statistical modelling of the effect of meteorological parameters on occurrence of measles

STATISTICAL MODELLING

OF THE EFFECT OF

METEOROLOGICAL

PARAMETERS ON

OCCURRENCE OF MEASLES

IN AKURE.

ADEYEMI ADEDAYO OLALEKAN

(2016)

1

CERTIFICATION

I certify that this Project “entitled Statistical modelling of the effect of meteorological

parameters on the occurrence of measles in Akure, Ondo state, Nigeria.” is as a result of the

research undertaken by Adeyemi Adedayo Olalekan with matriculation number

MET/11/4698 and was carried out under the supervision of Dr A. Akinbobola of the

department of Meteorology and Climate Science, Federal university of technology, Akure,

Ondo State

………………………………… ……………………

Dr A. Akinbobola Date / Signature

………………………………… ……………………...

Dr. E.C. Okogbue Date / Signature

(H.O.D.)

2

DEDICATION

This report is dedicated to Almighty God, my source of inspiration and guidance throughout

the period as undergraduate and also to my loving parent Mr. & Mrs. Adeyemi and siblings for their

support throughout the period.

3

ACKNOWLEDGEMENT

My sincere gratitude goes to almighty God for granting me good health, peace and success

throughout my undergraduate program.

Also my appreciation goes to my parent Mr. and Mrs. Adeyemi for their unending love,

support both financially and morally, may the good Lord bless you.

To my dear brothers and sister; Adeyemi Oluwaseun, Adeyemi Temitope, Adeyemi Titilayo

and Adeyemi Tobiloba and to my roommates and friends; Adegbite Oluwatoba and Obafemi

Timilehin for their love and support throughout the Undergraduate program

I also want to express my profound thanks to my project supervisor: Dr. Ademola

Akinbobola who has always been there as father, a lecturer, a friend and a disciplinarian in the

course of undergoing this research.

Finally, I thank the entire lecturers of the Department of Meteorology and Climate Sciences,

F.U.T.Akure, The H.O.D. Dr Okogbue, Prof. Omotosho, Dr.A.Akinbobola, Prof. Odekunle, Prof.

A.A. Balogun,, Dr. A.Adefisan, Dr. I. Balogun, Dr. V. Ajayi, Dr. Oluleye, Mr R.A. Balogun, Mr. K.

Ladipo, Mr. A.B. Okunlola, Mrs B.M. Dada and Mr Gbode for their assistance in the course of this

study.

4

LIST OF TABLES

Table 1. Seasonal index for Measles Occurrence

Table 2. Quarterly index for Measles Occurrence

Table 3. Mean Monthly Correlation between measles occurrence and meteorological

variables using Spearman’s Rank Correlation coefficient

Table 4. Multiple linear regression Model equations

Table 5. Validating Measles’s Model 1

Table 6. Validating Measles’s Model 2

Table 7. Probability of monthly occurrence of Measles

Table 8. Relative Risk of meteorological parameters with respect to measles.

5

LIST OF FIGURES

Fig 1. Map of Ondo State showing Akure.

Fig 2 Map of Nigeria showing the study area, Akure.

Fig 3. Graph of annual distribution of measles

Fig 4. Mean Monthly variation of Relative Humidity and measles

Fig 5. Mean Monthly variation of Min Temp and measles

Fig 6. Mean Monthly variation of Max Temp and measles

Fig 7. Mean Monthly variation of rainfall and measles

Fig 8. Mean Monthly variation of Solar Radiation and measles

Fig 9. Seasonal trend in reported measles cases

Fig 10. Seasonal Variation of Measles with RH

Fig 11. Seasonal Variation of Measles with Tmin

Fig 12. Seasonal Variation of Measles with Tmax

Fig 13 Seasonal Variation of Measles with Rainfall

Fig 14. Seasonal Variation of Measles with Solar Radiation

Fig 15. Observed and Predicted measles patients for Model 1


6

Abstract

This research work is aimed at evaluating the effect of meteorological parameters on the prevalence

of measles using statistical method so as to help make policies related to current control and possible

future elimination strategies. Measles occurrence is a major public health concern in this country and

it is an infectious, vaccine-preventable disease. It kills an estimated 750,000 children each year.

Over half of the deaths occur in Sub-Saharan Africa. Incidence is reducing with more children

getting vaccinated.

The weather variables used are monthly Relative humidity, rainfall, solar radiation, maximum and

minimum temperature and were obtained from meteorological department of the ministry of

agriculture, fisheries and forest reserve, Akure, Ondo state. Monthly data of reported cases of

measles between 2009 and 2014 from State hospital, Akure Ondo state. Graphical representations

were used to determine the variation of weather parameters on monthly and seasonal pattern.

Spearman’s Rank correlation coefficient to test and identify the strength of the relationship between

the monthly measles incidence and the weather parameters. The P-value was used to determine the

significant level of the weather variables on the occurrence and the variables to be used in the

predictive equation. Poisson Multiple regression models in generalized Linear models (GLMs) were

used to develop predictive models. Also, Poisson probability distribution function was used to

determine the monthly probability of the occurrence of the diseases and to know the weather

variables that lead to statistical changes in clinical-reported malaria cases.

The weather variable mean monthly variation shows that Maximum temperature, Minimum

temperature and solar radiation and measles occurrence are directly related and others are inversely

related. The correlation shows that there is a huge effect of these variables on measles especially

maximum and minimum temperature and solar radiation with 0.59, 0.62 and 0.64 all at 95%

confidence level. May and April showed highest mean monthly clinical reported cases of measles

and in terms of seasons, the transition season to early rainy season have highest reported cases of

measles occurrence which accounted for 41% of the total occurrence of reported cases of measles

occurrence. Two statistical models were developed in estimating the occurrence with the first model

developed using the whole weather parameters in the study and the second model developed using

only the weather parameters that have positive correlation at 95% confidence level with measles

prevalence. The probability of occurrence of the disease estimated by both models showed that

model 2 performed better though both models underestimated. Error analysis was done to determine

the reliability of the estimated values of both model 1 and model 2 and shown graphically. The

7

relative Risk of measles occurrence associated with relative humidity, Max temp, Min temp, rainfall,

solar radiation are 0.3312, 2.4539, 29.7181, 0.98399, 0.27831 respectively with Min temperature

having the highest risk.

