Winter dysentery is a highly contagious disease of cattle reported from many countries including the United States (Kahrs et al., 1973; Van Kruiningen et aZ., 1985) , Canada (MacPherson, 1957 ), Great Britain (Rolliion, 1948 , Sweden (Hedstrom and Isaksson, 1951) , Germany (RolIe et al., 1955) , France (Charton ef aI., 1%3; Espinasse et al., 1981) , Israel (Johnston, 1959; Komarov et uL, 1959) , Australia and New Zealand (Durham et al., 1979, Edwards and Sier, 1980) . The main clinical signs are a sudden outbreak of profuse diarrhoea or the occurrence of dark green to black soft faeces with the presence of blood in 5 to 10% of the animals. According to Van Kruiningen et al. (1985) , the diarrhoea is watery or porridge-like with a characteristic smell. Affected animals show respiratory signs such as nasolacrimal discharges or coughing and hyperthermia often precedes the onset of diarrhoea by 24-48 hours.
Epidemiological studies have show-n that winter dysentery most often affects housed cattle during November to April. At any one time, the percentage of affected animals in the herd usually ranges between 5 and 10% but may reach 30-50% of the animals after two or three days. The disease is characterized by high attack rates (up to 109% after four days) but low case fatality rates (< 1%).
Several hypotheses have been put forward concerning the origin of this disease since the first studies by and by in dairy herds in New Jersey. It was initially thought that winter dysentery was caused by Kbrio jejuni, now termed Campylobacter jejuni. However, more recent studies ruled out the role of this micro-organism in the aetiology of the disease (Charton et al., 1963; Ale&s et rrl, 1988) . The causative agent was suspected to be a virus by MacPherson (1957) , who reproduced the disease experimentally by inoculating faeces passed through a Seitz filter, although the virus concerned was not isolated. Various viruses have been incriminated, including those responsible for infectious bovine rhinotracheitis or mucosal disease, parainlluenza 3 virus or rotavirus, but these are now all discounted. A coronavirus similar to that responsible for calf diarrhoea was suggested as the causative agent in studies performed in France (Espinasse et al, 1981; 1982) , Sweden (Alenius et al., 1988) , the United States (Van Kruiningen et al., 1985; 1987; Saif et al., 1988) and Japan (Akashi ef al., 1980; Takahashi et al., 1980) .
In addition, the roles of several predisposing factors, such as sudden drops in temperature, housing conditions, calving or restricted feeding, have been emphasized by Espinasse et al. (1981) and Campbell and Cookiigham (1978) .
The losses due to the disease, especially in milk production, which may decrease by 25 to 100% over two weeks, were investigated by Rollinson (1948) , Hedstrom and Isaksson (1951) , Johnston (1959) and Charton et al. (1%3) . However, Campbell and Cookingham (1978) suggested that these earlier estimations were pessimistic. The aim of this study conducted in France during 1984-85 was to determine the factors responsible for the onset of the disease, their chronology, the clinical signs and the economic consequences to the farmer.
Fifteen herds of lo-40 dairy cows located near the Centre d'Application de l'Ecole V&&inaire d'Alfort at Champignelles (Yonne) were studied. The study was divided into two parts: a preliminary visit to collect data about the management and production, and several regular visits to record data about the housing conditions and on clinical observations. The management and the production variables During the first part of the survey, each herd was characterized by 34 variables divided into four groups: Gl, description of the farm (18 variables); G2, production (4 variables); G3, hygiene conditions (5 variables); G4, previous outbreaks of winter dysentery (6 variables).
The variables studied during the survey During the second part of the survey, the herds were each visited on 16 occasions, i.e. twice a week for 8 weeks from January to March, to record data on the housing conditions and on clinical observations of 8-10 cows per herd. A herd was considered to be affected by winter dysentery when a sudden outbreak of diarrhoea of a consistency ranging from soft stools to dysentery occurred and when both a high attack rate and decreased milk production were recorded as described by Kahrs et al. (1973) .
Seven variables pertaining to housing conditions were studied. These were the air temperature (TPl and TP2) and air velocity (VA1 and VA2) in two places, the moisture level in the stable (HYGR) and the mean temperature of the drinking water (TH20) and of the feed (TALI). Temperature variables were measured objectively while air velocity and moisture level were approximated from, respectively, a candle flame and the amount of condensation on the cold surfaces in the stable. Seven variables relative to the clinical observation of the cows were identified at each visit. These were the rectal temperature (TREC), fattening stages (GRAS), nasal discharge (RESP), the colour (DIARI), consistency (DIAR2) and smell (DIAR3) of the faeces, and the presence of coronavirus and/or Campyiobacter jejuni in a faecal sample of 20 g (CORO). The birth date of the animal (NAIS) and the lactation month (LM) were also recorded.
