Category: Swine Flu

The flu pandemic or swine flu in 2009 was an influenza pandemic that lasted from early 2009 to late 2010, and the second of the two pandemics involving H1N1 influenza virus (the first of which was the Spanish flu pandemic in 1918–1920), though in a new version. First identified in April 2009, the virus appeared to be a new strain of H1N1, resulting in a previous triple reassortment of dog, swine and human flu viruses further combined with a Eurasian pig flu virus leading to the word ‘swine flu’ It is estimated that 11–21 per cent of the world’s population (about 6.8 billion), or about 700 million–1.4 billion people, contracted the disease— more in absolute terms than the Spanish flu pandemic.

History

Initially referred to as an ‘outbreak’, the widespread H1N1 infection was first identified in the state of Veracruz, Mexico, with evidence that the virus had been present for months before it was officially referred to as an ‘epidemic’. The Mexican government closed most of the public and private facilities in Mexico City to limit the spread of the virus; however, it continued to spread globally, and in some cases the hospitals were too overwhelmed by infected people. American and Canadian laboratories first isolated the new virus from samples collected from people with flu in Mexico, Southern California and Texas in late April. The earliest known human case was soon traced to a case in a 5-year-old boy in La Gloria, Mexico, a rural town in Veracruz from March 9. In late April the World Health Organization (WHO) announced its first ever ‘public health emergency of international concern,’ or PHEIC, and in June the WHO and the U.S. CDC stopped reporting cases and announced the outbreak to be a pandemic. In November 2009, the pandemic began to taper, and by May 2010, the number of cases was in rapid decline. WHO Director-General Margaret Chan announced the end of the H1N1 pandemic on 10 August 2010 and announced that the H1N1 influenza outbreak has passed into the post- pandemic phase. According to the latest WHO estimates (as of July 2010), since it emerged in April 2009, the virus has killed more than 18,000 people, but they note that the estimated mortality (including unconfirmed or unreported deaths) from the H1N1 strain is ‘unquestionably higher’.

Classification

American media called the initial epidemic the ‘H1N1 influenza’ or ‘Swine Flu’. It is called the H1N1/09 pandemic virus by the WHO, while in the U.S. it is referred to as ‘novel influenza A (H1N1)’ or ‘2009 H1N1 flu’ by disease control and prevention centres. In the Netherlands, it was initially named ‘Pig Flu’ but is now called by the national health institution ‘New Influenza A (H1N1)’, while the media and the general public use the term ‘Mexican Flu’. South Korea and Israel thought briefly of naming it the ‘Mexican virus’. The term ‘North American influenza’ was introduced by the World Health Organization for Animals, and the European Commission has adopted the phrase ‘novel flu virus’.

Signs and Symptoms

H1N1 flu symptoms are like those of other influenzas, and may include fever, cough (usually a ‘dry cough’), headache, muscle or joint pain, sore throat, chills, exhaustion, and runny nose. In some cases, even diarrhea, vomiting, and neurological problems were reported. People at higher risk of serious complications include those over the age of 65, children under the age of 5, children with neurodevelopmental disorders, pregnant women (especially during the third trimester) and those of any age with underlying medical conditions such as asthma, diabetes, obesity, heart disease or a weakened immune system (e.g., immunosuppressive or HIV-infected medications).

Cause

The virus has been found to be a novel influenza strain for which current seasonal flu vaccines provide no protection. One U.S. research centre for disease control and prevention reported in May 2009 found that children do not have pre-existing immunity to the new strain, but that adults, particularly those over the age of 60, do have some immunity. Children had no cross-reactive antibody reaction to the new strain, adults between the ages of 18 and 60 had 6–9%, and older adults 33%.

Analysis of the genetic sequences of the first isolates, immediately shared by Nature and WHO on the GISAID site, soon found that the strain includes genes from five distinct flu viruses: North American swine influenza, North American avian influenza, human influenza and two typically found in Asia and Europe. More research has shown that some virus proteins are most like strains that cause mild symptoms in humans, leading virologist Wendy Barclay on 1 May 2009 to conclude that the initial signs are that the virus was unlikely to cause severe symptoms in most people.

In June 2010, Hong Kong scientists announced discovery of a new swine flu virus that is a combination of the H1N1 pandemic virus and viruses previously found in pigs. Pigs have been named the flu mixing device because they can be infected both by avian flu viruses, which never affect people directly, and by human viruses. If pigs are infected with more than one virus concurrently, the viruses will exchange genes, creating new variants that they pass on to humans and even spread among them.

Diagnosis

Confirmed diagnosis of H1N1 flu pandemic requires testing of the patient’s nasopharyngeal, nasal or oropharyngeal tissue swab. Real-time RT-PCR is the preferred study, as some cannot differentiate between H1N1 pandemic and normal seasonal flu. However, most people with flu symptoms do not need a specific H1N1 flu pandemic test because the test results do not usually affect the recommended course of treatment. The U.S. CDC suggests monitoring only for those with suspected flu, pregnant women and those with compromised immune systems who are hospitalized. The CDC announced a new test ‘CDC Influenza 2009 A (H1N1) pdm Real-Time RT-PCR Panel (IVD)’ on 22 June 2010. It uses a technique of molecular biology to identify influenza A viruses, and specifically the H1N1 virus of 2009. The new test would replace the previous RT-PCR real-time diagnostic test used during the 2009 H1N1 pandemic, which earned U.S. permission for emergency use. In April 2009 the Food and Drug Administration. Test reports are available within four hours and are reliable at 96 per cent.