This research will help in estimating measles prevalence and probability of occurrence overtime with

the model developed.

Keywords: measles, weather variables, seasons, statistical model.

8

Title Page…………………………………………………………………………………………………i

Certification……………………………………………………………………………………………..ii

Dedication………………………………………………………………………………………………iii

Acknowledgements…………………………………………………………………………………iv

List of Tables……………………………………………………………………………………………v

List of Figures…………………………………………………………………………………………..vi

Abstract………………………………………………………………………………………………….vii

TABLE OF CONTENTS

CHAPTER ONE .............................................................................................................................................. 10

INTRODUCTION ......................................................................................................................................... 10

1.1. Background to the Study ............................................................................................................... 10

1.2. Statement of the Research Problem ............................................................................................... 11

1.3. Research Questions ....................................................................................................................... 12

1.4. Aim And Objectives ...................................................................................................................... 12

1.5. Significance of the Study ............................................................................................................... 12

CHAPTER TWO ............................................................................................................................................. 14

LITERATURE REVIEW .............................................................................................................................. 14

CHAPTER THREE......................................................................................................................................... 20

RESEARCH METHODOLOGY .................................................................................................................. 20

3.1. Study Area .......................................................................................................................................... 20

3.2. Data..................................................................................................................................................... 21

CHAPTER FOUR ........................................................................................................................................... 23

RESULTS AND DISCUSSION .................................................................................................................... 23

4.1. Annual distribution of Measles occurrence ........................................................................................ 23

4.2. Monthly Variation of Meteorological variables with Measles occurrence ......................................... 23

4.3. Seasonal variation of meteorological parameters and measles Occurrence ....................................... 27

4.4. Statistical relationship between Measles and meteorological variables ............................................. 31

4.5. Weather-Disease Statistical Model ..................................................................................................... 32

4.6. Probability of occurrence of the diseases ........................................................................................... 35

4.7. Models Performance ........................................................................................................................... 36

4.8. Error Analysis ..................................................................................................................................... 36

4.9. Relative Risk of meteorological parameters with respect to measles. ................................................ 37

9

CHAPTER FIVE ............................................................................................................................................. 38

CONCLUSION AND RECOMMENDATION ............................................................................................ 38

5.1. Conclusion .......................................................................................................................................... 38

5.2. Recommendation ................................................................................................................................ 39

References ........................................................................................................................................................ 40

10

CHAPTER ONE

INTRODUCTION

1.1. Background to the Study

While our personal health may seem to relate mostly to prudent local environmental

exposures and health care access, sustained population health requires the life supporting “services

“of the biosphere. Population of all animal species depend on supplies of food and water, freedom

from excess infectious diseases and the physical safety and comfort conferred by climatic stability.

The world’s climate system is fundamental to this life support (WHO, Climate Change and Human

Health-Risk and Response, 2003).The characteristics, geographical distributions and seasonal

variations of many infectious diseases are prima facie evidence that their occurrence is linked to

weather and climate. Factors such as temperature, precipitation and humidity affects the life cycle of

many disease pathogens and vectors (both directly and indirectly through ecological changes) and

this can potentially affect the timing and intensity of disease outbreaks. Human societies have had

long experience of naturally occurring climatic vicissitudes. Many diseases are highly sensitive to

changing temperatures and precipitation. These include common diseases such as malaria, typhoid,

cough, diarrhoea, measles, asthma, meningitis and dengue fever as well as other major killer such as

malnutrition and diarrhoea (WHO, Climate Change and Human Health, 2007).

Researchers such as (Kenneth, Thomas, & Rebecca, 2008) have attempted to explore this

important aspect of human life: health and how the weather elements of a place could be a

major determinant of the state of health of the people. Some of these effects are positive while

others are negative, some are direct and some indirect. These clearly indicate the nature of

relationship that exists between weather and disease variables.

Climate variability and its impacts on human health are areas of research that have been

receiving very much attention from scientists and policy makers all around the world over the last

11

decade or so. The subject of climate change and variability is older than the subject of its impacts

(Chen, 2002) Climatic factors influence the emergence and re-emergence of infectious diseases in

addition to multiple human, biological, and ecological determinants.

Climatologists have identified upward trends in global temperatures and now estimate

an unprecedented rise of 2.0 degrees centigrade by the year 2100. Of major concern is that these

changes can affect the introduction and dissemination of many serious infectious diseases (Patz,

1996)

The temporal and spatial changes in temperature, precipitation and humidity expected to

occur under different climatic scenarios could affect the biology and ecology of vectors and

intermediate hosts and consequently the risk of disease transmission (WHO, 2005). Climatic

factors, particularly temperature and rainfall, may have a profound impact on transmission cycles of

diseases by influencing the availability of vector breeding sites, extending vector longevity, altering

host breeding of migration pattern and maintaining aggregation of vectors and host around water

bodies (Hensel, 1979)

1.2. Statement of the Research Problem

The studies of the relationship between weather and the occurrence of diseases have taken

various approaches globally; (Akinyemi Gabriel Omonijo, 2011) studied the Effect of thermal

environment on the temporal, spatial and seasonal occurrence of measles in Ondo state, Nigeria.

Their result shows that a high transmission of measles occurred in the months of January to May

during the dry season when human thermal comfort indices are very high. (Bukhari, 2009) Studied

temperature and rainfall variability and the outbreak of meningitis and measles in Zaria L.G.A. He

observed that measles showed a negative relationship with rainfall. There is no study to the best of

12

my knowledge that correlates several climatic elements with common diseases and builds a statistical

model to show the effect and help predict future occurrences especially in the study area.

