2. – The characteristics, geographical distributions and seasonal variations of many infectious
diseases are prima facie evidence that their occurrence is linked to weather and climate.
– 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 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. 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 centers of civilization 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, 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).
3. • What is the relationship between this disease
and weather condition?
• Does the occurrence of this disease exhibit
seasonality?
4. AIM
To evaluate the effect of meteorological
parameters on the prevalence of measles using
statistical methods
OBJECTIVES
• Assessing the correlation between meteorological
parameters and the prevalence of measles
• Generation of a weather-disease statistical model
which can be used to estimate the number of
occurrence.
5. 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)
6. 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.
7. METHODS OF ANALYSIS
• Graphical representations were used to determine the variation
of weather parameters on the monthly and seasonal pattern.
Spearman’s Rank correlation coefficient was used to test and
identify the strength of a relationship between the monthly
measles incidence and the weather parameters.
• Also, the seasonal and quarterly index were found using the
formulas;
8. METHODS OF ANALYSIS
• Poisson probability distribution function were 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
9. METHODS OF ANALYSIS
• The Relative Risk of the effects of weather
parameters on the occurrence of Measles was
established from multiple linear equations derived.
10. 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 was experienced.
Measles = -2.5429*year + 5210.1
50
60
70
80
90
100
110
120
130
140
2008 2009 2010 2011 2012 2013 2014 2015
MeaslesCases
Years
13. Mean Monthly Correlation between measles occurrence and
meteorological variables using Spearman’s Rank Correlation coefficient
14. Weather-Disease Statistical Model
Two models were developed for each of 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
15. Probability of occurrence of the diseases using Poisson
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 have
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.
16. Error Analysis
It is obviously shown that Model 2 is more reliable than model. This is
because the estimated value is closer to the observed value for model 2
than model 1.
17. Relative Risk of meteorological parameters with respect to measles.
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
18. 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 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 immunization, 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
19. • 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 advanced 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.
20. • 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,.
• Furuse Y, S. A. (2010). Origin of measles virus:divergence from rinderpest virus
between the 11th and 12th centuries. Virol J 7:52.
• GHC. (2009). Causes of child death. Washington DC: Global Health Council.
• 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).
• McNeil. (1976). Plagues and peoples.
• 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.
• WHO. (2007). AFRO Measles Surveillance . Feedback Bulletin June pp 1–7.
• WHO. (2009). Measles Fact Sheet. Geneva Switzerland:
http://www.who.int/mediacentre/facrsheets/fs286/en.