Treatment

A variety of approaches were prescribed to help relieve symptoms, including adequate consumption and rest of the liquids. Over – the counter pain medications such as acetaminophen and ibuprofen do not kill the virus but can be effective in reducing symptoms. Those under 16 with any flu-type symptoms should not use aspirin and other salicylate drugs because of the possibility of developing Reye’s syndrome. If the fever is mild and there are no other complications it is not recommended to take fever medication.

References

1. https://www.cdc.gov/flu/pandemic-resources/2009-h1n1-pandemic.html
2. https://www.who.int/csr/disease/swineflu/frequently_asked_questions/about_disease/en/
3. https://www.statnews.com/2019/06/11/h1n1-swine-flu-10-years-later/
4. https://www.nhs.uk/conditions/swine-flu/
5. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001558
6. https://www.cdc.gov/flu/pandemic-resources/2009-h1n1-pandemic.html
7. https://www.cdc.gov/flu/pandemic-resources/2009-h1n1-pandemic.html
8. https://www.who.int/csr/disease/swineflu/frequently_asked_questions/about_disease/en/
9. https://www.who.int/csr/disease/swineflu/frequently_asked_questions/about_disease/en/
10. https://www.statnews.com/2019/06/11/h1n1-swine-flu-10-years-later/
11. https://www.statnews.com/2019/06/11/h1n1-swine-flu-10-years-later/
12. https://www.nhs.uk/conditions/swine-flu/
13. https://www.nhs.uk/conditions/swine-flu/
14. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001558
15. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001558

Abstract

The major and challenging process of Swine Flu confirmation has promoted attempts to model it with the use of circulant triangular fuzzy numbers. In this paper, calculate the Six different indications using occurrence relationship (Ro) and conformability relationship (Rc) based on expert medical reports and analysis of related patients with Swine Flu and present some operations on circulant triangular fuzzy numbers matrices (TFNMs). The first row of the circulant(TFNMs) play important role in this study.

Key Words:

Triangular fuzzy numbers matrices (TFNMs), Circulant triangular fuzzy numbers matrices (CTFNMs), Occurrence relation (Ro), Conformability relation(Rc), and Swine Flu.

I. Introduction

In 1965, computer scientist L.A.Zadeh used the theory of a fuzzy concept for the first time in a scientific sense. Certain models for understanding and teaching the medical diagnosis process using fuzzy set theory vary in the degree to which they attempt to deal with different aspects of complications, such as

1. Relative important of symptoms,
2. Symptom patterns of disease stage,
3. Relation between diseases themselves,
4. Stages of hypothesis formation,
5. Preliminary and final diagnosis within-diagnosis process.

Such models also form the basis for a computerized medical expert program, which is useful for physicians in the diagnosis of certain identified categories of disease, which encourages us to create a model related to SWINE FLU disease, which is commonly observed in India. Implemented the concept TFM. In this paper, we give few basic dentition recalls TFM and CFM and its operations. In section, II discussed the definition of CTFM and its operations. In section III, Swine Flu diagnosis problems using CTFM relations are discussed. Finally, section IV presents the conclusion of this work.

II. Preliminaries

Definition 2.1.

A TFM represented as, where are all real value denotes the membership height and the left-hand and right-hand spreads of the mean value respectively, and membership height is defined as follows:

A TFN is said to be normalized if = 1 and it can be represented as.

If then is called standardized FN. Throughout this paper, we used normalized TFN.

Definition 2.2

TFNM of order m×n is defined as =(aij)m×n where aij = (aijL,aijM,aijU) is the ijth element of.aijL,aijUare the left and right spreads of aij respectively andaijM is the middling value.

Definition 2.3

Let = (aij)n×n and = (bij)n×n be two CTFM of same order. then

• (i) Addition Operation
• (+) = (aij + bij)n×n where aij + bij = (aijL + bijL,aijM + bijM,aijU + bijU) is the ijth element of (+)
• (ii) Subtraction Operation
• (-) = (aij −bij)n×n where aij −bij = (aijL −bijU,aijM −bijM,aijU −bijL) is the ijth element of (-) The same condition holds for CTF membership number.

Definition 2.4

Let = (aij)m×p and = (bij)p×n be two CTFNM. Then the Multiplication Operation:

• (.) = (cij)m×n, where (cij) = p X k=1 aik ·bkj for i = 1,2,…,m and j = 1,2,….,n.

Definition 2.5

Let and be two fuzzy relations on (p,q) and (q,r) respectively then the max-avg composition is denoted as is defined as

• (P,R) = {[(p,q),max r

Definition 2.6

Let = (aij)n×n where aij = (aijL,aijM,aijU) and = (bij)n×n where bij = (bijL,bijM,bijU) be two TFNM of same order. Then the maximum operation on it is given by Lmax = max(,) = (sup{aij,bij}) where sup{aij,bij} = (sup(aijL,bijL),sup(aijM,bijM),sup(aijU,bijU)) is the ijth element of max(,).

Definition 2.7

Let = (a1,a2,a3) be a TFN then AM() = . The same condition holds for TM membership number.

Definition 2.8

An n×n circulant matrix has the form

Thus a circulant matrix is determined by its first row.