1.3. Research Questions

1. What is the relationship between this disease and weather condition?

2. Does the occurrence of this disease exhibit seasonality?

1.4. Aim And Objectives

Aim

To evaluate the effect of meteorological parameters on the prevalence of measles

using statistical methods

Objectives

The objectives of this project are to;

assess the correlation between meteorological parameters and the prevalence of measles

generate a weather-disease statistical model which can be used to estimate the number of

occurrence.

1.5. Significance of the Study

Climate is an inevitable dominant element of man’s environment and powerful factors

in his well-being. Its critical elements such as temperature, rainfall and humidity can

influence diseases. Although medical science has made remarkable progress in fighting

diseases through modern technology, the health of the human population is still

influenced to a great extent by weather and climate. It is also good to know that the

13

environmental conditions that favour persistence of the vectors that transmit most of these

diseases need also to be considered.

This work investigates if any relationship exists between Max and Min temperature,

relative humidity, solar radiation and rainfall and measles occurrences and if these

parameters can be used to explain the monthly and seasonal variation in measles

occurrence. The parameters which have the most significant influence on the disease are

further studied using statistical analysis to ascertain this level of significance.

This study is quite significant because it will help in determining the role that

meteorological elements play in measles outbreak and transmission. It will also provide

information on what enhances the survival of the causative agents of the diseases. This

study therefore wishes to device a statistical model to help in prediction of future

occurrences of measles in the study area

14

CHAPTER TWO

LITERATURE REVIEW Every day man, wherever he may be on the earth’s surface has to live with the weather. In

regions of the world where weather exhibits day to day changes, it is general to open a conversation

with a few commands about the weather, with the result that we are made more and more aware of

the elements and the part they play in our lives. The geographical location is a key determinant of the

sensitivity of a species to environmental change. A change in the suitability of the weather within the

current geographical distribution of the disease will after the development, survival and reproduction

rate of vectors and pathogens and so affect the intensity of disease transmission and resultant

exposure of the population to the disease.

Scientific interest in the role of the environment, including weather and climate, in the

dynamics of infectious disease has been further stimulated by the growing problems of emergence

and re-emergency of infectious diseases despite series of intervention policies worldwide (Lipp E.K.,

2002). For example, measles has remained a public health challenge despite the enormous efforts by

the World Health Organization (WHO) and United Nations Children’s Fund (UNICEF) towards

reducing the global burden of this disease.

Measles, not just another viral exanthema is a highly communicable disease predominantly of

the pre-school and early school-age children (2-5 years). It is one of the six killer diseases of

childhood caused by a paramyxovirus commonly seen in the tropics. It is characterized by fever,

cough, coryza, and conjunctivitis. In other words, measles is also called rubella, and is a highly

contagious respiratory infection that is caused by a virus. It causes a total – body skin rash and flu-

like symptoms, including a fever, cough, and runny nose. The measles virus is from the family

parmyxovirus which normally grows, in the cell that lines the back of the throat and lungs. Measles

is a human disease and is not known to occur in animals. When the virus is breathing into the

respiratory tract of an uninfected individual, it passes through the cells of the tiny bronchioles into

15

the blood stream; the virus enters into the lymphatic system and begins to multiply or about 10-14

days (incubation period) (Duke, 2003).

Since measles is caused by a virus, there is no specific medical treatment and the virus has to

run its course. But a child who is infected should be sure to receive plenty of fluid and rest, and be

kept from spreading the infection to others (Healthscout, 2010). Severe measles is more likely among

poorly nourished young children, especially those with insufficient vitamin A, or whose immune

systems have been weakened by HIV/AIDS or other diseases. Most measles-related deaths are

caused by complications associated with the disease. Complications are more common in children

under the age of five, or adults over the age of 20. The most serious complications include blindness,

encephalitis (an infection that causes brain swelling) severe diarrhoea and related dehydration, ear

infection or severe respiratory infections such as pneumonia. As high as 10% of measles cases result

in death among populations with high levels of malnutrition and a lack of adequate health care

(Coleman, 2010). Unvaccinated young children are at highest risk of measles and its complications,

including death. Any non-immune person (who has not been vaccinated or previously recovered

from the disease) can become infected. In 2008, about 83% of the world’s children received one dose

of measles vaccine by their first birthday through routine health services-up from 72% in 2000. Two

doses of the vaccine are recommended to ensure immunity, as about 15% of vaccinated children fail

to develop immunity from the first dose.

(Akinbobola A., 2010) used Monthly temperature, relative humidity and rainfall for the

period 1990- 2003 for Akure, a city in the southwest to establish a relationship with Measles

occurrences and got threshold values of these Meteorological variables showing that high measles

attack occurred for a distinct temperature range of 32-340C if the relative humidity is not too high

(60%-80%).

(Akinbobola A., 2010) investigated if any relationship exists between temperature, relative

humidity and rainfall and measles occurrences and if these parameters can be used to explain the

16

monthly, inter-annual and seasonal variation in measles occurrence but three weather variables will

not explicitly show the relationship compared to five weather parameters.

The extensiveness of the disease at any time depends upon the number of susceptible persons

in a community. Children who are malnourished are mostly, susceptible to the disease. Young

infants between 4 and 6 months are vulnerable to measles because of reduced maternal anti bodies in

women with immunosuppressive disease (Coleman, 2010). The disease kills an average of one out of

every ten afflicted children in rural areas where treatment possibilities are very few or even non-

existent. Though as simple as the disease may look and despite that it is preventable, it is one of the

diseases with high mortality rate in children (Ray, 1970) . Most deaths are caused by complications

of the viral infection. There is increased mortality rate in infants below the age of nine (9) months

who are too young to have been vaccinated against the disease since rates of sero-conversion are

low. Measles is a handicap for the future of the community since it wipes out or disables a large

proportion of its children; therefore it calls for intervention in order to enhance or promote the future

of the community. (Ray, 1970). Despite the advent of measles vaccine, the illness remains one of the

most severe infectious diseases of the developing world. Over two Million children die of measles

each year. The greatest mortality rate is seen in the poor regions of the world where access to basic

health care services such as clinics, vaccines etc are limited (Coleman, 2010).

