Time Series Analysis On The Total Number Of Patients Treated For Malaria Fever

Time Series Analysis On The Total Number Of Patients Treated For Malaria Fever

Abstract

This design work birled the rate at which people are infected with malaria the least square system used for analysis showed that people are infected with malaria irrespective of the time and seasons of a consecutive time,

There’s no conspicuous direction as regarding the number of case treated for malaria over time.

Also, the analysis from autoregressive moving average report shows that both autoregressive and affecting normal of order four were both applicable while the report from autocorrelation and autocovanance doesn’t indicate any conspicuous trend in the number of cases treated for malaria.

Chapter One

Preface

The term time series refers to one the quantitative system used in determination pattern in data collected over timee.g daily yearly, daily or monthly.

Time service is the statistic tool or methodology that can be used to transfigure once experience to prognosticate unborn event which would enable the experimenter or association to plan.

It gives information about how the particular case of study has been carrying in the history and present and similar information can be used in vaticination The number of people treated for malaria fever at the otan Ayegbaju operation sanitarium. Comprehensive health centre otan. We’re going to seen how change do over mouths in each time in the circumstance of the complaint in the sanitarium. As a result of this, we will be suitable to know certain factor responsible for increase or drop in the rate of infection of the complaint over the period of time.

Record of time series data can be made in the following ways-

THROUGH CUMULATIVE FIGURES- these represent value of input through the quarter. We must always bear in mind the different when handling time series data and as certain which particular type we’re dealing with in every case.

CUMULATIVE TYPE ADDED compendium- some cases when an added compendium introduced for the accretive type of data the figure which are related to month of the time and not the aggregate for month. further more the characteristic movement, seasonal variation Irregular variation in the analysis of time series, we’ve two types of model are generally accepted as good approximation of the true data association among the element of observed data, they’re the most generally assumed relationship between time series and its factors. These are cumulative model and Multiplicative mode. All time series contain at least on of four of its factors. These factors are-

1. Long term trend

2. Seasonal variation

3. Cyclical variation

4. Irregular or arbitrary variation value

IRREGULAR OR arbitrary element

This venation can not BE attributed to any of three preliminarily bandied element in the sense that’s changeable.

Irregulars flotation can be cause by numerous factor similar as war, flood tide failure and other mortal as action. Two type of irregular variation may exit in a time seriesviz. minor and major irregularities minor irregularities show up as serivtooth like pattern are under the long term trend. These irregularited are in association long term operation

Models Of Time Series

We generally denote the element of time series as T, TS, C and I There Are Two Types Of ultramodern That Are Applicable For Joining element Of Time Series these ultramodernists are cumulative and multiplicative ultramodern.

The cumulative moder assumes that the stopcock of the original data is the sum aggregate of other four rudiments it is

T = T S C I where

T is value of the firstly conserved data( dependent)

T is the value of temporal or trend

S is the value ofr cyclical venation and

I is the value of irregular venation

Mufti active ultramodern on the other hand assumes that the value of the observed data is the Y = TSCI

Background Of Study

one of the factor that determine the population of a country, state original governmente.t.c is death rate, that’s to say, the further the complaint infected the population of such an area andvice-versa. The fact urged the pen into the study of daily number of people given treatment for malaria at the comprehensive health centre otan ayegbaju, in addition to that it’s done to know rainfall infected people come to the sanitarium fur test or they stay back due to the old custom tone drug.

In order to carry our the analysis data will be collected from diurnal record of the sanitarium at record department over some times to get all necessity information so as to carry out calculation and prognosticate about the nearest future by using secondary system of data collection.

compass and content of the study

This design work was carried out on the number of people treated for malaria fever between time 2001 to 2010. The data was collected from the comprehensive health centre Otan Ayegbaju osun state.

Aim And Objective Of The Study

to know whether the monthly spread of malaria is adding pr dwindling.

ii. To formulate a model that can best explain the relationship between malaria increases over the times

iii. To use the ultramodern to read the circumstance of malaria

iv. To compass the graph of the original datai.e circumstance of malaria against time correlogram and moving average.

SOURCES OF DATA COLLECTION

Data used for exploration are of two main sources these source are primary and secondary data.

Primary data are fresh data which are collected for the task at hand. An illustration of similar is tale enrollment for cards.

Secondary data on the other hand are data dreaded in actuality. They’re firstly collected for some purpose other than probe current problem. They can be collected from academy, sanitarium association, government agencies, review, yearly or periodic reporte.t.c therefore a secondary data is used in this design.

Limitation Of The Study

As we know that a experimenter is bound to face certain problem. colorful problem were encountered before during and after data collection

The problem are-

Poor storehouse, which make the transfer of data delicate Data weren’t duly recorded and in some cases, we’ve missing value.

Also data for some period are missing from the record office those were available were inadequately recorded this establishing one of the major.

Disadvantage of a secondary data operation data

collection- before the data was released to me, I had to present a cover letter from the Tank and my pupil identify card and also promised that the data would be used for statistical purpose only.