Milk production was recorded regularly throughout the survey and for two months after the survey ended. The herd's milk production and bulk milk quality (fat and protein content) were recorded every two days and every fortnight, respectively.
A total of 209 environmental observations and 2208 clinical observations were used with Student's t test and with factorial analysis as developed by Benzecri (1980) and Fenelon (1981) and already used in epidemiological studies (Faye and Brochart, 1986) . The factorial analysis is used to understand the information contained in a measurement table. The table is made up of (a) individuals (visits, herds or cows) in the rows, and (b) the results of the observations (called variables). made on those individuals (e.g. air temperature at each visit, nasal discharge observed in each cow etc.), in the columns. Classical statistical methods analyse the variables separately: factorial analysis is able to describe the relationship between all the variables and all the individuals. For such results, several stages must be followed.
Each individual is characterized by both qualitative and quantitative variables. The codiig stage consists of transforming the initial table into a complete disjunctive form. Each variable is separated into different classes. For example, the variable diarrhoea (coded D) can take the modes absent (coded Dl), mild (D2) or severe (D3). The individual ' x ' affected by mild diarrhoea will get a '1' in the class D2 of the variable diarrhoea and a '0' in the classes Dl and D3. The table designed in thii way is called a 'BVRT table' and is used in the following steps.
The goal of the factorial analysis is to measure the similarity (or absence of similarity) between the individuals, each characterized by a succession of 0 and 1 values given to the different classes of the variables. The x2 distance is used to measure that similarity. This distance weights the class of each variable by the frequency of the positive answers. The position of each individual in the cloud of points is calculated, taking into account its succession of 0 and 1 values and its distance from all the other individuals. The same calculation is undertaken with each variable.
The information contained in the cloud of points is very confused. Reducing the data, in order to simplify the analysis, consists of projecting the cloud of points onto a set of planes. Each plane is defined by two axes called 'factorial axes'. The first axis is the principal direction of expansion of the cloud. The cloud is then projected on that axis with respect to the maximum dispersion of points. The second axis is orthogonal to the first one and represents the residual dispersion of the cloud.
Interpreting the data The output from the computer constitutes a set of figures, each representing a plane with two factorial axes. The points configure the projection of the cloud of individuals and of the cloud of variables. The interpretation step consists in grouping the more similar individuals and variables. Different computer programs may help in the interpretation of the factorial analysis (hierarchical ascending classification, aids to interpretation etc.) and have been used systematically (Jactel et aZ., 1990) . In the factorial analysis, the first three factorial axes are interpreted and the individuals and the variables whose contributions are the highest are retained. It is then possible to define a group of herds with frequent winter dysentery outbreaks by a set of management variables. It is also possible to describe and analyse a winter dysentery outbreak by a set of clinical epidemiological and economical variables.
Half the herds experienced the disease and both severe and mild outbreaks were recorded. In mild outbreaks the disease was characterized by a softening and slight change in the c&our of the faeces, which became brownish-green. This form was observed in four herds and was accompanied by low fatality rates and a moderate decrease in milk production but no weight loss in the animals. In severe outbreaks, the diarrhoea was profuse, watery and sometimes turned into dysentery. The faeces were reddish-brown. This form was accompanied by a noticeable weight loss in the animals and a marked decrease in milk production. In four weeks up to 100% of the herd contracted the disease. From the factorial analysis, the descriptive variables in both forms were very similar and it was therefore assumed that they were two different clinical expressions of the same disease.
Thirty-six faecal samples from animals affected by the mild or severe form of the disease were tested for coronavirus using the method described by Cauchy (1986) . Six samples were positive. The evolution of the clinical signs led to five subgroups beii distingukhed for each of the 16 visits. The first subgroup of these, Tl, corresponded to the observation of the animals during the visit V,, the second, T2, described the modalities assumed by these variables during the next visit (V .+I) three days after V,,, while sub-groups T3, T4 and T5 corresponded to the mod&es assumed by the variables during visits Vfl+* Vn+j and V,,+4 respectively, 6, 9 and 12 days after V,. This was done for each v&t. An example of the graphical analysis is given in Figure 1 . The axis F2, representing the chronology of clinical signs in affected animals, is opposed to the axis Fl representing healthy animals. These results, confiied by the hierarchical ascending &s&cation test and aids to interpretation program, led to the determination of a typical pattern of clinical signs observed in affected animals. This was hyperthermia (~39°C) for 48-72 h prior to the onset of digestive disorders, profuse and watery diarrhoea for 6-9 days, rectal temperature returning to normal (38S"C) at the commencement of the diarrhoea, which was accompanied by respiratory disorders (mucopurulent : nasal discharge without cough) persisting for 9-12 days, and by a marked weight loss in the animals for as long as a month.
