STATISTICAL MODELLING OF THE EFFECT OF

METEOROLOGICAL PARAMETERS ON THE OCCURRENCE OF

MEASLES IN AKURE,ONDO STATE

BY;Adeyemi Adedayo

Olalekan.(Meteorology and Climate

science)

INTRODUCTION– 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).

RESEARCH QUESTIONS

• What is the relationship between this disease and weather condition?

• Does the occurrence of this disease exhibit seasonality?

AIM AND OBJECTIVESAIM

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.

RESEARCH METHODOLOGYSTUDY 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)

RESEARCH METHODOLOGY

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.

RESEARCH METHODOLOGY

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;

RESEARCH METHODOLOGYMETHODS 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

RESEARCH METHODOLOGYMETHODS OF ANALYSIS

• The Relative Risk of the effects of weather parameters on the occurrence of Measles was established from multiple linear equations derived.

RESULTS AND DISCUSSIONAnnual 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.

2008 2009 2010 2011 2012 2013 2014 201550

60

70

80

90

100

110

120

130

140

f(x) = − 2.54285714285714 x + 5210.12380952381

Years

Mea

sles C

ases

RESULTS AND DISCUSSIONMonthly Variation of Meteorological variables with Measles occurrence

RESULTS AND DISCUSSIONSeasonal variation of meteorological parameters and measles

Occurrence

RESULTS AND DISCUSSIONMean Monthly Correlation between measles occurrence and

meteorological variables using Spearman’s Rank Correlation coefficient

RESULTS AND DISCUSSION

Weather-Disease Statistical ModelTwo 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

RESULTS AND DISCUSSIONProbability 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.

RESULTS AND DISCUSSIONError 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.

RESULTS AND DISCUSSIONRelative 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

CONCLUSIONThe 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

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 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.

REFERENCES• 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.