Definition 2.9

A Fuzzy matrix = [aijA᷉ ] is said to be CFM if all the elements of the can are determined completely by its first row. Suppose the first row of is [a1,a2,………..an]Then any element of can be determined [ throughout the element of the first row] as aij= a1(n –i+j+1) with a1(n+ k) = a1k

A CFM is a form of

With entries in [ 0 , 1 ].

Definition 2.10

Let A᷉= [aijA᷉ ] ϵ circulant FMm×n, according to the definition in the representation of the complement of the FM which is denoted by ° and then ° is called CF complement matrix if ° = [(1-aijA᷉)]m×n for all aijA᷉ [0,1]. Then the matrix obtained from the so-called membership value would be the following ° = aijA᷉ = [(1- aijA᷉ ] for all i and j.

Definition 2.11

A TFNM is said to be a circulant TFNM if all the elements of the can are determined completely by its first row. Suppose the first row of is then any element aij of can be determined (throughout the element of the first row) as aij= a1(n –i+j+1) witha1(n+ k) = a1k

A CTFM in the form of

III. Medical diagnosis fuzzy matrix

Let be the set of symptoms of certain diseases, is a set of diseases and is a set of patients. The elements of CTFNMare defined as = (aij)m×l where aij = (aijL,aijM,aijU) is the ijth element of , 0 ≤ aijL ≤ aijM ≤ aijU ≤ 10. Here aijL is the lower bound, aijM is the middling value and aijU is the upper bound.

Procedure 3.1

1. Step 1: Construct a CTFNM(F,) over, where F is a mapping given by F: → F(), and F() is a set of all CTF sets. This matrix is denoted by which is the fuzzy occurrence matrix or symptom-disease CTFM.
2. Step 2: Construct a CTFNM(F,) over, where F is a mapping given by F: → F(), and F() is a set of all CTF sets. This matrix is denoted by which is the fuzzy conformation matrix or symptom-disease CTF number matrix.
3. Step 3: Construct another CTFNM(F1,) over, where F1 is a mapping given by F1: → F(). This matrix is denoted by which is the patient-symptom CTFNM.
4. Step 4: Convert the elements of CTFNM into its membership function as follows: Membership function of aij = (aijL,aijM,aijU) is defined Asif 0 ≤ aijL≤ aijM≤ aijU≤1, where 0 ≤≤ 1. Now the matrix, and are converted into Circulant triangular fuzzy membership matrices namely, and.
5. Step 5: Compute the following relation matrices. = ()mem()()mem it is calculated using Definition 2.5. = ()mem()()mem and = ()mem() (J(−)()mem), where J is the triangular fuzzy membership matrix in which all entries are (1, 1, 1). (J(−)()mem) is the complement of ()mem and it is called a non-symptom-disease triangular fuzzy membership matrix. and are calculated using subtraction operation and = max{,}. The elements of ,,,is of the form yij = (yijL,yijM,yijU) where 0 ≤ yijL ≤ yijM ≤ yijU ≤ 1. = (−). It is calculated using a subtraction operation. The elements of is of the form zij = (zijL,zijM,zijU) ∈ [−1,1] where zijL ≤ zijM ≤ zijU.
6. Step 6: Calculate = AM(zij) and Row’i = Maximum of ith row which helps the decision maker to strongly confirm the disease for the patient.

Case Study:

Suppose there are three patients, and in a hospital with symptoms of fever, sore throat, nausea, and vomiting problem. Let the possible diseases relating to the above symptoms be Normal viral fever and early stage of Swine Flu and Final stage of Swine Flu.

Step 1: We consider the set = {,}as universal set where, and represent the symptoms fever, sore throat, nausea and vomiting problem respectively and the set = {,} where, and represent the parameters Normal viral fever and Early stage of Swine Flu and Final stage of Swine Flu. respectively. Suppose that

• F() = [< e1,(1,2,3) >,< e2,(2,3,4) >,< e3,,(1,3,4) >]
• F() = [< e1,(1,3,4) >,< e2,(1,2,3) >,< e3,(2,3,4) >]
• F() = [< e1,(2,3,4) >,< e2,(1,3,4) >,< e3,(1,2,3) >]

The CTFNM(F,) is a parameterized family (F(), F(), F()) of all TFC number matrix over the set S and are determined from expert medical documentation. Thus the CTFNM(F,) represents a relation matrix and it gives an approximate description of the CTFNM medical knowledge of the two diseases and their symptoms given by =

1. Step 2:

Again we take the set = {,}as a universal set where, and represent the symptoms of fever, sore throat, nausea, and vomiting problem respectively and the set = {,} were, and represent the parameters of Normal viral fever and Early stage of Swine Flu and Final stage of Swine Flu respectively. Suppose that

• F() = [,,]
• F() = [,,]
• F() = [,,]

The CTFNM(F,) is a parameterized family (F(), F(), F()) of all triangular fuzzy Circulant number matrix over the set S and are determined from expert medical documentation. Thus the CTFNM(F,) represents a relation matrix and it gives an approximate description of the CTFNM medical knowledge of the two diseases and their symptoms given by =

1. Step 3:

Consider = {,,}as the universal set where ,and represent patients respectively and = {,,} as the set of parameters suppose that, F1() = [,,] F1() = [,< ,(1,3,4) >, ] F1() = [,,] The CTFNM(F1,) is another parameterized family of triangular fuzzy number matrix and gives a collection of approximate description of the patient-symptoms in the hospital. Thus the triangular fuzzy number matrix (F1,) represents a relation matrix called the patient-symptom matrix given by =

1. Step 4:
2. Step 5: The following relation matrices are computed
3. Step 6: From the above analysis it is obvious that patients P1 and P3 suffer from Swine Flu whereas patient P3 faces Normal Viral fever.

