An objective sign of seasonality is the concentration of morbidity in a short period of the year. When analyzing seasonality, it is necessary to provide a quantitative description of the characteristics of the distribution of diseases throughout the year, determine the beginning and duration of the seasonal increase in morbidity, and determine the proportion of diseases that are caused by seasonal factors.

Most often, extensive indicators are used to analyze seasonality, that is, the proportion of diseases of each month in the annual number of diseases is calculated. It is assumed that the share of the average monthly level with an even distribution of diseases throughout the year is 8.33%. Months in which the share exceeds this number are considered seasonal increase months.

More precisely, seasonality is revealed when calculating indicators of seasonal fluctuations (the ratio of the average daily monthly number of diseases to the average daily annual number, as a percentage). If the monthly seasonal fluctuation rate is less than 100%, then the influence of seasonal factors on morbidity is absent or minimal. When exceeding 100%, the influence of seasonal factors is significant and sometimes decisive.

Seasonality factor– the ratio of the number of diseases that occurred in the months of increase to the total number of diseases for the year as a percentage.

Kc = number of diseases in the months of seasonal growth / number of diseases per year * 100.

Conventionally, increase months include those in which the number of diseases exceeds the monthly average.

Seasonality index– the ratio of the number of diseases in the months of seasonal increase to the number of diseases that arose in the off-season period.

Is = number of diseases in the months of seasonal growth / number of diseases in other months.

Table 2.2. Example of calculating seasonal fluctuations

The monthly average (1456 / 12 = 121) is exceeded in the 6th, 7th, 8th and 9th month. Thus, the seasonality coefficient (169+275+272+165) / 1456 x100% = 60%.

Seasonality index = 169+275+272+165) / (27+65+55+101+96+88+64+79) = 1.5.

This indicator answers the question: how many times the number of diseases in the months of increase exceeds the off-season level.

The distribution of incidence between months (weeks) of a year or months of several years makes it possible to detect the time of risk. Studying the seasonality of morbidity allows us to draw conclusions regarding the ways of infection spread and changes in people’s behavior throughout the year that increase the risk of morbidity. Analyze the seasonal incidence of the total population, as well as age, occupational and other groups of the population and groups. The months with the maximum and minimum number of diseases, the beginning and end of the seasonal rise, and the proportion of diseases that are recorded during the rise are noted. To exclude randomness in determining seasonality, the duration of the period for which it is determined should be several (3-5) years.

When analyzing the annual dynamics of morbidity, determining the time of risk and the reasons that predetermine it allows appropriate measures to be taken ahead of time in order to achieve a reduction in the morbidity rate during the months of seasonal growth.

Analysis of incidence by territory determined by administrative and geographical boundaries. The morbidity rate is analyzed and compared by medical districts, medical associations, districts, cities, regions, and countries. Infectious respiratory diseases spread faster in cities than in villages. Cities have higher population densities and more intense communication between people. Zoonotic diseases that humans contract from animals are predominantly found in rural areas and natural hotspots. In order to visually depict and analyze the uneven distribution of infectious diseases across the territory, it is advisable to use a cartogram on which intensive morbidity indicators or cases of diseases are plotted according to the places where the disease is registered. The reasons for the uneven territorial spread of intestinal infections may be the unfavorable sanitary condition of the locality, the presence or absence of food establishments, water supply and sewerage, the possibility of infection of products during their transportation, manufacturing and storage. Therefore, it is necessary to identify risk areas, that is, areas in which social and natural factors predetermine a high level of morbidity. The unevenness of the epidemic process across the territory may depend on the volume and quality of preventive and anti-epidemic measures and the completeness of registration of infectious diseases. To determine the reasons for such unevenness, it is advisable to analyze the long-term dynamics of incidence in different territories. Important practical conclusions can be drawn from the analysis of information in a given territory about the sources of infectious agents, transmission routes and epidemic foci. For example, when studying focality, attention is paid to the number of outbreaks with single and multiple diseases, indicators that characterize focality in apartments, preschool institutions and schools. The focality indicators for a number of years are compared. The following indicators of focality are determined:

Fociality index= number of diseases / number of all outbreaks.

