algorithms. Only the best individual's information is shared in PSO, and the entire search process is tracking the optimal solution. So, the PSO algorithm has faster convergence than the genetic algorithm. And due to its advantages of high accuracy and fast convergence, the PSO algorithm is widely used in BESS capacity configuration [33,34]. Therefore, this article chooses the PSO algorithm for solving BESS capacity configuration.

To reasonably obtain the location of BESS, the PSO algorithm is used to solve for the optimal capacity of BESS, and the optimal location of BESS is selected from candidate nodes. Meanwhile, BESS adopts the active power control strategy, including plant-level control and local control, which quickly eliminates the power exceeding the limit of the AC line, suppresses the power fluctuation of the power grid, and ensures the safe and stable operation ability of the power grid.

The specific process is shown in Figure 1, and the solution steps are as follows:

$$x\_{i\rangle} = x\_{\rangle}^{\text{min}} + rand() \cdot (x\_{\rangle}^{\text{max}} - x\_{\rangle}^{\text{min}}) \tag{19}$$

where *i* = 1, 2, ... , *N* is a D-dimension vector; *j* = 1, 2, ... , *d*; *rand*() represents random numbers between 1 and 0; *x*max *j* , *x*min *j* are the maximum and minimum values of particles, respectively;

(8) Calculate the fitness value for particles by using (20). It is updated local optimal position and global optimal position by using Equations (21) and (22).

$$p\_i = \frac{F\_i}{\sum\_{k=1}^{N} F\_k} \tag{20}$$

$$v\_{i\bar{j}}(t+1) = w \times v\_{i\bar{j}}(t) + \varepsilon\_1 \times rand() \times \left(p\_{i\bar{j}}(t) - x\_{i\bar{j}}(t)\right) + \varepsilon\_2 \times rand() \times \left(p\_{i\bar{j}}(t) - x\_{i\bar{j}}(t)\right) \tag{21}$$

$$\mathbf{x}\_{i\bar{j}}(t+1) = \mathbf{x}\_{i\bar{j}}(t) + \upsilon\_{i\bar{j}}(t+1) \tag{22}$$

where *Fi* is the corresponding fitness value for particles I; *vij*(*t* + 1), *vij*(*t*) are the velocity of the ith particle at *t* + 1, *t* times, respectively; *w* is the inertia factor; *w* = 0.8, *c*1 and *c*2 are the learning rate; *c*1 = 0.9, *c*2 = 0.9. *pij*(*t*), *pgj*(*t*) respectively represent the individual optimal value and the global optimal value of particles;

(9) Output optimal solution. If the iteration number is greater than the set value, then output the Parote optimal. Otherwise, return to step (7).

**Figure 1.** Solution flow of BESS optimization configuration.