IV. Conclusion

Medicine is one of the fields where fuzzy set theory was recognized early on. The doctor generally gathers patient knowledge from the results of the past history, laboratory test results, and other investigative procedures like x-rays and ultra-sonic rays, etc. The information provided by each of these sources brings varying degrees of ambiguity with it. Thus the best and most accurate definitions of disease entities often use ambiguous linguistic words. Thus in this paper, a Fuzzy set structure has been used to model the medical diagnostic process and decision-making process in several different approaches.

References

1. L.A. Zadeh, Fuzzy sets, Information and Control, 8 (1965) 338-353.
2. H.K.Baruah, Towards forming a field of fuzzy sets, International Journal of Energy, Information and Communications, 2(1) (2011) 16 – 20.
3. H.K.Baruah, The theory of fuzzy sets: beliefs and realities, International Journal of Energy, Information and Communication, 2(2) (2011) 1 – 22.
4. R.Bellman and L.A.Zadeh, Decision Making in a fuzzy environment, Management Science, 17 (1970) B144-B164.
5. A.O.Esogbue and R.C.Elder, Fuzzy diagnosis decision models, Fuzzy Sets and Systems, 3 (1980) 1 – 9.
6. A.R.Meenakshi, Fuzzy Matrix theory, and applications, MJP Publishers, 2008.
7. E.Sanchez, Resolution of composite fuzzy relation equations, Information and Control, 30 (1976) 38 – 48
8. A.K.Shyamal and M.Pal, Triangular fuzzy matrices, Iranian Journal of Fuzzy Systems, 4(1) (2007) 75-87

Chapter – I. Introduction

Background of the study

“The swine flu is evolution, isn’t it? In a way, it’s an evolution of flu.

-Alan Tudyk”

Swine flu is an acute respiratory disease, caused by a strain of Hemophilus influenza type A virus known as H1N1, officially referred as novel A/H1N1. The virus is a mixture of four known strains of influenza a virus: One endemic in humans, one endemic in birds, and two endemics in pigs (swine). Swine influenza was first proposed to be a disease related to human influenza during the 1918 flu pandemic, which was known as Spanish flu, (infected about 500 million people and caused approximately 50 million deaths) At the end of March 2009, an outbreak of novel influenza A/H1N1 infection occurred in Mexico, followed by ongoing spread to all over the world in a short period.1

On 11 June 2009, the World Health Organization (WHO) raised its pandemic alert to the highest level, phase 6, meaning that; the A/H1N1 flu had spread in more than two continents. On June 2010, it had caused over 18,172 deaths in more than 214 countries and overseas territories or communities. Most illnesses, especially the severe ones and deaths had occurred among healthy young adults. Transmission of the new strain is human-to-human. Its symptoms are similar to those of influenza in general. It includes fever, cough, sore throat, body aches, headache, chills, and fatigue. The flu can make chronic health problems worse.2

Vaccines are available for different kinds of swine flu. However, vaccines against the new strain are developed, with safety profiles like seasonal flu vaccines. Knowledge, attitude, and practice (KAP) of people regarding swine flu are a cornerstone in the prevention of virus spread and outbreak. India is ranked 3 rd among the most affected countries for cases and deaths of swine flu globally.3

The highest number of cases were reported in 2009 (27,236), followed by 2010 (20,604) and 2012 (5,054 cases). The highest number of swine flu deaths took place in 2011 (1,763), followed by 2009 (981) and 2012 (405). A sheer volume of cases could easily overstretch already fragile and overburdened health services, especially in developing countries, and cause considerable suffering in human populations around the world. Swine flu has killed 261 people in India in the first 3 months of 2013, with most deaths reported from Rajasthan and Gujarat. A total of 2,329 people tested positive for the Influenza A (H1N1) virus, in 35 states and union territories.4

In a northern state of India, Punjab, total number of confirmed cases was 182 and 42 deaths. These deaths initiated chain of media reports and local physician’s articles on measures to prevent Swine flu being published in the newspaper dailies. When levels of worry are generally low, acting to increase the volume of mass media and advertising coverage is likely to increase the perceived efficacy of Trust in government/media information was more strongly associated with greater self-efficacy and hand washing; whereas trust in informal information was strongly associated with perceived health threat and avoidance behavior.5

‘The Government has been successful in providing information to people on swine flu. Even television channels have played a major role in educating people by inviting doctors and experts in their studios every day to provide information about the deadly virus,’ Information and Broadcasting Minister, Government of India said. The best we citizens can do is to keep ourselves informed about the happenings and the steps we can take to prevent the spread of the flu.6

Adequate amounts of vaccines and antivirals are unlikely to be available early on in a pandemic and later could become ineffective because of resistance. . These factors have focused attention on the use of non-pharmaceutical public health interventions to inhibit human to human transmission and fuelled interest in answering important questions about influenza epidemiology and transmission.7

Need for the study

H1N1 flu is a new influenza virus causing the current pandemic. This new virus was first detected in people in the United States in April 2009. The virus is spreading from person to person in the same way that regular seasonal influenza viruses spread. H1N1 flu is not caused by eating pork or pork products. H1N1 flu is not a food-borne disease; it is a respiratory disease (CDC, 2009b).8

Illness with the new H1N1 flu virus has ranged from mild to severe. Although the vast majority of people who have contracted H1N1 flu have recovered without needing medical treatment, hospitalizations and deaths have occurred.9

The purpose of the study was to find out if students of colleges, Sangli have adequate knowledge of the disease and whether or not the students viewed H1N1 influenza as a threat to their health. 10

This study also investigated knowledge about possible preventive measures that can be taken among the students.