Fociality index with multiple diseases = number of foci with 2 or more cases / total number of foci * 100.

To analyze morbidity by population groups, characteristics such as age, profession, gender, living conditions, and immunization are distinguished. Analysis of morbidity by age, profession and among other population groups, as well as in teams, is carried out according to intensive indicators per 1 thousand, 10 thousand, 100 thousand persons of a given age, profession, etc. In addition, the proportion of morbidity of a given group or collective in the overall morbidity is determined (extensive indicator). The most significant feature of the population with which the possibility of the disease is associated is its age composition. Age groups are allocated according to the research program, the purpose of which is to identify the causes of the prevailing morbidity in people of a certain age. For example, a possible distribution of the population into the following age groups: 0-1, 1-2, 3-6, 4-7, 7-14, 15-19, >19 years. Morbidity in age groups indicates which age group is most effectively affected by a particular pathogen transmission mechanism, how effective immunoprophylaxis is, and what features of life and behavior of this population contribute to an increase in morbidity.

Occupational risk groups for intestinal infections include employees of food production and processing enterprises, water supply systems, trade and public catering establishments.

Within the boundaries of social groups of the population, risk groups are distinguished. In particular, these are teams of preschool institutions and schools. In some risk groups, morbidity due to respiratory or intestinal infections may be observed for a long time. In order to identify the causes of morbidity in groups, they compare it among different groups, analyze the number of outbreaks that have arisen in them, the number of cases in each outbreak, and thus establish the reasons for the high incidence in them. Identification of risk groups and communities makes it possible to establish epidemic cause-and-effect relationships of morbidity in these groups and communities with risk factors.

Infectious diseases are identified by a doctor at the patient’s place of residence or at an appointment in a clinic. For each patient, on the day he is identified, the clinic sends an emergency message to the epidemic department of the SES. An epidemiological examination of the outbreak is carried out by an epidemiologist or his assistant. The purpose of an epidemiological examination of outbreaks is to identify the source of infection from which the infection occurred, the factors and routes of transmission of the pathogen. The following areas of work in the outbreak are distinguished:

· identification of the causes and conditions of outbreak occurrence;

· development and implementation of anti-epidemic measures to eliminate the outbreak;

· medical observation of the outbreak;

· analysis of the effectiveness of measures taken to eliminate the outbreak.

To identify the causes and conditions for the occurrence of an outbreak, the following methods are used:

· interviewing the patient (collecting an epidemiological history);

· carrying out laboratory tests (for the patient and contacts);

· study of medical documentation about morbidity in the outbreak area 1-4 weeks before identifying the patient (to find the source - the patient or the carrier).

The survey is carried out in the form of a conversation, for which it is necessary to know the features of the epidemiology of this infectious disease. During the inspection of the outbreak, attention is paid to those features that are important in the epidemiology of this disease: living conditions, the sanitary condition of the outbreak, the nature of the water supply. To identify the pathogen, laboratory methods (bacteriological, immunological) are widely used. The data obtained during the epidemiological survey of the outbreak is entered into the epidemiological surveillance map, and the results of the team survey are drawn up in the form of a report. All materials from the epidemiological survey are analyzed and, on their basis, conclusions are formulated about the causes of the outbreak and its approximate boundaries. Taking into account the characteristics of the epidemic focus, a specific plan for its elimination is being developed in the following areas:

· hospitalization of the patient or his isolation at home;

· measures for healthy individuals who are in the outbreak (laboratory examination, immunoglobulin prophylaxis, observation by local personnel);

· disinfection, disinsection, deratization.

An epidemic outbreak is considered eliminated if, during the maximum incubation period, no new cases of disease have arisen in the outbreak and all necessary anti-epidemic measures have been taken in it. Investigating an outbreak or epidemic has its own challenges because it involves a number of people being infected over a short period of time. The first cases of the disease at the beginning of the epidemic, the increase in the number of diseases, the peak of the epidemic and the decrease in incidence are determined.

An outbreak (epidemic) is investigated by a doctor-epidemiologist, but if necessary, other specialists (infectious disease specialist, bacteriologist, hygienist) also take part in it.

