Resources » Informer » Q Fever with MEHAS

MEHAS is a model for the evaluation of the dispersion of highly reactive substances. It allows to determine the dispersion of particles or aerosols. In this article the MEHAS model is applied to the spread of a biological pathogen, which causes the so-called "Q Fever". Between the years 2007 to 2010 infected goats on Dutch farms led to an epidemic among humans [2].

Q Fever

Q-Fever is an acute illness caused by the bacterium Coxiella burnetii. The natural reservoir of these bacteria are cattle, sheep, goats, cats, but also other animals like ticks. Infection in humans mainly occurs through the inhalation of dust contaminated with the bacteria, through contact with infected animals or animal products and foods (drinking of non-pasteurised milk) [1][8]. Infection from human to human is also possible [8]. For infection with the illness via inhalation the amount of 10 bacteria suffices [5] and is even possible with a single bacterium [4]. The bacterium measures between 0.2 - 1.0 µm, and is known in three different forms. In the form resembling a spore the bacterium can remain infectious for more than 40 months [8].


Between infection and manifestation of symptoms there is a time period between 3 and 30 days (incubation period)[8]. In many people an infection causes mild symptoms resembling a flu. They subsist within one to two weeks. In about 50% of the cases two to three weeks after infection fever, chills, sweating, exhaustion, and headaches appear. More serious symptoms include pneumonia, hepatitis and myocarditis. In the acute form, the illness is fatal in about 1-2% [7]. A treatment with antibiotics is important in order to prevent the infection to become chronic [1]. In about 5% of cases the infection takes a serious course and requires hospitalisation of the patient [3]. The bacterium has the capacity to remain dormant in a patient and to cause repeated re-occurrence of the illness [8].

Spread of the Disease

Q Fever occurs worldwide. Among others an outbreak occurred in 1987 in Switzerland, where residents along a route used for moving sheep from mountain pastures back to the valley became infected with Q Fever. 2007 - 2009 several thousand people became ill in the Netherlands. In the USA about 100 to 200 cases are registered every year [3]. 

Q Fever south of Vorerendaal, Netherlands

In March 2009 Q Fever was diagnosed on a goat farm near the city of Vorerendaal in the Netherlands. Consequently there were more than 250 confirmed cases of Q-Fever in humans [2]. In this scenario all cases were caused by the spread of the pathogen from one single farm. In the following graph [2] the farm is marked with a red dot (see also red arrow). The black dots show the places of residence of people who fell ill. It is interesting to note, that cases of infection occurred in people, who had never been in contact with the goats and lived more than 6km from the farm. 

Assumptions for the Dispersion Calculation

The altitude of the dispersion of 3m and the wind speed of 4m/sec have been adopted from relevant literature [2]. The amount of the dispersed substance and the time frame of the dispersion are unknown. Therefore a reference zone is determined, which is then compared to a different zone located at a distance elsewhere. The goal is to determine the relative deposition, that is the relationship of the deposition on the ground in both areas. The depositions thus derived in the model can then be compared with the occurring cases of illness per areal unit. In order for this to become possible we have chosen a sector and within the sector two zones, where the population density is about the same. In the following illustration we have marked out two zones A and B, which appear in the same sector and have approximately the same population density. 

Further it is assumed, that the amount of deposition has an effect on the amount of infections among humans.

The particle size is an important entry parameter. We adopt the value of 1?m in aerosol form according to literature [4]. In order for a particle to be inhalable into the lungs it has to be smaller than 10?m [6].

For the remaining entry values the standard values of the model have been adopted.

Comparison of relative deposition with cases of illness per area

The amount of cases in zone A is 9 and in zone B is 10. The area of zone B is about 4 times bigger than the area of zone A. Cases per areal unit therefore are 4x higher in A than in B. If we compare this ratio with the depositions in zone A and B (in both cases in the center of the zone) we have in A 0,045?g/m2 and in B 0,0099?g/m2. The relation is 0,045 / 0,0099 = 4,7. The amount of dispersed particles entered was chosen randomly in the model, because it is unknown. Because the deposition is changing proportionally with a change in dispersed mass and a comparison is made with a zone of reference, the amount entered into the model, as the sole changed parameter, does not influence the result. 


In a case like the above:

The estimated deposition in zone A (2,4km from the farm) is 4,7x bigger than in zone B (6,4km from the farm). The area of zone B is approximately 4x bigger than the area of zone A. For Zone B 8 cases of illness are expected, in reality there were 10.

Case with a sector to the north

If the sector with zones A and B is moved towards the north, there are 16 cases of illness in this zone A, and according to the estimate there should be about 14 cases in zone B. Effectively only two cases were found.

In both cases the number of cases of illness differs already in zone A, used as the reference zone, although zone A has the same distance to the source. In the first case there are 9, in the second 16 cases of illness. There may be different reasons, which have an influence on this, like e.g. wind flow conditions, age structure [3] etc. 


The two cases show, that MEHAS makes it possible to estimate the number of cases of illness in comparison the a zone of reference, even if the dispersed amount of bacteria and the dynamics of dispersion are not known.

In a further article we would like to estimate the bacteria emission rate of a goat stable and with this calculate danger areas.


[1] Q-Fieber - Bundesamt für Gesundheit - accessed 29.9.2014

[2] F.J. Sauter, W.A.J. van Pul, A.N. Swart, R.M. ter Schegget, V. Hackert, W. van der Hoek, Airborne dispersion of Q fever, National Institute for Public Health and the Environment Netherlands, RIVM 2011, Report 210231007/2011

[3] Center for Disease Control and Prevention - Q Fever - Symptoms, Diagnosis and Treatmend. accessed 1.10.2014

[4] R.M.Jones, M.Nicas, A.E.Hubbard, A.L.Reingold, The Infectious Dose of Coxiella burnetii (Q Fever), Applied Biosafety (2006), 11 (1) S. 32-41

[5] Center for Disease Control and Prevention - Rickettsial Agents - accessed 1.10.2014

[6] Particles: Size Makes All the Difference, National Institute of Environmental Health Sciences - aaccessed 1.10.2014

[7] European Centre for Disease Prevention and Control. 2010. Technical report: Risk assessment on Q fever. accessed 1.10.2014

[8] Arbeitskreis Blut, Coxiella burnetii – Pathogenic Agent of Q (Query) Fever, Transfus Med Hemother (2014),41, S. 60–72




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Q Fever with MEHAS