1. What is the current knowledge of students regarding H1N1 influenza?
2. What kind of general information do students have regarding symptoms of H1N1 influenza?
3. What kind of general information do students have regarding the transmission of H1N1 influenza?
4. What kind of general knowledge do students have regarding the treatment of H1N1 influenza?
5. What kind of general knowledge do students have regarding the prevention of H1N1 influenza?
6. What is the students‟ perceived level of threat for contracting H1N1 influenza?
7. Does the students‟ knowledge about H1N1 influenza vary with their perceived level of threat for contracting H1N1 influenza?

Statement of problem

“A study to assess the effectiveness of planned teaching programme on knowledge related to prevention of swine flu among the college going student in selected colleges of Sangli Miraj and kupwad corporation area.”

Objectives of the problem

1. To Assess the existing knowledge among college-going students related to prevention of swine flu in colleges in selected colleges of Sangli, Miraj kupwad corporation area.
2. To evaluate the effectiveness of planned teaching programme on students’ knowledge.

Abstract:

Swine Flu cases are rarely observed but are to be found subsequently increasing in Countries like India. They can be treated if it is detected in early stage. However many times it is difficult to predict as the symptoms are almost similar to other viral fevers. In this paper we propose Artificial Intelligent System for Efficient Swine Flu Detection (ESFD) using Naive Bayesian Classifier on SFdataset created from Knowledge based system. The ESFD is first trained by using training set with symptoms observed in Swine Flu cases and Naive Bayesian Classifier and then test set is applied to predict the if the disease if prevailing or not.

Keywords: Artificial Intelligent System, Efficient Swine Flu Detection (ESFD), Naive Bayesian Classifier, Swine Flu

1. Introduction

The subclass of Influenza A Virus is a virus Influenza a HIN1 causing Swine Flu and was foremost frequent reason behind human influenza also called flu in the year 2009. H1N1 leaves a few strains that are prevalent in human that creates a slight part of every influenza- resembling sickness and a little part of entire influenza that are seasonal. Additional strains of specified virus of other types are also observed in birds and in pigs[1].

The Swine flu is originally generated by viruses related to influenza that are disease related to respiration that affect respiratory organs in pigs, leading to listless actions, nasal secretions, decreased hunger and barking cough. The same types of symptoms are observed in people affected by Human flu as in pigs affected by Swine flu [2, 3].

In USA the Swine Influenza flu was initially in around 1930 seduded in pigs. A lot of investigations were being done. However, the Swine Flu if first observed in few places in Mexico and is then coined as H1N1. Later in USA the same H1N1 virus is observed in people and drastically that has spread a lot and more than 10000 persons are badly affected in over forty plus countries. In 2011 the lastest one is specified to be H3N2v with an influenza A H3N2v. [5,6].

1.1 Existing System

Flu viruses are spread mostly from an individual to another individual using any medium either by sneezing or direct cough from patients suffering from this virus. This can sometimes be affected even by touching the surfaces such as furniture or walls which are touched by the patient by which some people will be hospitalized. If specified virus is not detected on time and eradication then diseases like Swine Flu will be spread rapidly. The manual approaches are time taking [7].

1.2 Proposed System

The proposed System on Artificial Intelligent System is used for efficient Swine Flu Detection which is reliable. This requires a proper training set and test set for training the system. Once the system is trained the test set is used for verifying the system [8].

2. Efficient Swine Flu Detection using Naive Bayesian Classifier

2.1. Swine Flu Symptoms

The symptoms such as headache, Joint Pains, Muscle Pains, Chills, running nose, dry cough and fatigue in any influenzas and they are similar in Swine Flu. However few cases have been reporting that include Diarrhea, fever, Vomiting, and neurological issues.

The high risk is observed in individual with severe complications include elderly age group, kids less than five year of age, children with neurodevelopment anomalies, women who are pregnant and such people who are prone with medical conditions that are abnormal and underlying such as diabetic patients, people suffering from asthma or cardiac patients and such people who are immune suppressed, people suffering from obesity in any age group [2,9].

2.1.1. Severe case Symptoms

It is stated by WHO so as to medical image in stern case patients is strictly completely variant in Swine Flu compared to disease pattern observed throughout epidemics. If the people with sick medical history are pretended and expected headed for high risk, numerous critical cases are also observed in hale and hearty people with good medical history. Though research is presently going on the influencing factors that amplify the drastic danger of rigorous unhealthiness don’t seem to be presently understood. Patients with severe health condition are found to be okay initially. But around three to five days generally they begin to get worse after the onset of symptoms. Fast deterioration is being observed with failure in respiration in several patients within twenty four hours. Immediate Respiratory Support and Intensive Care Unit support is required with mechanical ventilation in case of most patients [5].

Immediate medical attention is recommended by CDC if any experience of warning signs of emergency is observed in person attacked with swine flu [7].