Epidemiological significance The disease is determined by its prevalence, frequency of registration (indicators of morbidity, mortality, lethality are studied and compared), the trend of the epidemiological process, the duration of the period of epidemic trouble are determined, the maximum and minimum levels of morbidity are compared, the ratio of manifest and asymptomatic forms is calculated.

Social significance infection is associated with the harm it causes to human health and the disorganizing effect of morbidity on various forms of life and activity of the population.

Economic significance infection is assessed by the losses that are caused to the national economy through the limitation of labor resources, the diversion of forces and funds to combat infectious diseases. Economic losses - direct and indirect (outpatient examination, inpatient treatment, payments for sick leave, loss of production by society due to illness, disability, mortality).

QUESTIONS FOR SELF-CHECK:

1. What is the difference between intensive and extensive indicators?

2. What is the difference between case-control and case-control analytic techniques?

"cohort study"?

3. What types of experimental research in epidemiology do you know?

4. Define the concept of “epidemiological diagnosis” and “retrospective epidemiological analysis”.

5. What manifestations of the epidemic process include cyclicity,

seasonality, epidemic trend?

6. What is the ultimate goal of a retrospective epidemiological analysis?

7. How can you visualize long-term and annual dynamics?

morbidity?

8. What is the purpose of an epidemiological survey of an outbreak?

9. At what levels are anti-epidemic measures aimed at in the outbreak?

10. Documentation that is completed in connection with the identification of a source of infection.

Seasonality

Seasonality - this is a persistent pattern of development of the epizootic process, manifested by a significant increase in the incidence of animals in certain months of the year. It is taken into account when planning and carrying out anti-epizootic measures, especially when determining the optimal timing of mass preventive immunization of animals against a particular disease.

The seasonality indicator is the ratio of the incidence rate of each month to the number of cases of the disease for the year (years) and is expressed as a percentage:

C = M/r * 100, where

C - seasonality;

M is the number of cases of the disease for a certain month;

r is the total number of cases of the disease over the years taken;

100 - conversion to percentage.

Seasonality factor

Seasonality coefficient (Ks) represents the ratio of the number of cases of animal diseases in the months of seasonal growth to the number of diseases per year and shows. What proportion of animal disease cases per year (years) occur in the months of seasonal growth (as a percentage). Months of seasonal increase in incidence include months in which the incidence was higher than the monthly average for the year (years):

The remaining months include those in which the incidence was equal to or lower than the monthly average. The monthly average is determined by dividing the sum of cases of the disease for the year by 12 in absolute numbers or percentages. The higher the seasonality coefficient, the more pronounced the seasonality of the disease. Seasonality is weakly expressed when the seasonality coefficient is 51 - 60%, to an average degree - when the seasonality coefficient is from 61 to 75% and clearly expressed seasonality when Kc is more than 75%.

Average month = =

Seasonality factor:

The seasonality coefficient is 59.86%, which is within the range of 51 - 60%, which indicates a weak expression of seasonality. Trichophytosis of carnivores occurs at any time of the year, but a greater number of sick dogs and cats are observed in the summer.

Seasonality index

Seasonality index shows how many times more cases of the disease are registered in the months of the seasonal rise than in other months:

Is = Zsez / Z m sez, where

IS - seasonality index;

Zsez - morbidity during periods of seasonal rise;

Z m sez - average interseasonal incidence.

During the months of the seasonal rise, 1.5 times more cases of trichophytosis were registered than in other months.

Table 9. Dynamics of the incidence of carnivorous trichophytosis in dogs and cats according to the clinic of IP Zubkov (Blagoveshchensk) in 2007-2011.

Building a seasonality chart

R - radius in the diagram corresponds to the average value, i.e.:

563/12 = 46.92 goals

Circle R = 46.92 / 563 * 100 = 8.3%

Scale 1:2 R=4.15%

Rice. 39.

Conclusion: Based on the calculations made, the seasonality graph and the data in Table 9, I can conclude that the seasonality of animal disease with carnivorous trichophytosis in the veterinary clinic of IP Zubkov is weakly expressed (Kc = 59.86%); the disease is 1.5 times more likely to manifest itself in the warm season during the months of seasonal growth (from May to September) than in the rest of the time.