Genetically and anti-genetically, MeV is related closely to viruses that are pathogens of

sheep, goat and cattle ( (T, 1999); (Sheshberadaran H, 1986); weather patterns are known to play a

significant role in the transmission of such viruses ( (AG, 2007); (I.C., 2003)). Measles virus is

assumed to have evolved in an environment where the above mentioned animals and humans live in

close proximity (McNeil, 1976) after the commencement of livestock farming and domestication of

animals in the early centres of civilisation in the Middle East (Furuse Y, 2010). Today, measles

remains one of the top ten leading causes of death globally ( (Strebel P, 2003); (WHO, 2007); (GHC,

17

2009)) and remains prevalent in many developing countries, especially in parts of Africa and Asia

where more than 20 million measles cases are reported annually (WHO, 2009)

In Nigeria, measles is an important cause of childhood morbidity and mortality. In January

2007, a total of 1,346 patients were admitted to hospital due to measles, of which 62% of cases were

aged 1–4 years and 23% were between 5 and 14 years of age (WHO, 2007). Measles outbreaks have

been increasingly common in Nigeria (WHO, 2008) despite the adoption of the WHO’s four-pronged

strategy and The National Program on Immunization with the aim of reducing fatal cases of measles

to near zero. (Persinger.P., 1975) found that variability in occurrences of many diseases is related to

seasonal trends in temperatures, (although significant year-to-year differences do occur). Bronchitis,

peptic ulcers, adrenal ulcer, glaucoma, goitre and eczema are related to seasonal variations in

temperature, (W., 1963).

The studies of (Egunjobi, 1993) and (Adetunji O.O., 2007) stated that measles in Nigeria

sometimes occurs immediately after the end of the rainy season, and often reaches epidemic

proportions in the dry season during February, March and April.

There is a general agreement that weather has a profound influence on human wellbeing.

Most of the researches have been done by medical scientists and a minor amount of the work has

been performed by climatologists. For example; some of the researchers suggest that extreme

weather events appear to have the greatest influence on health. (Driscoll, 1971b) correlated daily

mortality for 10 cities with weather conditions in January, April, July and October and found that

large diurnal variations in temperature, dew-point and pressure were associated with many high

mortality days. In addition, hot humid weather with concomitant high pollutant concentrations were

also contributory mechanisms other studies do not attribute large variations in mortality to extreme

events, but rather to the normal seasonal changes in weather (Ericsson, 2010).

18

Climate has potential to alter the average exposure of human populations to vector-borne

diseases by changing the geographical distribution of condition that is suitable for the vector and

diseases pathogen. An increase in global temperature will result in an expansion of warm

temperature regiment into higher altitude and latitudes. Any associated changes in rainfall in tropical

and subtropical zones will also render habitats more or less suitable for vector. In addition, the

implications of the asymmetrical increases of temperature with global warming for the epidemiology

of victor-borne disease need to be clarified. Greater effects of low temperatures on vectors survival,

behaviour, and disease transmission in cold limited climates than from smaller and less frequent

increases in extreme maximum temperatures. These changes would make temperature environments

more receptive to many tropical vector-borne diseases while having less negative efforts on tropical

environments (Gagge, 1979).

Measles and Meningitis rarely occur, but when they occur, it is usually during the hot period

(Marcus, 2012). Socioeconomic, poor hygiene/sanitation and environmental conditions are major

driving factors responsible for the seasonal fluctuations in measles transmission in Ondo State,

Nigeria. (Akinyemi Gabriel Omonijo, 2011)

The disease kills an average of one out of every ten infected children especially in the tropics.

Most deaths are caused by complications of the viral infection such as when it is super imposed by

other bacterial infections such as streploredpneumonea or when it suppresses the immunity of the

individual. Between 1992 – 1997, the West Africa sub-region reported the highest measles morbidity

(121 measles cases per100,000 inhabitants) while significant, reduction in reported measles

incidence and mortality was observed in South Africa sub-region (Coleman, 2010). While the

African nations are still battling with the disease, the Asian and European Countries have long ago

eliminated it to almost an in-existing rate. In China, the average annual measles incidence decreased

from 9.0 to 5.7 cases per 100,000 population and mortality rate falls from less than 0.3-0.1 death per

million population as at 2000 (Coleman, 2010). Most of the studies like on association between

19

climatic variation and disease incidence could not account for the complex web of factors that

influence diseases and thus may not be reliable indicators of future change. Most of the studies only

used a single parameter of climate i e. temperature and correlate it with a single disease.

Meteorological Factors and Measles Occurrence in Akure,Ondo State, Nigeria (Akinbobola, 2010)

made use of just three weather parameters (Temperature, Rainfall, Relative humidity) to show the

relationship with measles occurrence. There is no statistical model to the best of my knowledge that

has been developed to show the effect of meteorological parameters on measles especially in the

study area. This is one of the concerns of this study. This is the gap that this study will have to fill.

The disease is most prevalent when the temperatures is in range of between 32oC to 34oC

and relative humidity of between 60% and 80%.Furthermore, at temperatures below 32oC and

relative Humidity of less than 60%, there were very few measles patients (Akinbobola, 2010).

Persinger, 1980 found that variability in occurrences of many diseases is related to seasonal trends in

temperatures, (although significant year-to-year differences do occur). Bronchitis, peptic ulcers,

adrenal ulcer, glaucoma, goitre and eczema are related to seasonal variations in temperature, (Trop,

1963).

20

CHAPTER THREE

RESEARCH METHODOLOGY

3.1. Study Area

Akure (7° 15′ 0ˈˈN, 5° 11′ 42ˈˈE) is a city in the south-western Nigeria and is the largest city

and capital of Ondo state. The city has population of 588,000 based on 2006 population census. It

has a tropical wet-and-dry climate. Ondo State has a mean annual rainfall of about 1,500 mm and

2,000 mm in the derived savannah and humid forest zones, respectively (Adefolalu, 1997)

Fig 1. Map of Ondo State showing Akure.