In adults they embody problem in respiratory or squatness of breath, pressure and pain within abdomen or chest, confusion, fast giddiness, , severe vomiting persistently and temperature.

In kids they include difficulty in breathing or speed breathing, skin color turns blue, not accepting fluids, feeling lazy and sleepy, non-interacting, behaves irritable that the children usually don’t hold, symptoms related to other flu that recover but eventually turns to worst cough and high temperature, high temperature with a rash, not eating, having no tear when crying.

2.2.2. Complications

In Swine Flu attacked healthy patients who are suffering from obesity complications are being observed and who are attacked with prior respiratory disease and as principally they are infected by pneumonia disease. This happens most usually in adults who may be required to place under ventilation in ICT due to acute respiratory organ injury. Infections due to bacteria are more commonly observed in children. The prominent cause of secondary bacterial pneumonia is due to Staphylococcus aurous, along with MRSA with a higher mortality rate. Cardiac complications and Neuromuscular are uncommon but there’s an opportunity of occurrence. Fulminate myocarditis and pulmonary emboli were other complications detected [4].

History And Definition:

H1N1 influenza is otherwise called swine influenza. It’s called swine influenza in light of the fact that previously, the individuals who got it had direct contact with pigs. That changed quite a long while prior when another infection raised that spread among individuals who hadn’t been close to pigs.

The capacity to follow episodes of swine influenza in people goes back to examination of the 1918 Spanish flu pandemic, which contaminated 33% of the total populace (an expected 500 million individuals) and caused around 50 million passing. In 1918, the reason for human flu and its connects to avian and swine flu was not comprehended. The appropriate responses didn’t start to rise until the 1930s when related flu infections (presently known as H1N1 infections) were secluded from pigs and afterward people. In 2009, H1N1 was spreading quickly around the globe, so the World Health Organization considered it a pandemic. From that point forward, individuals have kept on becoming ill from swine influenza, yet not the same number of.

While swine influenza isn’t as unnerving as it appeared to be a couple of years prior, it’s as yet essential to shield yourself from getting it. Like regular influenza, it can cause progressively genuine medical issues for certain individuals.

Symptoms:

These, as well, are essentially equivalent to seasonal influenza. They can include:

• Fever
• Cough
• Fatigue
• Body aches
• Sore throat
• Chills
• Stuffy or runny nose
• Headache

Like customary influenza, swine influenza can prompt progressively significant issues including pneumonia, a lung contamination, and other breathing issues. What’s more, it can make a sickness like diabetes or asthma more terrible. On the off chance that you have side effects like shortness of breath, severe vomiting, torment in your paunch or sides, discombobulating, or disarray, call your primary care physician or 997 immediately.

How Do You Prevent It?

A similar route as the seasonal influenza. At the point when individuals who have it cough or sneeze, they shower small drops of the infection into the air. On the off chance that you interact with these drops, contact a surface (like a door handle or sink) where the drops landed, or contact something a tainted individual has as of late contacted, you can get H1N1 swine influenza.

Individuals who have it can spread it one day before they have any indications and upwards of 7 days after they become ill. Children can be infectious for up to 10 days.

In spite of the name, you can’t get swine influenza from eating bacon, ham, or some other pork item.

Diagnosis

To diagnose influenza, including swine influenza, your primary care physician will probably lead a physical test, search for signs and symptoms of flu, and conceivably request a test that recognizes flu infections.

There are a few tests used to analyze influenza, yet not every person who has this season’s cold virus should be tried. As a rule, realizing that somebody has this season’s flu virus doesn’t change the treatment plan.

The most ordinarily utilized test is known as a rapid influenza diagnostic test, which searches for substances (antigens) on a swab test from the nose or back of the throat. These tests can give results in around 15 minutes. In any case, results differ extraordinarily and are not constantly precise. Your primary care physician may determine you to have flu dependent on indications, in spite of a negative test outcome.

Increasingly delicate influenza tests are accessible in some specific clinics and labs.

Treatment:

Most people with influenza, including swine influenza, require just symptoms help. On the off chance that you have a chronic respiratory sickness, your doctor may recommend extra drugs to help assuage your side effects.

There are four FDA-approved antiviral medications that are in some cases recommended inside the main day or two of manifestations to lessen the seriousness of indications and perhaps the danger of intricacies. These include:

• Zanamivir (Relenza)
• Baloxavir (Xofluza)
• Oseltamivir (Tamiflu)
• Peramivir (Rapivab)

However, influenza infections can create protection from these medications.

To make the advancement of obstruction more outlandish and keep up provisions of these medications for the individuals who need them most, specialists hold antivirals for individuals at high danger of intricacies and the individuals who are in close contact with individuals who have high danger of inconveniences.

High-hazard bunches incorporate individuals who:

• Are more youthful than 19 years old and are getting long haul ibuprofen treatment. Utilizing ibuprofen during a viral ailment builds the danger of building up Reye’s disorder, an uncommon yet conceivably lethal malady, in these people.
• Are American Indians or Alaska Natives Alaskans Are in a hospital, nursing home or other long-term care facility.
• Are immunosuppressed because of specific drugs or HIV.
• Are more youthful than 5 years old, especially kids more youthful than 2 years.
• Are 65 years old or progressively settled..
• Are gargantuan, characterized as having a weight record over 40.
• Are pregnant or inside about fourteen days of conveyance, including ladies who have had pregnancy misfortune.
• Have certain interminable ailments, including asthma, emphysema, coronary illness, diabetes, neuromuscular ailment, or kidney, liver or blood infection.