With the release of UT 11.1.4.1 functionality has appeared in the program seasonal odds. Seasonal coefficients are designed to allow classification of items into various groups for the purpose of further automatic adjustment of sales plans taking into account the seasonality factor.

In this article we will look at how this functionality works.

Applicability

The article was written for the editors of UT 11.1 . If you use this edition, great - read the article and implement the functionality discussed.

If you are working with older versions of UT 11, then this functionality is current. The most noticeable difference between UT 11.3/11.4 and 11.1 edition is the Taxi interface. Therefore, in order to master the material in the article, reproduce the presented example on your UT 11 base. Thus, you will consolidate the material with practice :)

Creating and using seasonal odds

The use of seasonal coefficients in planning is determined by the “Seasonal coefficients” functional option. You can enable the use of this option on the “Administration” – “Marketing and Planning” program tab.

We can also specify the frequency of seasonal coefficients in the program. This setting is also set on the “Administration” – “Marketing and Planning” – “Frequency of specifying seasonal coefficients” tab. For example, let’s set the frequency to “Month”.

After checking these checkboxes in the item card and in the item type card, it became possible to specify a seasonal group.

Let's create a new seasonal group. Let’s go to the program tab “Regulatory and reference information” – “Settings and reference books” – “Seasonal groups”. For example, let’s call our new group “Furniture – Seasonal Group”.

To enlarge, click on the image.

The “Seasonal Groups” directory is used in sales planning and is intended to classify items into seasonal groups.

Now let’s fill in the “Seasonal group” field, for example, in one of our product items. For me this will be the nomenclature “Closet”.

To enlarge, click on the image.

After that, let's go back to the list of seasonal groups and calculate the seasonal coefficients. Those. Click the “Seasonal odds” button.

To enlarge, click on the image.

Seasonal coefficients are used in sales planning and are designed to record seasonal fluctuations in demand for product groups. Seasonal coefficient values ​​can be filled in manually or calculated using sales statistics.

A window for calculating/filling in coefficients will open in front of us. For each month, we can manually specify the coefficient value. But let’s automatically calculate the coefficients depending on sales statistics - click the “Calculate based on sales statistics” button.

To enlarge, click on the image.

In the window that opens, indicate, for example, a billing period of 1 year. The "To" field value will be automatically filled in with yesterday's date. Set the “Calculate by” switch to the “Number of sales” option.

I got this result:

To enlarge, click on the image.

“But how did you get these numbers?” you ask.
Let's calculate them manually.
So, according to the “Closet” nomenclature, I have the following sales:

• January – 4 pcs.
• February – 7 pcs.
• March – 3 pcs.

Total 14 cabinets. There are only 12 months in a year.

Let's calculate the monthly average: 14 / 12 = 1.167.

The seasonality coefficient is calculated using the formula:

Actual sales / Average monthly sales

Our result:

• January: 4 / 1.167 = 3.429.
• February: 7 / 1.167 = 6.
• March: 3 / 1.167 = 2.571.

The results obtained by manual calculation and those calculated automatically by the program are the same. Great!

Let's go to the "Graph" tab. Here we will see a chart of the change in sales ratio. The chart type can be changed in the same window if necessary.

To enlarge, click on the image.

Let's write down the calculated seasonal coefficients.

The obtained values ​​of seasonal coefficients can later be used to calculate the predicted number of sales of goods. Those. Now, when planning sales in “Types of plans” when using the “Advanced filling option (by sources)” in the default plan filling rule, you can set the “Change to seasonal coefficient” checkbox for the “Quantity filling source” in the selection settings.

To enlarge, click on the image.

Detailed information about the features of setting up the use of the extended filling option (according to sources) is described in the cases of the course “65 cases on UT11”. Therefore, I recommend that you read it.

Well, dear readers, in this article we looked at the mechanism for creating and using seasonal odds. In my opinion, this functionality is a new step in the development of planning functionality in the UT 11 program. Let's hope that it will continue to develop.