21

Fig. 2 Map of Nigeria showing the study area, Akure.

3.2. Data

The monthly meteorological data of Maximum temperature, Minimum temperature, Solar Radiation,

Rainfall and relative humidity from 2009 to 2014 was obtained from Ministry of Agriculture,

Fisheries and Forest Resources and monthly data of reported cases of malaria between 2009 and

2014 from State hospital, Akure Ondo state.

3.2.1. Method of Analysis:

Graphical representations to determine the variation of weather parameters on the monthly and

seasonal pattern.

Spearman’s Rank correlation coefficient to test and identify the strength of a relationship

between the monthly measles incidence and the weather parameters.

22

r = 1 - 6 ∑ 𝑑2

𝑛3−𝑛 or r = 1 –

6 ∑ 𝑑2

𝑛(𝑛2−1)

Where: d = difference btw two ranks

n = numbers of samples

The range of the spearman’s rank correlation coefficient is btw -1 to +1

The seasonal index using the formula;

Seasonal Index = Average of season

Total Average of all seasons∗ 100

Quarter Index = Average Quarter

Total average of all Quarters ∗ 100

Poisson probability distribution function will be used to build a model to determine the monthly

probability of the occurrence of the diseases and to know the weather variables that lead to

statistical changes in clinical-reported malaria cases

𝑃 (X =𝑥

𝜆) =

𝑒−𝜆λ𝑥

𝑥!

x = number of patients

λ = Monthly average

e = constant (2.87)

The Relative Risk of the effects of weather parameters on the occurrence of Measles will be

established from multiple linear equations derived. (Y = mx + c)

RR = exp(m) RR = Relative Risk

m = coefficient of the weather parameters

23

CHAPTER FOUR

RESULTS AND DISCUSSION

4.1. Annual distribution of Measles occurrence

From 2009 to 2014 the rate of reported clinical measles cases has been decreasing at the rate

of 3 patients per year (figure 3). Reported measles cases increased from 2009 to 2011, then there was

a sharp decrease from 2011 to 2012. Afterwards a sharp increase from 2012 to 2013 and finally a

decrease 2013 to 2014.

Fig 3. Graph of annual distribution of measles cases in Akure.

4.2. Monthly Variation of Meteorological variables with Measles occurrence

Variation between Relative Humidity and Measles Occurrence

Figure 4 shows the Monthly variation of Relative Humidity and Measles occurrence for the

year 2009 to 2014. It was observed that relative humidity was at its peak in August and September

while measles occurrence was in March. It can be seen that relative humidity has little effect on the

Measles = -2.5429*year + 5210.1

50

60

70

80

90

100

110

120

130

140

2008 2009 2010 2011 2012 2013 2014 2015

Mea

sles

Case

s

Years

24

occurrences of measles because no consistency was observed between relative humidity and measles

except the incidence of Measles that comes during the month of the first increase in Relative

Humidity. This is similar to the result of (Moses et al. 2012)

Fig 4. Mean Monthly variation of Relative Humidity and measles cases in Akure

Variation between Minimum Temperature and Measles Occurrence

Fig 5 shows the monthly variation between minimum temperature and Measles. The peak of

minimum temperature occurred on March followed by February, measles occurrence followed the

same pattern, and this shows that high minimum temperature has a strong effect on the occurrences

of measles, this is similar to the findings of (Akinbobola 2006).

Fig 5. Mean Monthly variation of Min Temp and measles cases in Akure

0

5

10

15

20

68.070.072.074.076.078.080.082.0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mea

sles

cas

es

Rel

ativ

e H

um

idit

y (

%)

MonthsRelative Humidity measles

0

5

10

15

20

17.0

18.0

19.0

20.0

21.0

22.0

23.0


Mea

sles

cas

es

Mim

imu

m T

emp

erat

ure

(ºC

)

MonthsTmin measles

25

Variation between Maximum Temperature and Measles Occurrence

Fig 6 shows the monthly variation between Maximum temperature and Measles. The peak of

Maximum temperature occurred on March followed by February, measles occurrence followed the

same pattern, and this shows that high Maximum temperature has a strong effect on the occurrences

of measles, this is similar to the findings of (Akinbobola 2006).

Fig 6. Mean Monthly variation of Max Temp and measles cases in Akure

Variation between Maximum Temperature and Measles Occurrence

Fig 7 shows the Monthly variation between Rainfall and Measles. It was observed that

Measles incidences were high during the months of March and April which matches with the months

with little rainfall, this revealed that there is a slight relationship between rainfall and Measles

Occurrence.

0

5

10

15

20

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0


Mea

sles

Max

imu

m T

emp

erat

ure

(ºC

)

MonthsTmax Measles

26

Fig 7. Mean Monthly variation of rainfall and measles cases in Akure

Variation between Solar Radiation and Measles Occurrence

Fig 8 shows the Monthly Variation of Solar Radiation and Measles Occurrence. It shows

Solar Radiation being at its peak in January and February which are associated with dry season.

Measles occurrence being at its peak in March which is the peak of dry season. This shows that there

is a strong relationship between Solar radiation and Measles Occurrence for the year 2009 to 2014.

Fig 8. Mean Monthly variation of Solar Radiation and measles cases in Akure

02468101214161820

0.0

50.0

100.0

150.0

200.0

250.0

Mea

sles

case

s

Ra

infa

ll (

mm

)

Monthsrainfall measles

0

2

4

6

810

12

14

16

18

20

0.00

2.00

4.00

6.00

8.0010.00

12.00

14.00

16.00

18.00

20.00


Mea

sles

Sola

r R

adia

tio

n

(MJ/

m^2

/day

)

Monthssolar rad measles

27

4.3. Seasonal variation of meteorological parameters and measles Occurrence

The transition season, between Dry Season and Wet season, is the season with the highest

occurrence of measles with 41% of the total annual occurrence. Measles cases are more prevalent

during transition and dry season where Relative humidity and Rainfall are moderate but maximum

and minimum temperature and Solar Radiation are high

Table 1.Seasonal index for Measles occurrence

S/N SEASONS SEASONAL INDEX (%)

1 Dry Season 39

2 Transition 41

3 Wet Season 20

Table 2.Quarterly index for Measles occurrence

S/N QUARTERS QUARTERLY INDEX (%)

1 1st Quarter 33.3

2 2nd Quarter 30.3

3 3rd Quarter 12.1

4 4th Quarter 24.2

28

In table 1, the dry season has the highest occurrence of measles with the value of 39% followed by

the transition season with value 41% and wet season with value 20% meanwhile the quarterly index

in table 2 revealed that the 1st quarter has the highest occurrence of measles with value 33.3%

followed by the 2nd quarter, 30.3% then the 4th quarter, 24.2% and finally the 3rd quarter with value

12.1%.