References:

1. Cold, F., Health, E., Disease, H., Disease, L., Management, P., Conditions, S., Problems, S., Disorders, S., Checker, S., Blogs, W., Boards, M., Answers, Q., Guide, I., Doctor, F., A-Z, C., A-Z, S., Medications, M., Identifier, P., Interactions, C., Drugs, C., Pregnant, T., Management, D., Obesity, W., Recipes, F., Exercise, F., Beauty, H., Balance, H., Relationships, S., Care, O., Health, W., Health, M., Well, A., Sleep, H., Teens, H., Pregnant, G., Trimester, F., Trimester, S., Trimester, T., Baby, N., Health, C., Vaccines, C., Kids, R., Cats, H., Dogs, H., Mean?, M., Asleep?, H., Rise, R., Vaping, N., Equipment?, I., Boards, M., Blogs, W. and Center, N. (2019). H1N1 Flu Virus (Swine Flu): Symptoms, Causes, Tests, Treatments, Prevention. [online] WebMD. Available at: https://www.webmd.com/cold-and-flu/flu-guide/h1n1-flu-virus-swine-flu#2 [Accessed 2 Dec. 2019].
2. Medscape.com. (2019). What is the history of H1N1 influenza (swine flu) pandemics?. [online] Available at: https://www.medscape.com/answers/1807048-166821/what-is-the-history-of-h1n1-influenza-swine-flu-pandemics [Accessed 10 Dec. 2019].
3. Mayoclinic.org. (2019). Swine flu (H1N1 flu) – Diagnosis and treatment – Mayo Clinic. [online] Available at: https://www.mayoclinic.org/diseases-conditions/swine-flu/diagnosis-treatment/drc-20378106 [Accessed 10 Dec. 2019].

Swine flu is an acute respiratory tract infection caused by influenza A H1N1 which is characterized by chills, fever, sore throat, muscle pains, severe headache, coughing, weakness/fatigue, and general discomfort. In more serious cases, influenza causes pneumonia, which can be fatal, particularly in the elderly and patients with pre-existing illnesses [1]. Influenza spreads around the world in seasonal epidemics, resulting in the deaths of between 250,000 and 500,000 people every year, up to millions people affected in some pandemic years. On average 41,400 people died each year in the United States between 1979 and 2001 from influenza [2].

The pandemic influenza A (H1N1) started in southern part of Rajasthan in August 2009 and lasted until November 2010. A large number of H1N1 cases and deaths have been reported during this pandemic [1]. After that, in next two years till August 2012, Southern Rajasthan reported little Influenza activity. In September 2012, this influenza A H1N1 virus once again has resurfaced in this region of India. The number of new cases, including fatal cases, continued to increase since 13th September 2012 in Southern Rajasthan. It lasted till 28th February 2013. Swine flu cases again resurfaced in month of January 2015 in Southern region of India. First death reported on 2nd February 2015. Swine flu positivity continues to increase with peak in the month of February and March 2015. Recently sudden outbreak of swine flu was more severe with high mortality and morbidity in compare to that of year 2012. Epidemic was declared On 12 February 2015 in Rajasthan which declared end on 15 April 2015, however, screening of patients of Influenza-like illness was continue in swine flu OPD and infrequent cases of swine flu were found positive. 491(39.37%) patients were found positive for H1N1 and 62 were expired during this period.

Mortality profile is an analytical tool used to identify the various factors responsible for poor outcomes of disease and it can also used to evaluate quality & efficiency of healthcare providers. Aim of this study is to summarise the clinical and epidemiological factors as well as to identify the risk factors associated with mortality among swine flu cases.

Materials & methods

It is a cross-sectional, descriptive, hospital-based study conducted on deceased patients due to swine flu reported at Maharana Bhupal Government Hospital, Udaipur during the outbreak of Influenza A H1N1 in the year 2015. A total about 491 patients were found to be swine positive was 491(39.37%), out of them 62 (12.63%) were expired

A standardized pre-structured questionnaire with consent was filled by help of bed head tickets and by interview of attendants of deceased patients. The questionnaire included clinical and epidemiological data of patients like age, sex, residence, communication detail, clinical signs & symptoms, exposure history, type & numbers of sample collected, time of report received, treatment taken, co-morbid medical conditions, investigation reports & duration of hospitalization.

In our study overall case fatality rate was 12.63%. Our study reported higher CFR in compare to the study of Gerardo Chowell et al (2011) [3] which reported overall CFR 1.2% and highest (5.5%) among people over 60 years. In our study CFR for age group, 31-45 years (16.06%) were highest which is comparable with the study finding of Mahendra Singh et al (2013) [4] at Jodhpur, Rajasthan which stated that highest CFR (20.2%) in the age group of 15-45 years. In contrast to an inception-cohort study conducted by STEVEN A.R. et al (2009) [5] in all Australian and New Zealand intensive care units (ICUs) during the winter of 2009 which reported case fatality rate 14.3%, our study reported slightly lower CFR (12.63%). Our findings were in contrast to a study of CP Sharma et al (2012) [6] reported overall CFR observed was 9.7% which was slightly higher among males (10.2%) than females (9.2%). Our findings were also contrary to findings of study of Prakash B Patel et al (2015) [7] which reported overall CFR observed was 5.91% which was slightly higher among males (6.0%) than females (5.8%) and the difference was not statistically significant. Our study reported higher overall CFR and affected female population.

