To diminish the detrimental impact of diseases and to improve farm animal health, it has been suggested to enhance disease resilience of the animals. Good disease resilience can be described as the ability of animals to be minimally affected by challenges that can cause disease and, if affected, to recover quickly. At this moment, knowledge is limited on how to operationalise disease resilience into farm management, in favour of animal health. Knowledge is required whether disease resilience can be influenced and even predicted, so we know how animals will respond to disease challenges they may encounter. The aim of this thesis was to investigate potential influencing factors and possible predictive indicators of resilience to typical multifactorial production related diseases in livestock animals. Disease resilience was quantified in pigs by measuring the duration, recovery and severity of the symptoms after a challenge with a respiratory co-infection model of Porcine Respiratory Reproductive and Respiratory Syndrome Virus (PRRSV) and Actinobacillus pleuropneumoniae (A. pleuropneumoniae). The effect of housing condition on disease resilience was studied by the introduction of social and environmental enrichment to one study group in comparison to the resilience of pigs that were conventionally housed. The application of enrichment benefitted resilience of pigs to the coinfection, by an accelerated viral clearance and a reduction of the risk and severity of lung lesions. In dairy cows the calving was seen as challenge and the period after transition of the dry to the lactation phase was used to monitor various clinical deviations as proxies of resilience, because this is the time when cows become most susceptible to diseases. To test whether (dynamic aspects of) variables could predict disease outcome, a number of physiological, immunological and behavioural variables were measured in pigs and cows prior to the challenges and tested as (dynamic) indicators of resilience ((D)IORs). The indicators of resilience that were found could consist of single measurements, the average values of multiple measurements (IORs), or the dynamic features of continuous measurements (DIORs). In pigs a higher level of lymphocytes, naïve T helper cells, memory T cells and higher relative levels of granulocytes and raised concentrations of natural (auto-) antibodies (N(A)Abs) were found as predicting IORs for the severity of the co-infection. In dairy cows, high average of eating time, high variance in ear temperature and strict regularity in behaviour including rumination and activity, were found as (D)IORs for duration and severity of post-partum diseases, summarised as a Total Deficit Score. This thesis shows that disease resilience of animals improves when they are kept under conditions that better meet their needs. Subsequently, indicators were found that can predict how animals will respond to disease challenges. These (D)IORs provide insight in the disease resilience upon perturbations. As a consequence, the influencing factors ...