P.S. This is the last article in a series of articles reviewing the new release of UT 11.1.4.1. See you again!

Seasonality indices(I s )  specialist. indicators used in the study of seasonal fluctuations. Calculated using the formula:

where  is the average for each month for the study period;

 overall average monthly level for the study period.

Let's show the calculation seasonality index For example. Example 5.1 The following data is available for a construction company on the volume of work performed by month in 2001–2003. at an estimated cost.

To obtain this, we will average the levels of periods of the same name using the simple arithmetic average formula:

January December -

The average values ​​of the series levels for each month of the annual cycle are presented in the table of this example.

Where n- number of months.

The value of the overall average level can also be calculated using the final data for individual years:

Where n- number of years;

The sum of the average annual levels of the dynamics series.

In conclusion, we determine the seasonality indices for the months of the year using the formula:

January - February -

The calculated seasonality indices are presented in the example table.

Therefore, min. The construction company had the volume of work performed in January, and the maximum in August.

For a series of intra-annual dynamics in which the main growth trend is insignificant, the study of seasonality is based on the constant average method, which is the average of all levels considered. The simplest way is as follows: the average level is calculated for each year, and then the level of each month is compared with it (as a percentage).

However, monthly data from one year, due to the element of randomness, may not be reliable for identifying patterns of fluctuation. Therefore, in practice, monthly data are used for a number of years (usually at least three years). Then for each month it is calculated average value level for three years, then the average monthly level for the entire series and the ratio of the averages for each month to the overall average monthly level of the series are determined (as a percentage).

Tasks and exercises on your own

1. There are the following indicators for the enterprise:

Determine for the first half of the year:

1) the average monthly cost of working capital for the 1st and 2nd quarters and for the first half of the year;

2) basic growth rates and increases in the value of working capital; check the relationship between them;

3) average monthly growth rate and increase in the value of working capital;

4) absolute increase in the value of working capital in the second quarter compared to the first quarter.

2. Using the relationship between the dynamics indicators, determine the levels of the dynamics series and the chain dynamics indicators missing in the table using the following data on the production of products of the association enterprise (in comparable prices):

3. The following data is available on retail turnover in all sales channels in the region:

To study the general trend of retail trade turnover in the region by month for 2001–2003. swipe:

1) 1. transformation of source data by enlarging time periods:

a) a) at quarterly levels,

b) b) at annual levels;

2) 2 smoothing quarterly levels of retail turnover using a moving average.

Graphically depict the actual and smoothed levels of the dynamics series.

Draw conclusions about the nature of the trend in retail turnover across all sales channels in the region.

4. The following data are available on the intra-annual dynamics of the supply of cotton fabrics to the regional retail network by quarter for 2001–2003.

To analyze the intra-annual dynamics of the supply of cotton fabrics:

1) determine seasonality indices using the method of analytical straight line alignment;

2) graphically present the seasonal wave of supply of cotton fabrics by quarter of the year and draw conclusions.

5. The relative indicator of the dynamics of the number of officially registered unemployed in the region in the first half of the year was 95%, in the second half of the year  108%. How has the number of unemployed people changed over the year as a whole?

6. What should be the average annual growth rate so that in three years the volume of production increases by 10 million tons and amounts to 100 million tons?

7. Based on data on sales volumes of a foreign trade company (millions of dollars) for the period from 1998 to 2003, a trend equation was constructed:

Make a sales forecast for 2004 and 2005. with a probability of 95% if the relative error of the equation is 5.5%.

In practice, there are many large companies (especially in the regions of the country) that only after five to ten years of their existence come to the idea of ​​planning their activities. But before this insight, most companies were doing business as usual.

Why were they able to live without planning for so long? In my opinion, the turbulent pre-crisis economy with an abundance of credit resources is to blame. At any time, most companies could easily cover the cash shortage with the help of banks. However, times have changed, and we have to “make ends meet.” This is why planning is necessary. How can we calculate a company's liquidity for a certain period without planning sales, purchases, expenses, revenue receipts, etc.? Without planning - nothing.