Winter dysentery seemed most frequently to affect cows in early lactation. No significant correlation could be established between the frequency or severity of the disease and the age of the animal and coronavirus excretion seemed to be very transient, occurring only between 0 and 3 days after scouring developed. Alphanumeric labeis with HD: Letters code the variable 'Previous outbreaks of winter dysentery', using number codes for each year (0 = 1985, 2= 1983, 3= 1982, 4= 1981 ) and a last number which codes for the severity of the illness (l=no disease, 2 = mild form, 3 = severe form). Alphanumeric labels with SUR: Letter codes the variables 'Available surface/ animal', while the number codes for the class (1=2-2.3 m'/animal, 2 = 2.3-3 m2/animal, 3 = B 3 m2/animal).
The records of previous occurrences of the disease over a five-year period led to two groups of herds being distinguished (Figure 2 ). The fast group consisted of five herds regularly affected by winter dysentery. For these a severe outbreak was recorded every four years, with a mild outbreak usually occurring in the second year, between two severe outbreaks. Three of the four herds which were affected by a mild form of winter dysentery during the survey belonged to this group. The second group comprised 10 herds where the incidence of the disease was very irregular. The animals developed either mild or severe scouring. All three herds in which a severe outbreak was reported during the survey belonged to this group. A characteristic example of the herds regularly affected by winter dysentery (Fwe 2) was that of a small farm with two workers from the farmer's family and less than 20 dairy cows, which specialized in non-intensive milk production, the mean annual production per cow being ~4000 kg. On these farms, milk quality (fat content >3g.6% and protein content >30%) is emphasii more than milk quantity. As regards hygiene, the variable 'mean area available per cow' properly discrimmates this type of farm, in which the area is regularly either smaller (~2.3 m2/cow), or larger (>3 m2) than in the farms where the incidence of the disease is less frequent. On the other hand, no particular breed, agricultural area used or duration of stabling were characteristic of the herds regularly affected.
To determine the environmental predisposing factors in winter dysentery, our analyses took into account variations in the environment (temperature, air velocity), which play a more important role than their actual level (Lucey ef &., 19%, Jactel et uZ., 1990) prior to the onset of winter dysentery. Seven additional variables were used to characterize differences between visit Vl and later visits. Visits 1,2 and 16 were not taken into account because very little information was available. The temperature of the stable was much lower than usual on visits 3 and 4 and on visits 11, 12 and 13 ( Figure 3) . The air and water temperatures usually diiered significantly between two visits (Table I) . A strong positive correlation was observed between the temperature of the stable and that of drmking water: the correlation was less marked with the temperature of the diet (Table II) . The air velocity and moisture content variables were not significant. Moreover, the mean level of environmental temperature had little effect since some affected herds exhibited higher mean temperatures in the stable than healthy herds. The reciprocal was also checked. Thus, the variation of temperature between two visits appeared to play the major role. TPl : Imrer stable temperature ("C) DTl : Difference in inner stable temperature between two successive fohow-up visits TH20 : Temperature of drinking water ("C) DTH : Difference in temperature of the drmking water between two successive fohow-up visits TALI:
Temperature of feed (YZ) DTA : Difference in temperature of feed between two successive foIIow-up visits All 2 vahres are significant TPl : IMer stable temperature ("C) TH20 : Temperature of driig water CC) TALI : Temperature of feed ("C) EnvironmentaI variables were compared with cIinicaI variables to indicate the number of animak exhibiting a given sign together with the observation of a particuhu environmentaI parameter. AnaIysis of this table (facto&d anaIysis, hierarchicaI classification, aids to interpretation) showed that the onset of winter dysentery (miId and severe outbreaks) tends to be preceded by a sharp drop in the temperatures in the stables by more than 1.7T followed by a rise. The outbreak then occurs 2-4 days after the temperature decrease. The most predisposing temperatures were the temperature of the air inside the stable less than !Y'C and the temperature of d&king water and feed less than 6.K and 5.4T, respectively. These three indicators correlated with each other.
