Syndromic surveillance system based on near real-time cattle mortality monitoring

Torres, Gregorio J.; Ciaravino, Giovanna; Ascaso, San Román; Flores, Víctor; Romero, Luis; Simón, F

doi:10.1016/j.prevetmed.2015.03.003

Cited by 12 publications

(9 citation statements)

References 19 publications

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“…For monitoring, mortality figures need to be produced frequently to detect deviations as soon as possible (Perrin et al, 2012;Torres et al, 2015). This may result in different preferred definitions for parameters to monitor calf mortality than for communication purposes.…”

Section: Discussionmentioning

confidence: 99%

Quantifying calf mortality on dairy farms: Challenges and solutions

Santman-Berends

Schukken

Schaik

2019

Journal of Dairy Science

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Section: Discussionmentioning

confidence: 99%

Quantifying calf mortality on dairy farms: Challenges and solutions

Santman-Berends

Schukken

Schaik

2019

Journal of Dairy Science

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“…Mortality data are routinely collected for livestock and poultry species, either as part of herd management practices (e.g., in pigs and poultry) or as part of mandatory animal movement reporting or monitoring of animals found dead on farm (e.g., in cattle). These data have often been used for epidemiological purposes, for example, as part of syndromic surveillance (Alba et al., ; Tapprest et al., ; Torres et al., ), for detecting outbreaks of disease (Backer, Brouwer, van Schaik, & van Roermund, ; Bos et al., ) or to assess the impact of an epidemic (Perrin et al., ).…”

Section: Introductionmentioning

confidence: 99%

Inferring within-herd transmission parameters for African swine fever virus using mortality data from outbreaks in the Russian Federation

Guinat

Porphyre

Gogin

et al. 2017

Transbound Emerg Dis

106

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SummaryMortality data are routinely collected for many livestock and poultry species, and they are often used for epidemiological purposes, including estimating transmission parameters. In this study, we infer transmission rates for African swine fever virus (ASFV), an important transboundary disease of swine, using mortality data collected from nine pig herds in the Russian Federation with confirmed outbreaks of ASFV. Parameters in a stochastic model for the transmission of ASFV within a herd were estimated using approximate Bayesian computation. Estimates for the basic reproduction number varied amongst herds, ranging from 4.4 to 17.3. This was primarily a consequence of differences in transmission rate (range: 0.7–2.2), but also differences in the mean infectious period (range: 4.5–8.3 days). We also found differences amongst herds in the mean latent period (range: 5.8–9.7 days). Furthermore, our results suggest that ASFV could be circulating in a herd for several weeks before a substantial increase in mortality is observed in a herd, limiting the usefulness of mortality data as a means of early detection of an outbreak. However, our results also show that mortality data are a potential source of data from which to infer transmission parameters, at least for diseases which cause high mortality.

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“…Despite availability of data, representative overviews over mortality rates in cattle populations of countries are few (Pannwitz, 2015). Still, in the last decade, some countries are developing surveillance systems using national registry mortality data for the detection of early disease outbreaks (Struchen et al, 2015;Torres et al, 2015;Santman-Berends et al, 2016). There is no normal level for on-farm mortality in cattle herds, but increasing trend of mortality in dairy herds has been reported in several studies (Thomsen et al, 2004;McConnel et al, 2008;Raboisson et al, 2011;Alvåsen et al, 2012;Alvåsen et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Reasons and risk factors for on-farm mortality in Estonian dairy herds

et al. 2017

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Mortality of dairy cattle causes financial loss for the farmer and reflects animal welfare status. The aim of this study was to determine the on-farm mortality (unassisted death and euthanasia), reasons, as well as risk factors for on-farm mortality in Estonian dairy cattle. Data of years 2013 and 2014 about all cattle from dairy herds in Estonia was collected from the Estonian Agricultural Registers and Information Board. The dataset included records of 363,380 animals from 2,616 herds. Multivariable Weibull proportional hazard models with herd as random effect were composed for detecting significant associations between potential risk factors and on-farm mortality (composed outcome including unassisted death and euthanasia). Data from Estonian Livestock Performance Recording Ltd was used to determine the reasons for on-farm mortality.The overall mortality rate (MR) including unassisted death and euthanasia was 6.95 per 100 animal-years. Mortality was highest in male (MR = 55.96 per 100 animal-years, 95% CI 54.53; 57.42) and female (MR = 27.70 per 100 animal-years, 95% CI 26.99; 28.43) calves up to three months old and lowest in female cattle aged 12-18 months (MR = 0.99 per 100 animal-years, 95% CI 0.89; 1.10). The main farmers´ stated reasons for dairy cow mortality were 'Metabolic and digestive disorders' and 'Feet/claw disorders'. The main reasons for on-farm mortality among youngstock were 'Metabolic and digestive disorders', 'Respiratory and infectious diseases' and 'Other reasons'. The risk factor analysis was carried out in three age categories: <3 months, 3-23 months and ≥24 months. Estonian Holstein breed was associated with significantly higher risk of mortality compared to Estonian Red breed cattle in every age group. The hazard of on-farm mortality was significantly higher for herds with over 400 animal-years compared to smaller herds in all age groups. Regional differences in mortality hazard were present in the model of youngstock over 3 months and adult cattle. Seasonal differences in mortality rate were present and differed by age groups. This is the first study revealing the on-farm mortality and related risk factors in Estonian dairy cattle population.

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Syndromic surveillance system based on near real-time cattle mortality monitoring

Cited by 12 publications

References 19 publications

Quantifying calf mortality on dairy farms: Challenges and solutions

Quantifying calf mortality on dairy farms: Challenges and solutions

Inferring within-herd transmission parameters for African swine fever virus using mortality data from outbreaks in the Russian Federation

Reasons and risk factors for on-farm mortality in Estonian dairy herds

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