Fig 10 shows seasonal variation of Relative Humidity and measles. As showed in the graph,

the peak of measles occurs during the transition season to wet season which signifies that little

amount of relative humidity is favourable for the production of measles infection

Fig 10.Seasonal Variation of Measles with RH cases in Akure

Fig 11 shows the seasonal variation of minimum temperature and measles. The transition

season is the season with peak mean seasonal minimum temperature which is the season with the

highest measles cases and dry season is the season with lowest mean seasonal minimum temperature

and wet season is the season with lowest cases of measles, this showed that high minimum

temperature is highly favourable for measles and affect the measles infection.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

71.0

72.0

73.0

74.0

75.0

76.0

77.0

78.0

79.0

80.0

DRY SEASON TRANSITION WET SEASONM

easl

es

Rel

ativ

e H

um

idit

y (%

)

SeasonsRH MEASLES

29

Fig 11.Seasonal Variation of Measles with Tmin cases in Akure

Fig 12 shows the seasonal variation of maximum temperature and measles. This shows dry season

having the highest maximum temperature. Measles occurrence increases with the increase in the

maximum temperature and decreases with decrease in maximum temperature, which revealed that

maximum temperature has a direct and strong effect on measles case

Fig 12.Seasonal Variation of Measles with Tmax cases in Akure

0.0

2.0

4.0

6.0

8.0

10.0

12.0

20.4

20.6

20.8

21.0

21.2

21.4

21.6

21.8

22.0

22.2

22.4

DRY SEASON TRANSITION WET SEASON

Mea

sles

Min

imu

m T

emp

erat

ure

(̊c)

SeasonsTmin MEASLES

0.0

2.0

4.0

6.0

8.0

10.0

12.0

26.0

27.0

28.0

29.0

30.0

31.0

32.0

33.0

34.0


Mea

sles

Max

imum

Tem

per

ature

(̊c)

SeasonsTmax MEASLES

30

Fig 13 shows the seasonal variation of rainfall and measles. As showed in the graph that the

peak of measles occurs during the transition season while the lowest record of measles occurrence is

during the wet season which signifies that high rainfall is not favourable for the prevalence of

measles.

Fig 13 Seasonal Variation of Measles with Rainfall cases in Akure

Fig 14 shows the seasonal variation of measles and solar radiation. Measles occurrence has

its peak at the transition period and solar radiation is very high during that period too which signifies

that solar radiation is a major factor in measles occurrence.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0


Mea

sles

Rai

nfa

ll (

mm

)

SeasonsRAINFALL MEASLES

31

Fig 14.Seasonal Variation of Measles with Solar Radiation cases in Akure

4.4. Statistical relationship between Measles and meteorological variables

Table 3 shows the correlation Rainfall (RR), Relative Humidity (RH), Minimum temperature

(Tmin), Maximum temperature (Tmax), and Solar radiation (SR) have with measles occurrence.

Maximum temperature, Minimum temperature and Solar radiation showed a significant relationship

with measles occurrence at 95% confidence level while Rainfall and Relative humidity have a

negative correlation which means a decrease in Rainfall and Relative humidity leads to an increase in

measles occurrence.

Mean Monthly Correlation between measles occurrence and meteorological variables

using Spearman’s Rank Correlation coefficient

Table 3.

S/N Meteorological Parameters R R2 P-value

1 Rainfall (RR) -0.37063 0.137366 0.235621

2 Maximum Temperature (Tmax) 0.587413 0.345054 0.044609

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0


Mea

sles

So

lar

Rad

iati

on

(m

j/m

^2/d

ay

SeasonsSR MEASLES

32

3 Minimum Temperature (Tmin) 0.615385 0.378698 0.03317

4 Relative Humidity (RH) -0.60839 0.37014 0.035806

5 Solar Radiation (SR) 0.643357 0.413908 0.024003

4.5. Weather-Disease Statistical Model

Statistical models for estimating the occurrence of measles were developed using Poisson

Multiple Linear Regression in Generalized Linear Models, (GLMs) since it has been found out that

some of the meteorological parameters have high correlation with the diseases as it can be seen in

Table 3.

Two models were developed for the disease; Model 1 was developed using all the

meteorological parameters while model 2 was developed using only the meteorological parameters

that has positive correlation with measles occurrence. Five out of six years data were used to develop

the model while the 6th was used to validate the models. The Models are as follows:

Model 1:

Measles = 13.6474 - 1.105012765RH + 0.897680391Tmax + 3.391756234Tmin -0.016135948RR

- 1.279017197SR

Model 2:

Measles = 7.5416079 + 1.1085015Tmax + 3.0288238Tmin - 1.043331SR - 1.081946RH

In Model 1, Relative humidity, Maximum temperature, minimum temperature, rainfall and

solar radiation were used while in Model 2, Maximum temperature, Minimum temperature, Solar

radiation and Relative Humidity were used since they show strong effect on measles occurrence with

33

correlation of 0.587413, 0.615385, 0.643357 and -0.60839 respectively all at 95% confidence level.

The negative figure for Relative humidity shows that Relative humidity must be low for measles

occurrence.

Multiple linear Regression Models

The table below shows the Stepwise multiple linear Regression Models of the effect of weather

parameters on measles occurrence singularly and combined.