The overall attack rate ranged between 22 and 90% in the herds studied. In mild outbreaks, the maximum decrease in milk production compared to a theoretical lactation curve (Wood, 1%7) ranged between 6 and 11%. The overall production drop persisted for 8 to 15 days, after which former milk production levels were regained. It is noteworthy that when milk production returned to preclinical levels ( Figure 4 ) the observed lactation curve exhibited a rapid rise compared to the theoretical curve. In severe outbreaks the maximum decrease in milk production was 30% and lasted for an average of 2g days. The later return to initial level followed the same pattern as for the mild outbreaks. In late lactation, winter dysentery could lead to premature drying-off. In both cases, the mean fat and protein content of bulk milk declined by 1.5 g/l in the assay performed immediately after the period of diarrhoea. In this evahtation of the economic consequences, the cost of treatment and the indirect effects of weight loss on reproduction performance were not taken into account (Table III) . 
The analysis of 2208 observations confmed the increase in rectal temperature 48-72 h prior to the onset of diarrhoea (Rollinson, 1948; Roberts, 195% Kahrs et uZ., 1973 Scott ef uZ., 1973 , a phenomenon also reported by Van Kruiningen et ~2. (1985) in their experimental study. In contrast, neither coughing nor strong smelling faeces was observed in our survey.
As in previous studies (Scott et al., lm, Horner et al., 1976) , no Campylobader jejuni was detected in the faeces (Table IV) . This observation, together with the results of the coronavirus test on the faeces, confiims the possible involvement of a coronavirus in the aetiology of winter dysentery (Komarov et &., 195% Charton et al., l%% Espinasse et al., 1981; Van Kruiningen et uZ., 1985) and its association with calf coronavirus (Horner et al., 1976; Durham et al., lm, Akashi et al., 19a, Espinasse et uZ., 1981) . A recent study by Van Kruiigen et ~2. (1987) , which seemed to demonstrate a marked antigenic relationship between the coronavirus isolated from winter dysentery outbreaks and calf diarrhoea coronavirus, was confirmed by Saif et uZ. (1988) . Nasal discharge is always observed in affected animals. Some other coronaviruses are known to bc involved in respiratory disorders (Thomas et d., 198Z Saif et uZ., 1986) . However, our survey indicates that faccal excretion of viral particles is very rapid and transient. It may even start during the period of hyperthermia prior to any sign of diarrhoea, which could explain the difficulty in detecting a coronavirus in affected cattle (Van Kruiningen et uL, 1985) . It would be advisable to examine the faeces for the presence of virus as soon as the rectal temperature exceeds 3YC. 3n The frequency of outbreaks of the disease in a herd may vary from a few months to 10 years and our observations suggest that immunity may last for two years. In herds reguhuly exposed to the disease the outbreaks wouid be rather miid, whereas when the inter& between recurrences are large (> 3 years) the outbreaks wouId be more severe on an economic basis.
The role of predisposing factors is cIearly emphasized by our study. A high incidence of the disease occurred on farms with traditionaI management conditions where the area avaiiable per animaI was Iarger or smaIIer than that usuaIIy recommended. These observations confirmed previous observations (Espinasse et aZ., 198% 1982) .
Environmental factors related to climatic conditions have already been demonstrated by other authors (Scott et al., lm, Campbell and Cookingham, 1978 Van Kruiningen et uZ., 1985) . The epidemiology of winter dysentery in France was associated with the winter months and early lactation in housed cattIe. In contrast with previous studies (Scott et aZ., 1973) , the age of the animaIs was not a determining factor in our study. The sudden drop in body temperature, due for example to drinking water, which precedes the onset of the disease, is in agreement with previous observations made by BuII (1957) , MacPherson (1957) and Johnston (1959) . More recentIy, Stermer et ~2. (1986) demonstrated that the ingestion of 3.9 kg of water at 1°C by a dairy cow led to a significant decrease in rectaI temperature of 0.3"C within 20 mm, and it is supposed that the intake of very cold feed or water induces a thermai stress which facihtates the onset of diarrhoea. Moreover, as suggested by CoiIins et aZ. (1987) and by Bugiin et aZ. (1989) , dairy cows may be heahhy carriers of coronavirus, so that a sudden change in the environmentaI conditions may provoke the development of cIiicaI features. These remarks confirm the concept that there is a relationship between the disease and the environment (Dennis, 1986) . The latter may, in some cases, contribute to the transmission of aetiologicaI agents and to reducing the host immune resistance (Dennis, 1986) .
AnaIysis of individuaI production curves shows more accurate estimation of miik loss than a study of the herd mean production (Kahrs et al., 1973) . Loss of miIk production ranged between 25% and 95% in severe cases but in miId outbreaks miIk 37g production was little affected. The later rapid rise in the lactation curve would correspond to the effect of treatment or to a reaction after a prolonged decrease in production (Lucey er &., 19%) . Generally speaking, economic studies have a tendency to overestimate the production losses but to underestimate the economic consequences to the farmer, which include the animal's weight loss, the effect on their future reproduction performance and the cost of treatment.