In our study mortality in the age group of 31-45 years was found to be highest 35.48% (22 cases). In contrast to study of Mahendra Singh et al (2013) [4] at Jodhpur, Rajasthan this showed 22.4% (13) deaths in the 30 to 45-year age group. In study conducted by CP Sharma et al (2012) [6] during 2009-2010 in same study area revealed maximum number of deaths 58.6% were reported in 21-40 year age group. Our findings were corroborating with the study findings of Viresh Ashok Nandimath et al (2015)[8] which reported that highest mortality (50.00%) were observed in 31-45 years age group. Our findings were also contrary to study of Hiren Jadawala et al (2015) [9] which reported highest number of deceased patients (43%) belonged to age group between 40-60 yrs. Our study findings are in contrast with the study of Dr. T. Vasanthi et al (2015) [10] which showed that case fatality was high in the age group greater than 65 years. Our study findings are in contrast with the study of Dr. Kshitij Domadia et al (2015) [11] which showed that Maximum mortality was found in the age group greater than 60 years (29.62%). The higher mortality in Southern Rajasthan region as compare to other regions of country may be due to the fact that the study was restricted to a small geographical area when compared to the entire country and sick patients were referred from the adjacent tribal regions and nearby states which have poor medical infrastructure which consequently led to loss of crucial delay in primary treatment.

Our study reported that majority of deceased patients 46(74.19%) belong to rural background. Mortality during swine flu outbreak was highest in females of rural background 27(43.55%). It coincides with the study conducted by CP Sharma et al (2012) [6] which showed that death were more in the cases belong to rural areas (58.6%) and rural females (52.8%). Our findings corroborates with the study of Chintal Vyas et al (2013) [12] which reported that death among females (34.2%) was higher than males (22.7%) in pandemic period. Our findings were also contrary to study of Hiren Jadawala et al (2015) [9] which reported that deaths were almost equally distributed among male and female. Our study findings corroborate with the study of Dr. Kshitij Domadia et al (2015) [11] which reported that more mortality was seen in females (24.28%) compared to males (19.70%). Reason of more swine flu morbidity and mortality in rural population might be lack of knowledge of preventive measures and delayed availability of treatment facilities.

In our study delay in hospitalization from onset of symptoms was observed median 6.5 days (range 2-20). Our findings corroborate with the study of Rogelio Perez-Padilla et al (2009) [13] which reported delay in hospitalization from onset of symptoms was median 6 days (range 4-13). In the study of Chintal Vyas et al (2013) [12] delay in hospitalization was much lower during pandemic period (4.6±1.7 days) while equivalent during the post-pandemic period (6.3±2.5 days) in compare to our study. Our study reported that majority of expired patients 44(70.97%) had delay of 4-7 days in admission after onset of symptoms. Our finding was similar to study of Hiren Jadawala et al (2015) [9] which reported that majority of expired patients (57.4%) has delay of more than 2 days in admission from onset of symptoms.

Our study reported that majority of expired patients 37(59.68%) were diagnosed within 4-7 days of onset of symptoms while majority of survived patients (38.90%) were diagnosed within 3-5 days of onset of symptoms.

Our study reported that majority of patients 42(67.74%) were expired within 3 days of hospitalization. Our study finding corroborates with the study of Viresh Ashok Nandimath et al (2015) [8] which reported that majority of patients 9 (75.00%) were expired within 2 days of hospitalization. In our study median duration of hospital stay in expired patients was 2 days (range 0-8) which is much lower than the study of Rogelio Perez-Padilla et al (2009) [13] which reported duration of hospitalization in expired patients was median 9 days (range 4-18). Our study reported that median duration from onset of symptoms to death was 9 days (range 4-24) which is much lower than the study of Rogelio Perez-Padilla et al (2009) [13] which reported median duration from onset of symptoms to death was 14 days (range 10-23). In our study mean duration of hospital stay in expired patients was 2.71± 2.07 days which is much lower than the study of Dr. Kshitij Domadia et al (2015) [11] which reported mean duration of hospitalization in expired patients was 9.9 days. It shows that the progression of disease was rapid in present outbreak.

In our study majority of expired patients, 37(59.68%) were not having any risk factors. Among expired patients Pregnancy (11.29%), Diabetes mellitus (12.90%), and hypertension (11.29%) were most common risk factors. In contrast to our findings, the study of Viresh Ashok Nandimath et al (2015) [8] reported that majority of expired patients 9(75.00%) were having any risk factors while hypertension and diabetes were most common risk factors similar to our study. In contrast to our findings, the study of CP Sharma et al (2012) [6] reported that high-risk factors were present in majority (79.31%) of expired patients while Pregnancy (27.5%) was most common followed by diabetes mellitus (17.2%) similar to our study. Our study findings corroborate with the study of Dr. V. R. Malkar et al (2012) [14] which showed that majority of patients 36(60.00%) were not having any risk factor. Study findings of Kashinkunti MD et al (2013) [15] reported that diabetes (50%) and hypertension (45.5%) are most common risk factors. In contrary to our study findings the study of Hiren Jadawala et al (2015) [9] reported that out of 47 patients, 26 (55.3%) had high-risk factors (like hypertension, diabetes, ischemic heart disease, thyroid disorder, liver disorder, seizures, malignancy, pregnancy) in which Hypertension (25.5%) was most common.