Table 4 multiple linear regression Model equations

S/N EQUATIONS

1. M1 = 27.49825 - 0.30217RH

2. M2 = -9.60097 + 0.460336MaxT

3. M3 = -35.5541 + 1.851642MinT

4. M4 = 5.517414 - 0.00645RR

5. M5 = -2.67716 + (0.508888SR)

6. M6 = - 100.1920035 + 2.168027582Tmax + 2.3827087244Tmin - 0.675342774SR

7. M7= 7.5416079 + 1.1085015Tmax + 3.0288238Tmin - 1.043331SR - 1.081946RH

8. M8 = 13.6474 - 1.105012765RH + 0.897680391Tmax + 3.391756234Tmin - 0.016135948RR

- 1.279017197SR

Models Validation

Validating Measles’s Model 1

34

Table 5.

MONTHS OBSERVED ESTIMATED RESIDUAL % DIFFERENCE

JANUARY 6 1 5 86

FEBRUARY 7 13 -6 -85

MARCH 4 21 -17 -422

APRIL 6 16 -10 -173

MAY 10 16 -6 -58

JUNE 8 14 -6 -78

JULY 0 11 -11 0

AUGUST 0 3 -3 0

SEPTEMBER 7 9 -2 -33

OCTOBER 4 14 -10 -258

NOVEMBER 5 10 -5 -106

DECEMBER 0 11 -11 0

Validating Measles’s Model 2

Table 6.

MONTHS OBSERVED PREDICTED RESIDUAL % DIFFERENCE

JANUARY 6 13 -7 -111

FEBRUARY 7 12 -5 -69

MARCH 4 20 -16 -398

APRIL 6 12 -6 -106

MAY 10 10 0 5

35

JUNE 8 10 -2 -29

JULY 0 6 -6 0

AUGUST 0 4 -4 0

SEPTEMBER 7 5 2 24

OCTOBER 4 9 -5 -125

NOVEMBER 5 8 -3 -63

DECEMBER 0 13 -13 0

4.6. Probability of occurrence of the diseases

Table 7 shows the probability of measles occurrence estimated using both models

Table 7. Probability of monthly occurrence of Measles

POISSON PROBABILITY ANALYSIS FOR MODEL 1 AND 2

MONTHS MODEL 1 MODEL 2

Jan 0.000175 0.018115673

Feb 0.029151 0.046928911

Mar 6.7E-06 1.47683E-05

Apr 0.002037 0.021389287

May 0.036813 0.123670819

Jun 0.027612 0.105373375

Jul 1.67E-05 0.001699051

Aug 0.031007 0.027549545

Sep 0.108873 0.11646686

Oct 0.001043 0.033850924

Nov 0.03267 0.086585709

36

Dec 2.16E-05 3.59639E-06

4.7. Models Performance

It’s found out that model 2 in the measles model performed better because meteorological

parameter with P-vales less than 0.05(at 95% confident level) were used and all of them has high

correlation. The models underestimated, this can lead to only two conclusions first the

meteorological parameters used in this study might not be the only environmental factors responsible

for the prevalence of these diseases. Secondly non climatic such as land cover, water bodies,

hygiene, population and public intervention factors are kept constant.

4.8. Error Analysis

The error analysis of the data was done to determine the reliability for the estimated values and

numerical values of the Poisson probability analysis in Table 7. The graphical representations of the

analysis are shown:


0

5

10

15

20

25

30


No

of

Pat

ien

ts

Months

Model 1

OBSERVED PREDICTED

37


4.9. Relative Risk of meteorological parameters with respect to measles.

S/N Weather Parameters Relative Risk

1 Relative Humidity (RH) 0.331207

2 Max Temp (Tmax) 2.453904

3 Min Temp (Tmin) 29.7181

4 Rainfall (RR) 0.983994

5 Solar Radiation (SR) 0.278311

The table above shows the relative risk of the weather parameters. It can be seen that 1oC

increase in minimum temperature is having more risk related to 1oC increase in maximum

temperature and 1% increase in rainfall. Also 1mm increase in rainfall is having more risk related to

1% increase in relative humidity all on measles occurrence.

0

5

10

15

20

25

30


Model 2

OBSERVED PREDICTED

38

CHAPTER FIVE

CONCLUSION AND RECOMMENDATION

5.1. Conclusion

After analysing both the meteorological and medical data set collected, it was found out that

meteorological parameters has significant effects on the occurrence of measles occurrence in Akure,

Southwestern, Nigeria. Measles occurrence in Akure from 2009 to 2014 has been on the decrease.

From time series analysis of the data collected measles did not occur throughout the year but the

incidence is more prevalent during the transition season and least occurrence was seen during the wet

season when relative humidity and rainfall are low.

The result shows that there is significantly direct association between maximum and

minimum temperature and solar radiation and measles occurrence. Relative humidity had an indirect

relationship with measles occurrence due to its high negative correlation showing that a decrease in

relative humidity leads to high risk of measles occurrence. Rainfall had no direct relationship with

measles occurrence.

The seasonal variation of the meteorological parameters and the disease analysed showed

more evidences that meteorological parameters has influence on the prevalence of the diseases than

the monthly variation.

The model developed underestimated due to the fact that meteorological parameters are

possibly not the only environmental factors that influence measles occurrence. Other factors that

determine the occurrence are; lack of immunisation, improper sanitation, and wind propagation,

contact with patient. Minimum temperature, maximum temperature, solar radiation and relative

humidity are predictors of measles occurrence in the study area.

Thresholds has been identified for various meteorological parameters used in the study area

such as solar radiation and temperature. Based on the result there is a clear seasonality in pattern of

reported measles cases.

39

5.2. Recommendation

Having found out the peak period of the reported cases of the disease and the meteorological

conditions (weather and climate) favourable for disease, the government at different level, private

and non – governmental can create awareness and campaign about the onset of the disease and the

period of maximum occurrence of the disease.

This research work would also serve as tool for health personnel for planning on future

management of measles and it will aid pharmacies in the production of vaccines as a way of reducing

the effect and curing the virus. In lieu of this, our hospitals should adopt efficient ways of data

archival to improve and encourage researches in this line of study.