So, the first important step in the budget of a trading company is sales planning. Although drawing up a sales plan is the responsibility of the sales department, I am committed to having sales plans drawn up by two departments: the sales department and the product management (purchasing) department, if there is one. Why complicate everything so much? This is a good cross-check of planned results, which are calculated by different methods.

The sales department draws up sales plans based on the capabilities of its clients, plans for the development of new markets, etc. However, sales people often do not have the analytical skills that are necessary when drawing up a sales plan. The product management department, whose functions include inventory management, pricing, budgeting, analytics and product promotion, should come to the rescue.

One way or another, below I provide for your consideration a practical methodology for calculating a sales plan:

1. Calculation of seasonality coefficients for past sales periods;

2. Calculation of sales targets based on:

• seasonality coefficients;
• growth (fall) trends;
• internal development plan of the enterprise;
• expert adjustment of calculated planned indicators.

Calculation of seasonality coefficients for past sales periods

Seasonality coefficients are determined by the formula:

k seasonality = Si / sS

where Si is the actual amount of sales for a particular month,

sS – average monthly sales amount for the year.

Table No. 1 shows a practical example of calculation.

Table 1. Calculation of seasonality coefficients

As can be seen from table No. 1, to calculate the seasonality coefficient, actual monthly sales for three years were taken. At the end of the table, average monthly sales were calculated for each year using the MS Excel formula: AVERAGE(). Then, in the lower block of the table, seasonality coefficients were calculated for each month of the year. Take, for example, the calculation of the coefficient for the first month of 2005, which was found as follows: 18,500 / 30,725 = 0.60.

To smooth out external factors that could affect seasonality in a particular year, we averaged the seasonality coefficients for each month (bottom row of Table 1). It is very important that the sum of all seasonality coefficients for the year is “12.00”, which indicates the correctness of the calculations.

Calculation of sales targets

Based on: seasonality coefficients, growth (decline) trends, internal development plan of the enterprise.

Economic growth (decline) trend

When calculating the amounts of planned sales, it is necessary to take into account the economic development trend. It is extremely wrong to rely only on internal company information (in our case, sales for past periods). Before the crisis, many companies made their plans solely based on their own historical data. And it worked until the crisis shook their minds. And the shock for most trading companies was inflated inventories, formed according to sales data, which in no way predicted a deterioration in the country’s economy. Yes, a company's actual sales reflect current trends that are embedded in the economy, but they do not predict its turning points. The trend coefficient is rather an expert indicator that cannot be accurately calculated. It is based on an analysis and forecast of the development of the economic situation. The topic of economic analysis is a separate large-scale issue that will not be discussed in this article.

Internal enterprise development plan

This is an enterprise plan for the next financial year, which contains information about company development strategies. Such information may include plans for developing sales channels, increasing the company’s customer base, etc., which are ultimately reflected in the planned level of sales growth.

So, after calculating the seasonality coefficients, we proceed to calculating the monthly amounts of planned sales.

Table 2. Calculation of monthly planned sales amounts

First, we calculate the total planned sales amount for the new period (line “Sales (plan) – 2008” in table No. 2). The company's management determined the sales growth rate for 2008 at the level of 1.2 - 20% increase in actual sales of the past year. Since actual sales for 2007 amounted to 495,545 USD, the planned level for 2008 will be: 551,454 USD. (495,545 USD + 20%).

Then in the “k trend” line we determine the coefficients for each month. In the above example (Table 2) it can be seen that in the second quarter we expect an increase in business activity in the country by 5% (coefficient 1.05), and in the third and fourth quarters by 10%.

In the line “k seasonality” we insert the previously calculated (Table 1) averaged seasonality coefficients.

To determine the amount of planned sales, it is necessary to make the following calculation for each month: the average monthly amount of planned sales (551,454 cu / 12 months = 45,955 cu) multiplied by k seasonality and k trend.

Expert adjustment of calculated planned indicators

After calculating the sales plan for the new period, it is necessary to logically comprehend the resulting figures. This allows you to identify places where the formulas may not have taken into account a number of factors that live in your head and are difficult to describe in numbers. The adjusted sales plan becomes a draft and is subject to review by company management. What management decides is a completely different story...