The model has to be improved in more advance manner to increase its accuracy, this can be done

using more year data set and more meteorological parameters.

More research work has to be done to cover a larger geographical location so that the model will

be effectively utilized.

40

References

A.G., O. (Weather variables and the occurrence of specific diseases of West African Dwarf (WAD) goats in

Ondo State). 2007. Nigeria : Proceedings of the International Conference on the Impacts of Extreme

Weather and Climate on Socio-Economic Development in Africa, Nigerian Meteorological Society,

Akure, 1–15 November, pp 34–38.

Adefolalu. (1997). Hydro-ecozone mapping of Ondo/Ekiti State,Nigeria. Ondo State. project Final report, pp.

141.

Adetunji O.O., O. E. (2007). Measles among hospitalized Nigerian children. Internet J Pediatr and Neonatol

7(1):1–11.

AG, O. (2007). Weather variables and the occurrence of specific. Proceedings of the International Conference

on the Impacts of Extreme Weather and Climate on Socio-Economic Development in Africa. Nigeria:

Nigerian Meteorological Society, Akure,.

Akinbobola A., O. J. (2010). Meteorological Factors and Measles Occurrence in Akure,Ondo State, Nigeria.

J.Meteorol. Rel. Sci., 4 35–47 (2010).

Akinyemi Gabriel Omonijo, A. M. (2011). Effect of thermal environment on the temporal,spatial and seasonal

occurrence of measles. Nigeria: Int J Biometeorol (2012) 56:873–885.

Bukhari, T. (2009). Temperature and Rainfall Variability and Outbreak of Meningitis and Measles in Zaria

L.G.A of Kaduna State, Nigeria. An Unpublished B.Sc Dissertation, Department of Geography,

Ahmadu Bello University, Zaria.

Chen, A. A. (2002). Investigating the link between early season caribean rainfall and the EL Nino + 1 year.

International Journal of Climatology, 22, PP 87 – 106.

Coleman, T. (2010). Forecasting Illness: The Weather-Health Connectionth. http://www.al-hikmah.org/fore-

casting illness.htn.

Driscoll. (1971b). Relationship between Meteorological Variables and Mortality Rate. Chicago Manucipal

press plc.

Duke. (2003). Environmental Health Principles. Fatcher pub.

Egunjobi. (1993). Spatial distribution of mortality from leading notifiable diseases in Nigeria. Soc Sci Med

36(10):1267–1272.

Ericsson, C. (2010). How Does Our Cold Weather Affect Our Health. Retrieved 27th March, 2009 from

http://www.usatoday.com/weather/health/whealth.htm.

Furuse Y, S. A. (2010). Origin of measles virus:divergence from rinderpest virus between the 11th and 12th

centuries. Virol J 7:52.

Gagge, A. (1979). The Role of Humidity during Warm Discomfort in Indoor Climate: Effects on Human

Comfort, Performance and Health in Residential, Commercial and Light Industry Building. Edited by

Fanger P.O and.

GHC. (2009). Causes of child death. Washington DC: Global Health Council.

41

Healthscout. (2010). Measles-Synptoms, Treatment and Prevention.

http://www.Hea;thscout.com/ency/68/732/main.

Hensel, H. (1979). Thermoreception and Human Comfort in INDOOR CLIMATE;Effect on Human

Comfort,Performance and Health in Residential,Commercial and Light-industry Building. Edited by

Fanger,P.O and Valbjorn,O.Gometed Press,London.

I.C., O. (2003). Incidence and modulating effects of environmental factors on trypanosomosis, peste des petit

ruminants (PPR) and bronchopneumonia of West African dwarf goats in Imo state, Nigeria. Livest Res

Rural Dev 15 (9) (Available at: http://www.lrrd.org/lrrd15/9/okoli159.htm).

Kenneth, L., Thomas, R., & Rebecca, J. a. (2008). Climate and vector- borne diseases. . American Journaj of

Preventive Medicine. Vol 35 (5), pp 436-450.

Lipp E.K., H. A. (2002). Effects of Global Climate on Infections Diseases: The Cholera Model “Clinical

Microbiology". Reviews, vol. 15, No. 4, October, pp. 757-770.

http://www.mbio,ncsu.edu/SL/MB590790/Cholare/cholera.pdf. downloaded June,2011.

McNeil. (1976). Plagues and peoples.

Patz, J. A. (1996). Global Climate Change and Emerging Infections Diseases. Journal of the medical

Association,” vol . 275, No.3, PP 217 -223.

Persinger, O. (1980). Lag responses in mood reports to change in the weather matrix. Inter. J . Biomet; 19,

108 -114.

Persinger.P. (1975). Our Environment and Our Health. Lisbon publishers.

Ray, V. (1970). Economic Activities in Zaria”in Journal of Zaria and its Region,Vol4. Department of Geography

Ahmadu Bello University, Zaria.

Sheshberadaran H, N. E. (1986). The antigenic relationship between measles, canine distemper and

rinderpest viruses studied with monoclonal antibodies. J Gen Virol 67:1381–1392.

Strebel P, S. C.-B. (2003). The unfinished measles immunization agenda. J Infect Dis 15(187):1–7.

T, B. (1999). Morbillivirus infections, with special emphasis on. Vet Microbiol 69:3–13.

W., T. (1963). The Burden of Climate on Man. Queen Lister Press.

WHO. (2003). Climate Change and Human Health-Risk and Response. France.

WHO. (2005). World Malaria Report.

WHO. (2007). AFRO Measles Surveillance . Feedback Bulletin June pp 1–7.

WHO. (2007). Climate Change and Human Health. http:/www.euro.who.int/mediacentre/PRI.

WHO. (2008). Measles Surveillance and Outbreak. WHO-African Region 17 April pp 1-4.

WHO. (2009). Measles Fact Sheet. Geneva Switzerland:

http://www.who.int/mediacentre/facrsheets/fs286/en.

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